Search results for: energy performance certificate EPBD

Authors: Vera Lucia da S. Ventura, Valmir Alves Ventura, Marcelo E. Fernandes, Marcelo T. Okano, Osmildo S. Santos, Heide Landi

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The paper aims to present the attitude of consumers regarding the environmental practices adopted by Brazilian organizations. It is understood organizations adopt practices about environment is essential, as their internal processes as external actions, the corporative and social changes are considered in this scene. It is observed consumers are important, therefore, more and more they analyze the responsible performance of Brazilian organizations. It was performed a quantitative research through questionnaire for achieving the objectives of this study. The sample was composed by 336 people at capacity consumption fully. The survey results demonstrate environmental management can be an excellent tool for conquering consumers, because consumers realize the great responsibility assumed by organizations regarding to the environment, nowadays. This finding was possible because most of the respondents answered the environmentally responsible behavior of organizations is decisive factor at the purchase’s moment. However, the data revealed consumers do not realize the practices adopted by companies. This lack of awareness may prejudice environmentally responsible organizations’ worth by consumers.

Keywords: environmental management, sustainability, conscious consumption, Brazilian organizations

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Authors: Siriporn Okonogi, Temsiri Suwan, Sakornrat Khongkhunthian, Jakkapan Sirithunyalug

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In this study, silver nanoparticles (AgNPs) were prepared using ascorbic acid as a reducing agent and silver nitrate as a precursor. The effects of gelatin (G) on particle characteristics and dental pathogen inhibition were investigated. The spectra of AgNPs and G-AgNPs were compared using UV-Vis and Energy-dispersive X-ray (EDX) spectroscopy. The obtained AgNPs and G-AgNPs showed the maximum absorption at 410 and 430 nm, respectively, and EDX spectra of both systems confirmed Ag element. Scanning electron microscope showed that AgNPs and G-AgNPs were spherical in shape. Particles size, size distribution, and zeta potential were determined using dynamic light scattering approach. The size of AgNPs and G-AgNPs were 56 ± 2.4 and 67 ± 3.6 nm, respectively with a size distribution of 0.23 ± 0.03 and 0.19 ± 0.02, respectively. AgNPs and G-AgNPs exhibited negative zeta potential of 24.1 ± 2.7 mV and 32.7 ± 1.2 mV, respectively. Minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) of the obtained AgNPs and G-AgNPs against three strains of dental pathogenic bacteria; Streptococcus gordonii, Streptococcus mutans, and Staphylococcus aureus were determined using broth dilution method. AgNPs and G-AgNPs showed the strongest inhibition against S. gordonii with the MIC of 0.05 and 0.025 mg/mL, respectively and the MBC of 0.1 and 0.05 mg/mL, respectively. Cytotoxicity test of AgNPs and G-AgNPs on human breast cancer cells using MTT assay indicated that G-AgNPs (0.1 mg/mL) was significantly stronger toxic than AgNPs with the cell inhibition of 91.1 ± 5.4%. G-AgNPs showed significantly less aggregation after storage at room temperature for 90 days than G-AgNPs.

Keywords: antipathogenic activity, ascorbic acid, cytotoxicity, stability

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Authors: B. R. Campomanes-Alvarez, P. Quiros, B. Fernandez

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Automatic Speech Recognition (ASR) is a machine-based process of decoding and transcribing oral speech. A typical ASR system receives acoustic input from a speaker or an audio file, analyzes it using algorithms, and produces an output in the form of a text. Some speech recognition systems use Hidden Markov Models (HMMs) to deal with the temporal variability of speech and Gaussian Mixture Models (GMMs) to determine how well each state of each HMM fits a short window of frames of coefficients that represents the acoustic input. Another way to evaluate the fit is to use a feed-forward neural network that takes several frames of coefficients as input and produces posterior probabilities over HMM states as output. Deep neural networks (DNNs) that have many hidden layers and are trained using new methods have been shown to outperform GMMs on a variety of speech recognition systems. Acoustic models for state-of-the-art ASR systems are usually training on massive amounts of data. However, audio files with their corresponding transcriptions can be difficult to obtain, especially in the Spanish language. Hence, in the case of these low-resource scenarios, building an ASR model is considered as a complex task due to the lack of labeled data, resulting in an under-trained system. Semi-supervised learning approaches arise as necessary tasks given the high cost of transcribing audio data. The main goal of this proposal is to develop a procedure based on acoustic semi-supervised learning for Spanish ASR systems by using DNNs. This semi-supervised learning approach consists of: (a) Training a seed ASR model with a DNN using a set of audios and their respective transcriptions. A DNN with a one-hidden-layer network was initialized; increasing the number of hidden layers in training, to a five. A refinement, which consisted of the weight matrix plus bias term and a Stochastic Gradient Descent (SGD) training were also performed. The objective function was the cross-entropy criterion. (b) Decoding/testing a set of unlabeled data with the obtained seed model. (c) Selecting a suitable subset of the validated data to retrain the seed model, thereby improving its performance on the target test set. To choose the most precise transcriptions, three confidence scores or metrics, regarding the lattice concept (based on the graph cost, the acoustic cost and a combination of both), was performed as selection technique. The performance of the ASR system will be calculated by means of the Word Error Rate (WER). The test dataset was renewed in order to extract the new transcriptions added to the training dataset. Some experiments were carried out in order to select the best ASR results. A comparison between a GMM-based model without retraining and the DNN proposed system was also made under the same conditions. Results showed that the semi-supervised ASR-model based on DNNs outperformed the GMM-model, in terms of WER, in all tested cases. The best result obtained an improvement of 6% relative WER. Hence, these promising results suggest that the proposed technique could be suitable for building ASR models in low-resource environments.

Keywords: automatic speech recognition, deep neural networks, machine learning, semi-supervised learning

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Authors: S. Delgado, C. Cerrada, R. S. Gómez

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This research introduces an approach to voxelizing the surfaces of triangular meshes with efficiency and accuracy. Our method leverages parallel equidistant scan-lines and introduces a Gap Detection technique to address the limitations of existing approaches. We present a comprehensive study showcasing the method's effectiveness, scalability, and versatility in different scenarios. Voxelization is a fundamental process in computer graphics and simulations, playing a pivotal role in applications ranging from scientific visualization to virtual reality. Our algorithm focuses on enhancing the voxelization process, especially for complex models and high resolutions. One of the major challenges in voxelization in the Graphics Processing Unit (GPU) is the high cost of discovering the same voxels multiple times. These repeated voxels incur in costly memory operations with no useful information. Our scan-line-based method ensures that each voxel is detected exactly once when processing the triangle, enhancing performance without compromising the quality of the voxelization. The heart of our approach lies in the use of parallel, equidistant scan-lines to traverse the interiors of triangles. This minimizes redundant memory operations and avoids revisiting the same voxels, resulting in a significant performance boost. Moreover, our method's computational efficiency is complemented by its simplicity and portability. Written as a single compute shader in Graphics Library Shader Language (GLSL), it is highly adaptable to various rendering pipelines and hardware configurations. To validate our method, we conducted extensive experiments on a diverse set of models from the Stanford repository. Our results demonstrate not only the algorithm's efficiency, but also its ability to produce 26 tunnel free accurate voxelizations. The Gap Detection technique successfully identifies and addresses gaps, ensuring consistent and visually pleasing voxelized surfaces. Furthermore, we introduce the Slope Consistency Value metric, quantifying the alignment of each triangle with its primary axis. This metric provides insights into the impact of triangle orientation on scan-line based voxelization methods. It also aids in understanding how the Gap Detection technique effectively improves results by targeting specific areas where simple scan-line-based methods might fail. Our research contributes to the field of voxelization by offering a robust and efficient approach that overcomes the limitations of existing methods. The Gap Detection technique fills a critical gap in the voxelization process. By addressing these gaps, our algorithm enhances the visual quality and accuracy of voxelized models, making it valuable for a wide range of applications. In conclusion, "Closing the Gap: Efficient Voxelization with Equidistant Scan-lines and Gap Detection" presents an effective solution to the challenges of voxelization. Our research combines computational efficiency, accuracy, and innovative techniques to elevate the quality of voxelized surfaces. With its adaptable nature and valuable innovations, this technique could have a positive influence on computer graphics and visualization.

Keywords: voxelization, GPU acceleration, computer graphics, compute shaders

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Authors: Kuo-Wei Hsu, Yen-Ting, Wu

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Recently, green marketing issues have emerged as the developing direction for local governments and social enterprises. At the same time, many social enterprises have considered how to effectively create a low-carbon and sustainable environment. Local government has a role to play in promoting low-carbon life styles and creating a green sustainable environment within this green marketing trend. Therefore, urban commercial centers have implemented relevant plans such as: Green Store, Green Action Shops, Green Restaurants and Green Hotels. The purpose of these plans to select the commercial center organizations have potential energy saving demonstration and environmental greenification. These organizations are willing to provide assistance counseling and become a green demonstration district, thereby promoting the major shopping district to take the initiative to enhance its green competitiveness. Finally, they create a new landscape for the commercial center. Studies on green marketing in commercial centers are seen as less attractive and only a few studies for commercial centers have focused on green marketing strategies. There is no empirical evidence for how commercial center managers evaluate a commercial center green marketing strategy. This research investigated the major commercial centers in Taichung City and found green marketing helps to enhance the connection between the urban commercial center value and society value, shape corporate image with social responsibility and create brand value, and therefore impact the increase of economic value.

Keywords: economic value added, green marketing, sustainable environment, urban commercial center.

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Authors: Zoran Visak, Joana Lopes, Vesna Najdanovic-Visak

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Within this study, the separation of nicotine from its aqueous solutions, using inorganic salt sodium chloride or ionic liquid (molten salt) ECOENG212® as salting-out media, was carried out. Thus, liquid-liquid equilibria of the ternary solutions (nicotine+water+NaCl) and (nicotine+water+ECOENG212®) were determined at ambient pressure, 0.1 MPa, at three temperatures. The related phase diagrams were constructed in two manners: by adding the determined cloud-points and by the chemical analysis of phases in equilibrium (tie-line data). The latter were used to calculate two important separation parameters - partition coefficients of nicotine and separation factors. The impacts of the initial compositions of the mother solutions and of temperature on the liquid-liquid phase separation and partition coefficients were analyzed and discussed. The results obtained clearly showed that both investigated salts are good salting-out media for the efficient and sustainable separation of nicotine from its solutions with water. However, when compared, sodium chloride exhibited much better separation performance than the ionic liquid.

Keywords: nicotine, liquid-liquid separation, inorganic salt, ionic liquid

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Authors: Nadjat Zerf Boudjettou, Fahima Nader, Rachid Chalal

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Knowledge management is to acquire and represent knowledge relevant to a domain, a task or a specific organization in order to facilitate access, reuse and evolution. This usually means building, maintaining and evolving an explicit representation of knowledge. The next step is to provide access to that knowledge, that is to say, the spread in order to enable effective use. Knowledge management in the medical field aims to improve the performance of the medical organization by allowing individuals in the care facility (doctors, nurses, paramedics, etc.) to capture, share and apply collective knowledge in order to make optimal decisions in real time. In this paper, we propose a knowledge management approach based on integration technique of heterogeneous data in the medical field by creating a data warehouse, a technique of extracting knowledge from medical data by choosing a technique of data mining, and finally an exploitation technique of that knowledge in a case-based reasoning system.

Keywords: data warehouse, data mining, knowledge discovery in database, KDD, medical knowledge management, Bayesian networks

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Authors: Tripureshwari Paul

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We are born with nature molded by nurture. Studies have confirmed the productive role of genes and environment on an individual. This study examines the relationship of parental genotype values on the intellectual ability of their children. Keeping in mind that academic achievement-learning capacity of student through normative education, a function of exposure to family environment and pathology with intellectual quotient of the individual. Purposive sampling was used and children between ages 11 and 12 years and their respective parents were involved. Raven’s Standard Progressive Matrices (RSPM), Family Pathology Scale (FPS) and Family Environment Scale (FES) were administered. The results found significant relationship of Offspring IQ to Parental IQ, maternal IQ demonstrating higher values of correlation. Female IQ was significant to maternal IQ and male IQ was significant to paternal IQ. With Academic Achievement not significantly correlated to IQ, it was determined that Competitive framework, freedom to expression and Recreational Orientation in family affect a child’s intellectual performance.

Keywords: academic achievement, environment, family environment, family pathology, genotype, intelligence quotient, maternal IQ, paternal IQ

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Authors: Nithin Krisshna Gunasekaran, Prathima Prabhu Tumkur, Nicole Nazario Bayon, Krishnan Prabhakaran, Joseph C. Hall, Govindarajan T. Ramesh

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Cerium oxide and turmeric have antioxidant properties, which have gained interest among researchers to study their applications in the field of biomedicine, such asanti-inflammatory, anticancer, and antimicrobial applications. In this study, the turmeric extract was prepared and mixed with cerium nitrate hexahydrate, stirred continuously to obtain a homogeneous solution and then heated on a hot plate to get the supernatant evaporated, then calcinated at 600°C to obtain the cerium oxide nanoparticles. Characterization of synthesized cerium oxide nanoparticles through Scanning Electron Microscopy determined the particle size to be in the range of 70 nm to 250 nm. Energy Dispersive X-Ray Spectroscopy determined the elemental composition of cerium and oxygen. Individual particles were identified through the characterization of cerium oxide nanoparticles using Field Emission Scanning Electron Microscopy, in which the particles were determined to be spherical and in the size of around 70 nm. The presence of cerium oxide was assured by analyzing the spectrum obtained through the characterization of cerium oxide nanoparticles by Fourier Transform Infrared Spectroscopy. The crystal structure of cerium oxide nanoparticles was determined to be face-centered cubic by analyzing the peaks obtained through theX-Ray Diffraction method. The crystal size of cerium oxide nanoparticles was determined to be around 13 nm by using the Debye Scherer equation. This study confirmed the synthesis of cerium oxide nanoparticles using turmeric extract.

Keywords: antioxidant, characterization, cerium oxide, synthesis, turmeric

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Authors: Nathakhun Wiroonsri

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There are various cluster validity measures used for evaluating clustering results. One of the main objectives of using these measures is to seek the optimal unknown number of clusters. Some measures work well for clusters with different densities, sizes and shapes. Yet, one of the weaknesses that those validity measures share is that they sometimes provide only one clear optimal number of clusters. That number is actually unknown and there might be more than one potential sub-optimal option that a user may wish to choose based on different applications. We develop two new cluster validity indices based on a correlation between an actual distance between a pair of data points and a centroid distance of clusters that the two points are located in. Our proposed indices constantly yield several peaks at different numbers of clusters which overcome the weakness previously stated. Furthermore, the introduced correlation can also be used for evaluating the quality of a selected clustering result. Several experiments in different scenarios, including the well-known iris data set and a real-world marketing application, have been conducted to compare the proposed validity indices with several well-known ones.

Keywords: clustering algorithm, cluster validity measure, correlation, data partitions, iris data set, marketing, pattern recognition

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Authors: Patarawan Sangnawakij, Sa-Aat Niwitpong

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In this paper, we proposed two new confidence intervals for the difference of coefficients of variation, CIw and CIs, in two independent gamma distributions. These proposed confidence intervals using the close form method of variance estimation which was presented by Donner and Zou (2010) based on concept of Wald and Score confidence interval, respectively. Monte Carlo simulation study is used to evaluate the performance, coverage probability and expected length, of these confidence intervals. The results indicate that values of coverage probabilities of the new confidence interval based on Wald and Score are satisfied the nominal coverage and close to nominal level 0.95 in various situations, particularly, the former proposed confidence interval is better when sample sizes are small. Moreover, the expected lengths of the proposed confidence intervals are nearly difference when sample sizes are moderate to large. Therefore, in this study, the confidence interval for the difference of coefficients of variation which based on Wald is preferable than the other one confidence interval.

Keywords: confidence interval, score’s interval, wald’s interval, coefficient of variation, gamma distribution, simulation study

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Authors: Selin Guney, Andres Riquelme

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Among the main purposes of optimal and efficient forecasts of agricultural commodity prices is to guide the firms to advance the economic decision making process such as planning business operations and marketing decisions. Governments are also the beneficiaries and suppliers of agricultural price forecasts. They use this information to establish a proper agricultural policy, and hence, the forecasts affect social welfare and systematic errors in forecasts could lead to a misallocation of scarce resources. Various empirical approaches have been applied to forecast commodity prices that have used different methodologies. Most commonly-used approaches to forecast commodity sectors depend on classical time series models that assume values of the response variables are precise which is quite often not true in reality. Recently, this literature has mostly evolved to a consideration of fuzzy time series models that provide more flexibility in terms of the classical time series models assumptions such as stationarity, and large sample size requirement. Besides, fuzzy modeling approach allows decision making with estimated values under incomplete information or uncertainty. A number of fuzzy time series models have been developed and implemented over the last decades; however, most of them are not appropriate for forecasting repeated and nonconsecutive transitions in the data. The modeling scheme used in this paper eliminates this problem by introducing Markov modeling approach that takes into account both the repeated and nonconsecutive transitions. Also, the determination of length of interval is crucial in terms of the accuracy of forecasts. The problem of determining the length of interval arbitrarily is overcome and a methodology to determine the proper length of interval based on the distribution or mean of the first differences of series to improve forecast accuracy is proposed. The specific purpose of this paper is to propose and investigate the potential of a new forecasting model that integrates methodologies for determining the proper length of interval based on the distribution or mean of the first differences of series and Fuzzy Time Series- Markov Chain model. Moreover, the accuracy of the forecasting performance of proposed integrated model is compared to different univariate time series models and the superiority of proposed method over competing methods in respect of modelling and forecasting on the basis of forecast evaluation criteria is demonstrated. The application is to daily corn and soybean prices observed at three commercially important North Carolina markets; Candor, Cofield and Roaring River for corn and Fayetteville, Cofield and Greenville City for soybeans respectively. One main conclusion from this paper is that using fuzzy logic improves the forecast performance and accuracy; the effectiveness and potential benefits of the proposed model is confirmed with small selection criteria value such MAPE. The paper concludes with a discussion of the implications of integrating fuzzy logic and nonarbitrary determination of length of interval for the reliability and accuracy of price forecasts. The empirical results represent a significant contribution to our understanding of the applicability of fuzzy modeling in commodity price forecasts.

Keywords: commodity, forecast, fuzzy, Markov

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Authors: Wisam K. Hussam, Ali Alfeeli, Gergory J. Sheard

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Solar panels that use photovoltaic (PV) cells are popular for converting solar radiation into electricity. One of the major problems impacting the performance of PV panels is the overheating caused by excessive solar radiation and high ambient temperatures, which degrades the efficiency of the PV panels remarkably. To overcome this issue, an aluminum heat sink was used to dissipate unwanted heat from PV cells. The dimensions of the heat sink were determined considering the optimal fin spacing that fulfils hot climatic conditions. In this study, the effects of cooling on the efficiency and power output of a PV panel were studied experimentally. Two PV modules were used: one without and one with a heat sink. The experiments ran for 11 hours from 6:00 a.m. to 5:30 p.m. where temperature readings in the rear and front of both PV modules were recorded at an interval of 15 minutes using sensors and an Arduino microprocessor. Results are recorded for both panels simultaneously for analysis, temperate comparison, and for power and efficiency calculations. A maximum increase in the solar to electrical conversion efficiency of 35% and almost 55% in the power output were achieved with the use of a heat sink, while temperatures at the front and back of the panel were reduced by 9% and 11%, respectively.

Keywords: photovoltaic cell, natural convection, heat sink, efficiency

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Authors: Karim Sobh, Khaled El-Ayat, Fady Morcos, Amr El-Kadi

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Distributed applications deployed on LEO satellites and ground stations require substantial communication between different members in a constellation to overcome the earth coverage barriers imposed by GEOs. Applications running on LEO constellations suffer the earth line-of-sight blockage effect. They need adequate lab testing before launching to space. We propose a scalable cloud-based net-work simulation framework to simulate problems created by the earth line-of-sight blockage. The framework utilized cloud IaaS virtual machines to simulate LEO satellites and ground stations distributed software. A factorial ANOVA statistical analysis is conducted to measure simulator overhead on overall communication performance. The results showed a very low simulator communication overhead. Consequently, the simulation framework is proposed as a candidate for testing LEO constellations with distributed software in the lab before space launch.

Keywords: LEO, cloud computing, constellation, satellite, network simulation, netfilter

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Authors: Sabir Hussain, Mujtaba Hassan, Kashif Rasool, Asif Shahzad

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Fine particulate matter with an aerodynamic diameter of less than 2.5 μm (PM2.5) was collected in Islamabad from November 2022 to January 2023, at urban sites. The average mass concentrations of PM2.5 varied, ranging from 90.5 to 133 μg m−3 in urban areas. Environmental scanning electron microscopy (ESEM) analysis revealed that Islamabad's PM2.5 comprised soot aggregates, ashes, minerals, bio-particles, and unidentified particles. Results from inductively coupled plasma atomic emission spectroscopy (ICP-OES) indicated a gradual increase in total elemental concentrations in Islamabad PM2.5 in winter, with relatively high levels in December. Significantly different elemental compositions were observed in urban PM2.5. Enrichment factor (EF) analysis suggested that elements such as K, Na, Ca, Mg, Al, Fe, Ba, and Sr were of natural origin, while As, Cu, Zn, Pb, Cd, Mn, Ni, and Se originated from anthropogenic sources. Plasmid DNA assays demonstrated varying levels of potential toxicity in Islamabad PM2.5 collected from urban sites, as well as across different seasons. Notably, the urban winter PM2.5 sample exhibited much stronger toxicity compared to other samples. The presence of heavy metals in Islamabad PM2.5, including Cu, Zn, Pb, Cd, Cr, Mn, and Ni, may have synergistic effects on human health.

Keywords: islamabad particulate matter pm2.5, scanning electron microscopy with energy-dispersive x-ray spectroscopy(sem-eds), fourier transform infrared spectroscopy(ftir), inductively coupled plasma optical emission spectroscopy(icp-oes)

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Authors: Amir Reza Radmanesh, Sina Farajzadeh Khosroshahi, Hani Sadr

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The transient catalytic converter performance is governed by complex interactions between exhaust gas flow and the monolithic structure of the catalytic converter. Stringent emission regulations around the world necessitate the use of highly-efficient catalytic converters in vehicle exhaust systems. Computational fluid dynamics (CFD) is a powerful tool for calculating the flow field inside the catalytic converter. Radial velocity profiles, obtained by a commercial CFD code, present very good agreement with respective experimental results published in the literature. However the applicability of CFD for transient simulations is limited by the high CPU demands. In the present work, Geometric modeling ceramic monolith substrate is done with square shaped channel type of Catalytic converter and it is coated platinum and palladium. This example illustrates the effect of flow distribution on thermal response of a catalytic converter and different gas flow velocities, during the critical phase of catalytic converter warm up.

Keywords: catalytic converter, computational fluid dynamic, porous media, velocity distribution

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Authors: Farid Arya, Trevor Hyde, Andrea Trevisi, Paolo Basso, Danilo Bardaro

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Heat transfer through multiple pane windows can be reduced by creating a vacuum pressure less than 0.1 Pa between the glass panes, with low emittance coatings on one or more of the internal surfaces. Fabrication of vacuum glazing (VG) requires the formation of a hermetic seal around the periphery of the glass panes together with an array of support pillars between the panes to prevent them from touching under atmospheric pressure. Atmospheric pressure and temperature differentials induce stress which can affect the integrity of the glazing. Several parameters define the stresses in VG including the glass thickness, pillar specifications, glazing dimensions and edge seal configuration. Inherent stresses in VG can result in fractures in the glass panes and failure of the edge seal. In this study, stress in VG with different glass thicknesses is theoretically studied using Finite Element Modelling (FEM). Based on the finding in this study, suggestions are made to address problems resulting from the use of thinner glass panes in the fabrication of VG. This can lead to the development of high performance, light and thin VG.

Keywords: vacuum glazing, stress, vacuum insulation, support pillars

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Authors: Haoqi Gao, Koichi Ogawara

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Retinal vascular segmentation of color fundus is the basis of ophthalmic computer-aided diagnosis and large-scale disease screening systems. Early screening of fundus diseases has great value for clinical medical diagnosis. The traditional methods depend on the experience of the doctor, which is time-consuming, labor-intensive, and inefficient. Furthermore, medical images are scarce and fraught with legal concerns regarding patient privacy. In this paper, we propose a new Generative Adversarial Network based on CycleGAN for retinal fundus images. This method can generate not only synthetic fundus images but also generate corresponding segmentation masks, which has certain application value and challenge in computer vision and computer graphics. In the results, we evaluate our proposed method from both quantitative and qualitative. For generated segmented images, our method achieves dice coefficient of 0.81 and PR of 0.89 on DRIVE dataset. For generated synthetic fundus images, we use ”Toy Experiment” to verify the state-of-the-art performance of our method.

Keywords: retinal vascular segmentations, generative ad-versarial network, cyclegan, fundus images

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Authors: Saba Latif

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The use of social media among university students is a topic of ongoing debate, with conflicting views on its impact on academic achievement. This study aimed to explore the relationship between social media use and academic achievement among university students and to identify factors that may contribute to positive or negative effects. The study used a mixed-methods design, including a survey of 500 university students and qualitative interviews with a subset of participants. The survey results showed that social media use was prevalent among students, with Facebook and Instagram are the most commonly used platforms. The findings also indicated a positive relationship between social media use and academic achievement, with students who reported higher levels of social media use also reporting higher GPAs. However, the qualitative interviews revealed that excessive use of social media could be a distraction that hinders academic performance, especially when students use it to procrastinate or to stay up late at night. Overall, the findings suggest that social media use can have both positive and negative effects on academic achievement among university students. Responsible and balanced use of social media, such as setting limits on usage and avoiding procrastination, may help students maximize the benefits while minimizing the risks.

Keywords: social media, university, achievement, effective, learning

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Authors: G. M. Komaki, S. Mobin, E. Teymourian, S. Sheikh

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This paper addresses minimizing the makespan of the distributed permutation flow shop scheduling problem. In this problem, there are several parallel identical factories or flowshops each with series of similar machines. Each job should be allocated to one of the factories and all of the operations of the jobs should be performed in the allocated factory. This problem has recently gained attention and due to NP-Hard nature of the problem, metaheuristic algorithms have been proposed to tackle it. Majority of the proposed algorithms require large computational time which is the main drawback. In this study, a general variable neighborhood search algorithm (GVNS) is proposed where several time-saving schemes have been incorporated into it. Also, the GVNS uses the sophisticated method to change the shaking procedure or perturbation depending on the progress of the incumbent solution to prevent stagnation of the search. The performance of the proposed algorithm is compared to the state-of-the-art algorithms based on standard benchmark instances.

Keywords: distributed permutation flow shop, scheduling, makespan, general variable neighborhood search algorithm

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Authors: Arnab Majumdar, Srimani Sen

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In this paper, a vivid simulated study has been made on 35 GHz Ka-band window frequency in order to judge and compare the DC and high frequency properties of cubic ZnB-GaN with the existing hexagonal 4H-SiC. A flat profile p⁺pnn⁺ DDR structure of impatt is chosen and is optimized at a particular bias current density with respect to efficiency and output power taking into consideration the effect of mobile space charge also. The simulated results obtained reveals the strong potentiality of impatts based on both cubic ZnB-GaN and hexagonal 4H-SiC. The DC-to-millimeter wave conversion efficiency for cubic ZnB-GaN impatt obtained is 50% with an estimated output power of 2.83 W at an optimized bias current density of 2.5×10⁸ A/m². The conversion efficiency and estimated output power in case of hexagonal 4H-SiC impatt obtained is 22.34% and 40 W respectively at an optimum bias current density of 0.06×10⁸ A/m².

Keywords: cubic ZnB-GaN, hexagonal 4H-SiC, double drift impatt diode, millimetre wave, optimised bias current density, wide band gap semiconductor

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Authors: Raghvendra Singh Yadav, Ivo Kuřitka, Jarmila Vilcakova, Jaromir Havlica, Lukas Kalina, Pavel Urbánek, Michal Machovsky, Milan Masař, Martin Holek

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In this work, CoFe₂₋ₓGdₓO₄ (x=0.00, 0.05, 0.10, 0.15, 0.20) spinel ferrite nanoparticles are synthesized by sonochemical method. The structural properties and cation distribution are investigated using X-ray Diffraction (XRD), Raman Spectroscopy, Fourier Transform Infrared Spectroscopy and X-ray photoelectron spectroscopy. The morphology and elemental analysis are screened using field emission scanning electron microscopy (FE-SEM) and energy dispersive X-ray spectroscopy, respectively. The particle size measured by FE-SEM and XRD analysis confirm the formation of nanoparticles in the range of 7-10 nm. The electrical properties show that the Gd³⁺ doped cobalt ferrite (CoFe₂₋ₓGdₓO₄; x= 0.20) exhibit enhanced dielectric constant (277 at 100 Hz) and ac conductivity (20.17 x 10⁻⁹ S/cm at 100 Hz). The complex impedance measurement study reveals that as Gd³⁺ doping concentration increases, the impedance Z’ and Z’ ’ decreases. The influence of Gd³⁺ doping in cobalt ferrite nanoparticles on the magnetic property is examined by using vibrating sample magnetometer. Magnetic property measurement reveal that the coercivity decreases with Gd³⁺ substitution from 234.32 Oe (x=0.00) to 12.60 Oe (x=0.05) and further increases from 12.60 Oe (x=0.05) to 68.62 Oe (x=0.20). The saturation magnetization decreases with Gd³⁺ substitution from 40.19 emu/g (x=0.00) to 21.58 emu/g (x=0.20). This decrease follows the three-sublattice model suggested by Yafet-Kittel (Y-K). The Y-K angle increases with the increase of Gd³⁺ doping in cobalt ferrite nanoparticles.

Keywords: sonochemical method, nanoparticles, magnetic property, dielectric property, electrical property

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Authors: Dim En Nyaung, Thin Lai Lai Thein

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Due to the rapid increase of Internet, web opinion sources dynamically emerge which is useful for both potential customers and product manufacturers for prediction and decision purposes. These are the user generated contents written in natural languages and are unstructured-free-texts scheme. Therefore, opinion mining techniques become popular to automatically process customer reviews for extracting product features and user opinions expressed over them. Since customer reviews may contain both opinionated and factual sentences, a supervised machine learning technique applies for subjectivity classification to improve the mining performance. In this paper, we dedicate our work is the task of opinion summarization. Therefore, product feature and opinion extraction is critical to opinion summarization, because its effectiveness significantly affects the identification of semantic relationships. The polarity and numeric score of all the features are determined by Senti-WordNet Lexicon. The problem of opinion summarization refers how to relate the opinion words with respect to a certain feature. Probabilistic based model of supervised learning will improve the result that is more flexible and effective.

Keywords: opinion mining, opinion summarization, sentiment analysis, text mining

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Authors: Wanhyun Cho, Seongchae Seo, Soonja Kang

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In this work, we propose a variational technique for image contrast enhancement which utilizes global and local information around each pixel. The energy functional is defined by a weighted linear combination of three terms which are called on a local, a global contrast term and dispersion term. The first one is a local contrast term that can lead to improve the contrast of an input image by increasing the grey-level differences between each pixel and its neighboring to utilize contextual information around each pixel. The second one is global contrast term, which can lead to enhance a contrast of image by minimizing the difference between its empirical distribution function and a cumulative distribution function to make the probability distribution of pixel values becoming a symmetric distribution about median. The third one is a dispersion term that controls the departure between new pixel value and pixel value of original image while preserving original image characteristics as well as possible. Second, we derive the Euler-Lagrange equation for true image that can achieve the minimum of a proposed functional by using the fundamental lemma for the calculus of variations. And, we considered the procedure that this equation can be solved by using a gradient decent method, which is one of the dynamic approximation techniques. Finally, by conducting various experiments, we can demonstrate that the proposed method can enhance the contrast of colour images better than existing techniques.

Keywords: color image, contrast enhancement technique, variational approach, Euler-Lagrang equation, dynamic approximation method, EME measure

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Authors: Paul Saah, Charles Mbohwa, Nelson Sizwe Madonsela

Abstract:

Organisations that want to have a competitive edge over their rivals in their industry are becoming more and more aware of the value of staff training and development programs. This conceptual study's primary goal is to determine how staff development and training affect an organization's ability to succeed. A non-empirical methodological approach was chosen because this was a conceptual study, and a thorough literature analysis was conducted to determine the contribution of staff training and development to the performance of a commercial organization. Twenty of the 100 publications about employee training and development that were obtained from Google Scholar and regarded to be more pertinent were examined for this study. The impact of employee training and development in an organization was found and documented during the analyses. According to the study's findings, some of the major advantages of staff development and training include greater productivity, the discovery of employee potential, job satisfaction, the development of skills, less supervision, a decrease in turnover and absenteeism as well as less supervision and reduction of errors and accidents. The findings show that organisations that make significant investments in the training and development of their personnel are more likely to succeed than those who do not.

Keywords: impact, employment, training and development, success, business, organization

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Authors: Javier Fernandez, Mayte Medina, Rafael Fernandez de Canete, Nuria Alcain, Juan Carlos Ramos-Diaz

Abstract:

At present, the experimentation of anesthetic drugs on patients requires a regulation protocol, and the response of each patient to several doses of entry drug must be well known. Therefore, the development of pharmacological dose control systems is a promising field of research in anesthesiology. In this paper, it has been developed a non-linear compartmental the pharmacokinetic-pharmacodynamical model which describes the anesthesia depth effect in a sufficiently reliable way over a set of patients with the depth effect quantified by the Bi-Spectral Index. Afterwards, an Artificial Neural Network (ANN) predictive controller has been designed based on the depth of anesthesia model so as to keep the patient in the optimum condition while he undergoes surgical treatment. For the purpose of quantifying the efficiency of the neural predictive controller, a classical proportional-integral-derivative controller has also been developed to compare both strategies. Results show the superior performance of predictive neural controller during BiSpectral Index reference tracking.

Keywords: anesthesia, bi-spectral index, neural network control, pharmacokinetic-pharmacodynamical model

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Authors: Franky Michael Hamonangan Siagian, Affan Maulana, Himawan Tri Bayu Murti Petrus, Widi Astuti

Abstract:

In this study, the feasibility of the direct electrowinning method was used to produce zero-valent iron from red mud. The bauxite residue sample came from the Tayan mine, Indonesia, which contains high hematite (Fe₂O₃). Before electrolysis, the samples were characterized by various analytical techniques (ICP-AES, SEM, XRD) to determine their chemical composition and mineralogy. The direct electrowinning method of red mud suspended in NaOH was introduced at low temperatures ranging from 30 - 110 °C. Variations of current density, red mud: NaOH ratio and temperature were carried out to determine the optimum operation of the direct electrowinning process. Cathode deposits and residues in electrochemical cells were analyzed using XRD, XRF, and SEM to determine the chemical composition and current recovery. The low-temperature electrolysis current efficiency on Redmud can reach 20% recovery at a current density of 920,945 A/m². The moderate performance of the process was investigated with red mud, which was attributed to the troublesome adsorption of red mud particles on the cathode, making the reduction far less efficient than that with hematite.

Keywords: red mud, electrochemical reduction, Iron production, hematite

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Authors: Majdi Al Shdifat, Juan Chiachio, Esther Puertas, María L. Jalón, Álvaro Blanca-Hoyos

Abstract:

In recent years, the world has begun to adopt more sustainable practices in response to today's environmental and climate challenges. The construction sector is one of the most resource-intensive among others, so researchers are testing different types of materials with different processes and methodologies to achieve more environmentally and sustainably friendly buildings.Plastic is one of the most harmful materials for the environment. The global production of plastics has increased dramatically in recent decades, and it is one of the most widely used materials. However, plastic waste is not biodegradable and has a chemical composition that is stable for many years in the environment, both on land and in water bodies. Recycled plastics have been tested to be used in construction in many ways to reduce the amount of plastic in the environment and the use of raw materials in construction. In this context, the main objective of this research is to test the use of plastic fibers with one of the most promising materials to replace cement, which is rammed earth. In fact, rammed earth is considered one of the most environmentally friendly materials due to its use of local raw materials, recyclability, and low embodied energy. In this research, three different types of plastic fibers were used. Then, the blends were evaluated by considering their mechanical properties, including compressive strength and tensile strength. In addition, the non-destructive ultrasonic wave velocity was measured. The result shows excellent potential for the use of plastic fibers in rammed earth, especially in terms of compressive strength.

Keywords: mechanical characterization, plastic fibers reinforcement, rammed earth, sustainable material

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Authors: Getaneh Berie Tarekegn

Abstract:

Wireless communication via unmanned aerial vehicles (UAVs) has drawn a great deal of attention due to its flexibility in establishing line-of-sight (LoS) communications. However, in complex urban and dynamic environments, the movement of UAVs can be blocked by trees and high-rise buildings that obstruct directional paths. With reconfigurable intelligent surfaces (RIS), this problem can be effectively addressed. To achieve this goal, accurate channel estimation in RIS-assisted UAV-enabled wireless communications is crucial. This paper proposes an accurate channel estimation model using long short-term memory (LSTM) for a multi-user RIS-assisted UAV-enabled wireless communication system. According to simulation results, LSTM can improve the channel estimation performance of RIS-assisted UAV-enabled wireless communication.

Keywords: channel estimation, reconfigurable intelligent surfaces, long short-term memory, unmanned aerial vehicles

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Authors: Neeta Kumari, Gopal Pathak

Abstract:

Artificial neural network has been proved to be an efficient tool for non-parametric modeling of data in various applications where output is non-linearly associated with input. It is a preferred tool for many predictive data mining applications because of its power , flexibility, and ease of use. A standard feed forward networks (FFN) is used to predict the groundwater fluoride content. The ANN model is trained using back propagated algorithm, Tansig and Logsig activation function having varying number of neurons. The models are evaluated on the basis of statistical performance criteria like Root Mean Squarred Error (RMSE) and Regression coefficient (R2), bias (mean error), Coefficient of variation (CV), Nash-Sutcliffe efficiency (NSE), and the index of agreement (IOA). The results of the study indicate that Artificial neural network (ANN) can be used for groundwater fluoride prediction in the limited data situation in the hard rock region like western parts of Jharkhand with sufficiently good accuracy.

Keywords: Artificial neural network (ANN), FFN (Feed-forward network), backpropagation algorithm, Levenberg-Marquardt algorithm, groundwater fluoride contamination

Procedia PDF Downloads 555