Decomposition

Six lexical decision experiments were conducted to examine the influence of complex structure on the processing speed of English compounds. All experiments revealed that semantically transparent compounds (eg, rosebud) were processed more quickly than matched monomorphemic words (eg, giraffe). Opaque compounds (eg, hogwash) were also processed more quickly than monomorphemic words. However, when the experimental materials and/or procedure encouraged decomposition/integration, this advantage ...

In this paper, we prove strong type, weak type inequalities of Hardy-Littlewood maximal operator and fractional Hardy-Littlewood maximal operator on variable sequence spaces lp(Z). This is achieved using Calderon-Zygmund decomposition for sequences, properties of modular functional and Log Holder continuity.

This paper discusses both occupational health risks to workers and environmental health risks to residents and workers. The report presents an overview of the available health literature for causes of diseases, injuries, and accidents from solid waste management collection, treatment, and disposal technologies. Illnesses discussed include infectious diseases, allergies, respiratory damage, and cancers. Some diseases are derived from direct ingestion of infectious micro-organisms, others involve infection through contamination of the food chain, whereby animals or other vectors have ingested infectious micro-organisms. Injuries include joint and spinal damage, fractures, puncture wounds, damage to eyes and ears. Accidents include slides from unstable disposal piles, cave-ins of disposal site surfaces, fires, explosions, being caught in processing equipment, and being run over by mobile equipment. As the next steps toward improving occupational and environmental health and safety in s...

The aim of this paper is to identify the factors that have influenced changes in the amount of carbon dioxide (CO2) emitted from the Greek manufacturing sector as a whole and from three representative subsectors. By means of an algebraic decomposition method the changes recorded during the period 1985-95 are analysed into four distinct factors: output level, energy intensity, fuel mix and structural change. The results show that the observed reduction of industrial CO2 emissions is not only due to the decrease of industrial energy intensity but also to the recession of the Greek manufacturing sector. The two other factors, namely sectoral and fuel shifts were driving emissions upward, primarily because of the increasing share of electricity - intensive sectors and the growing dependence of electricity generation from lignite. It is concluded that for the reduction of industrial CO2 emissions in the long term, policy measures should aim at further promoting energy saving technologies...

In the context of increased scrutiny of the methods in forensic sciences, it is essential to ensure that the approaches used in forensic taphonomy to measure decomposition and estimate the postmortem interval are underpinned by robust evidence-based data. Digital photographs are an important source of documentation in forensic taphonomic investigations but the suitability of the current approaches for photographs, rather than real-time remains, is poorly studied which can undermine accurate forensic conclusions. The present study aimed to investigate the suitability of 2D colour digital photographs for evaluating decomposition of exposed human analogues (Sus scrofa domesticus) in a tropical savanna environment (Hawaii), using two published scoring methods; Megyesi et al., 2005 and Keough et al., 2017. It was found that there were significant differences between the real-time and photograph decomposition scores when the Megyesi et al. method was used. However, the Keough et al. metho...

In manufacturing facilities, shipping and receiving traditionally have been concentrated in one or two areas of the building. The widespread introduction of just-in-time shipping policies in US manufacturing companies during the past 15 years, as well as the common use of decentralized receiving in the Japanese automobile industry, has motivated the consideration (and occasionally the adoption) of dock configurations to support decentralized receiving (also known as perimeter receiving). By providing multiple access points along the perimeter of the building, decentralized receiving generally reduces the time and cost associated with moving purchased parts to the locations within the facility where they are used, but also results in increased cost of construction, maintenance, and operation for docks and associated truck access/egress, as well as additional interior floorspace requirements. We develop an optimization-based procedure to determine which of several possible dock areas to construct, how many doors or gates each should have, how many unloading and internal material handling staff should be assigned to each group, and which inbound materials each group should handle. We consider the amortized cost of constructing and equipping the docks, the amortized cost of material handling equipment to support the unloaders and material handlers, the cost of operating the material handling equipment, and labor costs for both unloading and internal material handling.

The structure of a large network (graph) can often be revealed by partitioning it into smaller and possibly more dense sub-networks that are easier to handle. One of such decompositions is based on “k-cores”, proposed in 1983 by Seidman. Together with connectivity components, cores are one among few concepts that provide efficient decompositions of large graphs and networks. In this

Infrared emission spectroscopy has been used to study the dehydroxylation behavior over the temperature range from 200 to 750 degrees Celsius of three major Al-minerals in bauxite: gibbsite (synthetic and natural), boehmite (synthetic and natural) and diaspore. A good agreement is found with the thermal analysis and differential thermal analysis curves of these minerals. Loss in intensity of especially the hydroxyl-stretching modes of gibbsite, boehmite and diaspore as function of temperature correspond well with the observed changes in the TGA/DTA patterns. The DTA pattern of gibbsite clearly indicates the formation of boehmite as an intermediate shown by a endotherm around 500 degrees Celsius. Dehydroxylation of gibbsite is followed by a loss of intensity of the 3620 and 3351 cm-1 OH-stretching bands and the corresponding deformation band around 1024 cm-1. Dehydroxylation starts around 220 degrees Celsius and is complete around 350 degrees Celsius. Similar observations were made for boehmite and diaspore. For boehmite dehydroxylation was observed to commence around 250 degrees Celsius and could be followed by especially the loss in intensity of the bands around 3319 and 3129 cm-1. The DTA pattern of diaspore is more complex with overlapping endotherms around 622 and 650 degrees Celsius. The dehydroxylation can be followed by the decrease in intensity of the OH-stretching bands around 3667, 3215 and 2972 cm-1. Above 550 degrees Celsius only a single band is observed that disappears after heating above 600 degrees Celsius corresponding to the two endotherms around 622 and 650 degrees Celsius in the DTA

Abstract-Biochemical changes related to stability of food waste compost are strongly affected by different composting techniques. For evaluation of compost maturity three locally fabricated composters and windrow process were examined at seven days interval up to 91 days by ...

1. We investigated the effect of moderate eutrophication on leaf litter decomposition and associated invertebrates in five reference and five eutrophied streams in central Portugal. Fungal parameters and litter N and P dynamics were followed in one pair of streams. Benthic invertebrate parameters that are considered useful in bioassessment were estimated in all streams. Finally, we evaluated the utility of decomposition as a tool to assess stream ecosystem functional integrity.2. Decomposition of alder and oak leaves in coarse mesh bags was on average 2.3–2.7× faster in eutrophied than in reference streams. This was attributed to stimulation of fungal activity (fungal biomass accrual and sporulation of aquatic hyphomycetes) by dissolved nutrients. These effects were more pronounced for oak litter (lower quality substrate) than alder. N content of leaf litter did not differ between stream types, while P accrual was higher in the eutrophied than in the reference stream. Total invertebrate abundances and richness associated with oak litter, but not with alder, were higher in eutrophied streams.3. We found only positive correlations between stream nutrients (DIN and SRP) and leaf litter decomposition rates in both fine and coarse mesh bags, associated sporulation rates of aquatic hyphomycetes and, in some cases, total invertebrate abundances and richness.4. Some metrics based on benthic invertebrate community data (e.g. % shredders, % shredder taxa) were significantly lower in eutrophied than in reference streams, whereas the IBMWP index that is specifically designed for the Iberian peninsula classified all 10 streams in the highest possible class as having ‘very good’ ecological conditions.5. Leaf litter decomposition was sufficiently sensitive to respond to low levels of eutrophication and could be a useful functional measure to complement assessment programmes based on structural parameters.

This research aims to model the process of data visualization (DV) and design to facilitate computational thinking (CT) of secondary-level students. As an interdisciplinary method for visualizing complex data, creating data visualizations requires learners to analyze data from knowledge of pure and applied science. This study investigates creating DV to facilitate CT as an interdisciplinary method which combines mathematics and information technology. The study was carried out using synthesised design-based research (DBR) method by conducting two cycles comprised of five phases: problem analysis within real-life context, design solution, develop solution, evaluate in practice and reflection. CT dimensions (decomposition, pattern recognition, abstraction, and algorithm design) were tested by using the Computational Thinking through Data Visualization Rubric and reflection form acquired during implementation. The sample consisted of secondary school students in the data visualization creation process with 27 in the first cycle and 31 in the second cycle. This research proposes a model to facilitate development of CT by DV with the analysis of complex data, creating an effective method by enabling analytics and visualizing data. The proposed DV development process facilitating CT has the potential to inform research on interdisciplinary learning environments.

Head injury is a major reason for morbidity and mortality worldwide and traumatic head injuries represent the major cause of neurological disability to a clot or hematoma caused by Haemorrhage (ICH) and is the The most common cause of ICH normally reported in our country are road traffic accidents (RTA) followed by falls and assaults. India is a populous country with over a billion every 100,000 population with deprived of these doctors. The unavailability of these specialists is a grave concern to the w care to the nation. The mainstay in the diagnosis of an ICH is the CT (Computed Tomography) scan of the head which is the definitive tool for accurate diagnosis of an ICH following trauma and provides an objective assessment of structural damage to brain. Accurate segmentation of the haemorrhage. This study is on segment Keywords: Intracranial decomposition; Brain haemorrhage segmentation is the first step before detecting the been done on the brain haemorrhage detection using methods like Convolutional neural network other efficient and advanced deep learning techniques. But that is resource intensive. It is also nec efficient when there is a large dataset Hssayeni and colleagues multiple slices and made it public. Second, used deep learning methods to perform segmentation and got a dice coefficient of 31% which is good compared to and colleagues [12] propose entropy based automatic unsupervised brain intracranial haemorrhage segmentation which comprises of FCM clustering, thresholding and edge based active contour methods and they get a better result with the combination than FCM clustering and active use deep learning to diagnose brain haemorrhage. They have used LeNet, GoogleNet and Inception dataset consisting of 100 cases collected from 115 hospitals and discovered LeNet is the among the three. Arjun Majumdar and colleagues haemorrhage instead of Head injury is a major reason for morbidity and mortality worldwide and traumatic head injuries represent the major cause of neurological disability. A traumatic brain injury to a clot or hematoma caused by an accident or any other trauma. (ICH) and is the most common and serious consequence of head injury which can be life The most common cause of ICH normally reported in our country are road traffic accidents (RTA) followed by falls and assaults. India is a populous country with over a billion every 100,000 population with most of them in the urban setup, Indian rural population of more than 70% is deprived of these doctors. The unavailability of these specialists is a grave concern to the w care to the nation. The mainstay in the diagnosis of an ICH is the CT (Computed Tomography) scan of the head which is the definitive tool for accurate diagnosis of an ICH following trauma and provides an objective assessment of ctural damage to brain. Accurate segmentation of the. This study is on segmentation of the brain haemorrhage Intracranial haemorrhage; Discrete wavelet transforms I. RELATED WORK Brain haemorrhage segmentation is the first step before detecting the been done on the brain haemorrhage detection using methods like Convolutional neural network other efficient and advanced deep learning techniques. But that is resource intensive. It is also nec efficient when there is a large dataset, which is not easily available in case of brain haemorrhage. Hssayeni and colleagues [1][2] have contributed in two ways, they collected a new dataset of 82 CT scans with ade it public. Second, used deep learning methods to perform segmentation and got a dice coefficient of 31% which is good compared to other deep learning techniques on small datasets. Indrajeet Kumar propose entropy based automatic unsupervised brain intracranial haemorrhage segmentation which comprises of FCM clustering, thresholding and edge based active contour methods and they get a better result with the combination than FCM clustering and active contour methods alone. use deep learning to diagnose brain haemorrhage. They have used LeNet, GoogleNet and Inception dataset consisting of 100 cases collected from 115 hospitals and discovered LeNet is the among the three. Arjun Majumdar and colleagues [8] haemorrhage instead of traditional methods and achieve a Head injury is a major reason for morbidity and mortality worldwide and traumatic head injuries traumatic brain injury (TBI) is damage to the brain, secondary an accident or any other trauma. This hematoma is known as an Intracranial most common and serious consequence of head injury which can be life The most common cause of ICH normally reported in our country are road traffic accidents (RTA) followed by falls and assaults. India is a populous country with over a billion people and there is approximately one radiologist for of them in the urban setup, Indian rural population of more than 70% is deprived of these doctors. The unavailability of these specialists is a grave concern to the w care to the nation. The mainstay in the diagnosis of an ICH is the CT (Computed Tomography) scan of the head which is the definitive tool for accurate diagnosis of an ICH following trauma and provides an objective assessment of ctural damage to brain. Accurate segmentation of the haemorrhage is the first step before detecting the brain haemorrhage images using discrete wavelet transforms. iscrete wavelet transforms; Segmentation; RELATED WORK Brain haemorrhage segmentation is the first step before detecting the haemorrhage in the brain. A lot of work has been done on the brain haemorrhage detection using methods like Convolutional neural network other efficient and advanced deep learning techniques. But that is resource intensive. It is also nec which is not easily available in case of brain haemorrhage. have contributed in two ways, they collected a new dataset of 82 CT scans with ade it public. Second, used deep learning methods to perform segmentation and got a dice deep learning techniques on small datasets. Indrajeet Kumar propose entropy based automatic unsupervised brain intracranial haemorrhage segmentation which comprises of FCM clustering, thresholding and edge based active contour methods and they get a better result contour methods alone. Tong Duc Phong and colleagues use deep learning to diagnose brain haemorrhage. They have used LeNet, GoogleNet and Inception dataset consisting of 100 cases collected from 115 hospitals and discovered LeNet is the most time [8] use a modified version of U-Net to detect the brain traditional methods and achieve an overall specificity of 98.6% on the small dataset. Brain Haemorrhage Segmentation using Dircrete Wavelet Transform. the terms of the Creative Commons Attribution License; Which Permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source Head injury is a major reason for morbidity and mortality worldwide and traumatic head injuries (TBI) is damage to the brain, secondary This hematoma is known as an Intracranial most common and serious consequence of head injury which can be life-threatening. The most common cause of ICH normally reported in our country are road traffic accidents (RTA) followed by falls people and there is approximately one radiologist for of them in the urban setup, Indian rural population of more than 70% is deprived of these doctors. The unavailability of these specialists is a grave concern to the well-being of the health care to the nation. The mainstay in the diagnosis of an ICH is the CT (Computed Tomography) scan of the head which is the definitive tool for accurate diagnosis of an ICH following trauma and provides an objective assessment of is the first step before detecting the brain images using discrete wavelet transforms. Thresholding; Wavelet haemorrhage in the brain. A lot of work has been done on the brain haemorrhage detection using methods like Convolutional neural network [2][3][5][11] and other efficient and advanced deep learning techniques. But that is resource intensive. It is also necessary and which is not easily available in case of brain haemorrhage. Murtada D. have contributed in two ways, they collected a new dataset of 82 CT scans with ade it public. Second, used deep learning methods to perform segmentation and got a dice deep learning techniques on small datasets. Indrajeet Kumar propose entropy based automatic unsupervised brain intracranial haemorrhage segmentation which comprises of FCM clustering, thresholding and edge based active contour methods and they get a better result Tong Duc Phong and colleagues [13] use deep learning to diagnose brain haemorrhage. They have used LeNet, GoogleNet and Inception-ResNet and a most time-consuming model Net to detect the brain overall specificity of 98.6% on the small dataset.