Search

Aggregation in relational databases is accomplished through hashing and sorting interval data, which is computationally expensive and scales poorly as the data volumes grow. In this paper, we show how quantitative interval and time-series data in relational attributes can be represented using delta summary values rather than absolute values. The need for sorting to determine the row corresponding to some maximum timestamp is negated, reducing the time complexity from at least O(n log(n)) towards O(n) and improving query execution times. We illustrate this new method in the relational algebra, present the implementation algorithmically, and test an implementation in two leading RDBMS products against the use of normal equivalents. We found this delta summation technique to be most effective for use cases with additive, numerical data upon which it is necessary to frequently obtain the latest values, and where the row cardinalities are in the order of 10^5. Our experiments found the proposed new delta summation technique could execute faster than the equivalent standard selection method by up to 22.4%, reducing the overall query cost in some circumstances by up to 24.0%, reducing I/O load by up to 60.6%, but with increased query costs for write operations, an increase in CPU time and memory allocation, uncertain performance with very low or very high cardinalities and inconsistent results across different RDBMS platforms., Comment: 10 pages

A multidisciplinary approach was applied to explore deeper knowledge about the reproductive biology and ecology of the green mussel (Perna viridis) by interlinking among ecological factors, ingested gut plankton, gonad fatty acid profile, and reproductive traits. Mussels were collected throughout the year from the coastal region of the Bay of Bengal of Bangladesh, and the histological analysis of 242 mussels revealed five stages of gametogenesis with an annual spawning season from January to April. The gonadosomatic index showed a strong correlation with the water quality parameters, ingested gut plankton groups, and gonadal fatty acids, and also displayed prominent effects of seasonality. In all datasets, we performed principal component analysis in which the first two principal components showed that seasonality explained >65% of the variability, and the multivariate spaces of seasonality corresponded to the gonad development. High salinity, nutrients, chlorophyll a, and plankton abundance in the water column were positively correlated with gonad development and the spawning of mussels. During the development stage, mussel ingested more plankton, particularly Bacillariophyceae and Pyrrophyceae, which were an important source of increased n-3 polyunsaturated fatty acids content in gonads. Furthermore, gonads accumulated more lipids and saturated fatty acids during the mature and spawning stages, which were probably utilized during embryogenesis and early larval development as energy sources and constituents of the cell membrane. This research provides a model toward a better understanding of reproductive biological traits and how these traits are interrelated with the surrounding environmental variables of the green mussel, which would be helpful in marine aquaculture and the sustainable exploitation of the fishery resources.

The migration of anadromous fish in heterogenic environments unceasingly imposes a selective pressure that results in genetic variation for local adaptation. However, discrimination of anadromous fish populations by fine-scale local adaptation is challenging because of their high rate of gene flow, highly connected divergent population, and large population size. Recent advances in next-generation sequencing (NGS) have expanded the prospects of defining the weakly structured population of anadromous fish. Therefore, we used NGS-based restriction site-associated DNA (NextRAD) techniques on 300 individuals of an anadromous Hilsa shad (Tenualosa ilisha) species, collected from nine strategic habitats, across their diverse migratory habitats, which include sea, estuary, and different freshwater rivers. The NextRAD technique successfully identified 15,453 single nucleotide polymorphism (SNP) loci. Outlier tests using the FST OutFLANK and pcadapt approaches identified 74 and 449 SNPs (49 SNPs being common), respectively, as putative adaptive loci under a divergent selection process. Our results, based on the different cluster analyses of these putatively adaptive loci, suggested that local adaptation has divided the Hilsa shad population into two genetically structured clusters, in which marine and estuarine collection sites were dominated by individuals of one genetic cluster and different riverine collection sites were dominated by individuals of another genetic cluster. The phylogenetic analysis revealed that all the riverine populations of Hilsa shad were further subdivided into the north-western riverine (turbid freshwater) and the north-eastern riverine (clear freshwater) ecotypes. Among all of the putatively adaptive loci, only 36 loci were observed to be in the coding region, and the encoded genes might be associated with important biological functions related to the local adaptation of Hilsa shad. In summary, our study provides both neutral and adaptive contexts

Rabbitfish (Siganidae) are coral reef fish that are distributed across diverse habitats that include estuaries, mangroves, reefs, and even seaweed mats. Given their ecological diversity and natural widespread distributions across the Indo-Pacific region, we were interested to investigate the evolutionary history of this group and patterns of divergence that have contributed to their present-day distributions. In the present study, samples were collected from the South China Sea to study taxonomic and phylogenetic relationships, and divergence times. We investigated the taxonomic relationships among modern rabbitfish species, reconstructed their molecular phylogeny, and estimated divergence times among selected lineages based on a fragment of the mtDNA cytochrome oxidase I (COI) and sequences of the nuclear rhodopsin retrogene (RHO). Our results indicate that modern rabbitfish likely originated in the Indo-West Pacific during the late Eocene [37.4 million years ago (mya)], following which they diverged into three major clades during the Pliocene/Pleistocene. Subsequent diversification and origins of the majority of siganids may likely be associated with episodes of paleo-oceanographic events, including greenhouse and glaciation events (Eocene–Miocene) as well as major plate tectonic events (Pliocene–Pleistocene). Some modern siganid species may naturally hybridize with congeneric species where their geographical ranges overlap. A comprehensive taxonomic analysis revealed that the phylogeny of Siganidae (cladogenesis of Clades I, II, and III) is characterized by divergence in several external morphological characters and morphometric parameters. Our study demonstrates that morphological characteristics, geographical heterogeneity, and environmental change have contributed to siganids’ historical diversification.

Alzheimer’s disease, an incurable chronic neurological disorder (NLD) that affects human memory and demises cognitive thinking ability with shrinkage of the brain area. Early detection of Alzheimer’s disease (AD) is the only hope to delay its effect. This study designed a computer-aided automated detection method that can detect mild cognitive impairment for AD from magnetic resonance image scans. The data-driven solution approach requires an extensive quantity of annotated images for diagnosis. However, obtaining a large amount of annotated data for medical application is a challenging task. We have exploited a deep convolutional generative adversarial network (DCGAN) for synthesizing high-quality images to increase dataset size. A fine-tuned CNN (VGG16 architecture) model works on images to extract the intuitive features for early diagnosis. The extracted features of images by VGG16 feed into the support vector machine for classification. This research has conducted copious experiments to validate the proposed method outperformed relative baselines on public datasets., ISBN för värdpublikation: 978-3-030-86992-2; 978-3-030-86993-9

Rabbitfish (Siganidae) are coral reef fish that are distributed across diverse habitats that include estuaries, mangroves, reefs, and even seaweed mats. Given their ecological diversity and natural widespread distributions across the Indo-Pacific region, we were interested to investigate the evolutionary history of this group and patterns of divergence that have contributed to their present-day distributions. In the present study, samples were collected from the South China Sea to study taxonomic and phylogenetic relationships, and divergence times. We investigated the taxonomic relationships among modern rabbitfish species, reconstructed their molecular phylogeny, and estimated divergence times among selected lineages based on a fragment of the mtDNA cytochrome oxidase I (COI) and sequences of the nuclear rhodopsin retrogene (RHO). Our results indicate that modern rabbitfish likely originated in the Indo-West Pacific during the late Eocene [37.4 million years ago (mya)], following which they diverged into three major clades during the Pliocene/Pleistocene. Subsequent diversification and origins of the majority of siganids may likely be associated with episodes of paleo-oceanographic events, including greenhouse and glaciation events (Eocene-Miocene) as well as major plate tectonic events (Pliocene-Pleistocene). Some modern siganid species may naturally hybridize with congeneric species where their geographical ranges overlap. A comprehensive taxonomic analysis revealed that the phylogeny of Siganidae (cladogenesis of Clades I, II, and III) is characterized by divergence in several external morphological characters and morphometric parameters. Our study demonstrates that morphological characteristics, geographical heterogeneity, and environmental change have contributed to siganids' historical diversification.

The overlay of the COVID-19 pandemic on the pandemic of physical inactivity has become a great concern. Both types of pandemics can decrease the health protection capacity and consequently increase complexity in human lives. This cross-sectional study intended to examine changes in physical activity and sedentary behaviors during the COVID-19 pandemic among university students in a second-tier city of Bangladesh. Two hundred and nine students responded to an online questionnaire administered via Google Survey. In addition to descriptive statistics, parametric and non-parametric tests for comparing means, medians and distributions were used to assess differences in activity traits before and during the COVID-19 pandemic. Results show that the occurrence of COVID-19 has significantly reduced the practice of walking and physical activities among the students. They are commonly motivated by introjected regulation. Father’s occupation and the type of family of a student have significant influences on the total physical activity in either situation. Bangladeshi university students have, particularly, been perceived as not generally used to vigorous physical activities. They are inactive compared to students from other countries. Thus, the public health policymakers and the corresponding authority should inspire the students to be more physically active by implementing different strategies such as increasing bicycling and walking facilities on the campus.

Alzheimer’s disease, an incurable chronic neurological disorder (NLD) that affects human memory and demises cognitive thinking ability with shrinkage of the brain area. Early detection of Alzheimer’s disease (AD) is the only hope to delay its effect. This study designed a computer-aided automated detection method that can detect mild cognitive impairment for AD from magnetic resonance image scans. The data-driven solution approach requires an extensive quantity of annotated images for diagnosis. However, obtaining a large amount of annotated data for medical application is a challenging task. We have exploited a deep convolutional generative adversarial network (DCGAN) for synthesizing high-quality images to increase dataset size. A fine-tuned CNN (VGG16 architecture) model works on images to extract the intuitive features for early diagnosis. The extracted features of images by VGG16 feed into the support vector machine for classification. This research has conducted copious experiments to validate the proposed method outperformed relative baselines on public datasets., ISBN för värdpublikation: 978-3-030-86992-2; 978-3-030-86993-9

Background: Sleep problems in children with Autism Spectrum Disorder (ASD) are highly prevalent, but little information is available on this issue in low- to middle-income countries (LMIC) such as Bangladesh. Therefore, the present study investigated the prevalence and socio-demographic determinants of ASD sleep disturbances in a comparison with typically developing children (TDC). Methods: A cross-sectional interview study was carried out within a total of 446 Bangladeshi mothers, whose children's mean age was 8.1 +/- 2.9 years (151 ASD [8.5 +/- 2.7 years] and 295 TDC [7.9 +/- 2.9 years]); in addition to socio-demographics, the Child Sleep Habit Questionnaire (CSHQ) was used, and a cut-off score of 41 out of 93 points considered as reflecting sleep problems. Results: About 89.7% of the children reported having problems in sleep, with ASD reporting higher frequency vs TDC (94.00% vs 87.50%; chi(2)=4.678, p=0.031). The overall mean CSHQ score was 48.7 +/- 7.6 in total sample, whereas ASD children reported higher scores compared to TDCs (50.9 +/- 8.1 vs 47.5 +/- 7.0, p< 0.001). Similarly, subscales of CSHQ such as sleep duration (4.23 +/- 1.56 vs 3.90 +/- 1.31, p=0.017), sleep anxiety (7.23 +/- 2.05 vs 6.45 +/- 1.92, p< 0.001), night waking (3.82 +/- 1.07 vs 3.17 +/- 1.89, p< 0.001), parasomnias (8.86 +/- 2.06 vs 7.85 +/- 2.27, p< 0.001), and sleep disordered breathing (4.02 +/- 2.92 vs 3.43 +/- 2.07, p=0.014) were more problematic among ASD compared to TDC. Lastly, 28.5% of ASD reported taking sleep-related medications vs 0.3% for TDC (n=1). Conclusion: Bangladeshi ASD children are highly likely to manifest sleep disturbances, which warrant urgent implementation of parental educational and support programs to mitigate the impact of sleep problems in ASD families.

Naznin Sultana,1,2 Md Asaduzzaman,3 Firoj al Mamun,1,4 Ismail Hosen,1,4 Qian Yu,5 Amir H Pakpour,6,7 David Gozal,8 Mohammed A Mamun1,4 1CHINTA Research Bangladesh (Centre for Health Innovation, Networking, Training, Action and Research – Bangladesh), Savar, Dhaka, Bangladesh; 2Department of Public Health, North South University, Dhaka, Bangladesh; 3Department of Public Health, American International University – Bangladesh, Dhaka, Bangladesh; 4Department of Public Health and Informatics, Jahangirnagar University, Savar, Dhaka, Bangladesh; 5Exercise Psychophysiology Laboratory, Institute of KEEP Collaborative Innovation, School of Psychology, Shenzhen University, Shenzhen, 518060, People’s Republic of China; 6Social Determinants of Health Research Center, Research Institute for Prevention of Non-Communicable Diseases, Qazvin University of Medical Sciences, Qazvin, Iran; 7Department of Nursing, School of Health and Welfare, Jönköping University, Jönköping, Sweden; 8Department of Child Health and the Child Health Research Institute, The University of Missouri School of Medicine, Columbia, MO, USACorrespondence: Qian YuExercise Psychophysiology Laboratory, Institute of KEEP Collaborative Innovation, School of Psychology, Shenzhen University, 518060, People’s Republic of ChinaEmail yuqianmiss@163.comMohammed A MamunCHINTA Research Bangladesh, Savar, Dhaka, 1342, BangladeshTel +880-1738592653Email mamunphi46@gmail.comBackground: Sleep problems in children with Autism Spectrum Disorder (ASD) are highly prevalent, but little information is available on this issue in low- to middle-income countries (LMIC) such as Bangladesh. Therefore, the present study investigated the prevalence and socio-demographic determinants of ASD sleep disturbances in a comparison with typically developing children (TDC).Methods: A cross-sectional interview study was carried out within a total of 446 Bangladeshi mothers, who

Systematic transportation network and orderly circulation system is vital for the effective functioning of a land port facility. Benapole Port, the largest land port of entry in Bangladesh, located on Bangladesh- India border in Jashore. It handles most of the cross-border trade as well as a large number of passengers and trucks of goods are passed through this point every day. Due to lack of systematic organization and orderly traffic circulation control the port facility is unable to function at its full efficiency, resulting in traffic congestions, processing delay and systematic losses. The key challenge is to resolve the circulation network adequately and systematically to maneuver the cross-border traffic and cargo by the means of providing well organized traffic network, compaction of facilities, dedicated circulation system and smooth process flow of port activities. Through conduction of extensive site survey and in-depth data analysis the fundamental issues and remedial requirements forecasts are identified. By the means of computer aided simulation for comparison between multiple alternatives of traffic network distribution and circulation system the possible most suitable alternative can be assessed. The efficiency of this prevailed circulation system is supported by the results of the simulation, which can be able to optimize the addressed issues, also ensures maximized efficiency of the port functioning. Furthermore, either as a goods or tourists exchange point, Benapole Port has paramount importance and have potential to become a landmark establishment.

Bangladesh Ready-Made Garments (RMG) had become essentially throughout the most recent decades in each term yet lamentably nature of privately created merchandise is as yet an issue. As indicated by BGMEA report (2014) RMG comprises around 40% assembling just as half of all out workforce and 78% of all out fare profit produce from this area. A large portion of representatives in RMG are ladies around 90% where 4.2 million individuals are working in this part. Be that as it may, the potential development and prospect in Bangladesh RMG division is colossal. Furthermore, for utilizing this open doors it should concentrated on legitimate usage of HR approaches to accentuation quality execution. In ongoing information based monetary condition human asset is the most essential asset as hierarchical adequacy and productivity is to a great extent relies upon the use limit of this asset particularly if there should arise an occurrence of Ready-made Garments industry in Bangladesh. However, the substances for RMG associations are that their kin remain underestimated, under prepared and underutilized. Because of ill-advised HR rehearses work turmoil rate in RMG is high and representative profitability rate is lower than its rivals. As RMG is the rising part for additional venture, presently a day a much concern is expected to improve and support pieces of clothing organizations development. Human Resource Management requires significantly more worry in Bangladesh RMG area. In this perspective, literary works demonstrated that, HR rehearses didn't get a lot of consideration which should be estimated and examination in the part of Bangladesh RMG areas quality execution improvement as in RMG division HR is the fundamental favourable position for modern development and feasible upper hand in business. So this investigation is required to lift up this desire to satisfy this examination hole.

Systematic transportation network and orderly circulation system is vital for the effective functioning of a land port facility. Benapole Port, the largest land port of entry in Bangladesh, located on Bangladesh- India border in Jashore. It handles most of the cross-border trade as well as a large number of passengers and trucks of goods are passed through this point every day. Due to lack of systematic organization and orderly traffic circulation control the port facility is unable to function at its full efficiency, resulting in traffic congestions, processing delay and systematic losses. The key challenge is to resolve the circulation network adequately and systematically to maneuver the cross-border traffic and cargo by the means of providing well organized traffic network, compaction of facilities, dedicated circulation system and smooth process flow of port activities. Through conduction of extensive site survey and in-depth data analysis the fundamental issues and remedial requirements forecasts are identified. By the means of computer aided simulation for comparison between multiple alternatives of traffic network distribution and circulation system the possible most suitable alternative can be assessed. The efficiency of this prevailed circulation system is supported by the results of the simulation, which can be able to optimize the addressed issues, also ensures maximized efficiency of the port functioning. Furthermore, either as a goods or tourists exchange point, Benapole Port has paramount importance and have potential to become a landmark establishment.

Bangladesh Ready-Made Garments (RMG) had become essentially throughout the most recent decades in each term yet lamentably nature of privately created merchandise is as yet an issue. As indicated by BGMEA report (2014) RMG comprises around 40% assembling just as half of all out workforce and 78% of all out fare profit produce from this area. A large portion of representatives in RMG are ladies around 90% where 4.2 million individuals are working in this part. Be that as it may, the potential development and prospect in Bangladesh RMG division is colossal. Furthermore, for utilizing this open doors it should concentrated on legitimate usage of HR approaches to accentuation quality execution. In ongoing information based monetary condition human asset is the most essential asset as hierarchical adequacy and productivity is to a great extent relies upon the use limit of this asset particularly if there should arise an occurrence of Ready-made Garments industry in Bangladesh. However, the substances for RMG associations are that their kin remain underestimated, under prepared and underutilized. Because of ill-advised HR rehearses work turmoil rate in RMG is high and representative profitability rate is lower than its rivals. As RMG is the rising part for additional venture, presently a day a much concern is expected to improve and support pieces of clothing organizations development. Human Resource Management requires significantly more worry in Bangladesh RMG area. In this perspective, literary works demonstrated that, HR rehearses didn't get a lot of consideration which should be estimated and examination in the part of Bangladesh RMG areas quality execution improvement as in RMG division HR is the fundamental favourable position for modern development and feasible upper hand in business. So this investigation is required to lift up this desire to satisfy this examination hole.

Bangladesh Ready-Made Garments (RMG) had become essentially throughout the most recent decades in each term yet lamentably nature of privately created merchandise is as yet an issue. As indicated by BGMEA report (2014) RMG comprises around 40% assembling just as half of all out workforce and 78% of all out fare profit produce from this area. A large portion of representatives in RMG are ladies around 90% where 4.2 million individuals are working in this part. Be that as it may, the potential development and prospect in Bangladesh RMG division is colossal. Furthermore, for utilizing this open doors it should concentrated on legitimate usage of HR approaches to accentuation quality execution. In ongoing information based monetary condition human asset is the most essential asset as hierarchical adequacy and productivity is to a great extent relies upon the use limit of this asset particularly if there should arise an occurrence of Ready-made Garments industry in Bangladesh. However, the substances for RMG associations are that their kin remain underestimated, under prepared and underutilized. Because of ill-advised HR rehearses work turmoil rate in RMG is high and representative profitability rate is lower than its rivals. As RMG is the rising part for additional venture, presently a day a much concern is expected to improve and support pieces of clothing organizations development. Human Resource Management requires significantly more worry in Bangladesh RMG area. In this perspective, literary works demonstrated that, HR rehearses didn't get a lot of consideration which should be estimated and examination in the part of Bangladesh RMG areas quality execution improvement as in RMG division HR is the fundamental favourable position for modern development and feasible upper hand in business. So this investigation is required to lift up this desire to satisfy this examination hole.

Systematic transportation network and orderly circulation system is vital for the effective functioning of a land port facility. Benapole Port, the largest land port of entry in Bangladesh, located on Bangladesh- India border in Jashore. It handles most of the cross-border trade as well as a large number of passengers and trucks of goods are passed through this point every day. Due to lack of systematic organization and orderly traffic circulation control the port facility is unable to function at its full efficiency, resulting in traffic congestions, processing delay and systematic losses. The key challenge is to resolve the circulation network adequately and systematically to maneuver the cross-border traffic and cargo by the means of providing well organized traffic network, compaction of facilities, dedicated circulation system and smooth process flow of port activities. Through conduction of extensive site survey and in-depth data analysis the fundamental issues and remedial requirements forecasts are identified. By the means of computer aided simulation for comparison between multiple alternatives of traffic network distribution and circulation system the possible most suitable alternative can be assessed. The efficiency of this prevailed circulation system is supported by the results of the simulation, which can be able to optimize the addressed issues, also ensures maximized efficiency of the port functioning. Furthermore, either as a goods or tourists exchange point, Benapole Port has paramount importance and have potential to become a landmark establishment.

Systematic transportation network and orderly circulation system is vital for the effective functioning of a land port facility. Benapole Port, the largest land port of entry in Bangladesh, located on Bangladesh- India border in Jashore. It handles most of the cross-border trade as well as a large number of passengers and trucks of goods are passed through this point every day. Due to lack of systematic organization and orderly traffic circulation control the port facility is unable to function at its full efficiency, resulting in traffic congestions, processing delay and systematic losses. The key challenge is to resolve the circulation network adequately and systematically to maneuver the cross-border traffic and cargo by the means of providing well organized traffic network, compaction of facilities, dedicated circulation system and smooth process flow of port activities. Through conduction of extensive site survey and in-depth data analysis the fundamental issues and remedial requirements forecasts are identified. By the means of computer aided simulation for comparison between multiple alternatives of traffic network distribution and circulation system the possible most suitable alternative can be assessed. The efficiency of this prevailed circulation system is supported by the results of the simulation, which can be able to optimize the addressed issues, also ensures maximized efficiency of the port functioning. Furthermore, either as a goods or tourists exchange point, Benapole Port has paramount importance and have potential to become a landmark establishment.

Systematic transportation network and orderly circulation system is vital for the effective functioning of a land port facility. Benapole Port, the largest land port of entry in Bangladesh, located on Bangladesh- India border in Jashore. It handles most of the cross-border trade as well as a large number of passengers and trucks of goods are passed through this point every day. Due to lack of systematic organization and orderly traffic circulation control the port facility is unable to function at its full efficiency, resulting in traffic congestions, processing delay and systematic losses. The key challenge is to resolve the circulation network adequately and systematically to maneuver the cross-border traffic and cargo by the means of providing well organized traffic network, compaction of facilities, dedicated circulation system and smooth process flow of port activities. Through conduction of extensive site survey and in-depth data analysis the fundamental issues and remedial requirements forecasts are identified. By the means of computer aided simulation for comparison between multiple alternatives of traffic network distribution and circulation system the possible most suitable alternative can be assessed. The efficiency of this prevailed circulation system is supported by the results of the simulation, which can be able to optimize the addressed issues, also ensures maximized efficiency of the port functioning. Furthermore, either as a goods or tourists exchange point, Benapole Port has paramount importance and have potential to become a landmark establishment.

Surrounded by speakers of Indo-European, Dravidian and Tibeto-Burman languages, around 11 million Munda (a branch of Austroasiatic language family) speakers live in the densely populated and genetically diverse South Asia. Their genetic makeup holds components characteristic of South Asians as well as Southeast Asians. The admixture time between these components has been previously estimated on the basis of archaeology, linguistics and uniparental markers. Using genome-wide genotype data of 102 Munda speakers and contextual data from South and Southeast Asia, we retrieved admixture dates between 2000–3800 years ago for different populations of Munda. The best modern proxies for the source populations for the admixture with proportions 0.29/0.71 are Lao people from Laos and Dravidian speakers from Kerala in India. The South Asian population(s), with whom the incoming Southeast Asians intermixed, had a smaller proportion of West Eurasian genetic component than contemporary proxies. Somewhat surprisingly Malaysian Peninsular tribes rather than the geographically closer Austroasiatic languages speakers like Vietnamese and Cambodians show highest sharing of IBD segments with the Munda. In addition, we affirmed that the grouping of the Munda speakers into North and South Munda based on linguistics is in concordance with genome-wide data.

This Article contains errors in the Methods section, under subsection ‘Samples collection and genotyping’.

In this modern era, land transports are increasing dramatically. Moreover, self-driven car or the Advanced Driving Assistance System (ADAS) is now the public demand. For these types of cars, road conditions detection is mandatory. On the other hand, compared to the number of vehicles, to increase the number of roads is not possible. Software is the only alternative solution. Road Conditions Detection system will help to solve the issues. For solving this problem, Image processing, and machine learning have been applied to develop a project namely, Detection of Road Conditions Using Image Processing and Machine Learning Techniques for Situation Awareness. Many issues could be considered for road conditions but the main focus will be on the detection of potholes, Maintenance sings and lane. Image processing and machine learning have been combined for our system for detecting in real-time. Machine learning has been applied to maintains signs detection. Image processing has been applied for detecting lanes and potholes. The detection system will provide a lane mark with colored lines, the pothole will be a marker with a red rectangular box and for a road Maintenance sign, the system will also provide information of aintenance sign as maintenance sing is detected. By observing all these scenarios, the driver will realize the road condition. On the other hand situation awareness is the ability to perceive information from it’s surrounding, takes decisions based on perceived information and it makes decision based on prediction.

In this modern era, land transports are increasing dramatically. Moreover, self-driven car or the Advanced Driving Assistance System (ADAS) is now the public demand. For these types of cars, road conditions detection is mandatory. On the other hand, compared to the number of vehicles, to increase the number of roads is not possible. Software is the only alternative solution. Road Conditions Detection system will help to solve the issues. For solving this problem, Image processing, and machine learning have been applied to develop a project namely, Detection of Road Conditions Using Image Processing and Machine Learning Techniques for Situation Awareness. Many issues could be considered for road conditions but the main focus will be on the detection of potholes, Maintenance sings and lane. Image processing and machine learning have been combined for our system for detecting in real-time. Machine learning has been applied to maintains signs detection. Image processing has been applied for detecting lanes and potholes. The detection system will provide a lane mark with colored lines, the pothole will be a marker with a red rectangular box and for a road Maintenance sign, the system will also provide information of aintenance sign as maintenance sing is detected. By observing all these scenarios, the driver will realize the road condition. On the other hand situation awareness is the ability to perceive information from it’s surrounding, takes decisions based on perceived information and it makes decision based on prediction.

This Article contains errors in the Methods section, under subsection ‘Samples collection and genotyping’.

Surrounded by speakers of Indo-European, Dravidian and Tibeto-Burman languages, around 11 million Munda (a branch of Austroasiatic language family) speakers live in the densely populated and genetically diverse South Asia. Their genetic makeup holds components characteristic of South Asians as well as Southeast Asians. The admixture time between these components has been previously estimated on the basis of archaeology, linguistics and uniparental markers. Using genome-wide genotype data of 102 Munda speakers and contextual data from South and Southeast Asia, we retrieved admixture dates between 2000–3800 years ago for different populations of Munda. The best modern proxies for the source populations for the admixture with proportions 0.29/0.71 are Lao people from Laos and Dravidian speakers from Kerala in India. The South Asian population(s), with whom the incoming Southeast Asians intermixed, had a smaller proportion of West Eurasian genetic component than contemporary proxies. Somewhat surprisingly Malaysian Peninsular tribes rather than the geographically closer Austroasiatic languages speakers like Vietnamese and Cambodians show highest sharing of IBD segments with the Munda. In addition, we affirmed that the grouping of the Munda speakers into North and South Munda based on linguistics is in concordance with genome-wide data.

The upcoming 5th Generation (5G) of wireless networks is expected to lay a foundation of intelligent networks with the provision of some isolated Artificial Intelligence (AI) operations. However, fully-intelligent network orchestration and management for providing innovative services will only be realized in Beyond 5G (B5G) networks. To this end, we envisage that the 6th Generation (6G) of wireless networks will be driven by on-demand self-reconfiguration to ensure a many-fold increase in the network performanceandservicetypes.Theincreasinglystringentperformancerequirementsofemergingnetworks may finally trigger the deployment of some interesting new technologies such as large intelligent surfaces, electromagnetic-orbital angular momentum, visible light communications and cell-free communications – tonameafew.Ourvisionfor6Gis–amassivelyconnectedcomplexnetworkcapableofrapidlyresponding to the users’ service calls through real-time learning of the network state as described by the network-edge (e.g., base-station locations, cache contents, etc.), air interface (e.g., radio spectrum, propagation channel, etc.), and the user-side (e.g., battery-life, locations, etc.). The multi-state, multi-dimensional nature of the network state, requiring real-time knowledge, can be viewed as a quantum uncertainty problem. In this regard, the emerging paradigms of Machine Learning (ML), Quantum Computing (QC), and Quantum ML (QML) and their synergies with communication networks can be considered as core 6G enablers. Considering these potentials, starting with the 5G target services and enabling technologies, we provide a comprehensivereviewoftherelatedstate-of-the-artinthedomainsofML(includingdeeplearning),QCand QML, and identify their potential benefits, issues and use cases for their applications in the B5G networks. Subsequently,weproposeanovelQC-assistedandQML-basedframeworkfor6Gcommunicationnetworks whilearticulatingitschallengesandpotentialenablingtechnologiesatthenetwork-infrastruct

The upcoming 5th Generation (5G) of wireless networks is expected to lay a foundation of intelligent networks with the provision of some isolated Artificial Intelligence (AI) operations. However, fully-intelligent network orchestration and management for providing innovative services will only be realized in Beyond 5G (B5G) networks. To this end, we envisage that the 6th Generation (6G) of wireless networks will be driven by on-demand self-reconfiguration to ensure a many-fold increase in the network performanceandservicetypes.Theincreasinglystringentperformancerequirementsofemergingnetworks may finally trigger the deployment of some interesting new technologies such as large intelligent surfaces, electromagnetic-orbital angular momentum, visible light communications and cell-free communications – tonameafew.Ourvisionfor6Gis–amassivelyconnectedcomplexnetworkcapableofrapidlyresponding to the users’ service calls through real-time learning of the network state as described by the network-edge (e.g., base-station locations, cache contents, etc.), air interface (e.g., radio spectrum, propagation channel, etc.), and the user-side (e.g., battery-life, locations, etc.). The multi-state, multi-dimensional nature of the network state, requiring real-time knowledge, can be viewed as a quantum uncertainty problem. In this regard, the emerging paradigms of Machine Learning (ML), Quantum Computing (QC), and Quantum ML (QML) and their synergies with communication networks can be considered as core 6G enablers. Considering these potentials, starting with the 5G target services and enabling technologies, we provide a comprehensivereviewoftherelatedstate-of-the-artinthedomainsofML(includingdeeplearning),QCand QML, and identify their potential benefits, issues and use cases for their applications in the B5G networks. Subsequently,weproposeanovelQC-assistedandQML-basedframeworkfor6Gcommunicationnetworks whilearticulatingitschallengesandpotentialenablingtechnologiesatthenetwork-infrastruct

In this modern era, land transports are increasing dramatically. Moreover, self-driven car or the Advanced Driving Assistance System (ADAS) is now the public demand. For these types of cars, road conditions detection is mandatory. On the other hand, compared to the number of vehicles, to increase the number of roads is not possible. Software is the only alternative solution. Road Conditions Detection system will help to solve the issues. For solving this problem, Image processing, and machine learning have been applied to develop a project namely, Detection of Road Conditions Using Image Processing and Machine Learning Techniques for Situation Awareness. Many issues could be considered for road conditions but the main focus will be on the detection of potholes, Maintenance sings and lane. Image processing and machine learning have been combined for our system for detecting in real-time. Machine learning has been applied to maintains signs detection. Image processing has been applied for detecting lanes and potholes. The detection system will provide a lane mark with colored lines, the pothole will be a marker with a red rectangular box and for a road Maintenance sign, the system will also provide information of aintenance sign as maintenance sing is detected. By observing all these scenarios, the driver will realize the road condition. On the other hand situation awareness is the ability to perceive information from it’s surrounding, takes decisions based on perceived information and it makes decision based on prediction.

In this modern era, land transports are increasing dramatically. Moreover, self-driven car or the Advanced Driving Assistance System (ADAS) is now the public demand. For these types of cars, road conditions detection is mandatory. On the other hand, compared to the number of vehicles, to increase the number of roads is not possible. Software is the only alternative solution. Road Conditions Detection system will help to solve the issues. For solving this problem, Image processing, and machine learning have been applied to develop a project namely, Detection of Road Conditions Using Image Processing and Machine Learning Techniques for Situation Awareness. Many issues could be considered for road conditions but the main focus will be on the detection of potholes, Maintenance sings and lane. Image processing and machine learning have been combined for our system for detecting in real-time. Machine learning has been applied to maintains signs detection. Image processing has been applied for detecting lanes and potholes. The detection system will provide a lane mark with colored lines, the pothole will be a marker with a red rectangular box and for a road Maintenance sign, the system will also provide information of aintenance sign as maintenance sing is detected. By observing all these scenarios, the driver will realize the road condition. On the other hand situation awareness is the ability to perceive information from it’s surrounding, takes decisions based on perceived information and it makes decision based on prediction.

Wi-Fi technology has strong potentials in indoor and outdoor sensing applications, it has several important features which makes it an appealing option compared to other sensing technologies. This paper presents a survey on different applications of Wi-Fi based sensing systems such as elderly people monitoring, activity classification, gesture recognition, people counting, through the wall sensing, behind the corner sensing, and many other applications. The challenges and interesting future directions are also highlighted.

In recent years silicon photonics has become a considerable mainstream technology, especially in telecommunications fields to overcome the limitations imposed by copper-based technology. Nanoscale photonic technologies have attracted a lot of attention to co-develop photonic and electronic devices on silicon (Si) to provide a highly integrated electronic–photonic platform. Silicon-on-insulator (SOI) technology that relies heavily on the contrasted indices of Si and SiO2, enables the design and integration of these photonic devices in submicronic scales, similar to the devices produced by a standard CMOS fabrication platform in the electronics industry. One of the key challenges with these submicronic waveguide devices is to enable efficient coupling with fibre, which is mainly due to the mode-field differences between fibre and the waveguide, and their relative misalignments. To overcome this challenge, various techniques including prism, butt and grating coupling have been proposed. Among them, although butt coupling is an elegant solution for low loss and wideband operation, it often requires post-processing for accurate polishing and dicing to taper the waveguide edges. Therefore, it is not suitable for wafer-scale testing. Grating couplers, which mostly perform out of the plane coupling between a fibre and a waveguide, are also an attractive solution as light can be coupled in and out everywhere on the chip, opening the way for wafer-scale testing. However, despite such advantages, grating couplers often exhibit low coupling efficiency (CE) due to downward radiation of light that propagates towards substrate through buried oxide (BOX) which comprises 35%-45% of total incident light. Grating couplers are also very sensitive to the wavelength of the light as different wavelengths exhibit specific diffraction properties at the grating, which cause a narrow coupling bandwidth. In this thesis we have studied various techniques to improve the coupling efficiency and c

This paper presents a mathematical approach to the future dynamic spectrum market, where multiple secondary operators compete to gain radio resources. The secondary network operators (SNOs) face various concurrent auctions. We discuss techniques, which can be used to select auctions to optimize their objectives and increase the winning probability. To achieve these goals, a matching problem is formulated and solved, where secondary operators are paired with auctions, which can provide spectrum with the highest expected quality of service (QoS). A total outlay optimization is structured for auctions with concealed reserve prices, which are only revealed to the secondary operators for some price upon request. More specifically, we solve a nonlinear problem to determine the minimum set of auctions by using the brute force algorithm. We further introduce a surplus maximization and demonstrate an auction mechanism of spectrum allocation by modifying the Bayesian-Nash equilibrium. The mathematical analyses highlight that the optimal choice is achievable through the proposed mathematical formulation.

Dynamic spectrum sharing aims to provide flexible spectrum usage and improve spectrum efficiency for cellular and noncellular networks. We propose two optimization models using stochastic optimization algorithms in which the secondary operator: 1) spends the minimal cost to achieve the target grade of service (GoS) assuming unrestricted budget or 2) gains the maximal profit to achieve the target GoS assuming restricted budget. We assume that there are spectrum resources available for secondary operators to borrow under a merchant mode. Results obtained from each model are then compared with results derived from algorithms in which spectrum borrowings are random. Comparisons showed that the gain in the results obtained from our proposed stochastic-optimization framework is significantly higher than heuristic counterparts. Second, post-optimization performance analysis of the operators in the form of blocking probability in various scenarios is investigated to determine the probable performance gain and degradation of the secondary and primary operators, respectively. We mathematically model the sharing agreement scenario and derive the closed-form solution of blocking probabilities for each operator. Results show how the secondary operator performs in terms of blocking probability under various offered loads and sharing capacity.

Dynamic spectrum sharing aims to provide flexible spectrum usage and improve spectrum efficiency for cellular and noncellular networks. We propose two optimization models using stochastic optimization algorithms in which the secondary operator: 1) spends the minimal cost to achieve the target grade of service (GoS) assuming unrestricted budget or 2) gains the maximal profit to achieve the target GoS assuming restricted budget. We assume that there are spectrum resources available for secondary operators to borrow under a merchant mode. Results obtained from each model are then compared with results derived from algorithms in which spectrum borrowings are random. Comparisons showed that the gain in the results obtained from our proposed stochastic-optimization framework is significantly higher than heuristic counterparts. Second, post-optimization performance analysis of the operators in the form of blocking probability in various scenarios is investigated to determine the probable performance gain and degradation of the secondary and primary operators, respectively. We mathematically model the sharing agreement scenario and derive the closed-form solution of blocking probabilities for each operator. Results show how the secondary operator performs in terms of blocking probability under various offered loads and sharing capacity.

Dynamic Spectrum Sharing (DSS) aims to provide opportunistic access to under-utilized spectrum in cellular networks for secondary network operators. In this paper we propose an algorithm using stochastic and optimization models to borrow spectrum bandwidths under the assumption that more resources exist for secondary access than the secondary network demand by considering a merchant mode. The main aim of the paper is to address the problem of spectrum borrowing in DSS environments, where a secondary network operator aims to borrow the required spectrum from multiple primary network operators to achieve a maximum profit under specific grade of service (GoS) and budget restriction. We assume that the primary network operators offer spectrum access opportunities with variable number of channels (contiguous and/or non- contiguous) at variable prices. Results obtained are then compared with results derived from a heuristic algorithm in which spectrum borrowing are random. Comparisons showed that the gain in the results obtained from our proposed stochastic-optimization framework is significantly higher than random counterpart.

Spectrum sharing between operators with exclusive licensing have become a major concern for mobile network operators and regulators to respond to the growing spectrum demand of the multimedia applications. One of the important issues in spectrum sharing is to determine the potential benefit when multi-operators share the resources under certain mutual agreements. The paper focuses on dynamic spectrum sharing management in next generation cellular networks. We propose three loss network models and derive the closed form expression of blocking probability, each having specific level of cooperation and interaction. The analytical frameworks are presented to analyze the benefits due to multi-operator cooperation for spectrum sharing. This quantifies the operators' gains and degradations of operators engaged in cooperative arrangements. We also analyze the overall network performance in terms of spectrum utilization and present a detailed comparisons between the proposed analytical frameworks.

Although federated cloud computing has emerged as a promising paradigm, autonomous orchestration of resource utilization within the federation is still required to be balanced on the basis of workload assignment at a given time. Such potential imbalance of workload allocation as well as resource utilization may lead to a negative cloudburst within the federation. The analytical models found in the literature do not provide explicit framework to provide dynamic measure of workload requirement within a cloud federation environment. An additional challenge is the adoption of operational restrictions from regulatory body, the federation, or the federation participants. The analytical models presented in this paper have addressed workload balancing within a federated cloud environment under the access control restrictions agreed between federation members. The proposed analytical models provide a closed form solution for access probability and resource utilization at a given time. The analytical results are evaluated at different degree of security within the cloud federation environment and efficiency of the proposed workload balancing models is demonstrated. The proposed models can be used for cloud services dimensioning to handle high computational demand.

Due to exponentially growing wireless applications and services, traffic demand is rapidly increasing. To cope with such growth, wireless network operators seek radio resource cooperation strategies for their users with the highest grade of service possible. In this paper, we propose a set of analytical models for dynamic spectrum access (DSA) to attain intranetwork resource sharing agreements and adopt such strategies by sharing radio resources. The proposed models focus on reducing blocking probability for a secondary network to attain wireless services as a tradeoff with a marginal increase in the blocking probability of a primary network in return for monetary rewards. We derived the global balance equation and an explicit expression of the blocking probability for each resource sharing model. The robustness of the proposed analytical models is evaluated under different scenarios by considering varying traffic intensities and different network sizes and adding reserved resources. The results show that the blocking probabilities can be significantly reduced with the proposed DSA framework in comparison to the existing local spectrum access schemes.

Dynamic Spectrum Sharing (DSS) aims to provide opportunistic access to under-utilized spectrum in cellular networks for secondary network operators. In this paper we propose an algorithm using stochastic and optimization models to borrow spectrum bandwidths under the assumption that more resources exist for secondary access than the secondary network demand by considering a merchant mode. The main aim of the paper is to address the problem of spectrum borrowing in DSS environments, where a secondary network operator aims to borrow the required spectrum from multiple primary network operators to achieve a maximum profit under specific grade of service (GoS) and budget restriction. We assume that the primary network operators offer spectrum access opportunities with variable number of channels (contiguous and/or non- contiguous) at variable prices. Results obtained are then compared with results derived from a heuristic algorithm in which spectrum borrowing are random. Comparisons showed that the gain in the results obtained from our proposed stochastic-optimization framework is significantly higher than random counterpart.

Due to exponentially growing wireless applications and services, traffic demand is rapidly increasing. To cope with such growth, wireless network operators seek radio resource cooperation strategies for their users with the highest grade of service possible. In this paper, we propose a set of analytical models for dynamic spectrum access (DSA) to attain intranetwork resource sharing agreements and adopt such strategies by sharing radio resources. The proposed models focus on reducing blocking probability for a secondary network to attain wireless services as a tradeoff with a marginal increase in the blocking probability of a primary network in return for monetary rewards. We derived the global balance equation and an explicit expression of the blocking probability for each resource sharing model. The robustness of the proposed analytical models is evaluated under different scenarios by considering varying traffic intensities and different network sizes and adding reserved resources. The results show that the blocking probabilities can be significantly reduced with the proposed DSA framework in comparison to the existing local spectrum access schemes.

The consequences of live and formulated feeds on carcass biochemical composition and growth performances of Malaysian mahseer, Tor tambroides larvae were assessed in indoor nursery rearing systems. Quadruplicate groups of T. tambroides larvae (0.07 ± 0.01 g, mean ± SE) were stocked in sixteen aquaria (60 × 30 × 30 cm), randomly arranged in four dietary treatments: larvae fed artemia (LA), moina (LM), daphnia (LD) and formulated feed (FF) with stocking density of 34 larvae per aquarium. An additional experiment was conducted with the same dietary treatments in four circular tanks (1000 L capacity) with stocking density of 100 larvae per tank. In both experiments, the larvae were fed to visually near satiation in two equal feedings per day, seven days per week for 75 days. Treatments results were compared using ANOVA (α = 0.05). At the end of the feeding trial, the significantly highest whole body crude protein and lipid contents were found in larvae fed FF compared to those fed live feeds. Whole body polyunsaturated fatty acids were highest in the larvae fed FF compared to those fed live feeds. The significantly highest RNA/DNA ratio was observed in larvae fed FF, followed by those fed LA, and the lowest values were observed in larvae fed LM or LD. The growth-related parameters (mean final weight, mean weight gain and specific growth rate) were significantly highest in treatment FF, followed by treatment LA and lowest in treatment LD or LM. The results of the present experiment demonstrated that the nursery rearing of T. tambroides larvae with formulated feed gave better results than feeding live feeds.

The main aim of this article is to examine the effect of working capital on profitability of Bangladeshi Textiles Companies. We collected data about a sample of 21 Bangladeshi Textiles Companies listed at the Chittagong Stock Exchange (CSE) from 2008 to 2012 and evaluated the data using multiple regression. The results reveal that working capital management and profitability is positively correlated in Bangladeshi Textiles Companies. The study further reveals that inventory of number of days, number of day’s accounts receivables, and cash conversion period are positively correlated with a firm’s profitability but the numbers of days accounts payable are negatively correlated. The present study contributes to the existing stock of knowledge by examining the effect of working capital management on profitability in the context of an emerging capital market such as Bangladesh.. Keywords: Working capital management, Returns on assets, Accounts payable, Current ratio, Leverage. Cash Conversion Cycle.

The main aim of this article is to examine the effect of working capital on profitability of Bangladeshi Textiles Companies. We collected data about a sample of 21 Bangladeshi Textiles Companies listed at the Chittagong Stock Exchange (CSE) from 2008 to 2012 and evaluated the data using multiple regression. The results reveal that working capital management and profitability is positively correlated in Bangladeshi Textiles Companies. The study further reveals that inventory of number of days, number of day’s accounts receivables, and cash conversion period are positively correlated with a firm’s profitability but the numbers of days accounts payable are negatively correlated. The present study contributes to the existing stock of knowledge by examining the effect of working capital management on profitability in the context of an emerging capital market such as Bangladesh.. Keywords: Working capital management, Returns on assets, Accounts payable, Current ratio, Leverage. Cash Conversion Cycle.

The main aim of this article is to examine the effect of working capital on profitability of Bangladeshi Textiles Companies. We collected data about a sample of 21 Bangladeshi Textiles Companies listed at the Chittagong Stock Exchange (CSE) from 2008 to 2012 and evaluated the data using multiple regression. The results reveal that working capital management and profitability is positively correlated in Bangladeshi Textiles Companies. The study further reveals that inventory of number of days, number of day’s accounts receivables, and cash conversion period are positively correlated with a firm’s profitability but the numbers of days accounts payable are negatively correlated. The present study contributes to the existing stock of knowledge by examining the effect of working capital management on profitability in the context of an emerging capital market such as Bangladesh.. Keywords: Working capital management, Returns on assets, Accounts payable, Current ratio, Leverage. Cash Conversion Cycle.

We develop a generalized loss network framework for capacity planning of a perinatal network in the UK. Decomposing the network by hospitals, each unit is analyzed with a GI/G/c/0 overflow loss network model. A two-moment approximation is performed to obtain the steady state solution of the GI/G/c/0 loss systems, and expressions for rejection probability and overflow probability have been derived. Using the model framework, the number of required cots can be estimated based on the rejection probability at each level of care of the neonatal units in a network. The generalization ensures that the model can be applied to any perinatal network for renewal arrival and discharge processes., Comment: 21 pages, 4 figures, 2 tables