Search results for: distribution network reconfiguration

Authors: Iman Shabanzadeh

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The purpose of this research is to understand the lived experience of private sector entrepreneurs in facing the sources of freedom and unfreedom and benefiting from opportunities and basic capabilities in the process of realizing entrepreneurial ability in order to get closer to the macro situation of the narrative of human development in Iranian society. Therefore, the main question of the present research is to figure out what sources of freedom and social opportunities and unfreedom entrepreneurs in Iran's society benefit from the process of transforming their potential entrepreneurial abilities into entrepreneurial and business enterprises. In terms of methodology, the current research method will be thematic analysis in the form of semi-structured interviews with entrepreneurs active in small and medium-sized enterprises in Tehran, whose process of establishing and expanding their entrepreneurial activity has been in the last two decades. By examining the possibilities and refusals of advancing these people in the three stages of 'Idea creation and desire for entrepreneurship’, ‘Starting and creating a business’, and finally, ‘Continuing and expanding the business’, the findings of the research show the impact of five main resources for people to realize their potential talents, from the stage of creating an idea to expanding their business. These sources include' family institution,’ ‘education institution,’ ‘social norms and beliefs,’ ‘government and market,’ and ‘personality components of the entrepreneur.’ Finally, the findings are reported in three levels of basic themes (fifteen items), organizing themes (five items), and comprehensive themes (one item) and in the form of a theme network.

Keywords: entrepreneurship, human development, capability, sources of freedom

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Authors: Mulu G. Gencha, Gebremedhin Simon, Menna Olango

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This study was conducted at Hawassa University (HwU) in the Southern Nation Nationalities Peoples Regional State (SNNPRS) of Ethiopia. The prime concern of this study was to examine the writing performances of experimental and control group students, perception of experimental group students, and subject instructors. The course was blended learning (BL). Blended learning is a hybrid of classroom and on-line learning. Participants were eighty students from the School of Computer Science. Forty students attended the BL delivery involved using Face-to-Face (FTF) and campus-based online instruction. All instructors, fifty, of School of Language and Communication Studies along with 10 FGD members participated in the study. The experimental group went to the computer lab two times a week for four months, March-June, 2012, using the local area network (LAN), and software (MOODLE) writing program. On the other hand, the control group, forty students, took the FTF writing course five times a week for four months in similar academic calendar. The three instruments, the attitude questionnaire, tests and FGD were designed to identify views of students, instructors, and FGD participants on BL. At the end of the study, students’ final course scores were evaluated. Data were analyzed using independent samples t-tests. A statistically, significant difference was found between the FTF and BL (p<0.05). The analysis showed that the BL group was more successful than the conventional group. Besides, both instructors and students had positive attitude towards BL. The final section of the thesis showed the potential benefits and challenges, considering the pedagogical implications for the BL, and recommended possible avenues for further works.

Keywords: blended learning, computer attitudes, computer usefulness, computer liking, computer confidence, computer phobia

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Authors: Julian Jaegers, Siegmar Wirtz, Viktor Scherer

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Wood pellets belong to the most established trade formats of wood-based fuels. Especially, because of the transportability and the storage properties, but also due to low moisture content, high energy density, and the homogeneous particle size and shape, wood pellets are well suited for power generation in power plants and for the use in automated domestic firing systems. Before they are thermally converted, wood pellets pass various transport and storage procedures. There they undergo different mechanical impacts, which leads to pellet breakage and abrasion and to an increase in fines. The fines lead to operational problems during storage, charging, and discharging of pellets, they can increase the risk of dust explosions and can lead to pollutant emissions during combustion. In the current work, the dependence of the formation of fines caused by breakage during pneumatic transport is analyzed experimentally and numerically. The focus lies on the influence of conveying velocity, pellet loading, pipe diameter, and the shape of pipe components like bends or couplings. A test rig has been built, which allows the experimental evaluation of the pneumatic transport varying the above-mentioned parameters. Two high-speed cameras are installed for the quantitative optical access to the particle-particle and particle-wall contacts. The particle size distribution of the bulk before and after a transport process is measured as well as the amount of fines produced. The experiments will be compared with results of corresponding DEM/CFD simulations to provide information on contact frequencies and forces. The contribution proposed will present experimental results and report on the status of the DEM/CFD simulations. The final goal of the project is to provide a better insight into pellet breakage during pneumatic transport and to develop guidelines ensuring a more gentle transport.

Keywords: DEM/CFD-simulation of pneumatic conveying, mechanical impact on wood pellets during transportation, pellet breakage, pneumatic transport of wood pellets

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Authors: Chih-Ping Chang

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Different from traditional objective evidence, social media evidence has its own characteristics with easily tampering, recoverability, and cannot be read without using other devices (such as a computer). Simply taking a screenshot from social network sites must be questioned its original identity. When the police search and seizure digital information, a common way they use is to directly print out digital data obtained and ask the signature of the parties at the presence, without taking original digital data back. In addition to the issue on its original identity, this conduct to obtain evidence may have another two results. First, it will easily allege that is tampering evidence because the police wanted to frame the suspect and falsified evidence. Second, it is not easy to discovery hidden information. The core evidence associated with crime may not appear in the contents of files. Through discovery the original file, data related to the file, such as the original producer, creation time, modification date, and even GPS location display can be revealed from hidden information. Therefore, how to show this kind of evidence in the courtroom will be arguably the most important task for ruling social media evidence. This article, first, will introduce forensic software, like EnCase, TCT, FTK, and analyze their function to prove the identity with another digital data. Then turning back to the court, the second part of this article will discuss legal standard for authentication of social media evidence and application of that forensic software in the courtroom. As the conclusion, this article will provide a rethinking, that is, what kind of authenticity is this rule of evidence chase for. Does legal system automatically operate the transcription of scientific knowledge? Or furthermore, it wants to better render justice, not only under scientific fact, but through multivariate debating.

Keywords: federal rule of evidence, internet forensic, printouts as evidence, social media evidence, United States v. Vayner

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Authors: Mengdi Zhang, Xufeng Liu, Zhenji Gao, Ying Li, Zhu Rao, Yi Huang

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As an open system, the earth is constantly undergoing the transformation and release of matter and energy. Fault zones are relatively discontinuous and fragile geological structures, and the release of material and energy inside the Earth is strongest in relatively weak fault zones. Earthquake events frequently occur in fault zones and are closely related to tectonic activity in these zones. In earthquake precursor observation, monitoring the spatiotemporal changes in the release of related gases near fault zones (such as radon gas, hydrogen, carbon dioxide, helium), and analyzing earthquake precursor anomalies, can be effective means to forecast the occurrence of earthquake events. Radon gas, as an inert radioactive gas generated during the decay of uranium and thorium, is not only a indicator for monitoring tectonic and seismic activity, but also an important topic for ecological and environmental health, playing a crucial role in uranium exploration. At present, research on soil radon gas mainly focuses on the measurement of soil gas concentration and flux in fault zone profiles, while research on the correlation between spatiotemporal concentration changes in the same region and its geological background is relatively little. In this paper, Tangshan area in north China is chosen as research area. An analysis was conducted on the seismic geological background of Tangshan area firstly. Then based on quantitative analysis and comparison of measurement radon concentrations of 2023 and 2010, combined with the study of seismic activity and environmental changes during the time period, the spatiotemporal distribution characteristics and influencing factors were explored, in order to analyze the gas emission characteristics of the Tangshan fault zone and its relationship with fault activity, which aimed to be useful for the future work in earthquake monitor of Tangshan area.

Keywords: radon, Northern China, soil gas, earthquake

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Authors: Sandeep Vasaikar, Lary Obi

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Rapid and discriminative genotyping methods are useful for determining the clonality of the isolates in nosocomial or household outbreaks. Multilocus sequence typing (MLST) is a nucleotide sequence-based approach for characterising bacterial isolates. The genetic diversity and the clinical relevance of the drug-resistant Klebsiella isolates from Mthatha are largely unknown. For this reason, prospective, experimental study of the molecular epidemiology of Klebsiella isolates from patients being treated in Mthatha over a three-year period was analysed. Methodology: PCR amplification and sequencing of the drug-resistance-associated genes, and multilocus sequence typing (MLST) using 7 housekeeping genes mdh, pgi, infB, FusAR, phoE, gapA and rpoB were conducted. A total of 32 isolates were analysed. Results: The percentages of multidrug-resistant (MDR), extensively drug-resistance (XDR) and pandrug-resistant (PDR) isolates were; MDR 65.6 % (21) and XDR and PDR with 0 % each. In this study, K. pneumoniae was 19/32 (59.4 %). MLST results showed 22 sequence types (STs) were identified, which were further separated by Maximum Parsimony into 10 clonal complexes and 12 singletons. The most dominant group was Klebsiella pneumoniae with 23/32 (71.8 %) isolates, Klebsiella oxytoca as a second group with 2/32 (6.25 %) isolates, and a single (3.1 %) K. varricola as a third group while 6 isolates were of unknown sequences. Conclusions/significance: A phylogenetic analysis of the concatenated sequences of the 7 housekeeping genes showed that strains of K. pneumoniae form a distinct lineage within the genus Klebsiella, with K. oxytoca and K. varricola its nearest phylogenetic neighbours. With the analysis of 7 genes were determined 1 K. variicola, which was mistakenly identified as K. pneumoniae by phenotypic methods. Two misidentifications of K. oxytoca were found when phenotypic methods were used. No significant differences were observed between ESBL blaCTX-M, blaTEM and blaSHV groups in the distribution of Sequence types (STs) or Clonal complexes (CCs).

Keywords: phylogenetic analysis, phylogeny, klebsiella phylogenetic, klebsiella

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Authors: Usama Abdulwahab, Mohammed Wahab

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This paper presents the application of uncapacitated facility location problems (UFLP) and 1-median problems to support decision making in blood supply chain networks. A plethora of factors make blood supply-chain networks a complex, yet vital problem for the regional blood bank. These factors are rapidly increasing demand; criticality of the product; strict storage and handling requirements; and the vastness of the theater of operations. As in the UFLP, facilities can be opened at any of $m$ predefined locations with given fixed costs. Clients have to be allocated to the open facilities. In classical location models, the allocation cost is the distance between a client and an open facility. In this model, the costs are the allocation cost, transportation costs, and inventory costs. In order to address this problem the median algorithm is used to analyze inventory, evaluate supply chain status, monitor performance metrics at different levels of granularity, and detect potential problems and opportunities for improvement. The Euclidean distance data for some Ontario cities (demand nodes) are used to test the developed algorithm. Sitation software, lagrangian relaxation algorithm, and branch and bound heuristics are used to solve this model. Computational experiments confirm the efficiency of the proposed approach. Compared to the existing modeling and solution methods, the median algorithm approach not only provides a more general modeling framework but also leads to efficient solution times in general.

Keywords: approximate dynamic programming, facility location, perishable product, inventory model, blood platelet, P-median problem

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Authors: Babar Rasheed

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The organizations are in continuous effort to increase their yield and to retain their associates, employees. Technology is considered an integral part of attaining apposite work practices, work environment, and employee engagement. Unconsciously, these advanced practices like work from home, personalized intra-network are disturbing employee work-life balance which ultimately increases psychological pressure on employees. The smart work practice is to develop business models and organizational practices with enhanced employee engagement, minimum trouncing of organization resources with persistent revenue and positive addition in global societies. Need of smart work practices comes from increasing employee turnover rate, global economic recession, unnecessary job pressure, increasing contingent workforce and advancement in technologies. Current practices are not enough elastic to tackle global changing work environment and organizational competitions. Current practices are causing many reciprocal problems among employee and organization mechanically. There is conscious understanding among business sectors smart work practices that will deal with new century challenges with addressing the concerns of relevant issues. It is aimed in this paper to endorse customized and smart work practice tools along knowledge framework to manage the growing concerns of employee engagement, use of technology, orgaization concerns and challenges for the business. This includes a Smart Management Information System to address necessary concerns of employees and combine with a framework to extract the best possible ways to allocate companies resources and re-align only required efforts to adopt the best possible strategy for controlling potential risks.

Keywords: employees engagement, management information system, psychological pressure, current and future HR practices

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Authors: Hala S. Mekawy

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Affordable housing delivery for low and lower middle income families is a prominent problem in many developing countries; governments alone are unable to address this challenge due to diverse financial and regulatory constraints, and the private sector's contribution is rare and assists only middle-income households even when institutional and legal reforms are conducted to persuade it to go down market. Also, the market-enabling policy measures advocated by the World Bank since the early nineties have been strongly criticized and proven to be inappropriate to developing country contexts, where it is highly unlikely that the formal private sector can reach low income population. In addition to governments and private developers, affordable housing delivery systems involve an intricate network of relationships between diverse ranges of actors. Collaboration between them was proven to be vital, and hence, an approach towards partnership schemes for affordable housing delivery has emerged. The basic premise of this paper is that addressing housing affordability challenges in Egypt demands direct public support, as markets and market actors alone would never succeed in delivering decent affordable housing to low and lower middle income groups. It argues that this support would ideally be through local level partnership schemes, with a leading decentralized local government role, and partners being identified according to specific local conditions. It attempts to identify major attributes that would ensure the fulfilment of the goals of such schemes in the Egyptian context. This is based upon evidence from diversified worldwide experiences, in addition to the main outcomes of a questionnaire that was conducted to specialists and chief actors in the field.

Keywords: affordable housing, partnership schemes, housing, urban environments

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Authors: Sammy Kibor, Filip Huyghe, Marc Kochzius, James Kairo

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Goldstripe Sardinella, Sardinella gibbosa, (Bleeker, 1849) is a commercially and ecologically important small pelagic fish common in the Western Indian Ocean region. The present study aimed to assess genetic diversity and population structure of the species in the Kenya-Tanzania transboundary area using mtDNA and msDNA markers. Some 630 bp sequence in the mitochondrial DNA (mtDNA) Cytochrome C Oxidase I (COI) and five polymorphic microsatellite DNA loci were analyzed. Fin clips of 309 individuals from eight locations within the transboundary area were collected between July and December 2018. The S. gibbosa individuals from the different locations were distinguishable from one another based on the mtDNA variation, as demonstrated with a neighbor-joining tree and minimum spanning network analysis. None of the identified 22 haplotypes were shared between Kenya and Tanzania. Gene diversity per locus was relatively high (0.271-0.751), highest Fis was 0.391. The structure analysis, discriminant analysis of Principal component (DAPC) and the pair-wise (FST = 0.136 P < 0.001) values after Bonferroni correction using five microsatellite loci provided clear inference on genetic differentiation and thus evidence of population structure of S. gibbosa along the Kenya-Tanzania coast. This study shows a high level of genetic diversity and the presence of population structure (Φst =0.078 P < 0.001) resulting to the existence of four populations giving a clear indication of minimum gene flow among the population. This information has application in the designing of marine protected areas, an important tool for marine conservation.

Keywords: marine connectivity, microsatellites, population genetics, transboundary

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Authors: Rifaldy, Misbahudin, Khalid Rizky, Ricky Aryanto, M. Alfiyan Bagus, Fahri Septianto, Firman Najib Wibisana, Excobar Arman

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Indonesia is a country that has a high level of disaster because it is on the ring of fire, and there are several regions with three major plates meeting in the world. So that disaster analysis must always be done to see the potential disasters that might always occur, especially in this research are landslides and liquefaction. This research was conducted to analyze areas that are vulnerable to landslides and liquefaction hazards and their relationship with the assessment of the issue of moving the new capital of the Republic of Indonesia to the island of Kalimantan with a total area of 612,267.22 km². The method in this analysis uses the Analytical Hierarchy Process and consistency ratio testing as a complex and unstructured problem-solving process into several parameters by providing values. The parameters used in this analysis are the slope, land cover, lithology distribution, wetness index, earthquake data, peak ground acceleration. Weighted overlay was carried out from all these parameters using the percentage value obtained from the Analytical Hierarchy Process and confirmed its accuracy with a consistency ratio so that a percentage of the area obtained with different vulnerability classification values was obtained. Based on the analysis results obtained vulnerability classification from very high to low vulnerability. There are (0.15%) 918.40083 km² of highly vulnerable, medium (20.75%) 127,045,44815 km², low (56.54%) 346,175.886188 km², very low (22.56%) 138,127.484832 km². This research is expected to be able to map landslides and liquefaction disasters on the island of Kalimantan and provide consideration of the suitability of regional development of the new capital of the Republic of Indonesia. Also, this research is expected to provide input or can be applied to all regions that are analyzing the vulnerability of landslides and liquefaction or the suitability of the development of certain regions.

Keywords: analytic hierarchy process, Borneo Island, landslide and liquefaction, vulnerability analysis

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Authors: Ravneet Kaur, Joydeep Acharya, Sudhanshu Gaur

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Artificial Intelligence (AI) has the potential to transform manufacturing by improving shop floor processes such as production, maintenance and quality. However, industrial datasets are notoriously difficult to extract in a real-time, streaming fashion thus, negating potential AI benefits. The main example is some specialized industrial controllers that are operated by custom software which complicates the process of connecting them to an Information Technology (IT) based data acquisition network. Security concerns may also limit direct physical access to these controllers for data acquisition. To connect the Operational Technology (OT) data stored in these controllers to an AI application in a secure, reliable and available way, we propose a novel Industrial IoT (IIoT) solution in this paper. In this solution, we demonstrate how video cameras can be installed in a factory shop floor to continuously obtain images of the controller HMIs. We propose image pre-processing to segment the HMI into regions of streaming data and regions of fixed meta-data. We then evaluate the performance of multiple Optical Character Recognition (OCR) technologies such as Tesseract and Google vision to recognize the streaming data and test it for typical factory HMIs and realistic lighting conditions. Finally, we use the meta-data to match the OCR output with the temporal, domain-dependent context of the data to improve the accuracy of the output. Our IIoT solution enables reliable and efficient data extraction which will improve the performance of subsequent AI applications.

Keywords: human machine interface, industrial internet of things, internet of things, optical character recognition, video analytics

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Authors: Jamal Radaideh

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Al-Asfar Lake is located about 14 km east of Al-Ahsa and is one of the most important wetland lakes in the Al Ahsa/Eastern Province of Saudi Arabia. Al-Ahsa is may be the largest oasis in the world, having an area of 20,000 hectares, in addition, it is of the largest and oldest agricultural centers in the region. The surplus farm irrigation water beside additional water supplied by treated wastewater from Al-Hofuf sewage station is collected by a drainage network and discharged into Al-Asfar Lake. The lake has good wetlands, sand dunes as well as large expanses of open and shallow water. Salt tolerant vegetation is present in some of the shallow areas around the lake, and huge stands of Phragmites reeds occur around the lake. The lake presents an important habitat for wildlife and birds, something not expected to find in a large desert. Although high evaporation rates in the range of 3250 mm are common, the water remains in the evaporation lakes during all seasons of the year is used to supply cattle with drinking water and for aquifer recharge. Investigations showed that high concentrations of nitrogen (N), phosphorus (P), biological oxygen demand (BOD), chemical oxygen demand (COD) and salinity discharge to Al Asfar Lake from the D2 drain exist. It is expected that the majority of BOD, COD and N originates from wastewater discharge and leachate from surplus irrigation water which also contribute to the majority of P and salinity. The significant content of nutrients and biological oxygen demand reduces available oxygen in the water. The present project aimed to improve the water quality of the lake using constructed wetland trains which will be built around the lake. Phragmites reeds, which already occur around the lake, will be used.

Keywords: Al Asfar lake, constructed wetland, water quality, water treatment

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Authors: A. M. Taleb, A. G. Tail, A. F. Kara, B. B. Djedouani, C. T. Moussa

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Forensic entomology is the use of insects to aid legal investigations. The main purpose of forensic entomology is to establish the postmortem interval (PMI). In order to estimate the PMI, a forensic entomologist compares the case data with certain reference information relevant to the particular location and time of year. This reference information, including the local distribution of species, are not available in Algeria. Therefore, experiments need to be conducted to provide references for entomological evidence. The objective of this study was to identify the necrophagous flies species which arrive first to carrion using liver baits in Ghardaia, South Algeria. The study was carried out during the spring season in the palmeral of Beni Isguen, Ghardaia which is well known by its hot arid climate. The experiment site (32°28’0’’ N, 3°42’0’’ E), is situated at an altitude of about 526 metres above mean sea level. On April the 4th, 2014, a number of three replicates of liver baited traps were placed in the shade and other three baits were exposed to the sun. Flying insects and larvae were captured and identified. After few minutes, flies invaded the traps which were exposed to the sun. In contrast, no flies were observed in the other traps. A total number of fourty five (45) adult specimens belonging to three taxa were identified: Calliphora vicina (Robineau-Desvoidy, 1830) (Diptera, Calliphoridae) (51.11 %), Lucilia sericata (Meigen, 1826) (Diptera, Calliphoridae) (33.33 %) and Sarcophaga africa (Wiedemann, 1824) (Diptera: Sarcophagidae) (15.55 %). Six hundred and three (603) maggots belonging to two taxa were identified: Calliphora vicina (76.28 %) and Lucilia sericata (23.71 %). The data obtained from this study provides baseline information regarding the carrion fauna of this area. It will also form a basis for similar studies in different geographical and climatological regions of Algeria.

Keywords: forensic entomology, liver baits, necrophagous fly, Ghardaia, South Algeria

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Authors: Ethan James

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Over one billion people worldwide suffer from some level of vision loss or blindness as a result of progressive retinal diseases. Many patients, particularly in developing areas, are incorrectly diagnosed or undiagnosed whatsoever due to unconventional diagnostic tools and screening methods. Artificial intelligence (AI) based on deep learning (DL) convolutional neural networks (CNN) have recently gained a high interest in ophthalmology for its computer-imaging diagnosis, disease prognosis, and risk assessment. Optical coherence tomography (OCT) is a popular imaging technique used to capture high-resolution cross-sections of retinas. In ophthalmology, DL has been applied to fundus photographs, optical coherence tomography, and visual fields, achieving robust classification performance in the detection of various retinal diseases including macular degeneration, diabetic retinopathy, and retinitis pigmentosa. However, there is no complete diagnostic model to analyze these retinal images that provide a diagnostic accuracy above 90%. Thus, the purpose of this project was to develop an AI model that utilizes machine learning techniques to automatically diagnose specific retinal diseases from OCT scans. The algorithm consists of neural network architecture that was trained from a dataset of over 20,000 real-world OCT images to train the robust model to utilize residual neural networks with cyclic pooling. This DL model can ultimately aid ophthalmologists in diagnosing patients with these retinal diseases more quickly and more accurately, therefore facilitating earlier treatment, which results in improved post-treatment outcomes.

Keywords: artificial intelligence, deep learning, imaging, medical devices, ophthalmic devices, ophthalmology, retina

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Authors: Bicky Agarwal, Ambika Behl, Rajiv Kumar, Ashish Dhamaniya

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India ranks second for having the largest road network in the world after the United States (U.S.). According to the National Asphalt Pavement Association (NAPA), the U.S. produced about 94.6 million tons of Reclaimed Asphalt Pavement (RAP) in 2021. Despite the benefits of RAP usage, it is not widely adopted in many countries, including India. Rising asphalt binder costs and environmental concerns have spurred interest in using RAP material in asphalt mixtures. However, increasing RAP content may have adverse effects on certain characteristics of asphalt mixtures, such as cracking resistance. Cracking is a common pavement issue that affects the lifespan and durability of hot-mix asphalt pavements. Assessing cracking resistance is crucial in pavement design. Various laboratory tests and performance indicators are utilized to evaluate cracking resistance. This study aims to use the Texas Overlay Tester (TOT) to assess the impact of reclaimed asphalt pavement (RAP) on the cracking resistance of Bituminous Concrete (BC-II) mixes. Following the Marshall Mix Design method, asphalt mixes with RAP contents of 0% (Control), 30%, 40%, 50%, and 60% were prepared and tested at their Optimum Binder Content (OBC). The ITS results showed that the control mix had an ITS value of 1.2 MPa, with slight decreases observed in mixes containing up to 60% RAP, although these changes were not statistically significant (p=0.538>0.05). The TSR tests indicated that all mixes exceeded the minimum requirement of 80%. The Texas Overlay Test (TOT) was used to evaluate cracking performance and revealed that higher RAP contents had a negative impact on fatigue resistance. The 50% RAP mix exhibited the highest CFE, indicating that it has the best resistance to crack propagation despite having a lower number of cycles to failure. All mixes were categorized as falling into the Soft-crack-resistant quadrant, indicating their ability to resist crack propagation while being more susceptible to crack initiation.

Keywords: RAP, BC-II, HMA, TOT

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Authors: Snezana Lazic, Zarko Gacevic, Mark Holmes, Ekaterina Chernysheva, Marcus Müller, Peter Veit, Frank Bertram, Juergen Christen, Yasuhiko Arakawa, Enrique Calleja

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The study reports how the pencil-like morphology of a homoepitaxially grown GaN nanowire can be exploited for the fabrication of a thin conformal InGaN nanoshell, hosting nonpolar, semipolar and polar single photon sources (SPSs). All three SPS types exhibit narrow emission lines (FWHM~0.35 - 2 meV) and high degrees of linear optical polarization (P > 70%) in the low-temperature micro-photoluminescence (µ-PL) experiments and are characterized by a pronounced antibunching in the photon correlation measurements (gcorrected(2)(0) < 0.3). The quantum-dot-like exciton localization centers induced by compositional fluctuations within the InGaN nanoshell are identified as the driving mechanism for the single photon emission. As confirmed by the low-temperature transmission electron microscopy combined with cathodoluminescence (TEM-CL) study, the crystal region (i.e. non-polar m-, semi-polar r- and polar c-facets) hosting the single photon emitters strongly affects their emission wavelength, which ranges from ultra-violet for the non-polar to visible for the polar SPSs. The photon emission lifetime is also found to be facet-dependent and varies from sub-nanosecond time scales for the non- and semi-polar SPSs to a few nanoseconds for the polar ones. These differences are mainly attributed to facet-dependent indium content and electric field distribution across the hosting InGaN nanoshell. The hereby reported pencil-like InGaN nanoshell is the first single nanostructure able to host all three types of single photon emitters and is thus a promising building block for tunable quantum light devices integrated into future photonic and optoelectronic circuits.

Keywords: GaN nanowire, InGaN nanoshell, linear polarization, nonpolar, semipolar, polar quantum dots, single-photon sources

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Authors: Gulnaz Erkomaishvili

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Introduction: The pandemic and the current economic crisis have created problems for entrepreneurship and, therefore for start-up entrepreneurs. The paper presents the challenges of start-up entrepreneurs in Georgia in the time of pandemic and the analysis of the state economic policy measures. Despite many problems, the study found that in 54.2% of start-ups surveyed under the pandemic, innovation opportunities were growing. It can be stated that the pandemic was a good opportunity to increase the innovative capacity of the enterprise. 52% of the surveyed start-up entrepreneurs managed to adapt to the current situation and increase the sale of their products/services through remote channels. As for the assessment of state support measures by start-up entrepreneurs, a large number of Georgian start-ups do not assess the measures implemented by the state positively. Methodology: The research process uses methods of analysis and synthesis, quantitative and qualitative, interview/survey, grouping, relative and average values, graphing, comparison, data analysis, and others. Main Findings: Studies have shown that for the start-up entrepreneurs, the main problem remains: inaccessible funding, workers' qualifications gap, inflation, taxes, regulation, political instability, inadequate provision of infrastructure, amount of taxes, and other factors. Conclusions: The state should take the following measures to support business start-ups: create an attractive environment for investment, availability of soft loans, creation of an insurance system, infrastructure development, increase the effectiveness of tax policy (simplicity of the tax system, clarity, optimal tax level ); promote export growth (develop strategy for opening up international markets, build up a broad marketing network, etc.).

Keywords: start-up entrepreneurs, startups, start-up entrepreneurs support programs, start-up entrepreneurs support economic policy

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Authors: Sreetama Ghosh, Sundara Ramaprabhu

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Carbon dioxide gas has been a major cause for the worldwide increase in green house effect, which leads to climate change and global warming. So CO₂ capture & sequestration has become an effective way to reduce the concentration of CO₂ in the environment. One such way to capture CO₂ in porous materials is by adsorption process. A potential material in this aspect is porous hexagonal boron nitride or 'white graphene' which is a well-known two-dimensional layered material with very high thermal stability. It had been investigated that the sample with hierarchical pore structure and high specific surface area shows excellent performance in capturing carbon dioxide gas and thereby mitigating the problem of environmental pollution to the certain extent. Besides, the presence of sulfur as well as nitrogen in the sample synergistically helps in the increase in adsorption capacity. In this work, a cost effective single step synthesis of highly porous boron nitride nanosheets doped with sulfur had been demonstrated. Besides, the CO₂ adsorption-desorption studies were carried on using a pressure reduction technique. The studies show that the nanosheets exhibit excellent cyclic stability in storage performance. Thermodynamic studies suggest that the adsorption takes place mainly through physisorption. The studies show that the nanosheets exhibit excellent cyclic stability in storage performance. Further, the surface modification of the highly porous nano sheets carried out by incorporating ionic liquids had further enhanced the capturing capability of CO₂ gas in the nanocomposite, revealing that this particular material has the potential to be an excellent adsorbent of carbon dioxide gas.

Keywords: CO₂ capture, hexagonal boron nitride nanosheets, porous network, sulfur doping

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Authors: Bhaargav Gupta P., Vignesh N., Nithish Kumar R., Rahul J., Nivetha Ruvah D.

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Delivering essential medical supplies to rural and underserved areas is challenging due to infrastructure limitations and logistical barriers, often resulting in inefficiencies and delays. Traditional delivery methods are hindered by poor road networks, long distances, and difficult terrains, compromising timely access to vital resources, especially in emergencies. This paper introduces an autonomous drone system engineered to optimize last-mile delivery. By utilizing advanced navigation and object-detection algorithms, such as region-based convolutional neural networks (R-CNN), our drones efficiently avoid obstacles, identify safe landing zones, and adapt dynamically to varying environments. Equipped with high-precision GPS and autonomous capabilities, the drones effectively navigate complex, remote areas with minimal dependence on established infrastructure. The system includes a dedicated mobile application for secure order placement and real-time tracking, and a secure payload box with OTP verification ensures tamper-resistant delivery to authorized recipients. This project demonstrates the potential of automated drone technology in healthcare logistics, offering a scalable and eco-friendly approach to enhance accessibility and service delivery in underserved regions. By addressing logistical gaps through advanced automation, this system represents a significant advancement toward sustainable, accessible healthcare in remote areas.

Keywords: region-based convolutional neural network, one time password, global positioning system, autonomous drones, healthcare logistics

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Authors: Amr Mohamed Bakry Ibrahim, Yingzhou Ni, Hao Cheng, Li Liang

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Tuna oil (omega-3 oil) has become increasingly popular in the last ten years, because it is considered one of the treasures of food which has many beneficial health effects for the humans. Nevertheless, the susceptibility of omega-3 oils to oxidative deterioration, resulting in the formation of oxidation products, in addition to organoleptic problems including “fishy” flavors, have presented obstacles to the more widespread use of tuna oils in the food industry. This study sought to evaluate the potential impact of Mentha piperita oil on physicochemical characteristics and oxidative stability of tuna oil microcapsules formed by spray drying using the partial substitution to whey protein isolate by carboxymethyl cellulose and pullulan. The emulsions before the drying process were characterized regarding size and ζ-potential, viscosity, surface tension. Confocal laser scanning microscopy showed that all emulsions were sphericity and homogeneous distribution without any visible particle aggregation. The microcapsules obtained after spray drying were characterized regarding microencapsulation efficiency, water activity, color, bulk density, flowability, scanning surface morphology and oxidative stability. The microcapsules were spherical shape had low water activity (0.11-0.23 aw). The microcapsules containing both tuna oil and Mentha piperita oil were smaller than others and addition of pullulan into wall materials improved the morphology of microcapsules. Microencapsulation efficiency of powdered oil ranged from 90% to 94%. Using Mentha piperita oil in the process of microencapsulation tuna oil enhanced the oxidative stability using whey protein isolate only or with carboxymethyl cellulose or pullulan as wall materials, resulting in improved storage stability and mask fishy odor. Therefore, it is foreseen using tuna-Mentha piperita oil mixture microcapsules in the applications of the food industries.

Keywords: Mentha piperita oil, microcapsule, tuna oil, whey protein isolate

Procedia PDF Downloads 355

Authors: Zainab Abbas Soharwardi, Chunli Su, Harold Wilson Tumwitike Mapoma, Syed Zahid Aziz, Mahmut Ince

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This study investigated the spatial variations of groundwater chemistry used by communities in Lahore city with emphasis on arsenic (As) and fluoride (F) levels. A total of 472 tubewell samples were collected from 7 towns and analyzed for physical and chemical parameters, including pH, turbidity, electrical conductivity (EC), total dissolved solids (TDS), total hardness, HCO3, Ca2+, Mg2+, Na+, K+, SO42-, Cl-, NO3-, NO2-, F- and As. There were significant spatial variations observed for total hardness, TDS, HCO3, NO3 and As. In general, the south-east of the city displayed higher TH and HCO3 while the north-east showed significantly higher As concentrations attributed to the heterogeneity of the aquifer and industrial activities. In most cases, As was higher than WHO limit value. Indiscriminate disposal of domestic and commercial wastewater into River Ravi is the cause of elevated NO3 observed in the north-west compared to other places in the area. Investigation of the groundwater type revealed facies in the order: Ca-Mg-HCO3-SO4 > Mg-Ca-HCO3-SO4 > Ca-Mg-HCO3-SO4-Cl > Mg-Ca-HCO3-SO4 > Ca-HCO3-SO4 > Ca-Mg-SO4-HCO3. The plausible mineralization control mechanism seems to be that of carbonate weathering, although silicate weathering is probable. Moreover, PHREEQC model results showed that the groundwater was under saturated with respect to evaporites (anhydrite, fluorite, gypsum and halite) while generally equilibrium to saturated with respect to aragonite, calcite and dolomite. The Hierarchical Cluster Analysis (HCA) showed that pH significantly affected As, F, NO3 and NO2 while HCO3 contributing most to the observed TDS values in Lahore. It is concluded that inherent mineral dissolution/ precipitation, pH, oxic conditions, anthropogenic activities, atmospheric transport/ wet deposition, microbial activities and surface soil characteristics play their significant roles in elevating both As and F in the city's groundwater.

Keywords: Lahore, arsenic, fluoride, groundwater

Procedia PDF Downloads 551

Authors: Pranudda Pimsee, Caroline Sablayrolles, Pascale De Caro, Julien Guyomarch, Nicolas Lesage, Mireille Montréjaud-Vignoles

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The MIGR’HYCAR research project was initiated to provide decisional tools for risks connected to oil spill drifts in continental waters. These tools aim to serve in the decision-making process once oil spill pollution occurs and/or as reference tools to study scenarios of potential impacts of pollutions on a given site. This paper focuses on the study of the distribution of polycyclic aromatic hydrocarbons (PAHs) and derivatives from oil spill in water as function of environmental parameters. Eight petroleum oils covering a representative range of commercially available products were tested. 41 Polycyclic Aromatic Hydrocarbons (PAHs) and derivate, among them 16 EPA priority pollutants were studied by dynamic tests at laboratory scale. The chemical profile of the water soluble fraction was different from the parent oil profile due to the various water solubility of oil components. Semi-volatile compounds (naphtalenes) constitute the major part of the water soluble fraction. A large variation in composition of the water soluble fraction was highlighted depending on oil type. Moreover, four environmental parameters (temperature, suspended solid quantity, salinity, and oil: water surface ratio) were investigated with the Taguchi experimental design methodology. The results showed that oils are divided into three groups: the solubility of Domestic fuel and Jet A1 presented a high sensitivity to parameters studied, meaning they must be taken into account. For gasoline (SP95-E10) and diesel fuel, a medium sensitivity to parameters was observed. In fact, the four others oils have shown low sensitivity to parameters studied. Finally, three parameters were found to be significant towards the water soluble fraction.

Keywords: mornitoring, PAHs, water soluble fraction, SBSE, Taguchi experimental design

Procedia PDF Downloads 329

Authors: Gihan Thilakarathna, Fernando Ishara, Rathnayake Yasith, Bandara A. M. R. Y.

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E-learning is a crucial component of intelligent education. Even in the midst of a pandemic, E-learning is becoming increasingly important in the educational system. Several e-learning programs are accessible for students. Here, we decided to create an e-learning framework for children. We've found a few issues that teachers are having with their online classes. When there are numerous students in an online classroom, how does a teacher recognize a student's focus on academics and below-the-surface behaviors? Some kids are not paying attention in class, and others are napping. The teacher is unable to keep track of each and every student. Key challenge in e-learning is online exams. Because students can cheat easily during online exams. Hence there is need of exam proctoring is occurred. In here we propose an automated online exam cheating detection method using a web camera. The purpose of this project is to present an E-learning platform for math education and include games for kids as an alternative teaching method for math students. The game will be accessible via a web browser. The imagery in the game is drawn in a cartoonish style. This will help students learn math through games. Everything in this day and age is moving towards automation. However, automatic answer evaluation is only available for MCQ-based questions. As a result, the checker has a difficult time evaluating the theory solution. The current system requires more manpower and takes a long time to evaluate responses. It's also possible to mark two identical responses differently and receive two different grades. As a result, this application employs machine learning techniques to provide an automatic evaluation of subjective responses based on the keyword provided to the computer as student input, resulting in a fair distribution of marks. In addition, it will save time and manpower. We used deep learning, machine learning, image processing and natural language technologies to develop these research components.

Keywords: math, education games, e-learning platform, artificial intelligence

Procedia PDF Downloads 159

Authors: Dhanuja S. Patil, Sanjay B. Waykar

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Event detection is a standout amongst the most key parts for distinctive sorts of area applications of video data framework. Recently, it has picked up an extensive interest of experts and in scholastics from different zones. While detecting video event has been the subject of broad study efforts recently, impressively less existing methodology has considered multi-model data and issues related efficiency. Start of soccer matches different doubtful circumstances rise that can't be effectively judged by the referee committee. A framework that checks objectively image arrangements would prevent not right interpretations because of some errors, or high velocity of the events. Bayesian networks give a structure for dealing with this vulnerability using an essential graphical structure likewise the probability analytics. We propose an efficient structure for analysing and summarization of soccer videos utilizing object-based features. The proposed work utilizes the t-cherry junction tree, an exceptionally recent advancement in probabilistic graphical models, to create a compact representation and great approximation intractable model for client’s relationships in an interpersonal organization. There are various advantages in this approach firstly; the t-cherry gives best approximation by means of junction trees class. Secondly, to construct a t-cherry junction tree can be to a great extent parallelized; and at last inference can be performed utilizing distributed computation. Examination results demonstrates the effectiveness, adequacy, and the strength of the proposed work which is shown over a far reaching information set, comprising more soccer feature, caught at better places.

Keywords: summarization, detection, Bayesian network, t-cherry tree

Procedia PDF Downloads 330

Authors: Budhvin T. Withana, Sulochana Rupasinghe

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The educational system faces a significant concern with regards to Dyslexia and Dysgraphia, which are learning disabilities impacting reading and writing abilities. This is particularly challenging for children who speak the Sinhala language due to its complexity and uniqueness. Commonly used methods to detect the risk of Dyslexia and Dysgraphia rely on subjective assessments, leading to limited coverage and time-consuming processes. Consequently, delays in diagnoses and missed opportunities for early intervention can occur. To address this issue, the project developed a hybrid model that incorporates various deep learning techniques to detect the risk of Dyslexia and Dysgraphia. Specifically, Resnet50, VGG16, and YOLOv8 models were integrated to identify handwriting issues. The outputs of these models were then combined with other input data and fed into an MLP model. Hyperparameters of the MLP model were fine-tuned using Grid Search CV, enabling the identification of optimal values for the model. This approach proved to be highly effective in accurately predicting the risk of Dyslexia and Dysgraphia, providing a valuable tool for early detection and intervention. The Resnet50 model exhibited a training accuracy of 0.9804 and a validation accuracy of 0.9653. The VGG16 model achieved a training accuracy of 0.9991 and a validation accuracy of 0.9891. The MLP model demonstrated impressive results with a training accuracy of 0.99918, a testing accuracy of 0.99223, and a loss of 0.01371. These outcomes showcase the high accuracy achieved by the proposed hybrid model in predicting the risk of Dyslexia and Dysgraphia.

Keywords: neural networks, risk detection system, dyslexia, dysgraphia, deep learning, learning disabilities, data science

Procedia PDF Downloads 70

Authors: Zhongmin Wang, Wudong Fan, Hengshan Zhang, Yimin Zhou

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In data-driven prognostic methods, the prediction accuracy of the estimation for remaining useful life of bearings mainly depends on the performance of health indicators, which are usually fused some statistical features extracted from vibrating signals. However, the existing health indicators have the following two drawbacks: (1) The differnet ranges of the statistical features have the different contributions to construct the health indicators, the expert knowledge is required to extract the features. (2) When convolutional neural networks are utilized to tackle time-frequency features of signals, the time-series of signals are not considered. To overcome these drawbacks, in this study, the method combining convolutional neural network with gated recurrent unit is proposed to extract the time-frequency image features. The extracted features are utilized to construct health indicator and predict remaining useful life of bearings. First, original signals are converted into time-frequency images by using continuous wavelet transform so as to form the original feature sets. Second, with convolutional and pooling layers of convolutional neural networks, the most sensitive features of time-frequency images are selected from the original feature sets. Finally, these selected features are fed into the gated recurrent unit to construct the health indicator. The results state that the proposed method shows the enhance performance than the related studies which have used the same bearing dataset provided by PRONOSTIA.

Keywords: continuous wavelet transform, convolution neural net-work, gated recurrent unit, health indicators, remaining useful life

Procedia PDF Downloads 139

Authors: Cathal Ffrench, Ryan Barrett, Mike Quayle

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It is accepted in social psychology that categorization leads to ingroup favoritism, without further thought given to the processes that may co-occur or even precede categorization. These categorizations move away from the conceptualization of the self as a unique social being toward an increasingly collective identity. Subsequently, many individuals derive much of their self-evaluations from these collective identities. The seminal literature on this topic argues that it is primarily categorization that evokes instances of ingroup favoritism. Apropos to these theories, we argue that categorization acts to enhance and further intergroup processes rather than defining them. More accurately, we propose categorization aids initial ingroup contact and this first contact is predictive of subsequent favoritism on individual and collective levels. This analysis focuses on Virtual Interaction APPLication (VIAPPL) based studies, a software interface that builds on the flaws of the original minimal group studies. The VIAPPL allows the exchange of tokens in an intra and inter-group manner. This token exchange is how we classified the first contact. The study involves binary longitudinal analysis to better understand the subsequent exchanges of individuals based on who they first interacted with. Studies were selected on the criteria of evidence of explicit first interactions and two-group designs. Our findings paint a compelling picture in support of a motivated contact hypothesis, which suggests that an individual’s first motivated contact toward another has strong predictive capabilities for future behavior. This contact can lead to habit formation and specific favoritism towards individuals where contact has been established. This has important implications for understanding how group conflict occurs, and how intra-group individual bias can develop.

Keywords: categorization, group dynamics, initial contact, minimal social networks, momentary contact

Procedia PDF Downloads 152

Authors: Michelle Hartanto, Risheka Suthantirakumar

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Introduction Staff redeployment, increased numbers of acutely unwell patients requiring resuscitation decision-making conversations, visiting restrictions, and varying guidance regarding resuscitation for patients with COVID-19 disrupted clinicians’ management of resuscitation and escalation decision-making processes. While it was generally accepted that the COVID-19 pandemic disturbed numerous aspects of the Recommended Summary Plan for Emergency Care and Treatment (ReSPECT) process in the United Kingdom, a process which establishes a patient’s CPR status and treatment escalation plans, the impact of the pandemic on clinicians’ attitudes towards these resuscitation and decision-making conversations was unknown. This was the first study to examine the impact of the COVID-19 pandemic on clinicians’ knowledge, skills, and attitudes towards the ReSPECT process. Methods A cross-sectional survey of clinicians at one acute teaching hospital in the UK was conducted. A questionnaire with a defined five-point Likert scale was distributed and clinicians were asked to recall their pre-pandemic views on ReSPECT and report their current views at the time of survey distribution (May 2020, end of the first COVID-19 wave in the UK). Responses were received from 171 clinicians, and self-reported views before and during the pandemic were compared. Results Clinicians reported they found managing ReSPECT conversations more challenging during the pandemic, especially when conducted over the telephone with relatives, and they experienced an increase in negative emotions before, during, and after conducting ReSPECT conversations. Our findings identified that due to the pandemic there was now a need for clinicians to receive training and support in conducting resuscitation and escalation decision-making conversations over the telephone with relatives and managing these processes.

Keywords: cardiopulmonary resuscitation, COVID-19 pandemic, DNACPR discussion, education, recommended summary plan for emergency care and treatment, resuscitation order

Procedia PDF Downloads 103

Authors: Yongyi Chen, Li Qin, Peng Jia, Yongqiang Ning, Yun Liu, Lijun Wang

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The distributed feedback (DFB) lasers are indispensable in optical phase array (OPA) used for light detection and ranging (LIDAR) techniques, laser communication systems and integrated optics, thanks to their stable single longitudinal mode and narrow linewidth properties. Traditional index-coupled (IC) DFB lasers with uniform gratings have an inherent problem of lasing two degenerated modes. Phase shifts are usually required to eliminate the mode degeneration, making the grating structure complex and expensive. High-quality antireflection (AR) coatings on both lasing facets are also essential owing to the random facet phases introduced by the chip cleavage process, which means half of the lasing energy is wasted. Gain-coupled DFB (GC-DFB) lasers based on the periodic gain (or loss) are announced to have single longitudinal mode as well as capable of the unsymmetrical coating to increase lasing power and efficiency thanks to facet immunity. However, expensive and time-consuming technologies such as epitaxial regrowth and nanoscale grating processing are still required just as IC-DFB lasers, preventing them from practical applications and commercial markets. In this research, we propose a low-cost, single-mode regrowth-free GC-DFB laser based on periodic surface p-contacts. The gain coupling effect is achieved simply by periodic current distribution in the quantum well caused by periodic surface p-contacts, introducing very little index-coupling effect that can be omitted. It is prepared by i-line lithography, without nanoscale grating fabrication or secondary epitaxy. Due to easy fabrication techniques, it provides a method to fabricate practical low cost GC-DFB lasers for widespread practical applications.

Keywords: DFB laser, gain-coupled, low cost, periodic p-contacts

Procedia PDF Downloads 129