Search results for: deep vibro techniques

Authors: Mohamed Benaicha, Mahdi Allam

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A new two stage electrochemical process as a safe, large area and low processing cost technique for the production of semi-conducting CuInSe2 (CIS) thin films is studied. CuIn precursors were first potentiostatically electrodeposited onto molybdenum substrates from an acidic thiocyanate electrolyte. In a second stage, the prepared metallic CuIn layers were used as substrate in the selenium electrochemical deposition system and subjected to a thermal treatment in vacuum atmosphere, to eliminate binary phase formation by reaction of the Cu2-x Se and InxSey selenides, leading to the formation of CuInSe2 thin film. Electrochemical selenization from aqueous electrolyte is introduced as an alternative to toxic and hazardous H2Se or Se vapor phase selenization used in physical techniques. In this study, the influence of film deposition parameters such as bath composition, temperature and potential on film properties was studied. The electrochemical, morphological, structural and compositional properties of electrodeposited thin films were characterized using various techniques. Results of Cyclic and Stripping-Cyclic Voltammetry (CV, SCV), Scanning Electron Microscopy (SEM) and Energy Dispersive X-Ray microanalysis (EDX) investigations revealed good reproducibility and homogeneity of the film composition. Thereby optimal technological parameters for the electrochemical production of CuIn, Se as precursors for CuInSe2 thin layers are determined.

Keywords: photovoltaic, CIGS, copper alloys, electrodeposition, thin films

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Authors: S. K. M. Basha

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Nallamalas one of the centres of Plant Diversity (CPD) (WWF&IUCN,1995) is located in the central eastern Ghats between latitudes 15.20’-16.30’N and Longitude 78.30-80.10E in Andhra Pradesh, extended to an area of 7640 Sq.Km. No Comprehensive work available for RET Plants in the study area, therefore the objective of the present paper is to document the RET Medicinal Plants of Nallamalias and their uses by the local people of the area. In India, one of the major resources to know about the number of plant species and their medicinal values is the groups who are habituated in near and deep forests. The most common groups in south Indian forests are Yanadis and Yerukulas. These two groups of people are residing in the forest, which is located very far from the modern society, towns and cities. They are following traditional methods obtained from their forefathers in all respects, including medication. They are the only source to know many medicinal plants in the areas where they reside and are also important to record the medicinal properties of various plant species which are not reported. The new reports may help in drug industry in order to develop pharmaceutical herbal medicine for human health. In the present study, nearly 150 rare species have been found to be used for various ailments. Out of these 23 species are critically endangered, over 25 are vulnerable and around 22 comes under the category of near threatened. Some important species like Christella dentate, Careya arborea are used for curing cough and cold. Piper attnuatum, piper nigrum are used for curing skin disease. Ipomoea mauritiana is used against male impotency.Glycosmis cochinensis, Entada perseatha are used as contraceptives. The roots of Andrographis nallamalayana and Acrocephalus indicus are used for leucorrhoea. While the stem barks of Gyrocarpus americanus is given orally for spider bite. Piper hymenophyllum leaves mixed with turmeric and gingerly oil is used externally for mouth ulcers in cattle. Piper nigrum fruits are used for skin diseases. Vernonia anthelmentica seeds are used for indigestion. It was widely distributed in this hills. Due to over exploitation this species was in declined condition. Sterculia urens which is a sorce of gum for tribal, due to over exploitation this species declaimed in these hills. Hence, there is an urgent need to conserve the medicinal plants and prevent their exploitation and extinction with the help of tribals. There is a need to adopt sustainable utilization, cultivation and micro propagation techniques. Medicinal plants are as potent and effective today as they were thousands of years ago. They are natures wonderful gift to mankind and are involved in India as a very rich ancient heritage of traditional systems medicine i.e., ayurveda, siddha and unani. Unfortunately, these traditions have been largely eroded because of lack of support and recognition as well as rapid destruction of natural habitats which has led to shrinkage of medicinal plants therefore the conservation of medicinal plants and the revitalization of local health traditions has been taken up on priority basis.

Keywords: RET plants CPD, IUCN, nallamalas, yanadis, yerukulas

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Authors: N. Singh, A. Khullar, R. Shrivastava, I. Singh, A. S. Kumar

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Steel forms the basis of any modern society and is essential to economic growth. India’s annual crude steel production has seen a consistent increase over the past years and is poised to grow to 300 million tons per annum by 2030-31 from current level of 110-120 million tons per annum. Steel industry is highly capital-intensive industry and to remain competitive, it is imperative that it invests in operational excellence. Due to inherent nature of the industry, there is large amount of variability in its supply chain both internally and externally. Production and productivity of a steel plant is greatly affected by the bottlenecks present in material flow logistics. The internal logistics constituting of transport of liquid metal within a steel melting shop (SMS) presents an opportunity in increasing the throughput with marginal capital investment. The study was carried out at one of the SMS of an integrated steel plant located in the eastern part of India. The plant has three SMS’s and the study was carried out at one of them. The objective of this study was to identify means to optimize SMS hot metal logistics through application of industrial engineering techniques. The study also covered the identification of non-value-added activities and proposed methods to eliminate the delays and improve the throughput of the SMS.

Keywords: optimization, steel making, supply chain, throughput enhancement, workforce productivity

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Authors: Jose Daniel Giraldo Arias, Camilo Rojas Gomez, David Villegas Delgado, Gullermo Idarraga Alarcon, Juan Meza Meza

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The reverse engineering processes are widely used in the industry with the main goal to determine the materials and the manufacture used to produce a component. There are a lot of characterization techniques and computational tools that are used in order to get this information. A study case of a reverse engineering applied to a secondary sandwich- hybrid type structure used in a helicopter is presented. The methodology used consists of five main steps, which can be applied to any other similar component: Collect information about the service conditions of the part, disassembly and dimensional characterization, functional characterization, material properties characterization and manufacturing processes characterization, allowing to obtain all the supports of the traceability of the materials and processes of the aeronautical products that ensure their airworthiness. A detailed explanation of each step is covered. Criticality and comprehend the functionalities of each part, information of the state of the art and information obtained from interviews with the technical groups of the helicopter’s operators were analyzed,3D optical scanning technique, standard and advanced materials characterization techniques and finite element simulation allow to obtain all the characteristics of the materials used in the manufacture of the component. It was found that most of the materials are quite common in the aeronautical industry, including Kevlar, carbon, and glass fibers, aluminum honeycomb core, epoxy resin and epoxy adhesive. The stacking sequence and volumetric fiber fraction are a critical issue for the mechanical behavior; a digestion acid method was used for this purpose. This also helps in the determination of the manufacture technique which for this case was Vacuum Bagging. Samples of the material were manufactured and submitted to mechanical and environmental tests. These results were compared with those obtained during reverse engineering, which allows concluding that the materials and manufacture were correctly determined. Tooling for the manufacture was designed and manufactured according to the geometry and manufacture process requisites. The part was manufactured and the mechanical, and environmental tests required were also performed. Finally, a geometric characterization and non-destructive techniques allow verifying the quality of the part.

Keywords: reverse engineering, sandwich-structured composite parts, helicopter, mechanical properties, prototype

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Authors: Ahmed Mahmoud Ahmed Abouelmagd

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The diversity is the usual remedy of the transmitted signal level variations (Fading phenomena) in radio frequency channels. Diversity techniques utilize two or more copies of a signal and combine those signals to combat fading. The basic concept of diversity is to transmit the signal via several independent diversity branches to get independent signal replicas via time – frequency - space - and polarization diversity domains. Coding and decoding processes can be an alternative remedy for fading phenomena, it cannot increase the channel capacity, but it can improve the error performance. In this paper we propose the use of replication decoding with BCH code class, and Viterbi decoding algorithm with convolution coding; as examples of coding and decoding processes. The results are compared to those obtained from two optimized selection space diversity techniques. The performance of Rayleigh fading channel, as the model considered for radio frequency channels, is evaluated for each case. The evaluation results show that the coding and decoding approaches, especially the BCH coding approach with replication decoding scheme, give better performance compared to that of selection space diversity optimization approaches. Also, an approach for combining the coding and decoding diversity as well as the space diversity is considered, the main disadvantage of this approach is its complexity but it yields good performance results.

Keywords: Rayleigh fading, diversity, BCH codes, Replication decoding, ‎convolution coding, viterbi decoding, space diversity

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Authors: K. Silakaew, P. Thongbai

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There is an increasing need for high–permittivity polymer–matrix composites (PMC) owing to the rapid development of the electronics industry. Unfortunately, the dielectric permittivity of PMC is still too low ( < 80). Moreover, the dielectric loss tangent is usually high (tan > 0.1) when the dielectric permittivity of PMC increased. In this research work, the dielectric properties of poly(vinylidene fluoride) (PVDF)–based nanocomposites can be significantly improved by incorporating by silver–BaTiO3 (Ag–BT) ceramic hybrid nanoparticles. The Ag–BT/PVDF nanocomposites were fabricated using various volume fractions of Ag–BT hybrid nanoparticles (fAg–BT = 0–0.5). The Ag–BT/PVDF nanocomposites were characterized using several techniques. The main phase of Ag and BT can be detected by the XRD technique. The microstructure of the Ag–BT/PVDF nanocomposites was investigated to reveal the dispersion of Ag–BT hybrid nanoparticles because the dispersion state of a filler can have an effect on the dielectric properties of the nanocomposites. It was found that the filler hybrid nanoparticles were well dispersed in the PVDF matrix. The phase formation of PVDF phases was identified using the XRD and FTIR techniques. We found that the fillers can increase the polar phase of a PVDF polymer. The fabricated Ag–BT/PVDF nanocomposites are systematically characterized to explain the dielectric behavior in Ag–BT/PVDF nanocomposites. Interestingly, largely enhanced dielectric permittivity (>240) and suppressed loss tangent (tan<0.08) over a wide frequency range (102 – 105 Hz) are obtained. Notably, the dielectric permittivity is slightly dependent on temperature. The greatly enhanced dielectric permittivity was explained by the interfacial polarization between the Ag and PVDF interface, and due to a high permittivity of BT particles.

Keywords: BaTiO3, PVDF, polymer composite, dielectric properties

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Authors: Valentin Père, Mathieu Milhé, Fabien Baillon, Jean-Louis Dirion

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Microgrids offer potential solutions to meet the need for local grid stability and increase isolated networks autonomy with the integration of intermittent renewable energy production and storage facilities. In such a context, sizing production and storage for a given network is a complex task, highly depending on input data such as power load profile and renewable resource availability. This work aims at developing an operating cost computation methodology for different microgrid designs based on the use of deep reinforcement learning (RL) algorithms to tackle the optimal operation problem in stochastic environments. RL is a data-based sequential decision control method based on Markov decision processes that enable the consideration of random variables for control at a chosen time scale. Agents trained via RL constitute a promising class of Energy Management Systems (EMS) for the operation of microgrids with energy storage. Microgrid sizing (or design) is generally performed by minimizing investment costs and operational costs arising from the EMS behavior. The latter might include economic aspects (power purchase, facilities aging), social aspects (load curtailment), and ecological aspects (carbon emissions). Sizing variables are related to major constraints on the optimal operation of the network by the EMS. In this work, an islanded mode microgrid is considered. Renewable generation is done with photovoltaic panels; an electrochemical battery ensures short-term electricity storage. The controllable unit is a hydrogen tank that is used as a long-term storage unit. The proposed approach focus on the transfer of agent learning for the near-optimal operating cost approximation with deep RL for each microgrid size. Like most data-based algorithms, the training step in RL leads to important computer time. The objective of this work is thus to study the potential of Batch-Constrained Q-learning (BCQ) for the optimal sizing of microgrids and especially to reduce the computation time of operating cost estimation in several microgrid configurations. BCQ is an off-line RL algorithm that is known to be data efficient and can learn better policies than on-line RL algorithms on the same buffer. The general idea is to use the learned policy of agents trained in similar environments to constitute a buffer. The latter is used to train BCQ, and thus the agent learning can be performed without update during interaction sampling. A comparison between online RL and the presented method is performed based on the score by environment and on the computation time.

Keywords: batch-constrained reinforcement learning, control, design, optimal

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Authors: Petcharat Lovichakorntikul, John Walsh

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Meditation is in the process of becoming a globalized practice and its benefits have been widely acknowledged. The first wave of internationalized meditation techniques and practices was represented by Chan and Zen Buddhism and a new wave of practice has arisen in Thailand as part of the Phra Dhammakaya temple movement. This form of meditation is intended to be simple and straightforward so that it can easily be taught to people unfamiliar with the basic procedures and philosophy. This has made Phra Dhammakaya an important means of outreach to the international community. One notable aspect is to encourage adults to become like children to perform it – that is, to return to a naïve state prior to the adoption of ideology as a means of understanding the world. It is said that the Lord Buddha achieved the point of awakening at the age of seven and Phra Dhammakaya has a program to teach meditation to both children and adults. This brings about the research question of how practitioners respond to the practice of meditation and how should they be taught? If a careful understanding of how children behave can be achieved, then it will help in teaching adults how to become like children (albeit idealized children) in their approach to meditation. This paper reports on action research in this regard. Personal interviews and focus groups are held with a view to understanding self-learning methods with respect to Buddhist meditation and understanding and appreciation of the practices involved. The findings are considered in the context of existing knowledge about different learning techniques among people of different ages. The implications for pedagogical practice are discussed and learning methods are outlined.

Keywords: Buddhist meditation, Dhammakaya, meditation technique, pedagogy, self-learning

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Authors: A. M. Tiara, Sudipto Chakraborty, S. K. Pal

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Impinging jets and their different configurations are important from the viewpoint of the fluid flow characteristics and their influence on heat transfer from metal surfaces due to their complex flow characteristics. Such flow characteristics results in highly variable heat transfer from the surface, resulting in varying cooling rates which affects the mechanical properties including hardness and strength. The overall objective of the current research is to conduct a fundamental investigation of the heat transfer mechanisms for an impinging coolant jet. Numerical simulation of the cooling process gives a detailed analysis of the different parameters involved even though employing Computational Fluid Dynamics (CFD) to simulate the real time process, being a relatively new research area, poses many challenges. The heat transfer mechanism in the current research is actuated by jet cooling. The computational tool used in the ongoing research for simulation of the cooling process is ANSYS Workbench software. The temperature and heat flux distribution along the steel strip with the effect of various flow parameters on the heat transfer rate can be observed in addition to determination of the jet impingement patterns, which is the major aim of the present analysis. Modelling both jet and air atomized cooling techniques using CFD methodology and validating with those obtained experimentally- including trial and error with different models and comparison of cooling rates from both the techniques have been included in this work. Finally some concluding remarks are made that identify some gaps in the available literature that have influenced the path of the current investigation.

Keywords: CFD, heat transfer, impinging jets, numerical simulation

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Authors: Enoch Ocran

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Film Pertinence maintains consistency in storytelling by sustaining the natural flow of action while evoking a particular feeling or emotion from the viewers with selected motion pictures. This study presents a thorough investigation of "Film Pertinence" in videography that examines its influence in Africa and around the world. This research delves into the dynamic realm of visual storytelling through film, with a specific focus on the concept of Film Pertinence (FP). The study’s primary objectives are to conduct a comparative analysis of videography tools and techniques employed in both African and international contexts, examining how they contribute to the achievement of organizational goals and the enhancement of cultural awareness. The research methodology includes a comprehensive literature review, interviews with videographers from diverse backgrounds in Africa and the international arena, and the examination of pertinent case studies. The investigation aims to elucidate the multifaceted nature of videographic practices, with particular attention to equipment choices, visual storytelling techniques, cultural sensitivity, and adaptability. This study explores the impact of cultural differences on videography choices, aiming to promote understanding between African and foreign filmmakers and create more culturally sensitive films. It also explores the role of technology in advancing videography practices, resource allocation, and the influence of globalization on local filmmaking practices. The research also contributes to film studies by analyzing videography's impact on storytelling, guiding filmmakers to create more compelling narratives. The findings can inform film education, tailoring curricula to regional needs and opportunities. The study also encourages cross-cultural collaboration in the film industry by highlighting convergence and divergence in videography practices. At its core, this study seeks to explore the implications of film pertinence as a framework for videographic practice. It scrutinizes how cultural expression, education, and storytelling transcend geographical boundaries on a global scale. By analyzing the interplay between tools, techniques, and context, the research illuminates the ways in which videographers in Africa and worldwide apply film Pertinence principles to achieve cross-cultural communication and effectively capture the objectives of their clients. One notable focus of this paper is on the techniques employed by videographers in West Africa to emphasize storytelling and participant engagement, showcasing the relevance of FP in highlighting cultural awareness in visual storytelling. Additionally, the study highlights the prevalence of film pertinence in African agricultural documentaries produced for esteemed organizations such as the Roundtable on Sustainable Palm Oil (RSPO), Proforest, World Food Program, Fidelity Bank Ghana, Instituto BVRio, Aflatoun International, and the Solidaridad Network. These documentaries serve to promote prosperity, resilience, human rights, sustainable farming practices, community respect, and environmental preservation, underlining the vital role of film in conveying these critical messages. In summary, this research offers valuable insights into the evolving landscape of videography in different contexts, emphasizing the significance of film pertinence as a unifying principle in the pursuit of effective visual storytelling and cross-cultural communication.

Keywords: film pertinence, Africa, cultural awareness, videography tools

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Authors: Abbas Ahmadi, Bijan Raie, Mohammad Reza Neyshabouri, Mohammad Ali Ghorbani, Farrokh Asadzadeh

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In recent years, vast areas of Urmia Lake in Dasht-e-Tabriz has dried up leading to saline sediments exposure on the surface lake coastal areas being highly susceptible to wind erosion. This study was conducted to investigate wind erosion and its relevance to soil physicochemical properties and also modeling of wind erodibility (WE) using artificial intelligence techniques. For this purpose, 96 soil samples were collected from 0-5 cm depth in 414000 hectares using stratified random sampling method. To measure the WE, all samples (<8 mm) were exposed to 5 different wind velocities (9.5, 11, 12.5, 14.1 and 15 m s-1 at the height of 20 cm) in wind tunnel and its relationship with soil physicochemical properties was evaluated. According to the results, WE varied within the range of 76.69-9.98 (g m-2 min-1)/(m s-1) with a mean of 10.21 and coefficient of variation of 94.5% showing a relatively high variation in the studied area. WE was significantly (P<0.01) affected by soil physical properties, including mean weight diameter, erodible fraction (secondary particles smaller than 0.85 mm) and percentage of the secondary particle size classes 2-4.75, 1.7-2 and 0.1-0.25 mm. Results showed that the mean weight diameter, erodible fraction and percentage of size class 0.1-0.25 mm demonstrated stronger relationship with WE (coefficients of determination were 0.69, 0.67 and 0.68, respectively). This study also compared efficiency of multiple linear regression (MLR), gene expression programming (GEP), artificial neural network (MLP), artificial neural network based on genetic algorithm (MLP-GA) and artificial neural network based on whale optimization algorithm (MLP-WOA) in predicting of soil wind erodibility in Dasht-e-Tabriz. Among 32 measured soil variable, percentages of fine sand, size classes of 1.7-2.0 and 0.1-0.25 mm (secondary particles) and organic carbon were selected as the model inputs by step-wise regression. Findings showed MLP-WOA as the most powerful artificial intelligence techniques (R2=0.87, NSE=0.87, ME=0.11 and RMSE=2.9) to predict soil wind erodibility in the study area; followed by MLP-GA, MLP, GEP and MLR and the difference between these methods were significant according to the MGN test. Based on the above finding MLP-WOA may be used as a promising method to predict soil wind erodibility in the study area.

Keywords: wind erosion, erodible fraction, gene expression programming, artificial neural network

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Authors: Kostas Kouvaris, Anastasia Eleftheriou, Georgios A. Sarantitis, Apostolos Chondronasios

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To achieve the objective of almost zero carbon energy solutions by 2050, the EU needs to accelerate the development of integrated, highly efficient and environmentally friendly solutions. In this direction, district heating and cooling (DHC) emerges as a viable and more efficient alternative to conventional, decentralized heating and cooling systems, enabling a combination of more efficient renewable and competitive energy supplies. In this paper, we develop a forecasting tool for near real-time local weather and thermal energy demand predictions for an entire DHC network. In this fashion, we are able to extend the functionality and to improve the energy efficiency of the DHC network by predicting and adjusting the heat load that is distributed from the heat generation plant to the connected buildings by the heat pipe network. Two case-studies are considered; one for Vransko, Slovenia and one for Montpellier, France. The data consists of i) local weather data, such as humidity, temperature, and precipitation, ii) weather forecast data, such as the outdoor temperature and iii) DHC operational parameters, such as the mass flow rate, supply and return temperature. The external temperature is found to be the most important energy-related variable for space conditioning, and thus it is used as an external parameter for the energy demand models. For the development of the forecasting tool, we use state-of-the-art deep neural networks and more specifically, recurrent networks with long-short-term memory cells, which are able to capture complex non-linear relations among temporal variables. Firstly, we develop models to forecast outdoor temperatures for the next 24 hours using local weather data for each case-study. Subsequently, we develop models to forecast thermal demand for the same period, taking under consideration past energy demand values as well as the predicted temperature values from the weather forecasting models. The contributions to the scientific and industrial community are three-fold, and the empirical results are highly encouraging. First, we are able to predict future thermal demand levels for the two locations under consideration with minimal errors. Second, we examine the impact of the outdoor temperature on the predictive ability of the models and how the accuracy of the energy demand forecasts decreases with the forecast horizon. Third, we extend the relevant literature with a new dataset of thermal demand and examine the performance and applicability of machine learning techniques to solve real-world problems. Overall, the solution proposed in this paper is in accordance with EU targets, providing an automated smart energy management system, decreasing human errors and reducing excessive energy production.

Keywords: machine learning, LSTMs, district heating and cooling system, thermal demand

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Authors: Nikolai N. Bogolubov, Andrey V. Soldatov

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Terahertz radiation occupies a range of frequencies somewhere from 100 GHz to approximately 10 THz, just between microwaves and infrared waves. This range of frequencies holds promise for many useful applications in experimental applied physics and technology. At the same time, reliable, simple techniques for generation, amplification, and modulation of electromagnetic radiation in this range are far from been developed enough to meet the requirements of its practical usage, especially in comparison to the level of technological abilities already achieved for other domains of the electromagnetic spectrum. This situation of relative underdevelopment of this potentially very important range of electromagnetic spectrum is known under the name of the 'terahertz gap.' Among other things, technological progress in the terahertz area has been impeded by the lack of compact, low energy consumption, easily controlled and continuously radiating terahertz radiation sources. Therefore, development of new techniques serving this purpose as well as various devices based on them is of obvious necessity. No doubt, it would be highly advantageous to employ the simplest of suitable physical systems as major critical components in these techniques and devices. The purpose of the present research was to show by means of conventional methods of non-equilibrium statistical mechanics and the theory of open quantum systems, that a thoroughly studied two-level quantum system, also known as an one-electron two-level 'atom', being driven by external classical monochromatic high-frequency (e.g. laser) field, can radiate continuously at much lower (e.g. terahertz) frequency in the fluorescent regime if the transition dipole moment operator of this 'atom' possesses permanent non-equal diagonal matrix elements. This assumption contradicts conventional assumption routinely made in quantum optics that only the non-diagonal matrix elements persist. The conventional assumption is pertinent to natural atoms and molecules and stems from the property of spatial inversion symmetry of their eigenstates. At the same time, such an assumption is justified no more in regard to artificially manufactured quantum systems of reduced dimensionality, such as, for example, quantum dots, which are often nicknamed 'artificial atoms' due to striking similarity of their optical properties to those ones of the real atoms. Possible ways to experimental observation and practical implementation of the predicted effect are discussed too.

Keywords: terahertz gap, two-level atom, resonant fluorescence, quantum dot, resonant fluorescence, two-level atom

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Authors: Rebar Mohammed Hussein

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Enhanced Recovery After Surgery (ERAS) protocols have transformed perioperative care, aiming to reduce surgical stress, optimize pain management, and accelerate recovery. This study evaluates the impact of ERAS on anesthesia management in high-risk surgical patients, focusing on opioid-sparing techniques and multimodal analgesia. A retrospective analysis was conducted on patients undergoing major surgeries within an ERAS program, comparing outcomes with a historical cohort receiving standard care. Key metrics included postoperative pain scores, opioid consumption, length of hospital stay, and complication rates. Results indicated that the implementation of ERAS protocols significantly reduced postoperative opioid use by 40% and improved pain management outcomes, with 70% of patients reporting satisfactory pain control on postoperative day one. Additionally, patients in the ERAS group experienced a 30% reduction in length of stay and a 20% decrease in complication rates. These findings underscore the importance of integrating ERAS principles into anesthesia practice, particularly for high-risk patients, to enhance recovery, improve patient satisfaction, and reduce healthcare costs. Future directions include prospective studies to further refine anesthesia techniques within ERAS frameworks and explore their applicability across various surgical specialties.

Keywords: ERAS protocols, high-risk surgical patients, anesthesia management, recovery

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Authors: Hareem Kausar

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Women of today’s world are energetic, enthusiastic and high-spirited. They tend to be the best in whatever they do and strive to accept and fulfil each challenge with utmost liveliness. The aim of the research was about studying the impact of Maternal Employment on the Child’s Behavioral Development. It was conducted as an initiative to study the impact factor in Pakistani culture and for deep insight to the subject using qualitative research methodology. The samples were interviewed through semi-structured interview method in three phases including two working mothers, two children and a day care center official and the data was collected and analyzed through content analysis. Further, it was linked with the literature from the west and the results show that children of working mothers tend to be sound mentally and physically but at some points they face the inner feeling of solitude. Overall, develop the mechanism in independence in their nature and behavior but maternal employment definitely affects the overall behavioral development of the children.

Keywords: maternal employment, child behavior- development, childhood, impact

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Authors: Hammadi Larbi, Bergane Cheikh

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Drilling for oil is done through many mechanisms. The goal is first to dig deep and then, after arriving at the oil source, to simply suck it up. And for this, it is important to know the role of oil-based drilling muds, which had many benefits for the drilling tool and for drilling generally, and also and essentially to know the rheological behavior of the emulsion system in particular water-in-oil inverse emulsions (Water/crude oil). This work contributes to the improvement of the stability and rheological properties of crude oil-based drilling mud by organophilic clay. Experimental data from steady-state flow measurements of crude oil-based drilling mud are classically analyzed by the Herschel-Bulkley model. The effects of organophilic clay type VG69 are studied. Microscopic observation showed that the addition of quantities of organophilic clay type VG69 less than or equal to 3 g leads to the stability of inverse Water/Oil emulsions; on the other hand, for quantities greater than 3g, the emulsions are destabilized.

Keywords: drilling, organophilic clay, crude oil, stability

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Authors: Karan Sharma, Ajay Kumar

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Epileptic seizure is a type of disease due to which electrical charge in the brain flows abruptly resulting in abnormal activity by the subject. One percent of total world population gets epileptic seizure attacks.Due to abrupt flow of charge, EEG (Electroencephalogram) waveforms change. On the display appear a lot of spikes and sharp waves in the EEG signals. Detection of epileptic seizure by using conventional methods is time-consuming. Many methods have been evolved that detect it automatically. The initial part of this paper provides the review of techniques used to detect epileptic seizure automatically. The automatic detection is based on the feature extraction and classification patterns. For better accuracy decomposition of the signal is required before feature extraction. A number of parameters are calculated by the researchers using different techniques e.g. approximate entropy, sample entropy, Fuzzy approximate entropy, intrinsic mode function, cross-correlation etc. to discriminate between a normal signal & an epileptic seizure signal.The main objective of this review paper is to present the variations in the EEG signals at both stages (i) Interictal (recording between the epileptic seizure attacks). (ii) Ictal (recording during the epileptic seizure), using most appropriate methods of analysis to provide better healthcare diagnosis. This research paper then investigates the effects of a noninvasive healing therapy on the subjects by studying the EEG signals using latest signal processing techniques. The study has been conducted with Reiki as a healing technique, beneficial for restoring balance in cases of body mind alterations associated with an epileptic seizure. Reiki is practiced around the world and is recommended for different health services as a treatment approach. Reiki is an energy medicine, specifically a biofield therapy developed in Japan in the early 20th century. It is a system involving the laying on of hands, to stimulate the body’s natural energetic system. Earlier studies have shown an apparent connection between Reiki and the autonomous nervous system. The Reiki sessions are applied by an experienced therapist. EEG signals are measured at baseline, during session and post intervention to bring about effective epileptic seizure control or its elimination altogether.

Keywords: EEG signal, Reiki, time consuming, epileptic seizure

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Authors: Jiahe Liu, HongyangYu, Feng Qian, Miao Luo

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This study aimed to address the problem of improving the confidence of key points by fusing multi-view information, thereby estimating human posture more accurately. We first obtained multi-view image information and then used the MvP algorithm to fuse this multi-view information together to obtain a set of high-confidence human key points. We used these as the input for the Spatio-Temporal Graph Convolution (ST-GCN). ST-GCN is a deep learning model used for processing spatio-temporal data, which can effectively capture spatio-temporal relationships in video sequences. By using the MvP algorithm to fuse multi-view information and inputting it into the spatio-temporal graph convolution model, this study provides an effective method to improve the accuracy of human posture estimation and provides strong support for further research and application in related fields.

Keywords: multi-view, pose estimation, ST-GCN, joint fusion

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Authors: Shimaa Nagro, Russell Campion

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Mobile devices are becoming ever more widely available, with growing functionality, and they are increasingly used as enabling technology to give students access to educational material anytime and anywhere. However, the design of educational material's user interfaces for mobile devices is beset by many unresolved research problems such as those arising from constraints associated with mobile devices or from issues linked to effective learning. The proposed research aims to produce: (i) a method framework for the design and evaluation of educational material’s interfaces to be delivered on mobile devices, in multimedia form based on Human Computer Interaction strategies; and (ii) a software tool implemented as a fast-track alternative to use the method framework in full. The investigation will combine qualitative and quantitative methods, including interviews and questionnaires for data collection and three case studies for validating the method framework. The method framework is a framework to enable an educational designer to effectively and efficiently create educational multimedia interfaces to be used on mobile devices by following a particular methodology that contains practical and usable tools and techniques. It is a method framework that accepts any educational material in its final lesson plan and deals with this plan as a static element, it will not suggest any changes in any information given in the lesson plan but it will help the instructor to design his final lesson plan in a multimedia format to be presented in mobile devices.

Keywords: mobile learning, M-Learn, HCI, educational multimedia, interface design

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Authors: C. V. Aravinda, H. N. Prakash

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In this paper, we tried to convey fusion and state of art pertaining to SIL character recognition systems. In the first step, the text is preprocessed and normalized to perform the text identification correctly. The second step involves extracting relevant and informative features. The third step implements the classification decision. The three stages which involved are Data acquisition and preprocessing, Feature extraction, and Classification. Here we concentrated on two techniques to obtain features, Feature Extraction & Feature Selection. Edge-hinge distribution is a feature that characterizes the changes in direction of a script stroke in handwritten text. The edge-hinge distribution is extracted by means of a windowpane that is slid over an edge-detected binary handwriting image. Whenever the mid pixel of the window is on, the two edge fragments (i.e. connected sequences of pixels) emerging from this mid pixel are measured. Their directions are measured and stored as pairs. A joint probability distribution is obtained from a large sample of such pairs. Despite continuous effort, handwriting identification remains a challenging issue, due to different approaches use different varieties of features, having different. Therefore, our study will focus on handwriting recognition based on feature selection to simplify features extracting task, optimize classification system complexity, reduce running time and improve the classification accuracy.

Keywords: word segmentation and recognition, character recognition, optical character recognition, hand written character recognition, South Indian languages

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Authors: Monday O. Ale, Adebukola A. Akintade, Olasunbo O. Orungbemi

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Fermentation as an important operation in the processing of cocoa beans is now affected by the recent climate change across the globe. The major requirement for effective fermentation is the ability of the material used to retain sufficient heat for the required microbial activities. Apart from the effects of climate on the rate of heat retention, the materials used for fermentation plays an important role. Most Farmers still restrict fermentation activities to the use of traditional methods. Improving on cocoa fermentation in this era of climate change makes it necessary to work on other materials that can be suitable for cocoa fermentation. Therefore, the objective of this study was to determine the effects of fermentation techniques on the quality of cocoa beans. The materials used in this fermentation research were heap-leaves (traditional), stainless steel, plastic tin, plastic basket and wooden box. The period of fermentation varies from zero days to 10 days. Physical and chemical tests were carried out for variables in quality determination in the samples. The weight per bean varied from 1.0-1.2 g after drying across the samples and the major color of the dry beans observed was brown except with the samples from stainless steel. The moisture content varied from 5.5-7%. The mineral content and the heavy metals decreased with increase in the fermentation period. A wooden box can conclusively be used as an alternative to heap-leaves as there was no significant difference in the physical features of the samples fermented with the two methods. The use of a wooden box as an alternative for cocoa fermentation is therefore recommended for cocoa farmers.

Keywords: fermentation, effects, fermentation materials, period, quality

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Authors: Beloulou Mohamed Lamine, Bouhouf Attef, Meliani Walid, Sellami Dalila, Lamara Abdelhak

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Introduction: Post-surgical chronic pain (PSCP) is a pathological condition with a rather complex etiopathogenesis that extensively involves sensitization processes and neuronal damage. The neuropathic component of these pains is almost always present, with variable expression depending on the type of surgery. Objective: To assess the presumed beneficial effect of Regional Anesthesia-Analgesia Techniques (RAAT) on the development of post-surgical chronic neuropathic pain (PSCNP) in various surgical procedures. Patients and Methods: A comparative study involving 510 patients distributed across five surgical models (mastectomy, thoracotomy, hernioplasty, cholecystectomy, and major abdominal-pelvic surgery) and randomized into two groups: Group A (240) receiving conventional postoperative analgesia and Group B (270) receiving balanced analgesia, including the implementation of a Regional Anesthesia-Analgesia Technique (RAAT). These patients were longitudinally followed over a 6-month period, with post-surgical chronic neuropathic pain (PSCNP) defined by a Neuropathic Pain Score DN2≥ 3. Comparative measurements through univariate and multivariate analyses were performed to identify associations between the development of PSCNP and certain predictive factors, including the presumed preventive impact (protective effect) of RAAT. Results: At the 6th month post-surgery, 419 patients were analyzed (Group A= 196 and Group B= 223). The incidence of PSCNP was 32.2% (n=135). Among these patients with chronic pain, the prevalence of neuropathic pain was 37.8% (95% CI: [29.6; 46.5]), with n=51/135. It was significantly lower in Group B compared to Group A, with respective percentages of 31.4% vs. 48.8% (p-value = 0.035). The most significant differences were observed in breast and thoracopulmonary surgeries. In a multiple regression analysis, two predictors of PSCNP were identified: the presence of preoperative pain at the surgical site as a risk factor (OR: 3.198; 95% CI [1.326; 7.714]) and RAAT as a protective factor (OR: 0.408; 95% CI [0.173; 0.961]). Conclusion: The neuropathic component of PSCNP can be observed in different types of surgeries. Regional analgesia included in a multimodal approach to postoperative pain management has proven to be effective for acute pain and seems to have a preventive impact on the development of PSCNP and its neuropathic nature or component, particularly in surgeries that are more prone to chronicization.

Keywords: chronic postsurgical pain, postsurgical chronic neuropathic pain, regional anesthesia and analgesia techniques (RAAT), neuropathic pain score dn2, preventive impact

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Authors: Mohammad Bakhshandeh

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Structural Health Monitoring (SHM) is a crucial and necessary practice that plays a vital role in ensuring the safety and integrity of critical structures, and in particular, bridges. The continuous monitoring of bridges for signs of damage or degradation through Bridge Health Monitoring (BHM) enables early detection of potential problems, allowing for prompt corrective action to be taken before significant damage occurs. Although all monitoring techniques aim to provide accurate and decisive information regarding the remaining useful life, safety, integrity, and serviceability of bridges, understanding the development and propagation of damage is vital for maintaining uninterrupted bridge operation. Over the years, extensive research has been conducted on BHM methods, and experts in the field have increasingly adopted new methodologies. In this article, we provide a comprehensive exploration of the various BHM approaches, including sensor-based, non-destructive testing (NDT), model-based, and artificial intelligence (AI)-based methods. We also discuss the challenges associated with BHM, including sensor placement and data acquisition, data analysis and interpretation, cost and complexity, and environmental effects, through an extensive review of relevant literature and research studies. Additionally, we examine potential solutions to these challenges and propose future research ideas to address critical gaps in BHM.

Keywords: structural health monitoring (SHM), bridge health monitoring (BHM), sensor-based methods, machine-learning algorithms, and model-based techniques, sensor placement, data acquisition, data analysis

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Authors: Ibrahim Ilker Ozyigit, Fatih Tabanli, Sezin Adinir

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In this study, Ascochyta blight resistance was investigated in 34 registered chickpea varieties, which are widely planting in different regions of Turkey. For this aim, molecular marker techniques, such as STMS, RAPD and ISSR were used. Ta2, Ta146 and Ts54 primers were used for STMS, while UBC733 and UBC681 primers for RAPD, and UBC836 and UBC858 primers for ISSR. Ta2, Ts54 and Ta146 (STMS), and UBC733 (RAPD) primers demonstrated the distinctive feature for Ascochyta blight resistance. Ta2, Ts54 and Ta146 primers yielded the quite effective results in detection of resistant and sensitive varieties. Besides, UBC 733 primer distinguished all kinds of standard did not give any reliable results for other varieties since it demonstrated all as resistant. In addition, monomorphic bands were obtained from UBC681 (RAPD), and UBC836 and UBC858 (ISSR) primers, not demonstrating reliable results in detection of resistance against Ascochyta blight disease. Obtained results informed us about both disease resistance and genetic diversity in registered Turkish chickpea varieties. This project was funded through the Scientific Research Projects of Marmara University under Grant Number FEN-C-YLP-070617-0365 and The Scientific and Technological Research Council of Turkey (TUBITAK) under Grant Number 113O070.

Keywords: plant genetics, ISSR, RAPD, STMS

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Authors: Ahmad Umar, Sheikh Akbar, Ahmed A. Ibrahim, Mohsen A. Alhamami

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The development of advanced materials for energy storage and gas sensing applications has gained significant attention in recent years. In this study, we synthesized and characterized PANI@MnO2@rGO ternary nanocomposites (NCs) to explore their potential in supercapacitors and gas sensing devices. The ternary NCs were synthesized through a multi-step process involving the hydrothermal synthesis of MnO2 nanoparticles, preparation of PANI@rGO composites and the assembly to the ternary PANI@MnO2@rGO ternary NCs. The structural, morphological, and compositional characteristics of the materials were thoroughly analyzed using techniques such as XRD, FESEM, TEM, FTIR, and Raman spectroscopy. In the realm of gas sensing, the ternary NCs exhibited excellent performance as NH3 gas sensors. The optimized operating temperature of 100 °C yielded a peak response of 15.56 towards 50 ppm NH3. The nanocomposites demonstrated fast response and recovery times of 6 s and 10 s, respectively, and displayed remarkable selectivity for NH3 gas over other tested gases. For supercapacitor applications, the electrochemical performance of the ternary NCs was evaluated using cyclic voltammetry and galvanostatic charge-discharge techniques. The composites exhibited pseudocapacitive behavior, with the capacitance reaching up to 185 F/g at 1 A/g and excellent capacitance retention of approximately 88.54% over 4000 charge-discharge cycles. The unique combination of rGO, PANI, and MnO2 nanoparticles in these ternary NCs offer synergistic advantages, showcasing their potential to address challenges in energy storage and gas sensing technologies.

Keywords: paniI@mnO2@rGO ternary NCs, synergistic effects, supercapacitors, gas sensing, energy storage

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Authors: Pawan Kumar, Dhananjoy Shaw, Manoj Kumar Rathi

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Purpose of the study were to compare between (a) frequencies among the four quartiles of percentile norms of autonomic variables from power events and (b) frequencies among the four quartiles percentile norms of autonomic variables from aerobic events of Indian sportspersons withdrawn from competitive games and sports in regard to number of samples falling in each quartile. The study was conducted on 430 males of 30 to 35 years of age. Based on the nature of game/sports the retired sportspersons were classified into power events (throwers, judo players, wrestlers, short distance swimmers, cricket fast bowlers and power lifters) and aerobic events (long distance runners, long distance swimmers, water polo players). Date was collected using ECG polygraphs. Data were processed and extracted using frequency domain analysis and time domain analysis. Collected data were computed with frequency, percentage of each quartile and finally the frequencies were compared with the chi square analysis. The finding pertaining to norm reference comparison of frequencies among the four quartiles of Indian sportspersons withdrawn from competitive games and sports from (a) power events suggests that frequency distribution in four quartile namely Q1, Q2, Q3, and Q4 are significantly different at .05 level in regard to variables namely, SDNN, Total Power (Absolute Power), HF (Absolute Power), LF (Normalized Power), HF (Normalized Power), LF/HF ratio, deep breathing test, expiratory respiratory ratio, valsalva manoeuvre, hand grip test, cold pressor test and lying to standing test, whereas, insignificantly different at .05 level in regard to variables namely, SDSD, RMSSD, SDANN, NN50 Count, pNN50 Count, LF (Absolute Power) and 30: 15 Ratio (b) aerobic events suggests that frequency distribution in four quartile are significantly different at .05 level in regard to variables namely, SDNN, LF (Normalized Power), HF (Normalized Power), LF/HF ratio, deep breathing test, expiratory respiratory ratio, hand grip test, cold pressor test, lying to standing test and 30: 15 ratio, whereas, insignificantly different at .05 level in regard to variables namely, SDSD, RMSSD. SDANN, NN50 count, pNN50 count, Total Power (Absolute Power), LF(Absolute Power) HF(Absolute Power), and valsalva manoeuvre. The study concluded that comparison of frequencies among the four quartiles of Indian retired sportspersons from power events and aerobic events are different in four quartiles in regard to selected autonomic functions, hence the developed percentile norms are not homogenously distributed across the percentile scale; hence strengthen the percentage distribution towards normal distribution.

Keywords: power, aerobic, absolute power, normalized power

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Authors: Atena Tazikeh Lemeski, Ismail Ozdamar

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This study presents a detailed analysis of harmonic distortion in a grid-connected photovoltaic (PV) system using real-time data captured from a solar power plant. Harmonics introduced by inverters in PV systems can degrade power quality and lead to increased Total Harmonic Distortion (THD), which poses challenges such as transformer overheating, increased power losses, and potential grid instability. This research addresses these issues by applying Fast Fourier Transform (FFT) to identify significant harmonic components and employing notch filters to target specific frequencies, particularly the 3rd harmonic (150 Hz), which was identified as the largest contributor to THD. Initial analysis of the unfiltered voltage signal revealed a THD of 21.15%, with prominent harmonic peaks at 150 Hz, 250 Hz and 350 Hz, corresponding to the 3rd, 5th, and 7th harmonics, respectively. After implementing the notch filters, the THD was reduced to 5.72%, demonstrating the effectiveness of this approach in mitigating harmonic distortion without affecting the fundamental frequency. This paper provides practical insights into the application of real-time filtering techniques in PV systems and their role in improving overall grid stability and power quality. The results indicate that targeted harmonic mitigation is crucial for the sustainable integration of renewable energy sources into modern electrical grids.

Keywords: grid-connected photovoltaic systems, fast Fourier transform, harmonic filtering, inverter-induced harmonics

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Authors: Meenu Mehta, Munish Garg

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The use of oral anticancer drugs from AYUSH system of medicine is widely increased among the society due to their low cost, enhanced efficacy, increased patient preference, lack of inconveniences related to infusion and they provide an opportunity to develop chronic treatment regimens. However, oral delivery of these drugs usually laid down by the limited bioavailability of the drug, which is associated with a wide variation. As most of the cytotoxic agents have a narrow therapeutic window and are dosed at or near the maximum tolerated dose, a wide variability in the bioavailability can negatively affect treatment result. It is estimated that 40% of active substances are poorly soluble in water. The improvement of bio-availability of drugs with such properties presents one of the greatest challenges in drug formulations. There are several techniques reported in literature. Among all these Self Emulsifying Drug Delivery System (SEDDS) has gained more attention due to enhanced oral bio-availability enabling a reduction in dose. Thus, SEDDS anticancer drugs will have the increased bioavailability and efficacy. These dosage form will provide societal benefit in a cost-effective manner as compared to other oral dosage forms. Present study reflects on the formulation strategies as SEDDS for oral anticancer agents of AYUSH system for enhanced bioavailability with proven efficacy by cancer cell lines.

Keywords: anticancer agents, AYUSH system, bioavailability, SEDDS

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Authors: Stephen L. Green, Alexander N. Gorban, Ivan Y. Tyukin

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Within the past decade, using Convolutional Neural Networks (CNN)’s to create Deep Learning systems capable of translating Sign Language into text has been a breakthrough in breaking the communication barrier for deaf-mute people. Conventional research on this subject has been concerned with training the network to recognize the fingerspelling gestures of a given language and produce their corresponding alphanumerics. One of the problems with the current developing technology is that images are scarce, with little variations in the gestures being presented to the recognition program, often skewed towards single skin tones and hand sizes that makes a percentage of the population’s fingerspelling harder to detect. Along with this, current gesture detection programs are only trained on one finger spelling language despite there being one hundred and forty-two known variants so far. All of this presents a limitation for traditional exploitation for the state of current technologies such as CNN’s, due to their large number of required parameters. This work aims to present a technology that aims to resolve this issue by combining a pretrained legacy AI system for a generic object recognition task with a corrector method to uptrain the legacy network. This is a computationally efficient procedure that does not require large volumes of data even when covering a broad range of sign languages such as American Sign Language, British Sign Language and Chinese Sign Language (Pinyin). Implementing recent results on method concentration, namely the stochastic separation theorem, an AI system is supposed as an operate mapping an input present in the set of images u ∈ U to an output that exists in a set of predicted class labels q ∈ Q of the alphanumeric that q represents and the language it comes from. These inputs and outputs, along with the interval variables z ∈ Z represent the system’s current state which implies a mapping that assigns an element x ∈ ℝⁿ to the triple (u, z, q). As all xi are i.i.d vectors drawn from a product mean distribution, over a period of time the AI generates a large set of measurements xi called S that are grouped into two categories: the correct predictions M and the incorrect predictions Y. Once the network has made its predictions, a corrector can then be applied through centering S and Y by subtracting their means. The data is then regularized by applying the Kaiser rule to the resulting eigenmatrix and then whitened before being split into pairwise, positively correlated clusters. Each of these clusters produces a unique hyperplane and if any element x falls outside the region bounded by these lines then it is reported as an error. As a result of this methodology, a self-correcting recognition process is created that can identify fingerspelling from a variety of sign language and successfully identify the corresponding alphanumeric and what language the gesture originates from which no other neural network has been able to replicate.

Keywords: convolutional neural networks, deep learning, shallow correctors, sign language

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Authors: Eslam Dabbish, Fortuna Ponte, Stefano Scoditti, Emilia Sicilia, Gloria Mazzone

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The medical techniques based on the use of light for activating the drug are occupying a prominent place in the cancer treatment due to their selectivity that contributes to reduce undesirable side effects of conventional chemotherapy. Among these therapeutic treatments, photodynamic therapy (PDT) and photoactivated chemotherapy (PACT) are emerging as complementary approaches for selective destruction of neoplastic tissue through direct cellular damage. Both techniques rely on the employment of a molecule, photosensitizer (PS), able to absorb within the so-called therapeutic window. Thus, the exposure to light of otherwise inert molecules promotes the population of excited states of the drug, that in PDT are able to produce the cytotoxic species, such as 1O2 and other ROS, in PACT can be responsible of the active species release or formation. Following the success of cisplatin in conventional treatments, many other transition metal complexes were explored as anticancer agents for applications in different medical approaches, including PDT and PACT, in order to improve their chemical, biological and photophysical properties. In this field, several crucial characteristics of candidate PSs can be accurately predicted from first principle calculations, especially in the framework of density functional theory and its time-dependent formulation, contributing to the understanding of the entire photochemical pathways involved which can ultimately help in improving the efficiency of a drug. A brief overview of the outcomes on some platinum and ruthenium-based PSs proposed for the application in the two phototherapies will be provided.

Keywords: TDDFT, metal complexes, PACT, PDT

Procedia PDF Downloads 103