Search results for: hybrid MAC

Authors: M. Bilal Ishfaq, Adnan N. Qureshi

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COVID-19 is the most infectious disease these days, it was first reported in Wuhan, the capital city of Hubei in China then it spread rapidly throughout the whole world. Later on 11 March 2020, the World Health Organisation (WHO) declared it a pandemic. Since COVID-19 is highly contagious, it has affected approximately 219M people worldwide and caused 4.55M deaths. It has brought the importance of accurate diagnosis of respiratory diseases such as pneumonia and COVID-19 to the forefront. In this paper, we propose a hybrid approach for the automated detection of COVID-19 using medical imaging. We have presented the hybridization of manually extracted and convolutional features. Our approach combines Haralick texture features and convolutional features extracted from chest X-rays and CT scans. We also employ a minimum redundancy maximum relevance (MRMR) feature selection algorithm to reduce computational complexity and enhance classification performance. The proposed model is evaluated on four publicly available datasets, including Chest X-ray Pneumonia, COVID-19 Pneumonia, COVID-19 CTMaster, and VinBig data. The results demonstrate high accuracy and effectiveness, with 0.9925 on the Chest X-ray pneumonia dataset, 0.9895 on the COVID-19, Pneumonia and Normal Chest X-ray dataset, 0.9806 on the Covid CTMaster dataset, and 0.9398 on the VinBig dataset. We further evaluate the effectiveness of the proposed model using ROC curves, where the AUC for the best-performing model reaches 0.96. Our proposed model provides a promising tool for the early detection and accurate diagnosis of COVID-19, which can assist healthcare professionals in making informed treatment decisions and improving patient outcomes. The results of the proposed model are quite plausible and the system can be deployed in a clinical or research setting to assist in the diagnosis of COVID-19.

Keywords: COVID-19, feature engineering, artificial neural networks, radiology images

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Authors: Bülent Kantar, Numan Ünaldı

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This paper presents a new method which includes robust and invisible digital watermarking on images that is colored. Colored images are used as watermark. Frequency region is used for digital watermarking. Discrete wavelet transform and discrete stationary wavelet transform are used for frequency region transformation. Low, medium and high frequency coefficients are obtained by applying the two-level discrete wavelet transform to the original image. Low frequency coefficients are obtained by applying one level discrete stationary wavelet transform separately to all frequency coefficient of the two-level discrete wavelet transformation of the original image. For every low frequency coefficient obtained from one level discrete stationary wavelet transformation, watermarks are added. Watermarks are added to all frequency coefficients of two-level discrete wavelet transform. Totally, four watermarks are added to original image. In order to get back the watermark, the original and watermarked images are applied with two-level discrete wavelet transform and one level discrete stationary wavelet transform. The watermark is obtained from difference of the discrete stationary wavelet transform of the low frequency coefficients. A total of four watermarks are obtained from all frequency of two-level discrete wavelet transform. Obtained watermark results are compared with real watermark results, and a similarity result is obtained. A watermark is obtained from the highest similarity values. Proposed methods of watermarking are tested against attacks of the geometric and image processing. The results show that proposed watermarking method is robust and invisible. All features of frequencies of two level discrete wavelet transform watermarking are combined to get back the watermark from the watermarked image. Watermarks have been added to the image by converting the binary image. These operations provide us with better results in getting back the watermark from watermarked image by attacking of the geometric and image processing.

Keywords: watermarking, DWT, DSWT, copy right protection, RGB

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Authors: Mohammadreza Salek Faramarzi, Touraj Taghikhany

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In this paper, seismic fragility assessment of a recently developed hybrid structural system, known as the strongback system (SBS) is investigated. In this system, to mitigate the occurrence of the soft-story mechanism and improve the distribution of story drifts over the height of the structure, an elastic vertical truss is formed. The strengthened members of the braced span are designed to remain substantially elastic during levels of excitation where soft-story mechanisms are likely to occur and impose a nearly uniform story drift distribution. Due to the distinctive characteristics of near-field ground motions, it seems to be necessary to study the effect of these records on seismic performance of the SBS. To this end, a set of 56 near-field ground motion records suggested by FEMA P695 methodology is used. For fragility assessment, nonlinear dynamic analyses are carried out in OpenSEES based on the recommended procedure in HAZUS technical manual. Four damage states including slight, moderate, extensive, and complete damage (collapse) are considered. To evaluate each damage state, inter-story drift ratio and floor acceleration are implemented as engineering demand parameters. Further, to extend the evaluation of the collapse state of the system, a different collapse criterion suggested in FEMA P695 is applied. It is concluded that SBS can significantly increase the collapse capacity and consequently decrease the collapse risk of the structure during its life time. Comparing the observing mean annual frequency (MAF) of exceedance of each damage state against the allowable values presented in performance-based design methods, it is found that using the elastic vertical truss, improves the structural response effectively.

Keywords: IDA, near-fault, probabilistic performance assessment, seismic fragility, strongback system, uncertainty

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Authors: Guilherme Giacomini, Ana Luiza Menegatti Pavan, Allan Felipe Fattori Alves, Marcela de Oliveira, Fernando Antonio Bacchim Neto, José Ricardo de Arruda Miranda, Seizo Yamashita, Diana Rodrigues de Pina

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The maxillary sinus (MS), part of the paranasal sinus complex, is one of the most enigmatic structures in modern humans. The literature has suggested that MSs function as olfaction accessories, to heat or humidify inspired air, for thermoregulation, to impart resonance to the voice and others. Thus, the real function of the MS is still uncertain. Furthermore, the MS anatomy is complex and varies from person to person. Many diseases may affect the development process of sinuses. The incidence of rhinosinusitis and other pathoses in the MS is comparatively high, so, volume analysis has clinical value. Providing volume values for MS could be helpful in evaluating the presence of any abnormality and could be used for treatment planning and evaluation of the outcome. The computed tomography (CT) has allowed a more exact assessment of this structure, which enables a quantitative analysis. However, this is not always possible in the clinical routine, and if possible, it involves much effort and/or time. Therefore, it is necessary to have a convenient, robust, and practical tool correlated with the MS volume, allowing clinical applicability. Nowadays, the available methods for MS segmentation are manual or semi-automatic. Additionally, manual methods present inter and intraindividual variability. Thus, the aim of this study was to develop an automatic tool to quantity the MS volume in CT scans of paranasal sinuses. This study was developed with ethical approval from the authors’ institutions and national review panels. The research involved 30 retrospective exams of University Hospital, Botucatu Medical School, São Paulo State University, Brazil. The tool for automatic MS quantification, developed in Matlab®, uses a hybrid method, combining different image processing techniques. For MS detection, the algorithm uses a Support Vector Machine (SVM), by features such as pixel value, spatial distribution, shape and others. The detected pixels are used as seed point for a region growing (RG) segmentation. Then, morphological operators are applied to reduce false-positive pixels, improving the segmentation accuracy. These steps are applied in all slices of CT exam, obtaining the MS volume. To evaluate the accuracy of the developed tool, the automatic method was compared with manual segmentation realized by an experienced radiologist. For comparison, we used Bland-Altman statistics, linear regression, and Jaccard similarity coefficient. From the statistical analyses for the comparison between both methods, the linear regression showed a strong association and low dispersion between variables. The Bland–Altman analyses showed no significant differences between the analyzed methods. The Jaccard similarity coefficient was > 0.90 in all exams. In conclusion, the developed tool to quantify MS volume proved to be robust, fast, and efficient, when compared with manual segmentation. Furthermore, it avoids the intra and inter-observer variations caused by manual and semi-automatic methods. As future work, the tool will be applied in clinical practice. Thus, it may be useful in the diagnosis and treatment determination of MS diseases. Providing volume values for MS could be helpful in evaluating the presence of any abnormality and could be used for treatment planning and evaluation of the outcome. The computed tomography (CT) has allowed a more exact assessment of this structure which enables a quantitative analysis. However, this is not always possible in the clinical routine, and if possible, it involves much effort and/or time. Therefore, it is necessary to have a convenient, robust and practical tool correlated with the MS volume, allowing clinical applicability. Nowadays, the available methods for MS segmentation are manual or semi-automatic. Additionally, manual methods present inter and intraindividual variability. Thus, the aim of this study was to develop an automatic tool to quantity the MS volume in CT scans of paranasal sinuses. This study was developed with ethical approval from the authors’ institutions and national review panels. The research involved 30 retrospective exams of University Hospital, Botucatu Medical School, São Paulo State University, Brazil. The tool for automatic MS quantification, developed in Matlab®, uses a hybrid method, combining different image processing techniques. For MS detection, the algorithm uses a Support Vector Machine (SVM), by features such as pixel value, spatial distribution, shape and others. The detected pixels are used as seed point for a region growing (RG) segmentation. Then, morphological operators are applied to reduce false-positive pixels, improving the segmentation accuracy. These steps are applied in all slices of CT exam, obtaining the MS volume. To evaluate the accuracy of the developed tool, the automatic method was compared with manual segmentation realized by an experienced radiologist. For comparison, we used Bland-Altman statistics, linear regression and Jaccard similarity coefficient. From the statistical analyses for the comparison between both methods, the linear regression showed a strong association and low dispersion between variables. The Bland–Altman analyses showed no significant differences between the analyzed methods. The Jaccard similarity coefficient was > 0.90 in all exams. In conclusion, the developed tool to automatically quantify MS volume proved to be robust, fast and efficient, when compared with manual segmentation. Furthermore, it avoids the intra and inter-observer variations caused by manual and semi-automatic methods. As future work, the tool will be applied in clinical practice. Thus, it may be useful in the diagnosis and treatment determination of MS diseases.

Keywords: maxillary sinus, support vector machine, region growing, volume quantification

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Authors: Getaneh Berie Tarekegn

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A precise localization system is crucial for many artificial intelligence Internet of Things (AI-IoT) applications in the era of smart cities. Their applications include traffic monitoring, emergency alarming, environmental monitoring, location-based advertising, intelligent transportation, and smart health care. The most common method for providing continuous positioning services in outdoor environments is by using a global navigation satellite system (GNSS). Due to nonline-of-sight, multipath, and weather conditions, GNSS systems do not perform well in dense urban, urban, and suburban areas.This paper proposes a generative AI-based positioning scheme for large-scale wireless settings using fingerprinting techniques. In this article, we presented a semi-supervised deep convolutional generative adversarial network (S-DCGAN)-based radio map construction method for real-time device localization. It also employed a reliable signal fingerprint feature extraction method with t-distributed stochastic neighbor embedding (t-SNE), which extracts dominant features while eliminating noise from hybrid WLAN and long-term evolution (LTE) fingerprints. The proposed scheme reduced the workload of site surveying required to build the fingerprint database by up to 78.5% and significantly improved positioning accuracy. The results show that the average positioning error of GAILoc is less than 0.39 m, and more than 90% of the errors are less than 0.82 m. According to numerical results, SRCLoc improves positioning performance and reduces radio map construction costs significantly compared to traditional methods.

Keywords: location-aware services, feature extraction technique, generative adversarial network, long short-term memory, support vector machine

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Authors: Xingyu Peng, Qingyuan Hu, Xuebin Zhu, Xi Yuan

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Neutron spectrometry has contributed much to the development of nuclear physics since 1932 and has also become an importance tool in several other fields, notably nuclear technology, fusion plasma diagnostics and radiation protection. Compared with neutron fluxes, neutron spectra can provide more detailed information on the internal physical process of neutron sources, such as fast neutron reactors, fusion plasma, fission-fusion hybrid reactors, and so on. However, high performance neutron spectrometer is not so commonly available as it requires the use of large and complex instrumentation. This work describes the development and characterization of a compact magnetic proton recoil (MPR) spectrometer for high-resolution measurements of fast neutron spectra. The compact MPR spectrometer is featured by its large recoil angle, small size permanent analysis magnet, short beam transport line and dual-purpose detector array for both steady state and pulsed neutron spectra measurement. A 3-dimensional electromagnetic particle transport code is developed to simulate the response function of the spectrometer. Simulation results illustrate that the performance of the spectrometer is mainly determined by n-p recoil foil and proton apertures, and an overall energy resolution of 3% is achieved for 14 MeV neutrons. Dedicated experiments using alpha source and mono-energetic neutron beam are employed to verify the simulated response function of the compact MPR spectrometer. These experimental results show a good agreement with the simulated ones, which indicates that the simulation code possesses good accuracy and reliability. The compact MPR spectrometer described in this work is a valuable tool for fast neutron spectra measurements for the fission or fusion devices.

Keywords: neutron spectrometry, magnetic proton recoil spectrometer, neutron spectra, fast neutron

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Authors: Intisar Al Busaidi, Rashid Al Maamari, Daowoud Al Mahroqi, Mahvash Karimi

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In recent years, some research studies have been performed on the hybrid application of polymer and low salinity water flooding (LSWF). Numerous technical and economic benefits of low salinity polymer flooding (LSPF) have been reported. However, as with any EOR technology, there are various risks involved in using LSPF. Ions exchange between porous media and brine is one of the Crude oil/ brine/ rocks (COBR) reactions that is identified as a potential risk in LSPF. To the best of our knowledge, this conclusion was drawn based on bulk rheology measurements, and no explanation was provided on how water chemistry changed in the presence of polymer. Therefore, this study aimed to understand rock/ brine interactions with high and low salinity brine in the absence and presence of polymer with Omani reservoir core plugs. Many single-core flooding experiments were performed with low and high salinity polymer solutions to investigate the influence of partially hydrolyzed polyacrylic amide with different brine salinities on cation exchange reactions. Ion chromatography (IC), total organic carbon (TOC), rheological, and pH measurements were conducted for produced aqueous phase. A higher increase in pH and lower polymer adsorption was observed in LSPF compared with conventional polymer flooding. In addition, IC measurements showed that all produced fluids in the absence and presence of polymer showed elevated Ca²⁺, Mg²⁺, K+, Cl- and SO₄²⁻ ions compared to the injected fluids. However, the divalent cations levels, mainly Ca²⁺, were the highest and remained elevated for several pore volumes in the presence of LSP. The results are in line with rheological measurements where the highest viscosity reduction was recorded with the highest level of Ca²⁺ production. Despite the viscosity loss due to cation exchange reactions, LSP can be an attractive alternative to conventional polymer flooding in the Marmul field.

Keywords: polymer, ions, exchange, recovery, low salinity

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Authors: Calvin A. Omolo, Rahul S. Kalhapure, Mahantesh Jadhav, Sanjeev Rambharose, Chunderika Mocktar, Thirumala Govender

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Treatment of infections is compromised by limitations of conventional dosage forms and drug resistance. Nanocarrier system is a strategy to overcome these challenges and improve therapy. Thus, the development of novel materials for drug delivery via nanocarriers is essential. The aim of the study was to synthesize a multi-arm polymer (6-mPEPEA) for enhanced activity of vancomycin (VM) against susceptible and resistant Staphylococcus aureus (MRSA). The synthesis steps of the star polymer followed reported procedures. The synthesized 6-mPEPEA was characterized by FTIR, ¹H and ¹³CNMR and MTT assays. VM loaded micelles were prepared from 6-mPEPEA and characterized for size, polydispersity index (PI) and surface charge (ZP) (Dynamic Light Scattering), morphology by TEM, drug loading (UV Spectrophotometry), drug release (dialysis bag), in vitro and in vivo efficacy against sensitive and resistant S. aureus. 6-mPEPEA was synthesized, and its structure was confirmed. MTT assays confirmed its nontoxic nature with a high cell viability (77%-85%). Unimolecular spherical micelles were prepared. Size, PI, and ZP was 52.48 ± 2.6 nm, 0.103 ± 0.047, -7.3 ± 1.3 mV, respectively and drug loading was 62.24 ± 3.8%. There was a 91% drug release from VCM-6-mPEPEA after 72 hours. In vitro antibacterial test revealed that VM-6-mPEPEA had 8 and 16-fold greater activity against S. aureus and MRSA when compared to bare VM. Further investigations using flow cytometry showed that VM-6-mPEPEA had 99.5% killing rate of MRSA at the MIC concentration. In vivo antibacterial activity revealed that treatment with VM-6-mPEPEA had a 190 and a 15-fold reduction in the MRSA load in untreated and VM treated respectively. These findings confirmed the potential of 6-mPEPEA as a promising bio-degradable nanocarrier for antibiotic delivery to improve treatment of bacterial infections.

Keywords: biosafe, MRSA, nanocarrier, resistance, unimolecular-micelles

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Authors: Keerthi Katam, Abhinav B. Tirunaghari, Vinod Vadithya, Toshiyuki Shimizu, Satoshi Soda, Debraj Bhattacharyya

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Researchers worldwide are increasingly focusing on the removal of carbon and nutrient from wastewater using algal-bacterial hybrid systems. Algae produce oxygen during photosynthesis, which is taken up by heterotrophic bacteria for mineralizing organic carbon to carbon dioxide. This phenomenon reduces the net mechanical aeration requirement of aerobic biological wastewater treatment processes. Consequently, the treatment cost is also reduced. Microalgae also participate in the treatment process by taking up nutrient (N, P) from wastewater. Algal biomass, if harvested, can generate value-added by-products. The aim of the present study was to compare the performance of two systems - System A (mixed microalgae and bacteria) and System B (diatoms and bacteria) in treating kitchen wastewater (KWW). The test reactors were operated at five different solid retention times (SRTs) -2, 4, 6, 8, and 10-days in draw-and-fill mode. The KWW was collected daily from the dining hall-kitchen area of the Indian Institute of Technology Hyderabad. The influent and effluent samples were analyzed for total organic carbon (TOC), total nitrogen (TN) using TOC-L analyzer. A colorimetric method was used to analyze anionic surfactant. Phosphorus (P) and chlorophyll were measured by following standard methods. The TOC, TN, and P of KWW were in the range of 113.5 to 740 mg/L, 2 to 22.8 mg/L, and 1 to 4.5 mg/L, respectively. Both the systems gave similar results with 85% of TOC removal and 60% of TN removal at 10-d SRT. However, the anionic surfactant removal in System A was 99% and 60% in System B. The chlorophyll concentration increased with an increase in SRT in both the systems. At 2-d SRT, no chlorophyll was observed in System B, whereas 0.5 mg/L was observed in System A. At 10-d SRT, the chlorophyll concentration in System A was 7.5 mg/L, whereas it was 4.5 mg/L in System B. Although both the systems showed similar performance in treatment, the increase in chlorophyll concentration suggests that System A demonstrated a better algal-bacterial symbiotic relationship in treating KWW than System B.

Keywords: diatoms, microalgae, retention time, wastewater treatment

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Authors: Arun Prasad, Abhishek Tiwari, Rekha Jaiswal, Vivek Chaudhary

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Background: Robotic Roux en Y gastric bypass is being done for some time but is technically difficult, requiring operating in both the sub diaphragmatic and infracolic compartments of the abdomen. This can mean a dual docking of the robot or a hybrid partial laparoscopic and partial robotic surgery. The Mini /One anastomosis /omega loop gastric bypass (MGB) has the advantage of having all dissection and anastomosis in the supracolic compartment and is therefore suitable technically for robotic surgery. Methods: We have done 208 robotic mini gastric bypass surgeries. The robot is docked above the head of the patient in the midline. Camera port is placed supra umbilically. Two ports are placed on the left side of the patient and one port on the right side of the patient. An assistant port is placed between the camera port and right sided robotic port for use of stapler. Distal stomach is stapled from the lesser curve followed by a vertical sleeve upwards leading to a long sleeve pouch. Jejunum is taken at 200 cm from the duodenojejunal junction and brought up to do a side to side gastrojejunostomy. Results: All patients had a successful robotic procedure. Mean time taken was 85 minutes. There were major intraoperative or post operative complications. No patient needed conversion or re-explorative surgery. Mean excess weight loss over a period of 2 year was about 75%. There was no mortality. Patient satisfaction score was high and was attributed to the good weight loss and minimal dietary modifications that were needed after the procedure. Long term side effects were anemia and bile reflux in a small number of patients. Conclusions: MGB / OAGB is gaining worldwide interest as a short simple procedure that has been shown to very effective and safe bariatric surgery. The purpose of this study was to report on the safety and efficacy of robotic surgery for this procedure. This is the first report of totally robotic mini gastric bypass.

Keywords: MGB, mini gastric bypass, OAGB, robotic bariatric surgery

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Authors: Mohamed S. Selim, Nesreen A. Fatthallah, Shimaa A. Higazy, Zhifeng Hao, Ping Jing Mo

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As marine fouling-release (FR) surfaces, two new superhydrophobic nanocomposite series of polydimethylsiloxane (PDMS) loaded with reduced graphene oxide (RGO) and graphene oxide/boehmite nanorods (GO-γ-AlOOH) nanofillers were created. The self-cleaning and antifouling capabilities were modified by controlling the nanofillers' shapes and distribution in the silicone matrix. With an average diameter of 10-20 nm and a length of 200 nm, γ-AlOOH nanorods showed a single crystallinity. RGO was made using a hydrothermal process, whereas GO-γ-AlOOH nanocomposites were made using a chemical deposition method for use as fouling-release coating materials. These nanofillers were disseminated in the silicone matrix using the solution casting method to explore the synergetic effects of graphene-based materials on the surface, mechanical, and FR characteristics. Water contact angle (WCA), scanning electron, and atomic force microscopes were used to investigate the surface's hydrophobicity and antifouling capabilities (SEM and AFM). The roughness, superhydrophobicity, and surface mechanical characteristics of coatings all increased the homogeneity of the nanocomposite dispersion. To examine the antifouling effects of the coating systems, laboratory tests were conducted for 30 days using specified bacteria.PDMS/GO-γ-AlOOH nanorod composite demonstrated superior antibacterial efficacy against several bacterial strains than PDMS/RGO nanocomposite. The high surface area and stabilizing effects of the GO-γ-AlOOH hybrid nanofillers are to blame for this. The biodegradability percentage of the PDMS/GO-γ-AlOOH nanorod composite (3 wt.%) was the lowest (1.6%), while the microbial endurability percentages for gram-positive, gram-negative, and fungi were 86.42%, 97.94%, and 85.97%, respectively. The homogeneity of the GO-γ-AlOOH (3 wt.%) dispersion, which had a WCA of 151° and a rough surface, was the most profound superhydrophobic antifouling nanostructured coating.

Keywords: superhydrophobic nanocomposite, fouling release, nanofillers, surface coating

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Authors: Felice De Luca, Vincenzo Galdi, Piera Stella, Vito Calderaro, Adriano Campagna, Antonio Piccolo

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Sustainable mobility has become one of the major issues of recent years. The challenge in reducing polluting emissions as much as possible has led to the production and diffusion of vehicles with internal combustion engines that are less polluting and to the adoption of green energy vectors, such as vehicles powered by natural gas or LPG and, more recently, with hybrid and electric ones. While on the one hand, the spread of electric vehicles for private use is becoming a reality, albeit rather slowly, not the same is happening for vehicles used for public transport, especially those that operate in the congested areas of the cities. Even if the first electric buses are increasingly being offered on the market, it remains central to the problem of autonomy for battery fed vehicles with high daily routes and little time available for recharging. In fact, at present, solid-state batteries are still too large in size, heavy, and unable to guarantee the required autonomy. Therefore, in order to maximize the energy management on the vehicle, the optimization of driving profiles offer a faster and cheaper contribution to improve vehicle autonomy. In this paper, following the authors’ precedent works on electric vehicles in public transport and energy management strategies in the electric mobility area, an eco-driving strategy for electric bus is presented and validated. Particularly, the characteristics of the prototype bus are described, and a general-purpose eco-drive methodology is briefly presented. The model is firstly simulated in MATLAB™ and then implemented on a mobile device installed on-board of a prototype bus developed by the authors in a previous research project. The solution implemented furnishes the bus-driver suggestions on the guide style to adopt. The result of the test in a real case will be shown to highlight the effectiveness of the solution proposed in terms of energy saving.

Keywords: eco-drive, electric bus, energy management, prototype

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Authors: Samuel Abraham Berhane, Lingbing Bu

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This paper presents the spatiotemporal variability of aerosols, clouds, and precipitation within the major cities in Eritrea and it investigates the relationship between aerosols, clouds, and precipitation concerning the presence of aerosols over the study region. In Eritrea, inadequate water supplies will have both direct and indirect adverse impacts on sustainable development in areas such as health, agriculture, energy, communication, and transport. Besides, there exists a gap in the knowledge on suitable and potential areas for cloud seeding. Further, the inadequate understanding of aerosol-cloud-precipitation (ACP) interactions limits the success of weather modification aimed at improving freshwater sources, storage, and recycling. Spatiotemporal variability of aerosols, clouds, and precipitation involve spatial and time series analysis based on trend and anomaly analysis. To find the relationship between aerosols and clouds, a correlation coefficient is used. The spatiotemporal analysis showed larger variations of aerosols within the last two decades, especially in Assab, indicating that aerosol optical depth (AOD) has increased over the surrounding Red Sea region. Rainfall was significantly low but AOD was significantly high during the 2011 monsoon season. Precipitation was high during 2007 over most parts of Eritrea. The correlation coefficient between AOD and rainfall was negative over Asmara and Nakfa. Cloud effective radius (CER) and cloud optical thickness (COT) exhibited a negative correlation with AOD over Nakfa within the June–July–August (JJA) season. The hybrid single-particle Lagrangian integrated trajectory (HYSPLIT) model that is used to find the path and origin of the air mass of the study region showed that the majority of aerosols made their way to the study region via the westerly and the southwesterly winds.

Keywords: aerosol-cloud-precipitation, aerosol optical depth, cloud effective radius, cloud optical thickness, HYSPLIT

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Authors: M. Torres, A. Moulay, M. Zhuldybina, M. Rozel, N. D. Trinh, C. Bois

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Printed electronics are a fast-growing market as their applications cover a large range of industrial needs, their production cost is low, and the additive printing techniques consume less materials than subtractive manufacturing methods used in traditional electronics. With the growing demand for printed electronics, there are concerns about their harmful and irreversible contribution to the environment. Indeed, it is estimated that 80% of the environmental load of a product is determined by the choices made at the conception stage. Therefore, examination through a life cycle approach at the developing stage of a novel product is the best way to identify potential environmental issues and make proactive decisions. Life cycle analysis (LCA) is a comprehensive scientific method to assess the environmental impacts of a product in its different stages of life: extraction of raw materials, manufacture and distribution, use, and end-of-life. Impacts and major hotspots are identified and evaluated through a broad range of environmental impact categories of the ReCiPe (H) middle point method. At the conception stage, the LCA is a tool that provides an environmental point of view on the choice of materials and processes and weights-in on the balance between performance materials and eco-friendly materials. Using the life cycle approach, the current work aims to provide a cradle-to-grave life cycle assessment of a roll-to-roll hybrid printed smart label designed for the food cold chain. Furthermore, this presentation will present the environmental impact of metallic conductive inks, a comparison with promising conductive polymers, evaluation of energy vs. performance of industrial printing processes, a full assessment of the impact from the smart label applied on a cellulosic-based substrate during the recycling process and the possible recovery of precious metals and rare earth elements.

Keywords: Eco-design, label, life cycle assessment, printed electronics

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Authors: Ganesh R. Bhand, N. B. Chaure

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Cadmium selenide (CdSe) quantum dots (QDs) were prepared by solvothermal route. Subsequently a inorganic QDs-organic semiconductor (copper phthalocyanine) nanocomposite (i.e CuPc:CdSe nanocomposites) were produced by different concentration of QDs varied in CuPc. The nanocomposite thin films have been prepared by means of spin coating technique. The optical, structural and morphological properties of nanocomposite films have been investigated. The transmission electron microscopy (TEM) confirmed the formation of QDs having average size of  4 nm. The X-ray diffraction pattern exhibits cubic crystal structure of CdSe with reflection to (111), (220) and (311) at 25.4ᵒ, 42.2ᵒ and 49.6ᵒ respectively. The additional peak observed at lower angle at 6.9ᵒ in nanocomposite thin films are associated to CuPc. The field emission scanning electron microscopy (FESEM) observed that surface morphology varied in increasing concentration of CdSe QDs. The obtained nanocomposite show significant improvement in the thermal stability as compared to the pure CuPc indicated by thermo-gravimetric analysis (TGA) in thermograph. The effect in the Raman spectra of composites samples gives a confirm evidence of homogenous dispersion of CdSe in the CuPc matrix and their strong interaction between them to promotes charge transfer property. The success of reaction between composite was confirmed by Fourier transform infrared spectroscopy (FTIR). The photo physical properties were studied using UV - visible spectroscopy. The enhancement of the optical absorption in visible region for nanocomposite layer was observed with increasing the concentration of CdSe in CuPc. This composite may obtain the maximized interface between QDs and polymer for efficient charge separation and enhance the charge transport. Such nanocomposite films for potential application in fabrication of hybrid solar cell with improved power conversion efficiency.

Keywords: CdSe QDs, cupper phthalocyanine, FTIR, optical absorption

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Authors: Michael Ayomoh, Adriaan Roux, Oyindamola Omotuyi

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This paper presents an efficient robot navigation model in a multi-target domain amidst static and dynamic workspace obstacles. The problem is that of developing an optimal algorithm to minimize the total travel time of a robot as it visits all target points within its task domain amidst unknown workspace obstacles and finally return to its initial position. In solving this problem, a classical algorithm was first developed to compute the optimal number of paths to be travelled by the robot amidst the network of paths. The principle of shortest distance between robot and targets was used to compute the target point visitation order amidst workspace obstacles. Algorithm premised on the standard polar coordinate system was developed to determine the length of obstacles encountered by the robot hence giving room for a geometrical estimation of the total surface area occupied by the obstacle especially when classified as a relevant obstacle i.e. obstacle that lies in between a robot and its potential visitation point. A stochastic model was developed and used to estimate the likelihood of a dynamic obstacle bumping into the robot’s navigation path and finally, the navigation/obstacle avoidance algorithm was hinged on the hybrid virtual force field (HVFF) method. Significant modelling constraints herein include the choice of navigation path to selected target points, the possible presence of static obstacles along a desired navigation path and the likelihood of encountering a dynamic obstacle along the robot’s path and the chances of it remaining at this position as a static obstacle hence resulting in a case of re-routing after routing. The proposed algorithm demonstrated a high potential for optimal solution in terms of efficiency and effectiveness.

Keywords: multi-target, mobile robot, optimal path, static obstacles, dynamic obstacles

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Authors: Michael Naderhirn, Marco Pavone

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Problem statement, motivation, and aim of work: So far, the development of control algorithms was done by control engineers in a way that the controller would fit a specification by testing. When it comes to the certification of an autonomous car in highly complex scenarios, the challenge is much higher since such a controller must mathematically guarantee to implement the rules of the road while on the other side guarantee aspects like safety and real time executability. What if it becomes reality to solve this demanding problem by combining Formal Verification and System Theory? The aim of this work is to present a workflow to solve the above mentioned problem. Summary of the presented results / main outcomes: We show the usage of an English like language to transform the rules of the road into system specification for an autonomous car. The language based specifications are used to define system functions and interfaces. Based on that a formal model is developed which formally correctly models the specifications. On the other side, a mathematical model describing the systems dynamics is used to calculate the systems reachability set which is further used to determine the system input boundaries. Then a motion planning algorithm is applied inside the system boundaries to find an optimized trajectory in combination with the formal specification model while satisfying the specifications. The result is a control strategy which can be applied in real time independent of the scenario with a mathematical guarantee to satisfy a predefined specification. We demonstrate the applicability of the method in simulation driving scenarios and a potential certification. Originality, significance, and benefit: To the authors’ best knowledge, it is the first time that it is possible to show an automated workflow which combines a specification in an English like language and a mathematical model in a mathematical formal verified way to synthesizes a controller for potential real time applications like autonomous driving.

Keywords: formal system verification, reachability, real time controller, hybrid system

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Authors: Fabio T. Costa, Sergio E. Moya, Marcelo H. Sousa

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Nanocomposites designed by embedding magnetic nanoparticles into a polymeric matrix stand out as ideal magnetic-hybrid and magneto-responsive materials as sorbents for removal of pollutants in environmental applications. Covalent coupling is often desired for the immobilization of species on these nanocomposites, in order to keep them permanently bounded, not desorbing or leaching over time. Moreover, unwanted adsorbates can be separated by successive washes/magnetic separations, and it is also possible to recover the adsorbate covalently bound to the nanocomposite surface through detaching/cleavage protocols. Thus, in this work, we describe the preparation and characterization of highly-magnetizable chloromethylated polystyrene-based nanocomposite beads for selective covalent coupling in environmental applications. For synthesis optimization, acid resistant core-shelled maghemite (γ-Fe₂O₃) nanoparticles were coated with oleate molecules and directly incorporated into the organic medium during a suspension polymerization process. Moreover, the cross-linking agent ethylene glycol dimethacrylate (EGDMA) was utilized for co-polymerization with the 4-vinyl benzyl chloride (VBC) to increase the resistance of microbeads against leaching. After characterizing samples with XRD, ICP-OES, TGA, optical, SEM and TEM microscopes, a magnetic composite consisting of ~500 nm-sized cross-linked polymeric microspheres embedding ~8 nm γ-Fe₂O₃ nanoparticles was verified. This nanocomposite showed large room temperature magnetization (~24 emu/g) due to the high content in maghemite (~45 wt%) and resistance against leaching even in acidic media. Moreover, the presence of superficial chloromethyl groups, probed by FTIR and XPS spectroscopies and confirmed by an amination test can selectively adsorb molecules through the covalent coupling and be used in molecular separations as shown for the selective removal of 4-aminobenzoic acid from a mixture with benzoic acid.

Keywords: nanocomposite, magnetic nanoparticle, covalent separation, pollutant removal

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Authors: Cathy SW. Chow, Kristina Shin, Faming Wang, B. C. L. So

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During some dynamic giant puppet performances, an ergonomically designed load carrier system is necessary for the puppeteers to carry a giant puppet body’s heavy load with minimum muscle stress. A load carrier (i.e. prototype) was designed with two small wheels on the foot; and a hybrid spring device on the knee in order to assist the sliding and knee bending movements respectively. Thus, the purpose of this study was to evaluate the effect of three load carriers including two other commercially available load mounting systems, Tepex and SuitX, and the prototype. Ten male participants were recruited for the experiment. Surface electromyography (sEMG) was used to collect the participants’ muscle activities during forward moving and bouncing and with and without load of 11.1 kg that was 60 cm above the shoulder. Five bilateral muscles including the lumbar erector spinae (LES), rectus femoris (RF), bicep femoris (BF), tibialis anterior (TA), and gastrocnemius (GM) were selected for data collection. During forward moving task, the sEMG data showed smallest muscle activities by Tepex harness which exhibited consistently the lowest, compared with the prototype and SuitX which were significantly higher on left LES 68.99% and 64.99%, right LES 26.57% and 82.45%; left RF 87.71% and 47.61%, right RF 143.57% and 24.28%; left BF 80.21% and 22.23%, right BF 96.02% and 21.83%; right TA 6.32% and 4.47%; left GM 5.89% and 12.35% respectively. The result above reflected mobility was highly restricted by tested exoskeleton devices. On the other hand, the sEMG data from bouncing task showed the smallest muscle activities by prototype which exhibited consistently the lowest, compared with the Tepex harness and SuitX which were significantly lower on lLES 6.65% and 104.93, rLES 23.56% and 92.19%; lBF 33.21% and 93.26% and rBF 24.70% and 81.16%; lTA 46.51% and 191.02%; rTA 12.75% and 125.76%; IGM 31.54% and 68.36%; rGM 95.95% and 96.43% respectively.

Keywords: exoskeleton, giant puppet performers, load carriage system, surface electromyography

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Authors: Reem Saad Alqahtani

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Drawing on minority literature, this research highlights the role of three contemporary Arab American writers, considering the significance of the historical and cultural contexts of the brutal attacks of 9/11. The focus of the research is to draw attention to the poetry of Samuel Hazo, Nathalie Handal, and Naomi Shihab Nye as representatives of the identity crisis, whose experiences left them feeling marginalized and alienated in both societies, and reflected as one of the ethnic American minority groups, as demonstrated in their poetry, with a special focus on hybridity, resistance, identity, and empowerment. The study explores the writers’ post-9/11 experience, affected by the United States’ long history of marginalization and discrimination against people of colour, placing Arab American literature with that of other ethnic American groups who share the same experience and contribute to composing literature characterized by the aesthetics of cultural hybridity, cultural complexity, and the politics of minorities to promote solidarity and coalition building. Indeed, the three selected Arab American writers have found a link between their narration and the identity of the exiled by establishing an identity that is a kind of synthesis of diverse identities of Western reality and Eastern nostalgia. The approaches applied in this study will include historical/biographical, postcolonial, and discourse analysis. The first will be used to emphasize the influence of the biographical aspects related to the community, identity, and resistance of the three poets on their poetry. The second is used to investigate the effects of postcolonialism on the poets and their responses to it, while the third understand the sociocultural, political, and historical dimensions of the texts, establishing these poets as representative of the Arab American experience. This study is significant because it will help shed light on the importance of the Arabic hybrid identity in creating resistance to minority communities within American society.

Keywords: Arab American, identity, hybridity, post-9/11

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Authors: Todd Pennington, Carol Wilkinson, Keven Prusak

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Traditional models of student teaching practices typically involve the placement of a student teacher with an experienced mentor teacher. However, due to the ever-decreasing number of quality placements, an alternative triad approach is the paired placement of student teachers with one mentor teacher in a community of practice. This study examined the paired-placement of student teachers in physical education to determine the benefits and drawbacks after a 14-week student teaching experience. PETE students (N = 22) at a university in the United States were assigned to work in a triad with a student teaching partner and a mentor teacher, making up eleven triads for the semester. The one exception was a pair that worked for seven weeks at an elementary school and then for seven weeks at a junior high school, thus having two mentor teachers and participating in two triads. A total of 12 mentor teachers participated in the study. All student teachers and mentor teachers volunteered and agreed to participate. The student teaching experience was structured so that students engaged in: (a) individual teaching (one teaching the lesson with the other observing), (b) co-planning, and (c) peer coaching. All students and mentor teachers were interviewed at the conclusion of the experience. Using interview data, field notes, and email response data, the qualitative data was analyzed using the constant comparative method. The benefits of the paired placement experience emerged into three categories (a) quality feedback, (b) support, and (c) collaboration. The drawbacks emerged into four categories (a) unrealistic experience, (b) laziness in preparation, (c) lack of quality feedback, and (d) personality mismatch. Recommendations include: providing in-service training prior to student teaching to optimize the triad experience, ongoing seminars throughout the experience specifically designed for triads, and a hybrid model of paired placement for the first half of student teaching followed by solo student teaching for the second half of the experience.

Keywords: community of practice, paired placement, physical education, student teaching

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Authors: Adriana Duarte, Sandra Sampaio, Catia Ferreira, Jaime I. N. R. Gomes

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With the fashion of faded worn garments the textile industry has moved from indigo and pigments to dyes that are fixed by cationization, with products that can be toxic, and that can show this effect after washing down the dye with friction and/or treating with enzymes in a subsequent operation. Increasingly they are treated with bleaches, such as hypochlorite and permanganate, both toxic substances. An alternative process is presented in this work for both garment and jet dyeing processes, without the use of pre-cationization and the alternative use of “particle-dyes”. These are hybrid products, made up by an inorganic particle and an organic dye. With standard soluble dyes, it is not possible to avoid diffusion into the inside of the fiber unless using previous cationization. Only in this way can diffusion be avoided keeping the centre of the fibres undyed so as to produce the faded effect by removing the surface dye and showing the white fiber beneath. With “particle-dyes”, previous cationization is avoided. By applying low temperatures, the dye does not diffuse completely into the inside of the fiber, since it is a particle and not a soluble dye, being then able to give the faded effect. Even though bleaching can be used it can also be avoided, by the use of friction and enzymes they can be used just as for other dyes. This fashion brought about new ways of applying reactive dyes by the use of previous cationization of cotton, lowering the salt, and temperatures that reactive dyes usually need for reacting and as a side effect the application of a more environmental process. However, cationization is a process that can be problematic in applying it outside garment dyeing, such as jet dyeing, being difficult to obtain level dyeings. It also should be applied by a pad-fix or Pad-batch process due to the low affinity of the pre-cationization products making it a more expensive process, and the risk of unlevelness in processes such as jet dyeing. Wit particle-dyes, since no pre-cationizartion is necessary, they can be applied in jet dyeing. The excess dye is fixed by a fixing agent, fixing the insoluble dye onto the surface of the fibers. By applying the fixing agent only one to 1-3 rinses in water at room temperature are necessary, saving water and improving the washfastness.

Keywords: denim, garment dyeing, worn look, eco-fashion

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Authors: A. M. John-Otumu, M. M. Rahman, O. C. Nwokonkwo, M. C. Onuoha

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Online social media networks have long served as a primary arena for group conversations, gossip, text-based information sharing and distribution. The use of natural language processing techniques for text classification and unbiased decision-making has not been far-fetched. Proper classification of this textual information in a given context has also been very difficult. As a result, we decided to conduct a systematic review of previous literature on sentiment classification and AI-based techniques that have been used in order to gain a better understanding of the process of designing and developing a robust and more accurate sentiment classifier that can correctly classify social media textual information of a given context between hate speech and inverted compliments with a high level of accuracy by assessing different artificial intelligence techniques. We evaluated over 250 articles from digital sources like ScienceDirect, ACM, Google Scholar, and IEEE Xplore and whittled down the number of research to 31. Findings revealed that Deep learning approaches such as CNN, RNN, BERT, and LSTM outperformed various machine learning techniques in terms of performance accuracy. A large dataset is also necessary for developing a robust sentiment classifier and can be obtained from places like Twitter, movie reviews, Kaggle, SST, and SemEval Task4. Hybrid Deep Learning techniques like CNN+LSTM, CNN+GRU, CNN+BERT outperformed single Deep Learning techniques and machine learning techniques. Python programming language outperformed Java programming language in terms of sentiment analyzer development due to its simplicity and AI-based library functionalities. Based on some of the important findings from this study, we made a recommendation for future research.

Keywords: artificial intelligence, natural language processing, sentiment analysis, social network, text

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Authors: Uzaira Rafique, Kousar Parveen

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The discharge volume and composition of Textile effluents gains scientific concern due to its hazards and biotoxcity of azo dyes. Azo dyes are non-biodegradable due to its complex molecular structure and recalcitrant nature. Serious attempts have been made to synthesize and develop new materials to combat the environmental problems. The present study is designed for removal of a range of azo dyes (Methyl orange, Congo red and Basic fuchsine) from synthetic aqueous solutions and real textile effluents. For this purpose, Metal (cobalt) doped alumina hybrids are synthesized and applied as adsorbents in the batch experiment. Two different aluminium precursor (aluminium nitrate and spent aluminium foil) and glucose are mixed following sol gel method to get hybrids. The synthesized materials are characterized for surface and bulk properties using FTIR, SEM-EDX and XRD techniques. The characterization of materials under FTIR revealed that –OH (3487-3504 cm-1), C-H (2935-2985 cm-1), Al-O (~ 800 cm-1), Al-O-C (~1380 cm-1), Al-O-Al (659-669 cm-1) groups participates in the binding of dyes onto the surface of hybrids. Amorphous shaped particles and elemental composition of carbon (23%-44%), aluminium (29%-395%), and oxygen (11%-20%) is demonstrated in SEM-EDX micrograph. Time-dependent batch-experiments under identical experimental parameters showed 74% congo red, 68% methyl orange and 85% maximum removal of basic fuchsine onto the surface of cobalt doped alumina hybrids probably through the ion-exchange mechanism. The experimental data when treated with adsorption models is found to have good agreement with pseudo second order kinetic and freundlich isotherm for adsorption process. The present study concludes the successful synthesis of novel and efficient cobalt doped alumina hybrids providing environmental friendly and economical alternative to the commercial adsorbents for the treatment of industrial effluents.

Keywords: alumina hybrid, adsorption, dopant, isotherm, kinetic

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Authors: Makhlouf Mourad, Messabih Sidi Mohamed, Bouchher Omar, Houali Farida, Benrachedi Khaled

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Mesoporous materials are very commonly used as adsorbent materials for removing phenolic compounds. However, the adsorption mechanism of these compounds is still poorly controlled. However, understanding the interactions mesoporous materials/adsorbed molecules is very important in order to optimize the processes of liquid phase adsorption. The difficulty of synthesis is to keep an orderly and cubic pore structure and achieve a homogeneous surface modification. The grafting of Si(CH₃)₃ was chosen, to transform hydrophilic surfaces hydrophobic surfaces. The aim of this work is to study the kinetics and thermodynamics of two volatile organic compounds VOC phenol (PhOH) and P hydroxy benzoic acid (4AHB) on a mesoporous material of type MCM-48 grafted with an organosilane of the Trimethylchlorosilane (TMCS) type, the material thus grafted or functionalized (hereinafter referred to as MCM-48-G). In a first step, the kinetic and thermodynamic study of the adsorption isotherms of each of the VOCs in mono-solution was carried out. In a second step, a similar study was carried out on a mixture of these two compounds. Kinetic models (pseudo-first order, pseudo-second order) were used to determine kinetic adsorption parameters. The thermodynamic parameters of the adsorption isotherms were determined by the adsorption models (Langmuir, Freundlich). The comparative study of adsorption of PhOH and 4AHB proved that MCM-48-G had a high adsorption capacity for PhOH and 4AHB; this may be related to the hydrophobicity created by the organic function of TMCS in MCM-48-G. The adsorption results for the two compounds using the Freundlich and Langmuir models show that the adsorption of 4AHB was higher than PhOH. The values ​​obtained by the adsorption thermodynamics show that the adsorption interactions for our sample with the phenol and 4AHB are of a physical nature. The adsorption of our VOCs on the MCM-48 (G) is a spontaneous and exothermic process.

Keywords: adsorption, kinetics, isotherm, mesoporous materials, Phenol, P-hydroxy benzoique acid

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Authors: Rui Wu

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In the volatile modern manufacturing environment, new orders randomly occur at any time, while the pre-emptive methods are infeasible. This leads to a real-time scheduling method that can produce a reasonably good schedule quickly. The dynamic Flexible Job Shop problem is an NP-hard scheduling problem that hybrid the dynamic Job Shop problem with the Parallel Machine problem. A Flexible Job Shop contains different work centres. Each work centre contains parallel machines that can process certain operations. Many algorithms, such as genetic algorithms or simulated annealing, have been proposed to solve the static Flexible Job Shop problems. However, the time efficiency of these methods is low, and these methods are not feasible in a dynamic scheduling problem. Therefore, a dynamic rule selection scheduling system based on the reinforcement learning method is proposed in this research, in which the dynamic Flexible Job Shop problem is divided into several parallel machine problems to decrease the complexity of the dynamic Flexible Job Shop problem. Firstly, the features of jobs, machines, work centres, and flexible job shops are selected to describe the status of the dynamic Flexible Job Shop problem at each decision point in each work centre. Secondly, a framework of reinforcement learning algorithm using a double-layer deep Q-learning network is applied to select proper composite dispatching rules based on the status of each work centre. Then, based on the selected composite dispatching rule, an available operation is selected from the waiting buffer and assigned to an available machine in each work centre. Finally, the proposed algorithm will be compared with well-known dispatching rules on objectives of mean tardiness, mean flow time, mean waiting time, or mean percentage of waiting time in the real-time Flexible Job Shop problem. The result of the simulations proved that the proposed framework has reasonable performance and time efficiency.

Keywords: dynamic scheduling problem, flexible job shop, dispatching rules, deep reinforcement learning

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Authors: Jihuai Wu, Zeyu Song, Zhang Lan, Liuxue Sun

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Solar energy is clean, open, and infinite, but solar radiation on the earth is fluctuating, intermittent, and unstable. So, the sustainable utilization of solar energy requires a combination of high-efficient energy conversion and low-loss energy storage technologies. Hence, a photo capacitor integrated with photo-electrical conversion and electric-chemical storage functions in single device is a cost-effective, volume-effective and functional-effective optimal choice. However, owing to the multiple components, multi-dimensional structure and multiple functions in one device, especially the mismatch of the functional modules, the overall conversion and storage efficiency of the photocapacitors is less than 13%, which seriously limits the development of the integrated system of solar conversion and energy storage. To this end, two typical photocapacitors were studied. A three-terminal photocapacitor was integrated by using perovskite solar cell as solar conversion module and symmetrical supercapacitor as energy storage module. A function portfolio management concept was proposed the relationship among various efficiencies during photovoltaic conversion and energy storage process were clarified. By harmonizing the energy matching between conversion and storage modules and seeking the maximum power points coincide and the maximum efficiency points synchronize, the overall efficiency of the photocapacitor surpassed 18 %, and Joule efficiency was closed to 90%. A voltage adjustable hybrid supercapacitor (VAHSC) was designed as energy storage module, and two Si wafers in series as solar conversion module, a three-terminal photocapacitor was fabricated. The VAHSC effectively harmonizes the energy harvest and storage modules, resulting in the current, voltage, power, and energy match between both modules. The optimal photocapacitor achieved an overall efficiency of 15.49% and Joule efficiency of 86.01%, along with excellent charge/discharge cycle stability. In addition, the Joule efficiency (ηJoule) was defined as the energy ratio of discharge/charge of the devices for the first time.

Keywords: joule efficiency, perovskite solar cell, photocapacitor, silicon solar cell, supercapacitor

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Authors: Ayman M. Mansour, Bilal Hawashin, Hesham Alsalem

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Faster delivery of laboratory test results is one of the most noticeable signs of good laboratory service and is often used as a key performance indicator of laboratory performance. Despite the availability of technology, the delivery time of clinical laboratory test results continues to be a cause of customer dissatisfaction which makes patients feel frustrated and they became careless to get their laboratory test results. The Medical Clinical Laboratory test results are highly sensitive and could harm patients especially with the severe case if they deliver in wrong time. Such results affect the treatment done by physicians if arrived at correct time efforts should, therefore, be made to ensure faster delivery of lab test results by utilizing new trusted, Robust and fast system. In this paper, we proposed a distributed Multi-Agent System to enhance and faster the process of laboratory test results delivery using SMS. The developed system relies on SMS messages because of the wide availability of GSM network comparing to the other network. The software provides the capability of knowledge sharing between different units and different laboratory medical centers. The system was built using java programming. To implement the proposed system we had many possible techniques. One of these is to use the peer-to-peer (P2P) model, where all the peers are treated equally and the service is distributed among all the peers of the network. However, for the pure P2P model, it is difficult to maintain the coherence of the network, discover new peers and ensure security. Also, security is a quite important issue since each node is allowed to join the network without any control mechanism. We thus take the hybrid P2P model, a model between the Client/Server model and the pure P2P model using GSM technology through SMS messages. This model satisfies our need. A GUI has been developed to provide the laboratory staff with the simple and easy way to interact with the system. This system provides quick response rate and the decision is faster than the manual methods. This will save patients life.

Keywords: multi-agent system, delivery process, GSM technology, clinical laboratory results

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Authors: Getaneh Berie Tarekegn

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A precise localization system is crucial for many artificial intelligence Internet of Things (AI-IoT) applications in the era of smart cities. Their applications include traffic monitoring, emergency alarming, environmental monitoring, location-based advertising, intelligent transportation, and smart health care. The most common method for providing continuous positioning services in outdoor environments is by using a global navigation satellite system (GNSS). Due to nonline-of-sight, multipath, and weather conditions, GNSS systems do not perform well in dense urban, urban, and suburban areas.This paper proposes a generative AI-based positioning scheme for large-scale wireless settings using fingerprinting techniques. In this article, we presented a novel semi-supervised deep convolutional generative adversarial network (S-DCGAN)-based radio map construction method for real-time device localization. We also employed a reliable signal fingerprint feature extraction method with t-distributed stochastic neighbor embedding (t-SNE), which extracts dominant features while eliminating noise from hybrid WLAN and long-term evolution (LTE) fingerprints. The proposed scheme reduced the workload of site surveying required to build the fingerprint database by up to 78.5% and significantly improved positioning accuracy. The results show that the average positioning error of GAILoc is less than 39 cm, and more than 90% of the errors are less than 82 cm. That is, numerical results proved that, in comparison to traditional methods, the proposed SRCLoc method can significantly improve positioning performance and reduce radio map construction costs.

Keywords: location-aware services, feature extraction technique, generative adversarial network, long short-term memory, support vector machine

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Authors: D. Berg, N. Hartley, L. Nasr

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This paper presents a conceptual model of human-robot interaction within service encounters and the effect on the well-being of both consumers and service providers. In this paper, service providers are those employees who work alongside frontline service robots. The significance of this paper lies in the knowledge created which outlines how frontline service robots can be effectively utilized in service encounters for the benefit of organizations and society as a whole. As this paper is conceptual in nature, the main methodologies employed are theoretical, namely problematization and theory building. The significance of this paper is underpinned by the shift of service robots from manufacturing plants and factory floors to consumer-facing service environments. This service environment places robots in direct contact with frontline employees and consumers creating a hybrid workplace where humans work alongside service robots. This change from back-end to front-end roles may have implications not only on the physical environment, servicescape, design, and strategy of service offerings and encounters but also on the human parties of the service encounter itself. Questions such as ‘how are frontline service robots impacting and changing the service encounter?’ and ‘what effect are such changes having on the well-being of the human actors in a service encounter?’ spring to mind. These questions form the research question of this paper. To truly understand social service robots, an interdisciplinary perspective is required. Besides understanding the function, system, design or mechanics of a service robot, it is also necessary to understand human-robot interaction. However not simply human-robot interaction, but particularly what happens when such robots are placed in commercial settings and when human-robot interaction becomes consumer-robot interaction and employee-robot interaction? A service robot in this paper is characterized by two main factors; its social characteristics and the consumer-facing environment within which it operates. The conceptual framework presented in this paper contributes to interdisciplinary discussions surrounding social robotics, service, and technology’s impact on consumer and service provider well-being, and hopes that such knowledge will help improve services, as well as the prosperity and well-being of society.

Keywords: frontline service robots, human-robot interaction, service encounters, well-being

Procedia PDF Downloads 180