Miracle-dz/UniFAR-FTUs huggingface dataset:
Sustainable nutrition: Opportunities, risks and uncertainties from environmental and health perspectives

Food production and consumption are key drivers of environmental pressures and are essential factors in the promotion and maintenance of health. Production of food occupies more than one third of global land areas and is estimated to be responsible for some 30% of global greenhouse gas emissions. At the same time, we live in a world where nearly one billion people go hungry and even more people suffer from problems related to overweight and associated diet-related chronic diseases. This raises the question of the sustainability of current food systems and diets. This thesis analyzes the potential and limitation for diet change to contribute more sustainable food systems. The results show that dietary change can reduce greenhouse gas emissions and land use demand of the diet, and simultaneously improve the nutritional quality and health effects of the diet. The positive synergies suggest that dietary change can play an important role in reaching future environmental and health goals. Assessments of environmental- and health effects of food consumption and production are hampered by uncertainty and variability, and awareness of the limitations in the quality of data and methods is crucial. Transparent presentation of data and methods is necessary for a proper evaluation of the reliability and significance of the results. Improvement of data and further development of methods are required to further increase the quality of the assessments. In this thesis, an interdisciplinary approach is used which combines methods originating from the fields of environmental-, nutritional- and health- studies. Life cycle assessment is used to quantify the greenhouse gas emissions and land use demand of food production, while the nutritional and health effects of food consumption are analyzed by using nutrient calculation and nutrition epidemiology. Integration of nutritional and health aspects into environmental assessments of food is an exciting development of the research field contributing to important new knowledge. To further broaden the perspectives and deepen the knowledge of sustainable food systems more aspects need to be covered.
Analysis of Leakage Field from Bounded Wave Electromagnetic Pulse Simulator

Parallelized finite-difference time-domain (FDTD) method combined with mirror image theory is used for simulating the leakage of flat-plate bounded wave electromagnetic pulse (EMP) simulator with hybrid configuration. Considering the simulator has the symmetric characteristic and the tangential electric fields are symmetrical at the two sides of the simulator’s symmetry plane, perfectly magnetic conductor (PMC) boundary is applied for the FDTD computing. The results of the method agree well with those of normal FDTD method. The results show that the leakage field from the simulator is affected by the ground conductive characteristic evidently, and the leakage fields near the ground should be concerned specially. The method in this paper is useful for the simulation of other bounded wave EMP simulators. And the method has the advantages of saving computing time and CPU storage.
GPs who volunteer to be first responders for out-of-hospital cardiac arrest: A qualitative study

Abstract Background Out-of-hospital cardiac arrest (OHCA) is a major cause of premature mortality. Survival is possible when timely cardiopulmonary resuscitation and defibrillation are available in the community. GPs are well placed to provide early OHCA care and significantly increased rates of survival are achieved when GPs participate in resuscitation. A novel project alerts volunteer GP first responders to nearby OHCAs in Ireland. Objectives To explore the reasons why GPs volunteer to be OHCA first responders and their experience of participation. Methods A qualitative study involving in-depth, semi-structured interviews followed by thematic analysis was undertaken in 2017/18. Fourteen GPs from differing geographical areas in Ireland, who volunteered as OHCA first-responders were recruited to participate by purposive methods. Results GP participation in OHCA voluntary first response was understood as a function of GPs relationship to the community, their ability to manage competing demands in their personal and professional lives and also specific participatory gains. GPs expressed both altruistic motivations and a sense of obligation. GPs described a complex, multifaceted role in providing OHCA first response; they derived an inherent sense of satisfaction in delivering potentially life-saving interventions but also in the provision of holistic, compassionate end-of-life care for patients and their families. Participation was not without psychosocial risk for GPs. Conclusion GPs volunteer to provide early OHCA emergency care because of their relationship to the community. Care provided is complex and includes both resuscitation and end-of-life care.
The Identification of the Elastic Support System of the Laboratory Truck Crane

In the paper the identification of the discrete-continuous model of the laboratory truck crane has been presented. In the theoretical model, the laboratory truck crane has been represented by three member type telescopic boom, hydraulic cylinder of crane radius change and the elastic support system. The free vibration problem of the analyzed system has been only considered in a rotary plane of the laboratory truck crane. For formulating and solving the free vibration problem of the analyzed system the Lagrange multiplier formalism has been employed. The identification of the discrete-continuous model has consisted in the determination of the spring constants substituting the elastic support system. Values of these spring constants have been determined on the basis of the solution of optimization problem and the experimental modal analysis. In optimization problem, the genetic algorithm has been used.
Influence of Synoptic-Scale Airmass Conditions on Seasonal Precipitation Patterns over North Carolina

This paper characterizes the influence of synoptic-scale airmass conditions on spatial and temporal patterns of precipitation in North Carolina over a 16-year period (2003-2018). National Center for Environmental Prediction Stage IV multi-sensor precipitation estimates were used to describe seasonal variations in precipitation in the context of prevailing airmass conditions classified using the spatial synoptic classification system. Spatial analyses identified significant clustering of high daily average precipitation amounts distributed along the lee side of the Appalachian Mountains and along the coastal plains. Significant and heterogenous clustering was prevalent in summer months and tended to coincide with land cover boundaries and complex terrain. Between the three geographic regions of North Carolina, highest precipitations amounts were received in western North Carolina during the winter and spring, but this signal shifted to eastern North Carolina in the summer and fall. Central North Carolina received the least amount of precipitation; however, there was substantial variability between regions due to prevailing airmass conditions. The summer months were dominated by maritime tropical airmass conditions with no clear shift in summertime airmass trends over the study period. Most days with recorded precipitation in the winter, spring, and fall occurred under dry moderate airmass conditions; however, the highest daily average precipitation and total precipitation occurred under the influence of maritime moderate airmasses. Importantly, there was an observed shift toward warmer and more humid airmass conditions in the winter, spring, and fall months throughout the study period (2003-2018), indicating a shift toward airmass conditions conducive to higher daily average rain rates in North Carolina.
A new 3D modelling paradigm for discrete model

Until few years ago, 3D modelling was a topic confined into a professional environment. Nowadays technological innovations, the 3D printer among all, have attracted novice users to this application field. This sudden breakthrough was not supported by adequate software solutions. The 3D editing tools currently available do not assist the non-expert user during the various stages of generation, interaction and manipulation of 3D virtual models. This is mainly due to the current paradigm that is largely supported by two-dimensional input/output devices and strongly affected by obvious geometrical constraints. We have identified three main phases that characterize the creation and management of 3D virtual models. We investigated these directions evaluating and simplifying the classic editing techniques in order to propose more natural and intuitive tools in a pure 3D modelling environment. In particular, we focused on freehand sketch-based modelling to create 3D virtual models, interaction and navigation in a 3D modelling environment and advanced editing tools for free-form deformation and objects composition. To pursuing these goals we wondered how new gesture-based interaction technologies can be successfully employed in a 3D modelling environments, how we could improve the depth perception and the interaction in 3D environments and which operations could be developed to simplify the classical virtual models editing paradigm. Our main aims were to propose a set of solutions with which a common user can realize an idea in a 3D virtual model, drawing in the air just as he would on paper. Moreover, we tried to use gestures and mid-air movements to explore and interact in 3D virtual environment, and we studied simple and effective 3D form transformations. The work was carried out adopting the discrete representation of the models, thanks to its intuitiveness, but especially because it is full of open challenges.
Two-stage method of Barkhausen noise measurement

A simple method of measuring the Barkhausen noise in two stages was developed. The purpose of the proposed method was to remove the magnetizing frequency component from the induced electromotive force signal without using complex analog or digital filters, which also suppress the Barkhausen noise. In the first stage, an instance of the electromotive force signal was temporarily recorded, and in the second stage another instance of the electromotive force signal subtracted from the previously recorded instance of the electromotive force signal was measured. Using this method we will obtain only the stochastic component of the electromotive force signal – the Barkhausen noise. The method is illustrated on measuring the Barkhausen noise in an electrical steel sample. The experiments proved that with this method we are able to remove the dominant continuous component from the induced electromotive force signal and we can simplify the measurement. The general problems associated with the method are discussed.
Microfluidic Brain Slice Chambers and Flexible Microelectrode Arrays for in vitro Localized Stimulation and Spatial Mapping of Neural Activities

In vitro neurobiological experiments using brain slices have played a key role in improving our understanding of the central nervous system. Microfabricated brain slice chambers and flexible semi-transparent multi-electrode arrays allow controlled local changes in the chemical environments as well as high resolution electrophysiology and optical recording of brain slices which would in turn provide further information of the complex neuronal system. In this study, we exploit the advantages of microfabricated devices, including a microfluidic brain slice chamber for localized chemical stimulation and a flexible microelectrode array for spatial mapping of neuronal activities, to investigate various biophysical properties of cortical spreading depression. First, a microfluidic brain slice chamber is designed and fabricated to permit localized chemical stimulation to specific brain slice cortical regions. We build a numerical finite element model to predict the injected plume shape and the resulting ion distributions in the stimulated brain slice. Various characterization methods, including particle tracking velocimetry, fluorescent imaging and tissue staining, are implemented to verify the fluid dynamics predicted by our model. With the ability to fine control the stimulation area, we vary the stimulation size as well as the extracellular potassium concentration ([K+]e) to study the conditions for the onset of cortical spreading depression. We find a strong correlation between the threshold concentration and the slice area exposed to the increased [K+]e. Our results show that CSD is inducible under the conditions expected in migraine aura.We then explore the use of a flexible microelectrode array to map spatiotemporal electrophysiological activities induced by localized chemical stimulation. We concurrently use the microfluidic device to initiate CSD in mice cortex and the electrode array to record electrical activities accompanying the spreading wave. Besides the consistency between optical and electrical recording, we observe the electrical responses similar to cortical spreading convulsion with a corresponding optical characteristic.In summary, the microfluidic brain slice chamber has been demonstrated with a localized stimulation capability and used to probe the cortical spreading depression initiation and propagation properties. The concurrent use of the microfluidic and microelectrode array techniques has been demonstrated and is shown to be promising in addressing scientific questions. Our work demonstrates a successful implementation of novel microfabricated tools for the neuroscience research.
Labor Rationing

This paper measures excess labor supply in equilibrium. We induce hiring shocks—which employ 24 percent of the labor force in external month-long jobs—in Indian local labor markets. In peak months, wages increase instantaneously and local aggregate employment declines. In lean months, consistent with severe labor rationing, wages and aggregate employment are unchanged, with positive employment spillovers on remaining workers, indicating that over a quarter of labor supply is rationed. At least 24 percent of lean self-employment among casual workers occurs because they cannot find jobs. Consequently, traditional survey approaches mismeasure labor market slack. Rationing has broad implications for labor market analysis. (JEL E24, J22, J23, J31, J64, O15, R23)
An Optimization Design for the Standard Manual Wheelchair

The standard manual wheelchair is commonly found in hospitals, rehabilitation centres, senior housing and airports for allowing patients, people with physical disabilities or elder people to move. In this thesis, a CAD model of the standard wheelchair is first obtained. Then, an optimization design is carried out based on this model. A sliding seat is designed to facilitate people moving by themselves from the wheelchair to any chair close by. On the other hand, a reclining backrest is also designed to make users feel more comfortable. Computer Simulations are performed to evaluate this design under static and dynamic conditions. A displacement range for the sliding seat in which the wheelchair will not overturn is determined. The optimization design shows significant improvements that can conveniently be introduced on the mechanical wheelchair in order to make it more useful and comfortable to the users.
Dominance Norms and Data for Spoken Ambiguous Words in British English

Words with multiple meanings (e.g. bark of the tree/dog) have provided important insights into several key topics within psycholinguistics. Experiments that use ambiguous words require stimuli to be carefully controlled for the relative frequency (dominance) of their different meanings, as this property has pervasive effects on numerous tasks. Dominance scores are often calculated from word association responses: by measuring the proportion of participants who respond to the word ‘bark’ with dog-related (e.g. “woof”) or tree-related (e.g. “branch”) responses, researchers can estimate people’s relative preferences for these meanings. We collated data from a number of recent experiments and pre-tests to construct a dataset of 29,542 valid responses for 243 spoken ambiguous words from participants from the United Kingdom. We provide summary dominance data for the 182 ambiguous words that have a minimum of 100 responses, and a tool for automatically coding new word association responses based on responses in our coded set, which allows additional data to be more easily scored and added to this database. All files can be found at: https://osf.io/uy47w/.
LIPA: A Learning-based Indexing and Prefetching Approach for Data Deduplication

In this paper, we present a learning based data deduplication algorithm, called LIPA, which uses the reinforcement learning framework to build an adaptive indexing structure. It is rather different from previous inline chunk-based deduplication methods to solve the chunk-lookup disk bottleneck problem for large-scale backup. In previous methods, a full chunk index or a sampled chunk index often is often required to identify duplicate chunks, which is a critical stage for data deduplication. The full chunk index is hard to fit in RAM and the sampled chunk index directly affects the deduplication ratio dependent on the sampling ratio. Our learning based method only requires little memory overheads to store the index but achieves the same or even better deduplication ratio than previous methods. In our method, after the data stream is broken into relatively large segments, one or more representative chunk fingerprints are chosen as the feature of a segment. An incoming segment may share the same feature with previous segments. Thus we use a key-value structure to record the relationship between features and segments: a feature maps to a fixed number of segments. We train the similarities of these segments to a feature represented as scores by the reinforcement learning method. For an incoming segment, our method adaptively prefetches a segment and the successive ones into cache by using multi-armed bandits model. Our experimental results show that our method significantly reduces memory overheads and achieves effective deduplication.
A Survey on Machine learning and IoT

Internet of Things is an intelligence devices that can connect to any other devices. Based on that ability to connect and its intelligence, it makes people to create an intelligence device and capable to learn like human brain. To accomplish that goal, machine learning is the solution. Combination between machine learning and IoT generate a revolution in human life and application industry. The combination also generate new trend in market. Therefore, in this paper will be discuss about machine learning including machine learning technique and algorithm and also discuss about IoT including architecture and elements. Then in this paper will discuss about some researches that have already done that combine IoT with machine learning approach, including issue and challenge in those researches.
Tree Stem Diameter Change Measurements and Sap Flow in Scots Pine

Abstract Tree stem diameter changes diurnally due to changes in the water content of the stem. These changes have been detected since the late 19 th century and their connection to various environmental factors has been reported. The diameter is largest at night just before sunrise and starts to decrease as dawn breaks. The minimum is usually found in the afternoon, the exact time depending on e.g. light conditions. Towards night the diameter starts to increase again. Typical amplitude of the variation is of the order of 10 -4 m on Scots pine trees ( Pinus sylvestris L.). In this thesis measurements of xylem and whole stem (i.e. over bark) diameter variations are analysed on the basis of two most widely accepted theories of sap flow in trees: the cohesion-tension theory of the ascent of sap and the Munch hypothesis of phloem flow. First, the connection between xylem diameter variations and transpiration is verified using a dynamic model of sap flow. The model is developed using transpiration measured by shoot chambers and further tested with transpiration scaled down from the whole ecosystem evapo-transpiration. Secondly, the dynamics of the variations themselves are studied by comparing measurements of xylem and whole stem diameter variations at different heights. A cross-correlation method is used to determine the time lags and the results are interpreted in the light of the theories. Time lags between xylem and whole stem diameter variations suggest that the transport of sugars in the phloem could be detected using these measurements. As a conclusion of this study, xylem diameter variations can be said to follow transpiration and the dynamics of the ascent of sap, while sugar transport affects the pattern of stem diameter variations. Both measurements are direct measures of the state of a tree, although converting the diameter variations to the units of transpiration or sap flow requires further studies with modelling work. As an easy and cheap measurement based on the physical properties of wood and flow through porous media xylem and stem diameter variations have their place among tree physiological and ecological measurements.
Research on Blockchain Hybrid Consensus Algorithm Based on Internet of Things

This article explains the relevant content and related concepts of the blockchain, and studies the architecture and several core technologies of the blockchain. This article introduces the consensus problem and the distributed consistency problem, and elaborates the principles of several consensus algorithms that are widely used in the blockchain, including the PoW consensus algorithm, the PoS consensus algorithm, and the PBFT consensus algorithm. In view of the shortcomings of a single consensus algorithm, a hybrid consensus algorithm was redesigned. This article proposes that the PBFT consensus algorithm has a high execution efficiency, and is mainly responsible for the processing of transactions and smart contracts to meet the consensus algorithm's demand for high execution efficiency. Open the Byzantine committee node rotation election function in PBFT to PoW nodes. The committee nodes are selected by PoW nodes, and any node can participate and become a PoW node to ensure the degree of decentralization of the consensus algorithm, and thereby ensure the security of the consensus algorithm. Experimental results and data show that compared with the original consensus algorithm, the hybrid consensus algorithm is better in terms of throughput and latency. The hybrid consensus algorithm combines the two-consensus algorithm consensus of PBFT and PoW to solve the problem of decentralization and performance. Contradiction.