Updated baseline for a staged Compact Linear Collider

This report describes the physics potential and experiments at a future multi- TeV e+e collider based on the Compact Linear Collider (CLIC) technology. The physics scenarios considered include precision measurements of known quantities as well as the discovery potential of physics beyond the Standard Model. The report describes the detector performance required at CLIC, taking into account the interaction point environment and especially beaminduced backgrounds. Two detector concepts, designed around highly granular calorimeters and based on concepts studied for the International Linear Collider (ILC), are described and used to study the physics reach and potential of such a collider. Detector subsystems and the principal engineering challenges are illustrated. The overall performance of these CLIC detector concepts is demonstrated by studies of the performance of individual subdetector systems as well as complete simulation studies of six benchmark physics processes. These full detector simulation and reconstruction studies include beaminduced backgrounds and physics background processes. After optimisation of the detector concepts and adopting the reconstruction algorithms the results show very efficient background rejection and clearly demonstrate the physics potential at CLIC in terms of precision mass and cross section measurements. Finally, an overview of future plans of the CLIC detector and physics study is given and a list of key detector R&D topics needed for detectors at CLIC is presented.

The top quark is the heaviest known fundamental particle. As it is the only quark that decays before it hadronizes, it allows us to probe the properties of bare quarks at the Large Hadron Collider. Highlights of a few recent precision measurements by the ATLAS Collaboration of the top quark using 13 TeV and 8 TeV collision data will be presented: top-quark pair and single top production cross sections including differential distributions will be presented alongside measurements of top-quark properties, including results using boosted top quarks, probe our understanding of top-quark production in the TeV regime. Measurements of the top-quark mass and searches for rare top quark decays are also presented.

An integrated platform for design and numerical analysis of shield tunnelling processes on different levels of detail

Hydrological modelling of urbanized catchments: A review and future directions

Solar flares afford an opportunity to observe processes associated with the acceleration and propagation of high-energy particles at a level of detail not accessible in any other astrophysical source. I will review some key results from previous high-energy solar flare observations, including those from the Compton Gamma-Ray Observatory, and the problems that they pose for our understanding of energy release and particle acceleration processes in the astrophysical environment. I will then discuss a program of high-energy observations to be carried out during the upcoming 2000–2001 solar maximum that is aimed at addressing and resolving these issues. A key element in this observational program is the High Energy Solar Spectroscopic Imager (HESSI) spacecraft, which will provide imaging spectroscopic observations with spatial, temporal, and energy resolutions commensurate with the physical processes believed to be operating, and will in addition provide the first true gamma-ray spectroscopy of an astrophysical source.

Today, the challenge in chemical and material synthesis is not only the development of new catalysts and supports to synthesize a desired product, but also the understanding of the interaction of the catalyst with the surrounding flow field. Computational Fluid Dynamics or CFD is the analysis of fluid flow, heat and mass transfer and chemical reactions by means of computer-based numerical simulations. CFD has matured into a powerful tool with a wide range of applications in industry and academia. From a reaction engineering perspective, main advantages are reduction of time and costs for reactor design and optimization, and the ability to study systems where experiments can hardly be performed, e.g., hazardous conditions or beyond normal operation limits. However, the simulation results will always remain a reflection of the uncertainty in the underlying models and physicochemical parameters so that in general a careful experimental validation is required. This chapter introduces the application of CFD simulations in heterogeneous catalysis. Catalytic reactors can be classified by the geometrical design of the catalyst material (e.g. monoliths, particles, pellets, washcoats). Approaches for modeling and numerical simulation of the various catalyst types are presented. Focus is put on the principal concepts for coupling the physical and chemical processes on different levels of details, and on illustrative applications. Models for surface reaction kinetics and turbulence are described and an overview on available numerical methods and computational tools is provided.

<p>Hydrodynamic models (numerical solutions of the Saint Venant equations) are at the core of simulating water movements in natural streams and drainage systems. They enable realistic simulations of water movement and are directly linked to physical system characteristics such as channel slope and diameter. This feature is important for man-made drainage structures as it enables straightforward testing of the effects of varying channel designs. In cities, models with hundreds up to tens of thousands of pipes are commonly used for drainage infrastructure. Their computational expense remains high and they are not suited for a systematic screening of design options, discussing water management options in workshops, as well as many real-time applications such as data assimilation.</p><p>Hydrologists have developed many approaches to enable faster simulations. All of these do, however, compromise on the physical detail of the simulated processes (for example, by simulating only flows using linear reservoirs), and usually also on the spatial and temporal resolution of the models (for example, by simulating only flows between key points in the system). The link to physical system characteristics is thus lost. Therefore, it is challenging to incorporate such approaches into planning workflows where changing city plans require a constant revision of water management options.</p><p>Recent advances in scientific machine learning enable the creation of fast machine learning surrogates for complex systems that preserve a high spatio-temporal detail and a physically accurate simulation. We present such an approach that employs generalized residue networks for the simulation of hydraulics in drainage systems. The key concept is to train neural networks that learn how hydraulic states (level, flow and surcharge volume) at all nodes and pipes in the drainage network evolve from one time step to another, given a set of boundary conditions (surface runoff). Training is performed against the output of a hydrodynamic model for a short time series.</p><p>Once trained, the surrogates generate the same results as a hydrodynamic model in the same level of detail, and they can be used to quickly simulate the effect of many different rain events and climate scenarios. Considering pipe networks with 50 to 100 pipes, our approach achieves NSE values in the order of 0.95 for the testing dataset. Simulations are performed 10 to 50 times faster than the hydrodynamic model. Training times are in the order of 25 minutes on a single CPU. The surrogates are system specific and need to be retrained when the physical system changes. To minimize this overhead, we train surrogates for small subsystems which can subsequently be linked into a model for a large drainage network.</p><p>Our approach is an initial application of scientific machine learning for the simulation of hydraulics that is readily combined with other recent developments. Future research should, in particular, explore the application of physics-informed loss functions for bypassing the generation of training data from hydrodynamic simulations, and of graph neural networks to exploit spatial correlation structures in the pipe network.</p>

Accurate positioning is needed for agricultural vehicles now and in the future. Position is currently needed for mapping, precision farming, auto steering vehicles and for agro robotic solutions. Thus two kinds of errors is defined for GPS receivers such as relative accuracy and the absolute accuracy, which is measured positions to their real position. In this paper to overcome the above said problems such as errors we have proposed Extended Kalman filter by adopting artificial bee colony (ABC) algorithm for dynamic tuning. Hence consider a kinematic tractor model where the system inputs are vehicle speed, front wheel steering angle and the midpoint of the rear wheel axle. Thus Extended Kalman filter an efficient and mathematical algorithm that processes imprecise observation of input data and creates an optimal estimate by providing a prediction model and an observation model is proposed. The first phase is prediction stage and the second phase is the update stage in the system to produce an a posteriori state estimate, by adjusting the previous a priori estimate. Moreover ABC optimization algorithm is used to generate an optimal precise output. Hence the developed model is implemented in the working platform of MATLAB and output is compared with the existing technique to evaluate the performance.

The use of models to represent biological, chemical, and physical processes that govern the fate and transport of environmental contaminants is an enduring feature of risk assessments. Data collection is costly and time-consuming. Measuring future conditions is impossible regardless of the resources available. For these reasons, rarely do analysts have sufficient empirical data for estimating risks in a population of interest across the desired dimensions of space and time. The appropriate level of complexity, detail, and resource investment in a modeling exercise should be established by the intended use of the results generated and by the expected performance of the available modeling options. At one end of the spectrum, the concentration of a contaminant in an environmental medium may be estimated quickly and inexpensively using an intermedia partition coefficient or simple steady state compartmental model. In contrast, a complex dynamic model requiring vast stores of input data, computer power, and run time may be used to estimate concentration. However, because models are generally used when data are scarce or nonexistent, our ability to assess the accuracy and precision of various model options is often limited. The research presented below exploits a relatively unusual opportunity to compare concentration measurements of polychlorinated biphenyls (PCBs) for multiple environmental media with predictions from simple compartmental fate and transport models using two-dimensional Monte Carlo analysis. Simple compartmental models are found to predict measurements quite well overall, although decisions about the treatment of variability and autocorrelation in the airborne load of contaminant with season, weather system, and location influence their performance. The models are assessed at two sites near New Bedford Harbor in Massachusetts, one characterized by higher and more variable contaminant concentrations, while the other is a comparison or "background" site. The difference between model performance in the two locations illustrates some characteristics of situations in which simple models are most appropriate. Under background conditions of relatively low and consistent contaminant levels, a two compartment model generates excellent predictions of the sum of PCB congener concentration in soil based on air concentration. Under more contaminated or more variable conditions, model results are less predictive; however, most fall within an order of magnitude of the data. A comparison of model performance for predicting concentration of the sum of PCB congeners vs for predictions of individual PCB congeners is also pursued. Individual congener concentrations in soil tend to be slightly overpredicted for lighter weight congeners, i.e., those more characteristic of the New Bedford Harbor region, while levels of heavier congeners tend to be underpredicted in circumstances where air concentrations are relatively low. A three compartment model representing air, soil, and plant matter is found to predict levels of PCBs in edible produce within about an order of magnitude of those measured, under the conditions considered. This work demonstrates the relevance and usefulness of results from simple and easily implemented models for fate and transport predictions.

In hydrology, a plethora of modelling approaches exist. They differ in several aspects, including the underlying hypotheses (empirical, conceptual vs. physically based) and the spatial discretisation (lumped, semi-distributed, gridded). The advent of machine learning or AI techniques further expands the spectrum of available modelling options. In this context, there is a growing scientific interest in systematically comparing existing models, understanding the reasons behind their relative performance, and applying multi-model approaches to increase the reliability of the outcomes. However, few research has been carried out so far to compare all these types of models in the same framework, namely using similar input data, pre-processing procedures, parameter optimisation algorithms and strategy, objective function, etc. WOLFHydro, developed by the HECE group at the University of Liège, offers such a framework. It addresses a flexible simulation tool organised in ‘modules’ and capable of representing any catchment, thus keeping a tuneable level of complexity and details in the description of all the physical processes at work, while remaining in the same modelling environment and starting from exactly the same input data. The software parcels out a catchment into sub-catchments or evaluation points, which are arranged in a topology network. Each module can contain a chosen type of model (physically based, conceptual, empirical) with the desired spatial representation (lumped, gridded, semi-distributed) to be assembled and facilitate the creation of hybrid models. The software also accommodates anthropogenic structures, such as dams, storage basins, or any other hydraulic structure defined by a set of operation rules customable by the user. The software currently contains a number of models developed in-house, as well as widely accepted ones (GR4H, VHM, etc). They have been validated and tested on several Belgian catchments, in particular for the 2021 extreme floods in the Vesdre and Amblève valleys. The software also features post-processing tools and a GUI interface to facilitate inspection of the results. Thanks to its versatility, WOLFHydro aims at reducing biases in model comparisons conducted in separated frameworks, improving our understanding of dominant hydrological processes, improving the evaluation of the influence model structure complexity, and carry out ensemble modelling.

Visser, J.M.; Duke-Sylvester, S.M.; Carter, J., and Broussard, W.P., III, 2013. A computer model to forecast wetland vegetation changes resulting from restoration and protection in coastal Louisiana. In: Peyronnin, N. and Reed, D. (eds.), Louisiana’s 2012 Coastal Master Plan Technical Analysis, Journal of Coastal Research, Special Issue No. 67, 51–59. Coconut Creek (Florida), ISSN 0749-0208. The coastal wetlands of Louisiana are a unique ecosystem that supports a diversity of wildlife as well as a diverse community of commercial interests of both local and national importance. The state of Louisiana has established a 5-year cycle of scientific investigation to provide up-to-date information to guide future legislation and regulation aimed at preserving this critical ecosystem. Here we report on a model that projects changes in plant community distribution and composition in response to environmental conditions. This model is linked to a suite of other models and requires input from those that simulate the hydrology and morphology of coastal Louisiana. Collectively, these models are used to assess how alternative management plans may affect the wetland ecosystem through explicit spatial modeling of the physical and biological processes affected by proposed modifications to the ecosystem. We have also taken the opportunity to advance the state-of-the-art in wetland plant community modeling by using a model that is more speciesbased in its description of plant communities instead of one based on aggregated community types such as brackish marsh and saline marsh. The resulting model provides an increased level of ecological detail about how wetland communities are expected to respond. In addition, the output from this model provides critical inputs for estimating the effects of management on higher trophic level species though a more complete description of the shifts in habitat.

A polymer solar cell-thermoelectric (PSC–TE) hybrid energy-harvesting system was designed and fabricated, which realizes harvesting electricity from solar light and solar heat simultaneously. A series of measurements has been performed to study the relationship between the PSC–TE hybrid system and individual devices. The PSC–TE system improved the total power output compared with individual PSCs when a temperature gradient across TE module was introduced. The physical process that determines the overall power generation of the PSC–TE hybrid system was also studied and analyzed. The optimal power output of PSC–TE hybrid system is given, which can act as a guideline for further optimizing the hybrid energy-harvesting system. Interestedly, we demonstrate that the hybrid system can drive a commercial light-emitting diode by effectively utilizing solar energy, while it cannot be realized by an individual device. The hybrid system is proved to be a more efficient way for obtaining electricity by integrating mul...

Research into Bose-Einstein condensates is currently a thriving field in physics. Much of the experimental research involves the detection of cold atom clouds under various conditions. Consequently, the fundamental and technical limitations of the imaging techniques and technologies utilized will have a dramatic influence on the results of these experiments. Therefore a detailed analysis of the restrictions on a Bose-Einstein condensate imaging system is overdue and will facilitate further developments in the field due to an increase in the signal to noise ratio; hence a greater level of detail available in experimental images. The work carried out in this thesis focuses on the properties of absorption imaging techniques, in the quest for an optimized imaging system. Optimization was proposed to be achieved through maximization of the signal to noise ratio by any and all means available. A detailed investigation of the diverse influences on the signal to noise ratio was performed with a focus on eliminating technical restrictions and minimizing fundamental constraints. By studying a 2f absorption imaging system the maximal signal to noise ratio of 35 was calculated. This corresponded to imaging a cloud of 105 87Rb atoms in a (50μm) box with a 31.8MHz detuned, 780nm laser at an intensity of 570W/m2 for 35μs. These optimal imaging parameters correspond to our imaging system being limited by the well depth of our detector, not by a fundamental physical process. Furthermore, current experimental noise is identified as the most significant inhibitor to the achievement of an optimized absorption image. This study represents a first analysis of the quality of current Bose-Einstein condensate imaging techniques and limitations.

Whitepaper Series on Next Generation Justice Platform, Paper 3: The Legal Framework

Introduction: Environmental data play a crucial role in social change, especially in ancient Yemen, where the natural environment is closely linked to ancient civilizations. The development of tribal areas in Yemen, such as Saba and Hamir, has been affected by the growth of trade, handicrafts and traditional industries. The integration of the natural environment has led to the growth of maritime navigation and the establishment of modern industrial and administrative units. Urbanization in these areas has diversified the components of tribal populations, requiring new social, political, economic and legal regulation. The population also had to adapt to modern standards and regulations, such as monetary handling in the labour market and production. The geographical and environmental dimension plays a significant role in the emergence and expansion of cities, which are often found in fertile and resource-abundant locations. Urban development in the Arabian Peninsula has a long history dating back to ancient times. The region has witnessed the first civilization in history and has witnessed significant urbanization. The middle Ages experienced significant urban development, with cities reaching unprecedented sizes. The nineteenth century saw a new phase of urban development, with urban land subject to economic processes, including competition. The discovery and extraction of oil in the twentieth century led to the emergence of new cities, highlighting the current urban landscape. The Arab population has grown rapidly, rising from 13% in 1900 to 49% in 2005(according to the Central Bureau of Statistics). Today, the Arab city community is a mix of cultures and nationalities, with institutions replacing the family, the warmer and the tribe in different societies. Aspects of life and social systems. It is estimated that half of the Arab population inhabits urban areas because of urban growth and urbanization, a health phenomenon that is indicative of civilized development and economic and social growth. Methodology: The physical changes in the Republic of Yemen and the identification of the city of Aden were studied by reviewing the studies that monitored these changes and their social, economic and population effects. The study relied on secondary data in the first place. Results: this research was conducted to study The Influence of Urbanization on Social Dynamics in Yemen: Insights from Environmental and Climate Data, we find that urban society's relationship with the natural environment influences its social and cultural environment, influenced by collective and individual traditions and economic habits. Rural societies are more connected to the natural environment, while urban communities live in relative isolation, resulting in a dominant social and human environment. Discussion: Urban growth refers to the physical framework for society's stability and existence, indicating social well-being and basic needs such as housing, water security, energy, and transportation. In the Arab world, urbanization dates back to ancient times, with cities growing up in the Arabian Peninsula. The Middle Ages saw major urban development, while the 19th century saw rapid urban population growth. Today, half of the Arab population is urban, reflecting civilization's development and economic and social growth. Yemen is experiencing significant urbanization, transforming from an urban environment to a diverse, modern society, influenced by social construction and cultural values. The natural environment influences social and cultural environments, with rural societies being more connected to it. Urbanization requires preserving rural social characteristics, strengthening inhabitants' sense of belonging, and promoting participation in community development plans. Spatial justice and regional balance are crucial in developing world urban environments, as they help overcome alienation and promote collective affiliation.