Search results for: classification techniques

Authors: Pratibha Srivastava, Chithra V. J., Sudhakar S., Nitin K. D.

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A closed-loop system comprises of a controller, a response system, and an actuating system. The controller, which is the system under test for us, excites the actuators based on feedback from the sensors in a periodic manner. The sensors should provide the feedback to the System Under Test (SUT) within a deterministic time post excitation of the actuators. Any delay or miss in the generation of response or acquisition of excitation pulses may lead to control loop controller computation errors, which can be catastrophic in certain cases. Such systems categorised as hard real-time systems that need special strategies. The real-time operating systems available in the market may be the best solutions for such kind of simulations, but they pose limitations like the availability of the X Windows system, graphical interfaces, other user tools. In this paper, we present strategies that can be used on a general purpose operating system (Bare Linux Kernel) to achieve a deterministic deadline and hence have the added advantages of a GPOS with real-time features. Techniques shall be discussed how to make the time-critical application run with the highest priority in an uninterrupted manner, reduced network latency for distributed architecture, real-time data acquisition, data storage, and retrieval, user interactions, etc.

Keywords: real time data acquisition, real time kernel preemption, scheduling, network latency

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Authors: Akindele Ojo, Olanike Ojo, Agatha Oseghale

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The empirical study examined the allocative efficiency of small holder tuber crop farmers in North central, Nigeria. Data used for the study were obtained from primary source using a multi-stage sampling technique with structured questionnaires administered to 300 randomly selected tuber crop farmers from the study area. Descriptive statistics, data envelopment analysis and Tobit regression model were used to analyze the data. The DEA result on the classification of the farmers into efficient and inefficient farmers showed that 17.67% of the sampled tuber crop farmers in the study area were operating at frontier and optimum level of production with mean allocative efficiency of 1.00. This shows that 82.33% of the farmers in the study area can still improve on their level of efficiency through better utilization of available resources, given the current state of technology. The results of the Tobit model for factors influencing allocative inefficiency in the study area showed that as the year of farming experience, level of education, cooperative society membership, extension contacts, credit access and farm size increased in the study area, the allocative inefficiency of the farmers decreased. The results on effects of the significant determinants of allocative inefficiency at various distribution levels revealed that allocative efficiency increased from 22% to 34% as the farmer acquired more farming experience. The allocative efficiency index of farmers that belonged to cooperative society was 0.23 while their counterparts without cooperative society had index value of 0.21. The result also showed that allocative efficiency increased from 0.43 as farmer acquired high formal education and decreased to 0.16 with farmers with non-formal education. The efficiency level in the allocation of resources increased with more contact with extension services as the allocative efficeincy index increased from 0.16 to 0.31 with frequency of extension contact increasing from zero contact to maximum of twenty contacts per annum. These results confirm that increase in year of farming experience, level of education, cooperative society membership, extension contacts, credit access and farm size leads to increases efficiency. The results further show that the age of the farmers had 32% input to the efficiency but reduces to an average of 15%, as the farmer grows old. It is therefore recommended that enhanced research, extension delivery and farm advisory services should be put in place for farmers who did not attain optimum frontier level to learn how to attain the remaining 74.39% level of allocative efficiency through a better production practices from the robustly efficient farms. This will go a long way to increase the efficiency level of the farmers in the study area.

Keywords: allocative efficiency, DEA, Tobit regression, tuber crop

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Authors: Paolo Fantozzi, Luigi Laura, Umberto Nanni

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The problem of Entity relation discovery in structured data, a well covered topic in literature, consists in searching within unstructured sources (typically, text) in order to find connections among entities. These can be a whole dictionary, or a specific collection of named items. In many cases machine learning and/or text mining techniques are used for this goal. These approaches might be unfeasible in computationally challenging problems, such as processing massive data streams. A faster approach consists in collecting the cooccurrences of any two words (entities) in order to create a graph of relations - a cooccurrence graph. Indeed each cooccurrence highlights some grade of semantic correlation between the words because it is more common to have related words close each other than having them in the opposite sides of the text. Some authors have used sliding windows for such problem: they count all the occurrences within a sliding windows running over the whole text. In this paper we generalise such technique, coming up to a Weighted-Distance Sliding Window, where each occurrence of two named items within the window is accounted with a weight depending on the distance between items: a closer distance implies a stronger evidence of a relationship. We develop an experiment in order to support this intuition, by applying this technique to a data set consisting in the text of the Bible, split into verses.

Keywords: cooccurrence graph, entity relation graph, unstructured text, weighted distance

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Authors: T. Thein, S. Kalyar Myo

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Human uses visual information for understanding the speech contents in noisy conditions or in situations where the audio signal is not available. The primary advantage of visual information is that it is not affected by the acoustic noise and cross talk among speakers. Using visual information from the lip movements can improve the accuracy and robustness of automatic speech recognition. However, a major challenge with most automatic lip reading system is to find a robust and efficient method for extracting the linguistically relevant speech information from a lip image sequence. This is a difficult task due to variation caused by different speakers, illumination, camera setting and the inherent low luminance and chrominance contrast between lip and non-lip region. Several researchers have been developing methods to overcome these problems; the one is lip reading. Moreover, it is well known that visual information about speech through lip reading is very useful for human speech recognition system. Lip reading is the technique of a comprehensive understanding of underlying speech by processing on the movement of lips. Therefore, lip reading system is one of the different supportive technologies for hearing impaired or elderly people, and it is an active research area. The need for lip reading system is ever increasing for every language. This research aims to develop a visual teaching method system for the hearing impaired persons in Myanmar, how to pronounce words precisely by identifying the features of lip movement. The proposed research will work a lip reading system for Myanmar Consonants, one syllable consonants (င (Nga)၊ ည (Nya)၊ မ (Ma)၊ လ (La)၊ ၀ (Wa)၊ သ (Tha)၊ ဟ (Ha)၊ အ (Ah) ) and two syllable consonants ( က(Ka Gyi)၊ ခ (Kha Gway)၊ ဂ (Ga Nge)၊ ဃ (Ga Gyi)၊ စ (Sa Lone)၊ ဆ (Sa Lain)၊ ဇ (Za Gwe) ၊ ဒ (Da Dway)၊ ဏ (Na Gyi)၊ န (Na Nge)၊ ပ (Pa Saug)၊ ဘ (Ba Gone)၊ ရ (Ya Gaug)၊ ဠ (La Gyi) ). In the proposed system, there are three subsystems, the first one is the lip localization system, which localizes the lips in the digital inputs. The next one is the feature extraction system, which extracts features of lip movement suitable for visual speech recognition. And the final one is the classification system. In the proposed research, Two Dimensional Discrete Cosine Transform (2D-DCT) and Linear Discriminant Analysis (LDA) with Active Contour Model (ACM) will be used for lip movement features extraction. Support Vector Machine (SVM) classifier is used for finding class parameter and class number in training set and testing set. Then, experiments will be carried out for the recognition accuracy of Myanmar consonants using the only visual information on lip movements which are useful for visual speech of Myanmar languages. The result will show the effectiveness of the lip movement recognition for Myanmar Consonants. This system will help the hearing impaired persons to use as the language learning application. This system can also be useful for normal hearing persons in noisy environments or conditions where they can find out what was said by other people without hearing voice.

Keywords: feature extraction, lip reading, lip localization, Active Contour Model (ACM), Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), Two Dimensional Discrete Cosine Transform (2D-DCT)

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Authors: Laila Jeebodh Desai

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Storytelling as a therapeutic intervention entails the narrating of events by externalising emotions, thoughts and responses to life-changing events such as loss and grief. This creates the opportunity for clients to engage with psychologists by projecting various beliefs and challenges, such as grief, through a range of therapeutic modalities. This study conducts an inquiry into the ways in which storytelling can be utilised by educational psychologists with adolescent clients to address behavioural challenges relating to grief. This qualitative study therefore aims to facilitate an understanding of the use and benefits of storytelling as a therapeutic intervention. This has been achieved by examining interviews with four educational psychologists who have utilised storytelling as a therapeutic intervention with adolescent clients to overcome challenges with grief. The participants (educational psychologists) discussed case studies during interviews, which provided evidence of their practical administration of storytelling as a therapeutic intervention incorporating integrated theoretical approaches through the use of blended therapeutic techniques. Behavioural challenges relating to grief were also predominant in the case study information provided by the participants. The participants further confirmed that the term ‘grief’ included different types of loss that were experienced among adolescent clients. The implications and recommendations of the findings encouraged the utilisation of storytelling as a therapeutic intervention with adolescent clients in addressing behavioural challenges related to grief, based on the outcome of the case studies discussed by the participants.

Keywords: storytelling, therapeutic intervention, adolescents, grief

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Authors: José de Jesús Rivero Oliva, Jesús Salomón Llanes, Manuel Perdomo Ojeda, Antonio Torres Valle

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Combinatorial explosion is a common problem to both predominant methods for solving fault trees: Minimal Cut Set (MCS) approach and Binary Decision Diagram (BDD). High memory consumption impedes the complete solution of very complex fault trees. Only approximated non-conservative solutions are possible in these cases using truncation or other simplification techniques. The paper proposes a method (CSolv+) for solving complex fault trees, without any possibility of combinatorial explosion. Each individual MCS is immediately discarded after its contribution to the basic events importance measures and the Top gate Upper Bound Probability (TUBP) has been accounted. An estimation of the Top gate Exact Probability (TEP) is also provided. Therefore, running in a computer cluster, CSolv+ will guarantee the complete solution of complex fault trees. It was successfully applied to 40 fault trees from the Aralia fault trees database, performing the evaluation of the top gate probability, the 1000 Significant MCSs (SMCS), and the Fussell-Vesely, RRW and RAW importance measures for all basic events. The high complexity fault tree nus9601 was solved with truncation probabilities from 10-²¹ to 10-²⁷ just to limit the execution time. The solution corresponding to 10-²⁷ evaluated 3.530.592.796 MCSs in 3 hours and 15 minutes.

Keywords: system reliability analysis, probabilistic risk assessment, fault tree analysis, basic events importance measures

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Authors: Mazita Mohd Diah, Anton V. Dolzhenko, Tin Wui Wong

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Nanoparticles, being small with a large specific surface area, increase solubility, enhance bioavailability, improve controlled release and enable precision targeting of the entrapped compounds. In this study, chitosan as polymeric permeation enhancer was conjugated to a polar pro-drug, carboxymethyl-5-fluorouracil (CMFU) to increase the skin drug permeation. Chitosan-CMFU conjugate was synthesized using chemical conjugation process through succinate linker. It was then transformed into nanoparticles via spray drying method. The conjugation was elucidated using Fourier Transform Infrared and Proton Nuclear Magnetic Resonance techniques. The nanoparticle size, size distribution, zeta potential, drug content, skin permeation and retention profiles were characterized. The conjugation was denoted using 1H NMR by new peaks at signal δ = 4.184 ppm (singlet, 2H for CH2) and 7.676-7.688 ppm (doublet, 1H for C6) attributed to CMFU in chitosan-CMFU NMR spectrum. The nanoparticles had profiles of particle size: 93.97 ±35.11 nm, polydispersity index: 0.40 ± 0.14, zeta potential: +18.25 ±2.95 mV and drug content: 6.20 ± 1.98 % w/w. Almost 80 % w/w CMFU in the form of nanoparticles permeated through the skin in 24 hours and close to 50 % w/w permeation occurred in first 1-2 hours. Without conjugation to chitosan and nanoparticulation, less than 40 % w/w CMFU permeated through the skin in 24 hours. The skin drug retention likewise was higher with chitosan-CMFU nanoparticles (15.34 ± 5.82 % w/w) than CMFU (2.24 ± 0.57 % w/w). CMFU, through conjugation with chitosan permeation enhancer and processed in nanogeometry, had its skin permeation and retention degree promoted.

Keywords: carboxymethyl-5-fluorouracil, chitosan, conjugate, skin permeation, skin retention

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Authors: Zhihao Zheng, Zhilin Wang, Linxin Liu

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In the research of automated knowledge question-answering systems, accuracy and efficiency are critical challenges. This paper proposes a knowledge graph-enhanced Retrieval-Augmented Generation (RAG) method, combined with a Graph Neural Network (GNN) structure, to automatically determine the correctness of knowledge competition questions. First, a domain-specific knowledge graph was constructed from a large corpus of academic journal literature, with key entities and relationships extracted using Natural Language Processing (NLP) techniques. Then, the RAG method's retrieval module was expanded to simultaneously query both text databases and the knowledge graph, leveraging the GNN to further extract structured information from the knowledge graph. During answer generation, contextual information provided by the knowledge graph and GNN is incorporated to improve the accuracy and consistency of the answers. Experimental results demonstrate that the knowledge graph and GNN-enhanced RAG method perform excellently in determining the correctness of questions, achieving an accuracy rate of 95%. Particularly in cases involving ambiguity or requiring contextual information, the structured knowledge provided by the knowledge graph and GNN significantly enhances the RAG method's performance. This approach not only demonstrates significant advantages in improving the accuracy and efficiency of automated knowledge question-answering systems but also offers new directions and ideas for future research and practical applications.

Keywords: knowledge graph, graph neural network, retrieval-augmented generation, NLP

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Authors: Salvana P. M. Costa, Tarcyla A. Gomes, Giovanna C. R. M. Schver, Leslie R. M. Ferraz, Cristovão R. Silva, Magaly A. M. Lyra, Danilo A. F. Fonte, Larissa A. Rolim, Amanda C. Q. M. Vieira, Miracy M. Albuquerque, Pedro J. Rolim-neto

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Efavirenz (EFV) is considered one of the most widely used anti-HIV drugs. However, it is classified as a drug class II (poorly soluble, highly permeable) according to the biopharmaceutical classification system, presenting problems of absorption in the gastrointestinal tract and thereby inadequate bioavailability for its therapeutic action. This study aimed to overcome these barriers by developing and obtaining solid dispersions (SD) in order to increase the EFZ bioavailability. For the development of SD with EFV, theoretical and practical studies were initially performed. Thus, there was a choice of a carrier to be used. For this, it was analyzed the various criteria such as glass transition temperature of the polymer, intra- and intermolecular interactions of hydrogen bonds between drug and polymer, the miscibility between the polymer and EFV. The choice of the obtainment method of the SD came from the analysis of which method is the most consolidated in both industry and literature. Subsequently, the choice of drug and carrier concentrations in the dispersions was carried out. In order to obtain DS to present the drug in its amorphous form, as the DS were obtained, they were analyzed by X-ray diffraction (XRD). SD are more stable the higher the amount of polymer present in the formulation. With this assumption, a SD containing 10% of drug was initially prepared and then this proportion was increased until the XRD showed the presence of EFV in its crystalline form. From this point, it was not produced SD with a higher concentration of drug. Thus, it was allowed to select PVP-K30, PVPVA 64 and the SOLUPLUS formulation as carriers, once it was possible the formation of hydrogen bond between EFV and polymers since these have hydrogen acceptor groups capable of interacting with the donor group of the drug hydrogen. It is worth mentioning also that the films obtained, independent of concentration used, were presented homogeneous and transparent. Thus, it can be said that the EFV is miscible in the three polymers used in the study. The SD and Physical Mixtures (PM) with these polymers were prepared by the solvent method. The EFV diffraction profile showed main peaks at around 2θ of 6,24°, in addition to other minor peaks at 14,34°, 17,08°, 20,3°, 21,36° and 25,06°, evidencing its crystalline character. Furthermore, the polymers showed amorphous nature, as evidenced by the absence of peaks in their XRD patterns. The XRD patterns showed the PM overlapping profile of the drug with the polymer, indicating the presence of EFV in its crystalline form. Regardless the proportion of drug used in SD, all the samples showed the same characteristics with no diffraction peaks EFV, demonstrating the behavior amorphous products. Thus, the polymers enabled, effectively, the formation of amorphous SD, probably due to the potential hydrogen bonds between them and the drug. Moreover, the XRD analysis showed that the polymers were able to maintain its amorphous form in a concentration of up to 80% drug.

Keywords: amorphous form, Efavirenz, solid dispersions, solubility

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Authors: Nirmal Kumar Verma, Pallavi Jha

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Terahertz (THz) radiation generation by propagation of two-color laser pulses in plasma is an active area of research due to its potential applications in various areas, including security screening, material characterization and spectroscopic techniques. Due to non ionizing nature and the ability to penetrate several millimeters, THz radiation is suitable for diagnosis of cancerous cells. Traditional THz emitters like optically active crystals when irradiated with high power laser radiation, are subject to material breakdown and hence low conversion efficiencies. This problem is not encountered in laser - plasma based THz radiation sources. The present paper is devoted to the simulation study of the enhanced THz radiation generation by propagation of two-color, linearly polarized laser pulses through magnetized plasma. The two laser pulses orthogonally polarized are co-propagating along the same direction. The direction of the external magnetic field is such that one of the two laser pulses propagates in the ordinary mode, while the other pulse propagates in the extraordinary mode through homogeneous plasma. A transverse electromagnetic wave with frequency in the THz range is generated due to the presence of the static magnetic field. It is observed that larger amplitude terahertz can be generated by mixing of ordinary and extraordinary modes of two-color laser pulses as compared with a single laser pulse propagating in the extraordinary mode.

Keywords: two-color laser pulses, terahertz radiation, magnetized plasma, ordinary and extraordinary mode

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Authors: Thomas Geninatti, Bruno Giacomo, Alessandro Grattoni

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Nanofluidic devices have been investigated for over a decade as promising platforms for the controlled release of therapeutics. The nanochannel drug delivery system (nDS), a membrane fabricated with high precision silicon techniques, capable of zero-order release of drugs by exploiting diffusion transport at the nanoscale originated from the interactions between molecules with nanochannel surfaces, showed the flexibility of the sustained release in vitro and in vivo, over periods of time ranging from weeks to months. To improve the implantable bio nanotechnology, in order to create a system that possesses the key features for achieve the suitable release of therapeutics, the next generation of nDS has been created. Platinum electrodes are integrated by e-beam deposition onto both surfaces of the membrane allowing low voltage (<2 V) and active temporal control of drug release through modulation of electrostatic potentials at the inlet and outlet of the membrane’s fluidic channels. Hence, a tunable administration of drugs is ensured from the nanochannel drug delivery system. The membrane will be incorporated into a peek implantable capsule, which will include drug reservoir, control hardware and RF system to allow suitable therapeutic regimens in real-time. Therefore, this new nanotechnology offers tremendous potential solutions to manage chronic disease such as cancer, heart disease, circadian dysfunction, pain and stress.

Keywords: nanochannel membrane, drug delivery, tunable release, personalized administration, nanoscale transport, biomems

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Authors: Saima Iftikhar Rida Shabbir, Zeeshan Hassan

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Water shortage, energy crisis and natural misfortunes are the glitches which reduce the efficacy of agricultural ecosystems especially in Pakistan where these are more frequent besides being intense. Accordingly, the agricultural water resources, food security and country’s economy are at risk. To address this, we have used Geospatial techniques incorporating ASTER Global DEM, Geological map, rainfall data, discharge data, Landsat 5 image of Swat valley in order to assess the viability of selected sites. The sites have been studied via GIS tools, Hydrological investigation and multiparametric analysis for their potentialities of collecting and securing the rain water; regulating floods by storing the surplus water bulks by check dams and developing them for power generation. Our results showed that Siat1-1 was very useful for low-cost dam with main objective of as Debris dam; Site-2 and Site 3 were check dams sites having adequate storing reservoir so as to arrest the inconsistent flow accompanied by catering the sedimentation effects and the debris flows; Site 4 had a huge reservoir capacity but it entails enormous edifice cost over very great flood plain. Thus, there is necessity of active Hydrological developments to estimate the flooded area using advanced and multifarious GIS and remote sensing approaches so that the sites could be developed for harnessing those sites for agricultural and energy drives.

Keywords: site suitability, check dams, SHP, terrain analysis, volume estimation

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Authors: Michela Quadrini

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Chord diagrams occur in mathematics, from the study of RNA to knot theory. They are widely used in theory of knots and links for studying the finite type invariants, whereas in molecular biology one important motivation to study chord diagrams is to deal with the problem of RNA structure prediction. An RNA molecule is a linear polymer, referred to as the backbone, that consists of four types of nucleotides. Each nucleotide is represented by a point, whereas each chord of the diagram stands for one interaction for Watson-Crick base pairs between two nonconsecutive nucleotides. A chord diagram is an oriented circle with a set of n pairs of distinct points, considered up to orientation preserving diffeomorphisms of the circle. A linear chord diagram (LCD) is a special kind of graph obtained cutting the oriented circle of a chord diagram. It consists of a line segment, called its backbone, to which are attached a number of chords with distinct endpoints. There is a natural fattening on any linear chord diagram; the backbone lies on the real axis, while all the chords are in the upper half-plane. Each linear chord diagram has a natural genus of its associated surface. To each chord diagram and linear chord diagram, it is possible to associate the intersection graph. It consists of a graph whose vertices correspond to the chords of the diagram, whereas the chord intersections are represented by a connection between the vertices. Such intersection graph carries a lot of information about the diagram. Our goal is to define an LCD equivalence class in terms of identity of intersection graphs, from which many chord diagram invariants depend. For studying these invariants, we introduce a new representation of Linear Chord Diagrams based on a set of appropriate topological operators that permits to model LCD in terms of the relations among chords. Such set is composed of: crossing, nesting, and concatenations. The crossing operator is able to generate the whole space of linear chord diagrams, and a multiple context free grammar able to uniquely generate each LDC starting from a linear chord diagram adding a chord for each production of the grammar is defined. In other words, it allows to associate a unique algebraic term to each linear chord diagram, while the remaining operators allow to rewrite the term throughout a set of appropriate rewriting rules. Such rules define an LCD equivalence class in terms of the identity of intersection graphs. Starting from a modelled RNA molecule and the linear chord, some authors proposed a topological classification and folding. Our LCD equivalence class could contribute to the RNA folding problem leading to the definition of an algorithm that calculates the free energy of the molecule more accurately respect to the existing ones. Such LCD equivalence class could be useful to obtain a more accurate estimate of link between the crossing number and the topological genus and to study the relation among other invariants.

Keywords: chord diagrams, linear chord diagram, equivalence class, topological language

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Authors: Emmanuel Iyamuremye

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Extreme rainfall events have caused significant damage to agriculture, ecology, and infrastructure, disruption of human activities, injury, and loss of life. They also have significant social, economic, and environmental consequences because they considerably damage urban as well as rural areas. Early detection of extreme maximum rainfall helps to implement strategies and measures, before they occur, hence mitigating the consequences. Extreme value theory has been used widely in modeling extreme rainfall and in various disciplines, such as financial markets, the insurance industry, failure cases. Climatic extremes have been analyzed by using either generalized extreme value (GEV) or generalized Pareto (GP) distributions, which provides evidence of the importance of modeling extreme rainfall from different regions of the world. In this paper, we focused on Peak Over Thresholds approach, where the Poisson-generalized Pareto distribution is considered as the proper distribution for the study of the exceedances. This research also considers the use of the generalized Pareto (GP) distribution with a Poisson model for arrivals to describe peaks over a threshold. The research used statistical techniques to fit models that used to predict extreme rainfall in Kigali. The results indicate that the proposed Poisson-GP distribution provides a better fit to maximum monthly rainfall data. Further, the Poisson-GP models are able to estimate various return levels. The research also found a slow increase in return levels for maximum monthly rainfall for higher return periods, and further, the intervals are increasingly wider as the return period is increasing.

Keywords: exceedances, extreme value theory, generalized Pareto distribution, Poisson generalized Pareto distribution

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Authors: Babak Azari, Afshin Pourahmad, Babak Sadeghi, Masuod Mokhtari

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In this work, Equisetnm arvense plant extract was used for preparing amorphous SiO₂. For preparing of SiO₂/zeolitic imidazolate framework-8 (ZIF-8) nanocomposite by solvothermal method, the synthesized SiO₂ was added to the synthesis mixture ZIF-8. The nanocomposite was characterized using a range of techniques. The photocatalytic activity of SiO₂/ZIF-8 was investigated systematically by degrading crystal violet as a cationic dye under Ultraviolet light irradiation. Among synthesized samples (SiO₂, ZIF-8 and SiO₂/ZIF-8), the SiO₂/ZIF-8 exhibited the highest photocatalytic activity and improved stability compared to pure SiO₂ and ZIF-8. As evidenced by Scanning Electron Microscopy and Transmission electron microscopy images, ZIF-8 particles without aggregation are located over SiO₂. The SiO₂ not only provides structured support for ZIF-8 but also prevents the aggregation of ZIF-8 Metal-organic framework in comparison to the isolated ZIF-8. The superior activity of this photocatalyst was attributed to the synergistic effects from SiO₂ owing to (I) an electron acceptor (from ZIF-8) and an electron donor (to O₂ molecules), (II) preventing recombination of electron-hole in ZIF-8, and (III) maximum interfacial contact ZIF-8 with the SiO₂ surface without aggregation or prevent the accumulation of ZIF-8. The results demonstrate that holes (h+) and •O₂- are primary reactive species involved in the photocatalytic oxidation process. Moreover, the SiO₂/ZIF-8 photocatalyst did not show any obvious loss of photocatalytic activity during five-cycle tests, which indicates that the heterostructured photocatalyst was highly stable and could be used repeatedly.

Keywords: nano, zeolit, potocatalist, nanocomposite

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Authors: Anup Anand Deshmukh, Catherine Soladie, Renaud Seguier

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Emotion plays a key role in many applications like healthcare, to gather patients’ emotional behavior. Unlike typical ASR (Automated Speech Recognition) problems which focus on 'what was said', it is equally important to understand 'how it was said.' There are certain emotions which are given more importance due to their effectiveness in understanding human feelings. In this paper, we propose an approach that models human stress from audio signals. The research challenge in speech emotion detection is finding the appropriate set of acoustic features corresponding to an emotion. Another difficulty lies in defining the very meaning of emotion and being able to categorize it in a precise manner. Supervised Machine Learning models, including state of the art Deep Learning classification methods, rely on the availability of clean and labelled data. One of the problems in affective computation is the limited amount of annotated data. The existing labelled emotions datasets are highly subjective to the perception of the annotator. We address the first issue of feature selection by exploiting the use of traditional MFCC (Mel-Frequency Cepstral Coefficients) features in Convolutional Neural Network. Our proposed Emo-CNN (Emotion-CNN) architecture treats speech representations in a manner similar to how CNN’s treat images in a vision problem. Our experiments show that Emo-CNN consistently and significantly outperforms the popular existing methods over multiple datasets. It achieves 90.2% categorical accuracy on the Emo-DB dataset. We claim that Emo-CNN is robust to speaker variations and environmental distortions. The proposed approach achieves 85.5% speaker-dependant categorical accuracy for SAVEE (Surrey Audio-Visual Expressed Emotion) dataset, beating the existing CNN based approach by 10.2%. To tackle the second problem of subjectivity in stress labels, we use Lovheim’s cube, which is a 3-dimensional projection of emotions. Monoamine neurotransmitters are a type of chemical messengers in the brain that transmits signals on perceiving emotions. The cube aims at explaining the relationship between these neurotransmitters and the positions of emotions in 3D space. The learnt emotion representations from the Emo-CNN are mapped to the cube using three component PCA (Principal Component Analysis) which is then used to model human stress. This proposed approach not only circumvents the need for labelled stress data but also complies with the psychological theory of emotions given by Lovheim’s cube. We believe that this work is the first step towards creating a connection between Artificial Intelligence and the chemistry of human emotions.

Keywords: deep learning, brain chemistry, emotion perception, Lovheim's cube

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Authors: Erdem Elibol, Musa Cadırcı, Nedim Tutkun

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Recently, colloidal quantum dots (CQDs) have drawn increasing attention due to their unique size tunability, which makes them potential candidates for numerous applications including photovoltaic, LEDs, and imaging. However, the main challenge to exploit CQDs properly is that there has not been an effective method to produce them with highly crystalline form and narrow size dispersion. Hot injection method is one of the widely used techniques to produce high-quality nanoparticles. In this method, the key parameter is to reduce the time for injection of the precursors into each other, which yields fast and constant nucleation rate and hence to highly monodisperse QDs. In conventional hot injection method, the injection of precursors is carried out using standard lab syringes with long needles. However, this technique is relatively slow and thus will result in poor optical properties in QDs. In this work, highly luminescent CdTe QDs were synthesised by transferring hot precursors into each other using cannula method. Unlike regular syringe technique, with the help of high pressure difference between two precursors’ flasks and wide cross-section of cannula, the hot cannulation process is too short which yields narrow size distribution and high quantum yield of CdTe QDs. Here QDs with full width half maximum (FWHM) of 28 nm was achieved. In addition, the photoluminescence quantum yield of our samples was measured to be about 21 ± 0.9 which is at least twice the previous record values for CdTe QDs wherein syringe was used to transfer precursors.

Keywords: CdTe, hot injection method, luminescent, quantum dots

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Authors: Rezzouq Asiya, Bouftou Abderrahim, Belfadil Doha, Taoufyk Azzeddine, El Bouchti Mehdi, Zyade Souad, Cherkaoui Omar, Majid Sanaa

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Plastic packaging is widely used, but its pollution is a major environmental problem. Solutions require new sustainable technologies, environmental management, and the use of bio-based polymers as sustainable packaging. Cellulose acetate (CA) is a biobased polymer used in a variety of applications such as the manufacture of plastic films, textiles, and filters. However, it has limitations in terms of thermal stability and rigidity, which necessitates the addition of plasticizers to optimize its use in packaging. Plasticizers are molecules that increase the flexibility of polymers, but their influence on the chemical and physical properties of films (CA) has not been studied in detail. Some studies have focused on mechanical and thermal properties. However, an in-depth analysis is needed to understand the interactions between the additives and the polymer matrix. In this study, the aim is to examine the effect of two types of plasticizers, glycerol (a conventional plasticizer) and an ionic liquid, on the transparency, mechanical, thermal and barrier properties of cellulose acetate (CA) films prepared by the solution-casting method . Various analytical techniques were used to characterize these films, including infrared spectroscopy (FT-IR), X-ray diffraction (XRD), thermogravimetric analysis (TGA), water vapor permeability (WVP), oxygen permeability, scanning electron microscopy (SEM), opacity, transmission analysis and mechanical tests.

Keywords: cellulose acetate, plasticizers, biopolymers, ionic liquid, glycerol.

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Authors: Nazanin Pilevari, Kamyar Mahmoodi

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The purpose of this research is to provide a model based on fuzzy inference system for evaluating empowerment of Knowledge management. The first prototype of the research was developed based on the study of literature. In the next step, experts were provided with these models and after implementing consensus-based reform, the views of Fuzzy Delphi experts and techniques, components and Index research model were finalized. Culture, structure, IT and leadership were considered as dimensions of empowerment. Then, In order to collect and extract data for fuzzy inference system based on knowledge and Experience, the experts were interviewed. The values obtained from designed fuzzy inference system, made review and assessment of the organization's empowerment of Knowledge management possible. After the design and validation of systems to measure indexes ,empowerment of Knowledge management and inputs into fuzzy inference) in the AYANDEH Bank, a questionnaire was used. In the case of this bank, the system output indicates that the status of empowerment of Knowledge management, culture, organizational structure and leadership are at the moderate level and information technology empowerment are relatively high. Based on these results, the status of knowledge management empowerment in AYANDE Bank, was moderate. Eventually, some suggestions for improving the current situation of banks were provided. According to studies of research history, the use of powerful tools in Fuzzy Inference System for assessment of Knowledge management and knowledge management empowerment such an assessment in the field of banking, are the innovation of this Research.

Keywords: knowledge management, knowledge management empowerment, fuzzy inference system, fuzzy Delphi

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Authors: Hanieh Sadat Jannesari, Hoori Jannesar, Alireza Hajian HosseinAbadi

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In general, buildings are responsible for a considerable share of consumed energy and carbon emissions worldwide and play a significant role in formulating sustainable development strategies. Therefore, a lot of effort is put into the design and construction of zero-energy buildings (ZEBs) to help eliminate the problems associated with the reduction of energy resources and environmental degradation. Two strategies are significant in designing ZEBs: minimizing the need for energy utilization in buildings (particularly for cooling and heating) through highly energy-efficient designs and using renewable energies and other technologies to meet the remaining energy needs. This paper reviews the works related to these two strategies concerning sustainable energy-saving solutions using renewable energy technologies and the Internet of Things in ZEBs. Drawing on the theories and recently implemented projects of energy engineers in ZEBs, we have reported the required technologies within the framework of this paper’s objectives. Overall, solutions based on renewable and sustainable technologies such as photovoltaic (PV) modules, thermal collectors, Phase Change Material (PCM) techniques, etc., are used in active and passive systems designed for various applications in such buildings as cooling, heating, lighting, cooking, etc. The results obtained from examining these projects show that it is possible to minimize the amount of energy required to be produced for and consumed by these buildings.

Keywords: active and passive renewable energy systems, internet of things, storage, zero energy buildings

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Authors: A. Degale Desta, Tamirat Kebamo

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Background: Recent advances in computer vision and pattern recognition have significantly improved activity recognition through video analysis, particularly with the application of Deep Convolutional Neural Networks (CNNs). One-stage detectors now enable efficient video-based recognition by simultaneously predicting object categories and locations. Such advancements are highly relevant in educational settings where CCTV surveillance could automatically monitor academic activities, enhancing security and classroom management. However, current datasets and recognition systems lack the specific focus on campus environments necessary for practical application in these settings.Objective: This study aims to address this gap by developing a dataset and testing an automated activity recognition system specifically tailored for educational campuses. The EthioCAD dataset was created to capture various classroom activities and teacher-student interactions, facilitating reliable recognition of academic activities using deep learning models. Method: EthioCAD, a novel video-based dataset, was created with a design science research approach to encompass teacher-student interactions across three domains and 18 distinct classroom activities. Using the Roboflow AI framework, the data was processed, with 4.224 KB of frames and 33.485 MB of images managed for frame extraction, labeling, and organization. The Ultralytics YOLOv8 model was then implemented within Google Colab to evaluate the dataset’s effectiveness, achieving high mean Average Precision (mAP) scores. Results: The YOLOv8 model demonstrated robust activity recognition within campus-like settings, achieving an mAP50 of 90.2% and an mAP50-95 of 78.6%. These results highlight the potential of EthioCAD, combined with YOLOv8, to provide reliable detection and classification of classroom activities, supporting automated surveillance needs on educational campuses. Discussion: The high performance of YOLOv8 on the EthioCAD dataset suggests that automated activity recognition for surveillance is feasible within educational environments. This system addresses current limitations in campus-specific data and tools, offering a tailored solution for academic monitoring that could enhance the effectiveness of CCTV systems in these settings. Conclusion: The EthioCAD dataset, alongside the YOLOv8 model, provides a promising framework for automated campus activity recognition. This approach lays the groundwork for future advancements in CCTV-based educational surveillance systems, enabling more refined and reliable monitoring of classroom activities.

Keywords: deep CNN, EthioCAD, deep learning, YOLOv8, activity recognition

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Authors: L. Almulla

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Native plants are better adapted to the local environment providing a more natural effect on landscape projects; their use will both conserve natural resources and produce sustainable greenery. Continuation of evaluation of additional native plants is essential to increase diversity of plant resources for greenery projects. Therefore, in this project an effort was made to study the mass multiplication of further native plants for greenery applications. Standardization of vegetative propagation methods is essential for conservation and sustainable utilization of native plants in restoration projects. Moreover, these simple propagation methods can be readily adapted by the local nursery sector in Kuwait. In the present study, various treatments were used to mass multiply selected plants using vegetative parts to secure maximum rooting and initial growth. Soft or semi-hardwood cuttings of selected native plants were collected from mother plants and subjected to different treatments. Pennisetum divisum can be vegetatively propagated by cuttings/off-shoots. However, Tamarix aucheriana showed maximum number of rooted cuttings and stronger vigor seedlings with the lowest growth hormone concentration. Standardizing the propagation techniques for the native plant species will add to the rehabilitation and landscape revegetation projects in Kuwait.

Keywords: Kuwait desert, landscape, rooting percentage, vegetative propagation

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Authors: Carmen Avilés Palacios, Camilo Muñoz Arenas, Joaquín García Alfonso, Jesús González Arteaga, Alberto Alcalde Calonge

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There is a need for review of the consumption and production models of industrialized states in accordance with the Paris Agreement and the Sustainable Development Goals (1) (OECD, 2016). This constitutes the basis of the bioeconomy (2) that is focused on striking a balance between economic development, social development and environmental protection. Bioeconomy promotes the adequate use and consumption of renewable natural resources (3) and involves developing new products and services adapted to the principles of circular economy: more sustainable (reusable, biodegradable, renewable and recyclable) and with a lower carbon footprint (4). In this context, Urban Forest Innovation Lab (UFIL) grows, an Urban Laboratory for experimentation focused on promoting entrepreneurship in forest bioeconomy (www.uiacuenca.es). UFIL generates local wellness taking sustainable advantage of an endogenous asset, forests. UFIL boosts forest bioeconomy opening its doors of knowledge to pioneers in this field, giving the opportunity to be an active part of a change in the way of understanding the exploitation of natural resources, discovering business, learning strategies and techniques and incubating business ideas So far now, 100 entrepreneurs are incubating their nearly 30 new business plans. UFIL has promoted an ecosystem to connect the rural-urban world that promotes sustainable rural development around the forest.

Keywords: bioeconomy, forestry, innovation, entrepreneurship

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Authors: Oladimeji Israel Ajayi

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Agricultural development has long been identified as a cornerstone for wealth creation and economic growth, particularly in emerging economies like Nigeria. This study examines the relationship between agricultural development and wealth creation in Nigeria, emphasizing the sector's potential in reducing poverty, creating employment, and boosting economic stability. Nigeria, endowed with fertile land and a favorable climate, has a significant agricultural base that, if fully leveraged, can transition the economy from oil dependency to a diversified and sustainable growth model. However, challenges such as limited access to credit, poor infrastructure, outdated farming techniques, and climate variability hinder optimal productivity. This research employs a mixed-methods approach, analyzing data from the Nigerian National Bureau of Statistics and the Food and Agriculture Organization to understand how investments in agriculture influence wealth indicators such as GDP growth, employment rates, and rural income levels. The findings reveal a strong positive correlation between agricultural investment and wealth creation, suggesting that strategic policies focusing on mechanization, credit accessibility, and sustainable practices could significantly boost agricultural productivity and contribute to wealth distribution in Nigeria. This study contributes to policy discourse by highlighting agriculture’s role as a transformative tool for economic resilience and sustainable wealth creation in Nigeria.

Keywords: agricultural development, poverty reduction, wealth creation, prosperity

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Authors: Andrey Khalov

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The rapid proliferation of unstructured data in IT infrastructure management demands innovative approaches for extracting actionable knowledge. This paper presents a framework for ontology-based knowledge extraction that combines relational graph neural networks (R-GNN) with large language models (LLMs). The proposed method leverages the DOLCE framework as the foundational ontology, extending it with concepts from ITSMO for domain-specific applications in IT service management and outsourcing. A key component of this research is the use of transformer-based models, such as DeBERTa-v3-large, for automatic entity and relationship extraction from unstructured texts. Furthermore, the paper explores how transfer learning techniques can be applied to fine-tune large language models (LLaMA) for using to generate synthetic datasets to improve precision in BERT-based entity recognition and ontology alignment. The resulting IT Ontology (ITO) serves as a comprehensive knowledge base that integrates domain-specific insights from ITIL processes, enabling more efficient decision-making. Experimental results demonstrate significant improvements in knowledge extraction and relationship mapping, offering a cutting-edge solution for enhancing cognitive computing in IT service environments.

Keywords: ontology expansion, synthetic dataset, transformer fine-tuning, concept extraction, DOLCE, BERT, taxonomy, LLM, NER

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Authors: Zuzana Chaloupková, Zdeňka Marková, Václav Ranc, Radek Zbořil

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Prostate cancer is now one of the most serious oncological diseases in men with an incidence higher than that of all other solid tumors combined. Diagnosis of prostate cancer usually involves detection of related genes or detection of marker proteins, such as PSA. One of the new potential markers is PSMA (prostate specific membrane antigen). PSMA is a unique membrane bound glycoprotein, which is considerably overexpressed on prostate cancer as well as neovasculature of most of the solid tumors. Commonly applied methods for a detection of proteins include techniques based on immunochemical approaches, including ELISA and RIA. Magnetically assisted surface enhanced Raman spectroscopy (MA-SERS) can be considered as an interesting alternative to generally accepted approaches. This work describes a utilization of MA-SERS in a detection of PSMA in human blood. This analytical platform is based on magnetic nanocomposites Fe3O4@Ag, functionalized by a low-molecular selector labeled as JB303. The system allows isolating the marker from the complex sample using application of magnetic force. Detection of PSMA is than performed by SERS effect given by a presence of silver nanoparticles. This system allowed us to analyze PSMA in clinical samples with limits of detection lower than 1 ng/mL.

Keywords: diagnosis, cancer, PSMA, MA-SERS, Ag nanoparticles

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Authors: Suelane Garcia Fontes, Silvio Luiz Stanzani, Pedro L. Pizzigatti Corrła Ronaldo G. Morato

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Environmental changes and major natural disasters are most prevalent in the world due to the damage that humanity has caused to nature and these damages directly affect the lives of animals. Thus, the study of animal behavior and their interactions with the environment can provide knowledge that guides researchers and public agencies in preservation and conservation actions. Exploratory analysis of animal movement can determine the patterns of animal behavior and with technological advances the ability of animals to be tracked and, consequently, behavioral studies have been expanded. There is a lot of research on animal movement and behavior, but we note that a proposal that combines resources and allows for exploratory analysis of animal movement and provide statistical measures on individual animal behavior and its interaction with the environment is missing. The contribution of this paper is to present the framework AniMoveMineR, a unified solution that aggregates trajectory analysis and data mining techniques to explore animal movement data and provide a first step in responding questions about the animal individual behavior and their interactions with other animals over time and space. We evaluated the framework through the use of monitored jaguar data in the city of Miranda Pantanal, Brazil, in order to verify if the use of AniMoveMineR allows to identify the interaction level between these jaguars. The results were positive and provided indications about the individual behavior of jaguars and about which jaguars have the highest or lowest correlation.

Keywords: data mining, data science, trajectory, animal behavior

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Authors: Gatot Dwi Hendro, Hayyan ul Haq

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This work aims to explore the strategy in embodying the ecological validity in creating the sustainability of public policy, particularly in strengthening the green economy in Indonesia. This green economy plays an important role in supporting the national development in Indonesia, as it is a part of the national policy that posits the primary priority in Indonesian governance. The green economy refers to the national development covering strategic natural resources, such as mining, gold, oil, coal, forest, water, marine, and the other supporting infrastructure for products and distribution, such as fabrics, roads, bridges, and so forth. Thus, all activities in those national development should consider the sustainability. This sustainability requires the strong commitment of the national and regional government, as well as the local governments to put the ecology as the main requirement for issuing any policy, such as licence in mining production, and developing and building new production and supporting infrastructures for optimising the national resources. For that reason this work will focus on the strategy how to embody the ecological values and norms in the public policy. In detail, this work will offer the method, i.e. legal techniques, in visualising and embodying the norms and public policy that valid ecologically. This ecological validity is required in order to maintain and sustain our collective life.

Keywords: ecological validity, sustainable development, coherence, Indonesian Pancasila values, environment, marine

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Authors: David P. Tantua, Abdulkader Helwan

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Biometrics has been recently used for the human identification systems using the biological traits such as the fingerprints and iris scanning. Identification systems based biometrics show great efficiency and accuracy in such human identification applications. However, these types of systems are so far based on some image processing techniques only, which may decrease the efficiency of such applications. Thus, this paper aims to develop a human palmprint identification system using multi-layer perceptron neural network which has the capability to learn using a backpropagation learning algorithms. The developed system uses images obtained from a public database available on the internet (CASIA). The processing system is as follows: image filtering using median filter, image adjustment, image skeletonizing, edge detection using canny operator to extract features, clear unwanted components of the image. The second phase is to feed those processed images into a neural network classifier which will adaptively learn and create a class for each different image. 100 different images are used for training the system. Since this is an identification system, it should be tested with the same images. Therefore, the same 100 images are used for testing it, and any image out of the training set should be unrecognized. The experimental results shows that this developed system has a great accuracy 100% and it can be implemented in real life applications.

Keywords: biometrics, biological traits, multi-layer perceptron neural network, image skeletonizing, edge detection using canny operator

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Authors: Shital Suresh Borse, Vijayalaxmi Kadroli

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E-commerce industries nowadays implement the latest AI, ML Techniques to improve their own performance and prediction accuracy. This helps to gain a huge profit from the online market. Ant Colony Optimization, Genetic algorithm, Particle Swarm Optimization, Neural Network & GWO help many e-commerce industries for up-gradation of their predictive performance. These algorithms are providing optimum results in various applications, such as stock price prediction, prediction of drug-target interaction & user ratings of similar products in e-commerce sites, etc. In this study, customer reviews will play an important role in prediction analysis. People showing much interest in buying a lot of services& products suggested by other customers. This ultimately increases net profit. In this work, a convolution neural network (CNN) is proposed which further is useful to optimize the prediction accuracy of an e-commerce website. This method shows that CNN is used to optimize hyperparameters of GWO algorithm using an appropriate coding scheme. Accurate model results are verified by comparing them to PSO results whose hyperparameters have been optimized by CNN in Amazon's customer review dataset. Here, experimental outcome proves that this proposed system using the GWO algorithm achieves superior execution in terms of accuracy, precision, recovery, etc. in prediction analysis compared to the existing systems.

Keywords: prediction analysis, e-commerce, machine learning, grey wolf optimization, particle swarm optimization, CNN

Procedia PDF Downloads 118