Search results for: deep feed forward neural network

Authors: Ana Neves, John Leander, Ignacio Gonzalez, Raid Karoumi

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Systematic monitoring and inspection are needed to assess the present state of a structure and predict its future condition. If an irregularity is noticed, repair actions may take place and the adequate intervention will most probably reduce the future costs with maintenance, minimize downtime and increase safety by avoiding the failure of the structure as a whole or of one of its structural parts. For this to be possible decisions must be made at the right time, which implies using systems that can detect abnormalities in their early stage. In this sense, Structural Health Monitoring (SHM) is seen as an effective tool for improving the safety and reliability of infrastructures. This paper explores the decision-making problem in SHM regarding the maintenance of civil engineering structures. The aim is to assess the present condition of a bridge based exclusively on measurements using the suggested method in this paper, such that action is taken coherently with the information made available by the monitoring system. Artificial Neural Networks are trained and their ability to predict structural behavior is evaluated in the light of a case study where acceleration measurements are acquired from a bridge located in Stockholm, Sweden. This relatively old bridge is presently still in operation despite experiencing obvious problems already reported in previous inspections. The prediction errors provide a measure of the accuracy of the algorithm and are subjected to further investigation, which comprises concepts like clustering analysis and statistical hypothesis testing. These enable to interpret the obtained prediction errors, draw conclusions about the state of the structure and thus support decision making regarding its maintenance.

Keywords: artificial neural networks, clustering analysis, model-free damage detection, statistical hypothesis testing, structural health monitoring

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Authors: R. G. D Jayawardena

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The Buddha was known by great wise men from 6th century B.C up to date as a superhuman being born in the world beyond the omnipotent. The Buddha’s doctrinal descriptions reflect his deep enlightenment about imperial and metaphysical knowledge. Buddhology undertaken for this study is an unexposed subject in metaphysical points. The Buddhist wall painting in Sri Lanka depicts deep metaphysical meaning than its simple perspective of estheticism. Buddhology, in some perspectives, has been interpreted as a complete natural science discovered by the Buddha to teach the way of honorable living in perfect happiness and peace of mind till death. Such interpretations which emphasized are based on textual studies. The Buddhology conducted through literal tradition is depicted in wall paintings in Sri Lanka are in visual art with specific techniques rules. The Buddhology, which is investigated on wall paintings, portrays the Buddha in the form of a superhuman being and as an unparalleled person among the Devas, Brahmas, Yakshas, Maras, and humans. The Buddha concept is known to Sri Lankan Buddhists as a person attained to full awakening of wisdom. In personality, the Buddha is depicted as a supernormal person in the world and a rare birth. In brief, the paper will discuss and illustrate the Buddha’s transcendental position and the reality of what he experienced and its authenticity.

Keywords: Buddhology, Metaphysic, Sri Lanka, paintings

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Authors: Wassim Jerbi, Abderrahmen Guermazi, Hafedh Trabelsi

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The optimum use of coverage in wireless sensor networks (WSNs) is very important. LEACH protocol called Low Energy Adaptive Clustering Hierarchy, presents a hierarchical clustering algorithm for wireless sensor networks. LEACH is a protocol that allows the formation of distributed cluster. In each cluster, LEACH randomly selects some sensor nodes called cluster heads (CHs). The selection of CHs is made with a probabilistic calculation. It is supposed that each non-CH node joins a cluster and becomes a cluster member. Nevertheless, some CHs can be concentrated in a specific part of the network. Thus, several sensor nodes cannot reach any CH. to solve this problem. We created an O-LEACH Orphan nodes protocol, its role is to reduce the sensor nodes which do not belong the cluster. The cluster member called Gateway receives messages from neighboring orphan nodes. The gateway informs CH having the neighboring nodes that not belong to any group. However, Gateway called (CH') attaches the orphaned nodes to the cluster and then collected the data. O-Leach enables the formation of a new method of cluster, leads to a long life and minimal energy consumption. Orphan nodes possess enough energy and seeks to be covered by the network. The principal novel contribution of the proposed work is O-LEACH protocol which provides coverage of the whole network with a minimum number of orphaned nodes and has a very high connectivity rates.As a result, the WSN application receives data from the entire network including orphan nodes. The proper functioning of the Application requires, therefore, management of intelligent resources present within each the network sensor. The simulation results show that O-LEACH performs better than LEACH in terms of coverage, connectivity rate, energy and scalability.

Keywords: WSNs; routing; LEACH; O-LEACH; Orphan nodes; sub-cluster; gateway; CH’

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Authors: Chun-Shien Lu

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Privacy-preserving query has recently received considerable attention in the signal processing and multimedia community. It is also a critical step in wireless sensor network for retrieval of sensitive data. The purposes of privacy-preserving query in both the areas of signal processing and sensor network are the same, but the similarity and difference of the adopted technologies are not fully explored. In this paper, we first review the recently developed methods of privacy-preserving query, and then describe in a comprehensive manner what we can learn from the mutual of both areas.

Keywords: encryption, privacy-preserving, search, security

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Authors: Chompunut Jantarasorn, Chutima Prommak

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This paper presents an intensive measurement studying of the network performance analysis when IEEE 802.11g Wireless Local Area Networks (WLAN) coexisting with IEEE 802.15.4 Wireless Personal Area Network (WPAN). The measurement results show that the coexistence between both networks could increase the Frame Error Rate (FER) of the IEEE 802.15.4 networks up to 60% and it could decrease the throughputs of the IEEE 802.11g networks up to 55%.

Keywords: wireless performance analysis, coexistence analysis, IEEE 802.11g, IEEE 802.15.4

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Authors: Maria Magdy Danial Riad

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The objective of this study was to investigate the effect of different protein sources with different degradability ratios during late gestation of ewes on colostrum composition and its IgG concentration, body weight change of dams, and birth weight of their lambs. Objectives: 35 multiparous native crossbred ewes (BW= 59±2.5kg) were randomly allocated to five dietary treatments (7 ewes / treatment) for 2 months prior to lambing. Methods: Experimental diets were isonitrogenous (12.27% CP) and isocaloric (2.22 Mcal ME/kg DM). In diet I (the control), solvent extract soybeans (SESM 33% RUP of CP), II feed grade urea (FGU 31% RUP), III slow release urea (SRU 31% RUP). As sources of undegradable protein, extruded expeller SBM-EESM 40 (37% RUP) and extruded expeller SBM-EESM 60 (41% RUP) were used in groups IV and V, respectively. Results showed no significant effect on feed intake, crude protein (CP), metabolizable energy (ME), and body condition score (BCS). Ewes fed the 37% RUP diet gained more (p<0.05) weight compared with ewes fed the 31% RUP diet (5.62 vs. 2.5kg). Ewes in EESM 60 had the highest levels of fat, protein, total solid, solid not fat, and immunoglobulin and the lowest in urea N content (P< 0.05) in colostrum during the first 24hrs after lambing. Conclusions: Protein source and RUP levels in ewes’ diets had no significant effect (P< 0.05) on lambs’ birth weight and ewes' blood biochemical parameters. Increasing the RUP content of diet during late gestation resulted in an increase in colostrum constituents and its IgG level but had no effect on ewes’ performance and their lambs’ outcome.

Keywords: colostrum, ewes, lambs output, pregnancy, undegradable protein

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Authors: Blair D. Macdonald

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After nearly one hundred years of its origin, foundational quantum mechanics remains one of the greatest unexplained mysteries in physicists today. Within this time, chaos theory and its geometry, the fractal, has developed. In this paper, the propagation behaviour with an iteration of a simple fractal, the Koch Snowflake, was described and analysed. From an arbitrary observation point within the fractal set, the fractal propagates forward by oscillation—the focus of this study and retrospectively behind by exponential growth from a point beginning. It propagates a potentially infinite exponential oscillating sinusoidal wave of discrete triangle bits sharing many characteristics of light and quantum entities. The model's wave speed is potentially constant, offering insights into the perception and a direction of time where, to an observer, when travelling at the frontier of propagation, time may slow to a stop. In isolation, the fractal is a superposition of component bits where position and scale present a problem of location. In reality, this problem is experienced within fractal landscapes or fields where 'position' is only 'known' by the addition of information or markers. The quantum' measurement problem', 'uncertainty principle,' 'entanglement,' and the classical-quantum interface are addressed; these are a problem of scale invariance associated with isolated fractality. Dual forward and retrospective perspectives of the fractal model offer the opportunity for unification between quantum mechanics and cosmological mathematics, observations, and conjectures. Quantum and cosmological problems may be different aspects of the one fractal geometry.

Keywords: measurement problem, observer, entanglement, unification

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Authors: Pawit Tangviroon, Apichit Svang-Ariyaskul

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As the limited availability of petroleum-based fuel has been a major concern, biodiesel is one of the most attractive alternative fuels because it is renewable and it also has advantages over the conventional petroleum-base diesel. At Present, productions of biodiesel generally perform by transesterification of vegetable oils with low molecular weight alcohol, mainly methanol, using chemical catalysts. Methanol is petrochemical product that makes biodiesel producing from methanol to be not pure renewable energy source. Therefore, ethanol as a product produced by fermentation processes. It appears as a potential feed stock that makes biodiesel to be pure renewable alternative fuel. The research is conducted based on two biodiesel production processes by reacting soybean oils with methanol and ethanol. Life cycle assessment was carried out in order to evaluate the environmental impacts and to identify the process alternative. Nine mid-point impact categories are investigated. The results indicate that better performance on Abiotic Depletion Potential (ADP) and Acidification Potential (AP) are observed in biodiesel production from methanol when compared with biodiesel production from ethanol due to less energy consumption during the production processes. Except for ADP and AP, using methanol as feed stock does not show any advantages over biodiesel from ethanol. The single score method is also included in this study in order to identify the best option between two processes of biodiesel production. The global normalization and weighting factor based on eco-taxes are used and it shows that producing biodiesel form ethanol has less environmental load compare to biodiesel from methanol.

Keywords: biodiesel, ethanol, life cycle assessment, methanol, soybean oil

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Authors: Jing Zhang

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To reduce the carbon footprint of mankind and become more sustainable is one of the major challenges in our era. Internet of Things (IoT) mainly resolves three problems: Things to Things (T2T), Human to Things, H2T), and Human to Human (H2H). Borrowing the classification of IoT, we can find carbon prints of industries also can be divided in these three ways. Therefore, monitoring the routes of generation and circulation of products may help calculate product carbon print. This paper does not consider any technique used by IoT itself, but the ideas of it look at the connection of products. Carbon prints are like a gene or mark of a product from raw materials to the final products, which never leave the products. The contribution of this paper is to combine the characteristics of IoT and the methodology of network science to find a way to calculate the product's carbon footprint. Life cycle assessment, LCA is a traditional and main tool to calculate the carbon print of products. LCA is a traditional but main tool, which includes three kinds.

Keywords: product carbon footprint, Internet of Things, network science, life cycle assessment

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Authors: Hushyar Azari

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Aim and background: Deep brain stimulation (DBS) is regarded as an important therapeutic choice for Parkinson's disease (PD). The two most common targets for DBS are the subthalamic nucleus (STN) and globus pallidus (GPi). This review was conducted to compare the clinical effectiveness of these two targets. Methods: A systematic literature search in electronic databases: Embase, Cochrane Library and PubMed were restricted to English language publications 2010 to 2021. Specified MeSH terms were searched in all databases. Studies which evaluated the Unified Parkinson's Disease Rating Scale (UPDRS) III were selected by meeting the following criteria: (1) compared both GPi and STN DBS; (2) had at least three months follow-up period; (3)at least five participants in each group; (4)conducted after 2010. Study quality assessment was performed using the Modified Jadad Scale. Results: 3577 potentially relevant articles were identified, of these, 3569 were excluded based on title and abstract, duplicate and unsuitable article removal. Eight articles satisfied the inclusion criteria and were scrutinized (458 PD patients). According to Modified Jadad Scale, the majority of included studies had low evidence quality which was a limitation of this review. 5 studies reported no statistically significant between-group difference for improvements in UPDRS ш scores. At the same time, there were some results in terms of pain, action tremor, rigidity, and urinary symptoms, which indicated that STN DBS might be a better choice. Regarding the adverse effects, GPi was superior. Conclusion: It is clear that other larger randomized clinical trials with longer follow-up periods and control groups are needed to decide which target is more efficient for deep brain stimulation in Parkinson’s disease and imposes fewer adverse effects on the patients. Meanwhile, STN seems more reasonable according to the results of this systematic review.

Keywords: brain stimulation, globus pallidus, Parkinson's disease, subthalamic nucleus

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Authors: Sanaz Kavianpour, Zuraini Ismail, Bharanidharan Shanmugam

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Social Network Sites (SNSs) can be served as an invaluable platform to transfer the information across a large number of individuals. A substantial component of communicating and managing information is to identify which individual will influence others in propagating information and also whether dissemination of information in the absence of social signals about that information will be occurred or not. Classifying the final audience of social data is difficult as controlling the social contexts which transfers among individuals are not completely possible. Hence, undesirable information diffusion to an unauthorized individual on SNSs can threaten individuals’ privacy. This paper highlights the information diffusion in SNSs and moreover it emphasizes the most significant privacy issues to individuals of SNSs. The goal of this paper is to propose a privacy-preserving model that has urgent regards with individuals’ data in order to control availability of data and improve privacy by providing access to the data for an appropriate third parties without compromising the advantages of information sharing through SNSs.

Keywords: anonymization algorithm, classification algorithm, information diffusion, privacy, social network sites

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Authors: Veerle Van Hoeck, Ingrid Somers, Dany Morisset

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Anti-nutritional factors such as non-starch polysaccharides (NSP) are present in viscous cereals used to feed poultry. Therefore, exogenous carbohydrases are commonly added to monogastric feed to degrade these NSP. Our hypothesis is that xylanase not only improves laying hen performance and digestibility but also induces a significant shift in microbial composition within the intestinal tract and, thereby, can cause a prebiotic effect. In this context, a better understanding of whether and how the chicken gut flora can be modulated by xylanase is needed. To do so, in the herein laying hen study, the effects of dietary supplementation of xylanase on performance, digestibility, and cecal microbiome were evaluated. A total of 96 HiSex laying hens was used in this experiment (3 diets and 16 replicates of 2 hens). Xylanase was added to the diets at concentrations of 0, 45,000 (15 g/t XygestTM HT) and 90,000 U/kg (30 g/t Xygest HT). The diets were based on wheat (~55 %), soybean, and sunflower meal. The lowest dosage, 45,000 U/kg, significantly increased average egg weight and improved feed efficiency compared to the control treatment (p < 0.05). Egg quality parameters were significantly improved in the experiment in response to the xylanase addition. For example, during the last 28 days of the trial, the 45,000 U/kg and the 90,000 U/kg treatments exhibited an increase in Haugh units and albumin heights (p < 0.05). Compared with the control, organic matter digestibility and N retention were drastically improved in the 45,000 U/kg treatment group, which implies better nutrient digestibility at this lowest recommended dosage compared to the control (p < 0.05). Furthermore, gross energy and crude fat digestibility were improved significantly for birds fed 90,000 U/kg group compared to the control. Importantly, 16S rRNA gene analysis revealed that xylanase at 45,000 U/kg dosages can exert a prebiotic effect. This conclusion was drawn based on studying the sequence variation in the 16S rRNA gene in order to characterize diverse microbial communities of the cecal content. A significant increase in beneficial bacteria (Lactobacilli spp and Enterococcus casseliflavus) was documented when adding 45,000 U/kg xylanase to the diet of laying hens. In conclusion, dietary supplementation of xylanase, even at the lowest dose of (45,000 U/kg), significantly improved laying hen performance and digestibility. Furthermore, it is generally accepted that a proper bacterial balance between the number of beneficial bacteria and pathogenic bacteria in the intestine is vital for the host. It seems that the xylanase enzyme is able to modulate the laying hen microbiome beneficially and thus exerts a prebiotic effect. This microbiome plasticity in response to the xylanase provides an attractive target for stimulating intestinal health.

Keywords: laying hen, prebiotic, XygestTM HT, xylanase

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Authors: Ricky Saputra, Rashmi Walvekar, Mohammad Khalid

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Landfills, water contamination, and toxic gas emission are a few impacts faced by the environment due to the increasing number of αof waste rubber tires (WRT). In spite of such concerning issue, only minimal efforts are taken to reclaim or recycle these wastes as their products are generally not-profitable for companies. Unlike the typical reclamation process, devulcanization is a method to selectively cleave sulfidic bonds within vulcanizates to avoid polymeric scissions that compromise elastomer’s mechanical and tensile properties. The process also produces devulcanizates that are re-processable similar to virgin rubber. Often, a devulcanizing agent is needed. In the current study, novel and sustainable ammonium chloride-based ternary deep eutectic solvents (TDES), with a different number of α-hydrogens, were utilised to devulcanize ground rubber tire (GRT) as an effort to implement green chemistry to tackle such issue. 40-mesh GRT were soaked for 1 day with different TDESs and sonicated at 37-80 kHz for 60-120 mins and heated at 100-140oC for 30-90 mins. Devulcanizates were then filtered, dried, and evaluated based on the percentage of by means of Flory-Rehner calculation and swelling index. The result shows that an increasing number of α-Hs increases the degree of devulcanization, and the value achieved was around eighty-percent, thirty percent higher than the typical industrial-autoclave method. Resulting bondages of devulcanizates were also analysed by Fourier transform infrared spectrometer (FTIR), Horikx fitting, and thermogravimetric analyser (TGA). The earlier two confirms only sulfidic scissions were experienced by GRT through the treatment, while the latter proves the absence or negligibility of carbon-chains scission.

Keywords: ammonium, sustainable, deep eutectic solvent, α-hydrogen, waste rubber tire

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Authors: Mária Csernoch, Piroska Biró

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The legends about “user-friendly” and “easy-to-use” birotical tools (computer-related office tools) have been spreading and misleading end-users. This approach has led us to the extremely high number of incorrect documents, causing serious financial losses in the creating, modifying, and retrieving processes. Our research proved that there are at least two sources of this underachievement: (1) The lack of the definition of the correctly edited, formatted documents. Consequently, end-users do not know whether their methods and results are correct or not. They are not aware of their ignorance. They are so ignorant that their ignorance does not allow them to realize their lack of knowledge. (2) The end-users’ problem-solving methods. We have found that in non-traditional programming environments end-users apply, almost exclusively, surface approach metacognitive methods to carry out their computer related activities, which are proved less effective than deep approach methods. Based on these findings we have developed deep approach methods which are based on and adapted from traditional programming languages. In this study, we focus on the most popular type of birotical documents, the text-based documents. We have provided the definition of the correctly edited text, and based on this definition, adapted the debugging method known in programming. According to the method, before the realization of text editing, a thorough debugging of already existing texts and the categorization of errors are carried out. With this method in advance to real text editing users learn the requirements of text-based documents and also of the correctly formatted text. The method has been proved much more effective than the previously applied surface approach methods. The advantages of the method are that the real text handling requires much less human and computer sources than clicking aimlessly in the GUI (Graphical User Interface), and the data retrieval is much more effective than from error-prone documents.

Keywords: deep approach metacognitive methods, error-prone birotical documents, financial losses, human and computer resources

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Authors: I. Touaїbia, E. Azzag, O. Narjes

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Power supply networks are growing continuously and their reliability is getting more important than ever. The complexity of the whole network comprises numerous components that can fail and interrupt the power supply for the end user. Underground distribution systems are normally exposed to permanent faults, due to specific construction characteristics. In these systems, visual inspection cannot be performed. In order to enhance service restoration, accurate fault location techniques must be applied. This paper describes the different faults that affect the underground distribution system of SONELGAZ (National Society of Electricity and Gas of Algeria), and cable fault location procedure with impulse reflection method (TDR), based in the analyses of the cable response of the electromagnetic impulse, allows cable fault prelocation. The results are obtained from real test in the underground distribution feeder from electrical network of energy distribution company of Souk-Ahras, in order to know the influence of cable characteristics in the types and frequency of faults.

Keywords: distribution networks, fault location, TDR, underground cable

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Authors: Jennifer Nutton, Gayle Ployer, Ky Scott, Jenny Morgan

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Working in a circle within higher education creates a decolonial space of mutual respect, responsibility, and reciprocity that facilitates collaborative learning and deep connections among learners and instructors. This approach is beyond simply facilitating a group in a circle but opens the door to creating a sacred space connecting each member to the land, to the Indigenous peoples who have taken care of the lands since time immemorial, to one another, and to one’s own positionality. These deep connections not only center human knowledges and relationships but also acknowledges responsibilities to land. Working in a circle as a relational pedagogical praxis also disrupts institutional power dynamics by creating a space of collaborative learning and deep connections in the classroom. Inherent within circle work is to facilitate connections not just academically but emotionally, physically, culturally, and spiritually. Recent literature supports the use of online talking circles, finding that it can offer a more relational and experiential learning environment, which is often absent in the virtual world and has been made more evident and necessary since the pandemic. These deeper experiences of learning and connection, rooted in both knowledge and the land, can then be shared with openness and vulnerability with one another, facilitating growth and change. This process of beginning with the land is critical to ensure we have the grounding to obstruct the ongoing realities of colonialism. The authors, who identify as both Indigenous and non-Indigenous, as both educators and learners, reflect on their teaching and learning experiences in circle. They share a relational pedagogical praxis framework that has been successful in educating future social workers, environmental activists, and leaders in social and human services, health, legal and political fields.

Keywords: circle work, relational pedagogies, decolonization, distance education

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Authors: Amir Andria Gad Shehata

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Minimizing the inventory cost, optimizing the inventory quantities, and increasing system operational availability are the main motivations to enhance forecasting demand of spare parts in a major power utility company in Medina. This paper reports in an effort made to optimize the orders quantities of spare parts by improving the method of forecasting the demand. The study focuses on equipment that has frequent spare parts purchase orders with uncertain demand. The pattern of the demand considers a lumpy pattern which makes conventional forecasting methods less effective. A comparison was made by benchmarking various methods of forecasting based on experts’ criteria to select the most suitable method for the case study. Three actual data sets were used to make the forecast in this case study. Two neural networks (NN) approaches were utilized and compared, namely long short-term memory (LSTM) and multilayer perceptron (MLP). The results as expected, showed that the NN models gave better results than traditional forecasting method (judgmental method). In addition, the LSTM model had a higher predictive accuracy than the MLP model.

Keywords: spare part, spare part inventory, inventory model, optimization, maintenanceneural network, LSTM, MLP, forecasting demand, inventory management

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Authors: Adedoyin Akintunde Adedayo, Onilude Abiodun Anthony

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The continuous usage of synthetic antibiotics in livestock production has led to the resistance of microbial pathogens. This has prompted research to find alternative sources. This study aims to compare the growth and intestinal health of broilers fed scent leaf meal (SLM) as an alternative to synthetic antibiotics. The study used a completely randomized design (CRD) with 300 one-week-old Arbor Acres broiler chicks. The chicks were divided into six treatments with five replicates of ten birds each. The feeding trial lasted 49 days, including a one-week acclimatization period. Commercial broiler diets were used. The diets included a negative control (no leaf meal or antibiotics), a positive control (0.10% oxy-tetracycline), and four diets with different levels of SLM (0.5%, 1.0%, 1.5%, and 2.0%). The supplementation of both oxy-tetracycline and SLM improved feed intake during the finisher phase. Birds fed SLM at a 1% inclusion level showed significantly (P<0.05) improved average body weight gain (ABWG), lowered feed-to-gain ratio, and cost per kilogram of weight gain compared to other diets. The mortality (2.0%) rate was significantly higher in the negative control group. White blood cell levels varied significantly (P<0.05) in birds fed SLM-supplemented diets, and the use of 2% SLM led to an increase in liver weight. However, welfare indices were not compromised.

Keywords: Arbor Acres, phyto-biotic, synthetic antibiotic, white blood cell, liver weight

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Authors: Maria Ezziani, Rabie Zine, Amine Amar, Ilhame Kissani

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This paper introduces a predictive model for urban transportation engineering, which is vital for efficient traffic management. Utilizing comprehensive datasets and advanced statistical techniques, the model accurately forecasts travel times by considering temporal variations and traffic dynamics. Machine learning algorithms, including regression trees and neural networks, are employed to capture sequential dependencies. Results indicate significant improvements in predictive accuracy, particularly during peak hours and holidays, with the incorporation of traffic flow and speed variables. Future enhancements may integrate weather conditions and traffic incidents. The model's applications range from adaptive traffic management systems to route optimization algorithms, facilitating congestion reduction and enhancing journey reliability. Overall, this research extends beyond travel time estimation, offering insights into broader transportation planning and policy-making realms, empowering stakeholders to optimize infrastructure utilization and improve network efficiency.

Keywords: predictive analytics, traffic flow, travel time estimation, urban transportation, machine learning, traffic management

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Authors: Wissal Ben Arfi, Lubica Hikkerova, Jean-Michel Sahut

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This study examines interfirm networks in the digital transformation era, and in particular, how tacit knowledge codification affects innovation success within digital platforms. Hence, one of the most important features of digital transformation and innovation process outcomes is the emergence of digital platforms, as an interfirm network, at the heart of open innovation. This research aims to illuminate how digital platforms influence inter-organizational innovation through virtual team interactions and knowledge sharing practices within an interfirm network. Consequently, it contributes to the respective strategic management literature on new product development (NPD), open innovation, industrial management, and its emerging interfirm networks’ management. The empirical findings show, on the one hand, that knowledge conversion may be enhanced, especially by the socialization which seems to be the most important phase as it has played a crucial role to hold the virtual team members together. On the other hand, in the process of socialization, the tacit knowledge codification is crucial because it provides the structure needed for the interfirm network actors to interact and act to reach common goals which favor the emergence of open innovation. Finally, our results offer several conditions necessary, but not always sufficient, for interfirm managers involved in NPD and innovation concerning strategies to increasingly shape interconnected and borderless markets and business collaborations. In the digital transformation era, the need for adaptive and innovative business models as well as new and flexible network forms is becoming more significant than ever. Supported by technological advancements and digital platforms, companies could benefit from increased market opportunities and creating new markets for their innovations through alliances and collaborative strategies, as a mode of reducing or eliminating uncertainty environments or entry barriers. Consequently, an efficient and well-structured interfirm network is essential to create network capabilities, to ensure tacit knowledge sharing, to enhance organizational learning and to foster open innovation success within digital platforms.

Keywords: interfirm networks, digital platform, virtual teams, open innovation, knowledge sharing

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Authors: Tahir Nawaz Cheema, Dumitru Baleanu, Ali Raza

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In this research work, we design intelligent computing through Bayesian Regularization artificial neural networks (BRANNs) introduced to solve the mathematical modeling of infectious diseases (Covid-19). The dynamical transmission is due to the interaction of people and its mathematical representation based on the system's nonlinear differential equations. The generation of the dataset of the Covid-19 model is exploited by the power of the explicit Runge Kutta method for different countries of the world like India, Pakistan, Italy, and many more. The generated dataset is approximately used for training, testing, and validation processes for every frequent update in Bayesian Regularization backpropagation for numerical behavior of the dynamics of the Covid-19 model. The performance and effectiveness of designed methodology BRANNs are checked through mean squared error, error histograms, numerical solutions, absolute error, and regression analysis.

Keywords: mathematical models, beysian regularization, bayesian-regularization backpropagation networks, regression analysis, numerical computing

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Authors: Nicolás Clavijo Buriticá, Laura Viviana Triana Sanchez

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This research develops a productive context associated with the aquaculture industry in northern Tolima-Colombia, specifically in the town of Lerida. Strategic aspects of chain of fish Production-Distribution, especially those related to supply network design of an association devoted to cultivating, farming, processing and marketing of fish are addressed. This research is addressed from a special approach of Supply Chain Management (SCM) which guides management objectives to the system sustainability; this approach is called Sustainable Supply Chain Management (SSCM). The network design of fish production-distribution system is obtained for the case study by two mathematical programming models that aims to maximize the economic benefits of the chain and minimize total supply chain costs, taking into account restrictions to protect the environment and its implications on system productivity. The results of the mathematical models validated in the productive situation of the partnership under study, called Asopiscinorte shows the variation in the number of open or closed locations in the supply network that determines the final network configuration. This proposed result generates for the case study an increase of 31.5% in the partial productivity of storage and processing, in addition to possible favorable long-term implications, such as attending an agile or not a consumer area, increase or not the level of sales in several areas, to meet in quantity, time and cost of work in progress and finished goods to various actors in the chain.

Keywords: Sustainable Supply Chain, mathematical programming, aquaculture industry, Supply Chain Design, Supply Chain Configuration

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Authors: Mukund Ramchandra Mogarekar, Pankaj Kumar, Shraddha V. More

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Background: Total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), very low-density lipoprotein cholesterol (VLDL-C), Apo B, and lipoprotein(a) was found as atherogenic factors while high-density lipoprotein cholesterol (HDL-C) was anti-atherogenic. Methods and Results: The study group consists of 40 MI subjects as cases and 40 healthy as controls. Monocytic PON 2 Lactonase (LACT) activity was measured by using Dihydrocoumarine (DHC) as substrate. Phenotyping was done by method of Mogarekar MR et al, serum AOPP by modified method of Witko-Sarsat V et al and Apo B by Turbidimetric immunoassay. PON 2 LACT activities were significantly lower (p< 0.05) and AOPPs & Apo B were higher in MI subjects (p> 0.05). Trimodal distribution of QQ, QR & RR phenotypes of study population showed no significant difference among cases and controls (p> 0.05). Univariate binary logistic regression analysis showed independent association of TC, HDL, LDL, AOPP, Apo B, and PON 2 LACT activity with MI and multiple forward binary logistic regression showed PON 2 LACT activity and serum Apo B as an independent predictor of MI. Conclusions- Decrease in PON 2 LACT activity in MI subjects than in controls suggests increased oxidative stress in MI which is reflected by significantly increased AOPP and Apo B. PON 1 polymorphism of QQ, QR and RR showed no significant difference in protection against MI. Univariate and multiple forward binary logistic regression showed PON 2 LACT activity and serum Apo B as an independent predictor of MI.

Keywords: advanced oxidation protein products, apolipoprotein-B, myocardial infarction, paraoxonase 2 lactonase

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Authors: Adiyasa Muda Zannatan

Abstract:

Cassava (Manihot esculenta) is a plant originating from Brazil. Cassava plants categorized as sixth major food in the world after wheat, rice, corn and potatoes. It has been cultivated on hilly land for 97 years since 1918 at Cireundeu village, West Java Province, Indonesia. Cireundeu traditional village located in the mountain valleys and has a hilly slope up to 38%. Cassava is used as the primary food in that area. Uniquely, Cassava productivity is stable and continues until now. The assessment of soil quality is taking soil samples in the area and analysis the soil in laboratory. The result of analysis that soil in the area is not degraded because it has optimum nutrient, organic matter, and high value of cation exchange capacity in soil even though it has been cultivated in scarp with high slope. Commonly, soil on scarp with high slope has a high rate erosion and poor nutrient. It proved that cassava is able to be an alternative technique of soil conservation in the areas that have a high slope. Beside that, cassava can be utilized as a plant food, feed, fertilizer, and energy. With the utilization of Cassava, the target of Sustainable Development Goals (SDG's) will be achieved with consideration three important components include economy, social, and environment. In economy, Cassava can to be the commercial product like processed food, feed, and alternative energy. In social, it will increase social welfare and will be hereditary. And for environment, Cassava prevents soil from erosion and keeps soil quality.

Keywords: Cassava, local wisdom, conservation, soil quality, sustainable

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Authors: J. S. Saini, P. P. K. Sandhu

Abstract:

The multi-hop nature of Wireless Mesh Networks and the hasty progression of throughput demands results in multi- channels and multi-radios structures in mesh networks, but the main problem of co-channels interference reduces the total throughput, specifically in multi-hop networks. Quality of Service mentions a vast collection of networking technologies and techniques that guarantee the ability of a network to make available desired services with predictable results. Quality of Service (QoS) can be directed at a network interface, towards a specific server or router's performance, or in specific applications. Due to interference among various transmissions, the QoS routing in multi-hop wireless networks is formidable task. In case of multi-channel wireless network, since two transmissions using the same channel may interfere with each other. This paper has considered the Destination Sequenced Distance Vector (DSDV) routing protocol to locate the secure and optimised path. The proposed technique also utilizes the Lempel–Ziv–Welch (LZW) based lossless data compression and intra cluster data aggregation to enhance the communication between the source and the destination. The use of clustering has the ability to aggregate the multiple packets and locates a single route using the clusters to improve the intra cluster data aggregation. The use of the LZW based lossless data compression has ability to reduce the data packet size and hence it will consume less energy, thus increasing the network QoS. The MATLAB tool has been used to evaluate the effectiveness of the projected technique. The comparative analysis has shown that the proposed technique outperforms over the existing techniques.

Keywords: WMNS, QOS, flooding, collision avoidance, LZW, congestion control

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Authors: Shashank Gupta, Iteash Agarwal, Vijayalaxmi Mogiligidda, Rajesh Kumar Krishnan, Sruthi Chennuri, Deepika Aggarwal, Anwesha Hoodati, Sheroy Cooper, Ranjan, Mohammad Bilal Sheik, Bhavya K. M., Manasa Hegde, M. Naveen Krishna, Amit Kumar Chauhan, Mallikarjun Korrapati, Sumit Singh, J. B. Singh, Sunil Sud, Sunil Gupta, Sidhartha Pant, Sankar, Neha Agrawal, Ashish Ranjan, Piyush Mohapatra, Roopak T., Arsh Ahmad, Nanjunda M., Dilip Singh

Abstract:

Major research interests on quantum key distribution (QKD) are primarily focussed on increasing 1. point-to-point transmission distance (1000 Km), 2. secure key rate (Mbps), 3. security of quantum layer (device-independence). It is great to push the boundaries on these fronts, but these isolated approaches are neither scalable nor cost-effective due to the requirements of specialised hardware and different infrastructure. Current and future QKD network requires addressing different sets of challenges apart from distance, key rate, and quantum security. In this regard, we present ChaQra -a sub-quantum network with core features as 1) Crypto agility (integration in the already deployed telecommunication fibres), 2) Software defined networking (SDN paradigm for routing different nodes), 3) reliability (addressing denial-of-service with hybrid quantum safe cryptography), 4) upgradability (modules upgradation based on scientific and technological advancements), 5) Beyond QKD (using QKD network for distributed computing, multi-party computation etc). Our results demonstrate a clear path to create and accelerate quantum secure Indian subcontinent under the national quantum mission.

Keywords: quantum network, quantum key distribution, quantum security, quantum information

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Authors: Shriya Shukla, Lachin Fernando

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Pneumonia, a leading cause of mortality in young children globally, presents significant diagnostic challenges, particularly in resource-limited settings. This study presents an approach to diagnosing pediatric pneumonia using Chest X-Ray (CXR) images, employing a lightweight MobileNetV2 model enhanced with synthetic data augmentation. Addressing the challenge of dataset scarcity and imbalance, the study used a Variational Autoencoder-Generative Adversarial Network (VAE-GAN) to generate synthetic CXR images, improving the representation of normal cases in the pediatric dataset. This approach not only addresses the issues of data imbalance and scarcity prevalent in medical imaging but also provides a more accessible and reliable diagnostic tool for early pneumonia detection. The augmented data improved the model’s accuracy and generalization, achieving an overall accuracy of 95% in pneumonia detection. These findings highlight the efficacy of the MobileNetV2 model, offering a computationally efficient yet robust solution well-suited for resource-constrained environments such as mobile health applications. This study demonstrates the potential of synthetic data augmentation in enhancing medical image analysis for critical conditions like pediatric pneumonia.

Keywords: pneumonia, MobileNetV2, image classification, GAN, VAE, deep learning

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Authors: Tatjana Jurić, Bojana Blagojević, Denis Uka, Ružica Ždero Pavlović, Boris M. Popović

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Natural deep eutectic solvents (NADES) represent a class of modern systems that have been developed as a green alternative to toxic organic solvents, which are commonly used as extraction media. It has been considered that hydrogen bonding is the main interaction leading to the formation of NADES. The aim of this study was phytochemical characterization and determination of the antioxidant and antibacterial activity of Mentha piperita leaf extracts obtained by six choline chloride-based NADES. NADES were prepared by mixing choline chloride with different hydrogen bond donors in 1:1 molar ratio following the addition of 30% (w/w) water. The mixtures were then heated (60 °C) and stirred (650 rpm) until the clear homogenous liquids were obtained. The Mentha piperita extracts were prepared by mixing 75 mg of peppermint leaves with 1 mL of NADES following by the heating and stirring (60 °C, 650 rpm) within 30 min. The content of six phenolics in extracts was determined using HPLC-PDA. The dominant compounds presented in peppermint leaves - rosmarinic acid and luteolin 7-O-glucoside, were extracted by NADES at a similar level as 70% ethanol. The microdilution method was applied to test the antibacterial activity of extracts. Compared with 70% ethanol, all NADES systems showed higher antibacterial activity towards Pseudomonas aeruginosa (Gram -), Staphylococcus aureus (Gram +), Escherichia coli (Gram -), and Salmonella enterica (Gram -), especially NADES containing organic acids. The majority of NADES extracts showed a better ability to neutralize DPPH radical than conventional solvent and similar ability to reduce Fe3+ to Fe2+ ions in FRAP assay. The obtained results introduce NADES systems as the novel, sustainable, and low-cost solvents with a variety of applications.

Keywords: antibacterial activity, antioxidant activity, green extraction, natural deep eutectic solvents, polyphenols

Procedia PDF Downloads 184

Authors: Marija Milašinović-Šeremešić, Valentina Semenčenko, Milica Radosavljević, Dušanka Terzić, Ljiljana Mojović, Ljubica Dokić

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Maize (Zea mays L.) is one of the most important cereal crops, and as such, one of the most significant naturally renewable carbohydrate raw materials for the production of energy and multitude of different products. The main goal of the present study was to investigate a suitability of selected maize hybrids of different genetic background produced in Maize Research Institute ‘Zemun Polje’, Belgrade, Serbia, for starch, bioethanol and animal feed production. All the hybrids are commercial and their detailed characterization is important for the expansion of their different uses. The starches were isolated by using a 100-g laboratory maize wet-milling procedure. Hydrolysis experiments were done in two steps (liquefaction with Termamyl SC, and saccharification with SAN Extra L). Starch hydrolysates obtained by the two-step hydrolysis of the corn flour starch were subjected to fermentation by S. cerevisiae var. ellipsoideus under semi-anaerobic conditions. The digestibility based on enzymatic solubility was performed by the Aufréré method. All investigated ZP maize hybrids had very different physical characteristics and chemical composition which could allow various possibilities of their use. The amount of hard (vitreous) and soft (floury) endosperm in kernel is considered one of the most important parameters that can influence the starch and bioethanol yields. Hybrids with a lower test weight and density and a greater proportion of soft endosperm fraction had a higher yield, recovery and purity of starch. Among the chemical composition parameters only starch content significantly affected the starch yield. Starch yields of studied maize hybrids ranged from 58.8% in ZP 633 to 69.0% in ZP 808. The lowest bioethanol yield of 7.25% w/w was obtained for hybrid ZP 611k and the highest by hybrid ZP 434 (8.96% w/w). A very significant correlation was determined between kernel starch content and the bioethanol yield, as well as volumetric productivity (48h) (r=0.66). Obtained results showed that the NDF, ADF and ADL contents in the whole maize plant of the observed ZP maize hybrids varied from 40.0% to 60.1%, 18.6% to 32.1%, and 1.4% to 3.1%, respectively. The difference in the digestibility of the dry matter of the whole plant among hybrids (ZP 735 and ZP 560) amounted to 18.1%. Moreover, the differences in the contents of the lignocelluloses fraction affected the differences in dry matter digestibility. From the results it can be concluded that genetic background of the selected maize hybrids plays an important part in estimation of the technological value of maize hybrids for various purposes. Obtained results are of an exceptional importance for the breeding programs and selection of potentially most suitable maize hybrids for starch, bioethanol and animal feed production.

Keywords: bioethanol, biomass quality, maize, starch

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Authors: Selim M. Khan, Aaron A. Goodarzi, Joshua M. Taron, Tryggve Rönnqvist

Abstract:

Indoor air quality, a prime determinant of health, is strongly influenced by the presence of hazardous radon gas within the built environment. As a health issue, dangerously high indoor radon arose within the 20th century to become the 2nd leading cause of lung cancer. While the 21st century building metrics and human behaviors have captured, contained, and concentrated radon to yet higher and more hazardous levels, the issue is rapidly worsening in Canada. It is established that Canadians in the Prairies are the 2nd highest radon-exposed population in the world, with 1 in 6 residences experiencing 0.2-6.5 millisieverts (mSv) radiation per week, whereas the Canadian Nuclear Safety Commission sets maximum 5-year occupational limits for atomic workplace exposure at only 20 mSv. This situation is also deteriorating over time within newer housing stocks containing higher levels of radon. Deep machine learning (LSTM) algorithms were applied to analyze multiple quantitative and qualitative features, determine the most important contributory factors, and predicted radon levels in the known past (1990-2020) and projected future (2021-2050). The findings showed gradual downwards patterns in Sweden, whereas it would continue to go from high to higher levels in Canada over time. The contributory factors found to be the basement porosity, roof insulation depthness, R-factor, and air dynamics of the indoor environment related to human window opening behaviour. Building codes must consider including these factors to ensure adequate indoor ventilation and healthy living that can prevent lung cancer in non-smokers.

Keywords: radon, building metrics, deep learning, LSTM prediction model, lung cancer, canada, sweden

Procedia PDF Downloads 112