Suitable Scenario Research Articles

Numerical simulation of near wellbore fracture propagation in interbedded continental shales with competing perforations

To reduce near wellbore fracture complexity in continental laminated shales, in-plane perforations completion technology is introduced. The global embedded cohesive elements are capable to simulating arbitrary fracture propagation growth while the laminae effects on fracture propagation path can be considered in established finite element model. Numerical simulations reflect that the existence of weak micro fractures only locally alter the fracture propagation path but laminae can change the fracture propagation path greatly. Furthermore, the in-situ stress anisotropy, pump rate, perforation intersection angle and perforation angle with laminae are critical factors for affecting the fracture propagation path and breakdown pressure. There is a great impact of laminae on the fracture branching at the contact interface and hydraulic fracture tends to cross laminae at high approaching angle but propagate horizontally along laminae at low approaching angle. In most cases, hydraulic fracture tends to propagate along the laminae rather than across the laminae. The adjustment of pump rate, fluid viscosity, perforation intersection angle and perforation angle with laminae can enhance the possibilities of hydraulic fracture across laminae. It was also found that hydraulic fractures induced from different perforations can interact and overlap with each other, resulting in different fracture geometry and pressure behavior for individual fracture. The fracture width of interior fracture is almost close at the near wellbore zone at the end of pumping, and exterior fractures generally deviated away from the perforation tunnel direction because of stress interference of neighboring fractures. Additionally, although a fracture initiated initially from a perforation tunnel to short distance, hydraulic fractures still finally would reorient itself to maximum principle in-situ stress direction, thus increase more chances of creating one large and main fracture along the maximum principle in-situ stress orientation with larger horizontal stress contrast. Therefore, the existence of laminae can enhance the potential of fracture complexity near wellbore but the selection of suitable perforation scenarios in one defining plane can increase the possibility of form a main fracture. The simulation results from this study offer some important insights to hydraulic fracturing design with multiple perforations on interbedded continental shales stimulation.

Answering Why and When?

Immersive analytics (IA) is a fast-growing research field that concerns improving and facilitating human sense making and data understanding through an immersive experience. Understanding the suitable application scenario that will benefit from IA enables a shift towards developing effective and meaningful applications. This paper aims to explore tasks and scenarios that can benefit from IA by conducting a systematic review of existing studies and mapping them according to the multi-level typology for abstract visualization tasks, which is also known as the what-why-how framework. The study synthesizes several works to answer the why within the context of multiple levels of specificity. In addition, this study also explores the application domains and IA guiding scenarios to address when scenarios best integrate with IA. Then, the paper discusses the IA evaluation types and research methods to evaluate an IA application that can promote effective user engagement in IA. Finally, the limitations and potential future works are discussed.

A Study Using the Hybrid Fuzzy AHP&TOPSIS Method in the Conversion of a LEED-Certified Education Building into a Nearly Zero-Energy Building in a Cold Climate

In cold climate regions, it is essential to design and manufacture energy-efficient buildings for both economic benefits and the reduction of environmental effects by controlling energy consumption. This study aimed to increase the cost-effective energy performance and approach the nearly zero energy building (nZEB) by taking the leadership in energy and environmental design (LEED) in the cold climate region of Turkey as a model. The results of single and mixed scenarios that increase energy efficiency were determined by making energy modelling of the building. By applying single and mixed energy efficiency scenarios, a maximum saving of 85.60 % per year in terms of primary energy, an improvement of 83.6 % in terms of global costs and a reduction of 86.4 % in CO2 emissions were obtained compared to the reference building. The payback period of the scenarios is between 3.8 years and 14.53 years. The most suitable single and mixed scenario was determined by a systematic hybrid model, in which the fuzzy analytical hierarchy process (FAHP) and technique for order of preference by similarity to ideal solution (TOPSIS) methods among multi-criteria decision-making methods are used together. The results showed that economic criteria were decisive in determining the most suitable scenario for cold climate regions. The results of this study revealed that there can be a realistic decision-support model for the creation of energy-efficient buildings for countries without the need for foreign certification.

Development of Monocular Vision based Autonomous Car

Abstract: The evolution of the artificial intelligence has served as the catalyst in the field of technology. We can now develop things which was once just an imagination. One of such creation is the birthof Autonomous cars. Days have come one can do their work even touching steering wheel, accelerator you will still be able to reach your target destination safely. Self-driving car (Autonomous cars) is a robotic vehicle that is designed to travel between destinations without human intervention. It is capable of sensing environment and navigate without human input. Autonomous cars must have control systems that are capable of analysing sensor data to distinguish between different cars on the road. The potential benefits of autonomous cars include reduced mobility and infrastructure costs, increased safety, increased mobility, increased customer satisfaction and reduced crime. Self-driving cars rely on software which needs to be thoroughly tested. Testing self-drivingcar software in real traffic is not only expensive but also dangerous and has already caused fatalities. Virtual tests, in which self-driving car software is tested in computer simulations, offer a more efficient and safer alternative compared to naturalistic field operational tests. However, creating suitable test scenarios is laborious and difficult. In this paper we combine procedural content generation, a technique commonly employed in modern video games, and search-based testing, a testing technique proven to be effective in many domains, in order to automatically create challenging virtual scenarios for testing self-driving car soft- ware. Our as Fault prototype implements this approach to generate virtual roads for testing lane keeping, one of the defining features of autonomous driving. Evaluation on two different self-driving car software systems demonstrates that as Fault can generate effective virtual road networks that succeed in revealing software failures, which manifest as cars departing their lane. Compared to random testing as Faultwas not only more efficient, but also caused up to twice as many lane departures

Long-lived particle phenomenology in the 2HDM+a model

Higgs decays displaced from the primary interaction vertex represent a striking experimental signature that is actively searched for by the ATLAS, CMS and LHCb collaborations. We point out that signals of this type appear in the context of the 2HDM+a model if the mixing angle θ of the two CP-odd weak spin-0 eigenstates is tiny and the dark matter (DM) sector is either decoupled or kinematically inaccessible. Utilising two suitable benchmark scenarios, we determine the constraints on the parameter space of the 2HDM+a model that are set by the existing LHC searches for long-lived particles (LLPs) in Higgs decays. We find that depending on the precise mass spectrum of the spin-0 states, mixing angles θ in the ballpark of a few 10−8 to 10−5 can be excluded based on LHC Run II data. This finding emphasises the unique role that searches for displaced signatures can play in constraining the parameter space of the 2HDM+a model. The ensuing DM phenomenology is also discussed. In particular, we show that parameter choices leading to an interesting LLP phenomenology can simultaneously explain the DM abundance observed in today’s Universe.

XR Technology Deployment in Value Creation

With increasing computing power and data transmission performance of information technologies, the application scenarios for Extended Reality (XR) technologies in industries are growing. Despite the ongoing scientific investigation of industrial XR applications for over 25 years, these technologies are still considered emerging. Within this paper, we present an industry- and business-process agnostic approach for classifying the deployment purposes of XR technologies in value creation. We identified two major research streams regarding the role of XR technologies in value creation: (1) the research initiatives focusing on business-process-specific use case analysis and (2) industry-oriented research reviews. This results in limited identification of suitable application scenarios for new use cases and restricted transferability of the existing use case to future deployments. First, we provide a qualitative analysis of the current research streams. Then, in the second step, by abstracting the XR technology from the existing business processes and the industry-specific context, the generic purposes for XR technologies in value creation are identified and defined. The summary of these deployment purposes results in a taxonomy that enables the identification and transfer of potential use cases of XR technologies in value creation.

Evaluation of Different Fault Diagnosis Methods and Their Applications in Vehicle Systems

A high level of automation in vehicles is accompanied by a variety of sensors and actuators, whose malfunctions must be dealt with caution because they might cause serious driving safety hazards. Therefore, a robust and highly accurate fault detection and diagnosis system to monitor the operational states of vehicle systems is an indispensable prerequisite. In the area of fault diagnosis, numerous techniques have been studied, and each one has pros and cons. Selecting the best approach based on the requirements or usage scenarios will save much needless work. In this article, the authors examine some of the most common fault diagnosis methods for their applicability to automated vehicle systems: the traditional binary logic method, the fuzzy logic method, the fuzzy neural method, and two neural network methods (the feedforward neural network and the convolutional neural network). For each approach, the diagnosis algorithms for vehicle systems were modeled differently. The analysis of the detection capabilities and the suitable application scenarios of each fault diagnosis approach for vehicle systems, as well as recommendations for selecting different methods for various diagnosis needs, are also provided. In the future, this can serve as an effective guide for the selection of a suitable fault diagnosis approach based on the application scenarios for vehicle systems.

Sector coupling of electricity, transport and cooling with high share integration of renewables in Indonesia

Sector coupling improves energy system efficiency by maximising potential synergies among energy sectors. This paper aims to assess the sector coupling of electricity, transport, and cooling on the Java and Bali islands, Indonesia. Future energy systems in 2040, focussing on decarbonised electricity sector with high electric vehicles deployment and district cooling penetration, were simulated using EnergyPLAN. A bottom-up calculation approach was applied to determine demand in the transport sector. In the cooling sector, geospatial analysis was employed to quantify cooling demand and locate potential district cooling networks in Jakarta. Six scenarios were explored based on their energy demand and supply characteristics. Modelling results show that the sector coupling of three sectors could reduce primary energy supply (PES), CO2 emissions and annual costs. The most suitable scenario has about 8% lower PES, 14% lower CO2 emissions and 2% less annual costs compared to the business-as-usual scenario. Results also show that transport electrification could only effectively and significantly decrease CO2 emissions if its electricity demand is produced from renewables. Transport electrification with large scale integration of renewables could also lower the annual costs by decreasing fossil fuel costs in the transport and electricity sectors.

RenoDSS – A BIM-based building renovation decision support system

Increasing the building renovation rate is a key initiative to drive energy efficiency in the sector and contribute to the European Green Deal. Within this paper, the BIM-based building renovation decision support system RenoDSS is described. RenoDSS (i) automatically generates renovation scenarios based on user preferences and the IFC representation (including second level space boundaries data) of the current building configuration (baseline), (ii) calculates baseline and renovation scenario KPIs, and (iii) identifies the most suitable scenarios by comparing the scenarios’ energy, finance, and sustainability KPIs. Validation conducted at two pilot sites shows that RenoDSS reduces the time for (i) identifying potential renovation scenarios, (ii) calculating their energy and LCA/LCC KPIs, and (iii) producing an IFC file for each renovation scenario by around 80% compared to tool-supported manual means. RenoDSS is used by (i) renovation designers that want to optimize the thermal building hull in combination with PV, solar thermal, and HVAC, as well as (ii) decision makers who want to collaboratively identify the most promising renovation option out of the ones generated by RenoDSS.

A review on semi-supervised clustering

Semi-supervised clustering (SSC), a technique integrating semi-supervised learning and clustering analysis, incorporates the given prior information (e.g., class labels and pairwise constraints) into clustering to guide the clustering process and improve the performance. In recent years, a large number of valuable works have emerged, focusing on theoretical research and application in different fields. In this paper, a detailed review of SSC is provided from a new perspective. Firstly, all SSC studies are organized as partition-based SSC, hierarchical-based SSC, density-based SSC, graph-based SSC, neural network-based SSC, Nonnegative Matrix Factorization-based SSC and random subspace technique-based SSC. Thus, the semi-supervised researches can be in-depth discussed in each clustering idea. Secondly, the general overviews are detailed in each category respectively, including the performance, the suitable scenarios and the way to add supervising information. Thirdly, the recent successful applications of SSC are summarized according to different backgrounds such as medical, biological, business, journalism, financial and so on. Based on this, some application caveats and development trends of SSC are particularly given in the end. This comprehensive review and analysis of SSC can provide an overall outline, the scope of research topics, and a relative complete analysis of existing SSC methods for researchers.

Interval transportation problem: feasibility, optimality and the worst optimal value

We consider the model of a transportation problem with the objective of finding a minimum-cost transportation plan for shipping a given commodity from a set of supply centers to the customers. Since the exact values of supply and demand and the exact transportation costs are not always available for real-world problems, we adopt the approach of interval programming to represent such uncertainty, resulting in the model of an interval transportation problem. The interval model assumes that lower and upper bounds on the data are given and the values can be independently perturbed within these bounds. In this paper, we provide an overview of conditions for checking basic properties of the interval transportation problems commonly studied in interval programming, such as weak and strong feasibility or optimality. We derive a condition for testing weak optimality of a solution in polynomial time by finding a suitable scenario of the problem. Further, we formulate a similar condition for testing strong optimality of a solution for transportation problems with interval supply and demand (and exact costs). Moreover, we also survey the results on computing the best and the worst optimal value. We build on an exact method for solving the NP-hard problem of computing the worst (finite) optimal value of the interval transportation problem based on a decomposition of the optimal solution set by complementary slackness. Finally, we conduct computational experiments to show that the method can be competitive with the state-of-the-art heuristic algorithms.

Modified Bacteriophage for Tumor Detection and Targeted Therapy

Malignant tumor is one of the leading causes of death in human beings. In recent years, bacteriophages (phages), a natural bacterial virus, have been genetically engineered for use as a probe for the detection of antigens that are highly expressed in tumor cells and as an anti-tumor reagent. Furthermore, phages can also be chemically modified and assembled with a variety of nanoparticles to form a new organic/inorganic composite, thus extending the application of phages in biological detection and tumor therapeutic. This review summarizes the studies on genetically engineered and chemically modified phages in the diagnosis and targeting therapy of tumors in recent years. We discuss the advantages and limitations of modified phages in practical applications and propose suitable application scenarios based on these modified phages.

Analysis of Charging Infrastructure for Private, Battery Electric Passenger Cars: Optimizing Spatial Distribution Using a Genetic Algorithm

To enable the deployment of battery electric vehicles (BEVs) as passenger cars in the private transport sector, suitable charging infrastructure is crucial. In this paper, a methodology for the efficient spatial distribution of charging infrastructure is evaluated by investigating a scenario with a 100% market penetration of BEVs of (around 1.3 million vehicles) in Berlin, Germany. The goal of the evaluated methodology is the development of various charging infrastructure scenarios—including public and private charging—which are suitable to cover the entire charging demand. Therefore, these scenarios are investigated in detail with a focus on the number of public charging points, their spatial distributions, the available charging power, and the necessary capital costs. For the creation of these charging infrastructure scenarios, a placement model is developed. As input, it uses the data of a multi-agent transport simulation (MATSim) scenario of the metropolitan area of Berlin to evaluate and optimize different distributions of charging infrastructure. The model uses a genetic algorithm and the principle of multi-objective optimization. The capital costs of the charging points and the mean detour car drivers must undertake are used as the optimization criteria. Using these criteria, we expect to generate cost-efficient infrastructure solutions that provide high usability at the same time. The main advantage of the method selected is that multiple optimal solutions with different characteristics can be found, and suitable solutions can be selected by subsequently using other criteria. Besides the generated charging scenarios for Berlin, the main goal of this paper is to provide a valid methodology, which is able to use the output data of an agent-based, microscopic transport simulation of an arbitrary city or area (or even real driving data) and calculate different suitable charging infrastructure scenarios regarding the different optimization criteria. This paper shows a possible application of this method and provides suggestions to improve the significance of the results in future works. The optimized charging infrastructure solutions for the Berlin scenario show capital costs of between EUR 624 and 2950 million. Users must cover an additional mean detour of 254 m to 590 m per charging process to reach an available charging point. According to the results, a suitable ratio between the charging points and vehicles is between 11:1 and 5:1. A share of fast charging infrastructure (>50 kW) of less than ten percent seems to be sufficient if it is situated at the main traffic routes and highly frequented places.

Analytic kludge waveforms for extreme-mass-ratio inspirals of a charged object around a Kerr-Newman black hole

We derive the approximate, ``analytic-kludge'' (AK) waveforms for the inspiral of a charged stellar-mass compact object (CO) into a charged massive Kerr-Newman (KN) black hole (BH). The modifications of the inspiral orbit due to the charges in this system can be attributed to three sources: the electric force between the CO and the MBH, the energy flow of the dipole electromagnetic radiation, and the deformation of the metric caused by the charge of the MBH. All these are encoded explicitly in the fundamental frequencies of the orbits, which are calculated analytically in the weak-field regime. By calculating the mismatch between the waveforms for charged and neutral EMRI systems with respect to space-borne detectors TianQin and LISA, we show that tiny charges in the system can produce distinct imprints on the waveforms. Finally, we perform parameter estimation for the charges using the Fisher information matrix method and find that the precision can reach the level of $10^{-5}$ in suitable scenarios. We also study the effects of charges on the parameter estimation of charge, where the effects from the charge of the MBH can be well explained by its effects on the cutoff of the inspiral.

MultI-messenger probe of Cosmic Ray Origins: MICRO project

We present the milestones achieved by the MICRO (MultI-messenger probe of Cosmic Ray Origins) project. This multi-institute project consists of a study of bursting astrophysical sources as candidate sources for ultra-high-energy cosmic rays (UHECRs). We aim at identifying source classes that correlate best with existing observational data (arrival direction distribution, energy spectrum and primary mass composition) and to constrain the origin of UHECRs on the basis of the largest datasets acquired to this day. The study of the secondary fluxes of neutrinos and gamma rays will provide a powerful test of the most suitable astrophysical scenarios. For these achievements, a public software enabling a joint fit of both composition and flux data as a function of energy and direction is being developed. In addition, a modelling of transient sources is foreseen, including the treatment of hadronic interactions within the sources, which represents a novelty for UHECR simulation codes. This contribution will highlight the first results of the project and detail upcoming software releases, which will benefit the astroparticle community at large.

Wideband Analysis and Prolongation of Surrounding TGVs Shielding Structure in 3-D ICs

Through glass vias (TGVs) with low substrate loss are subjected to signal distortion issues. This letter proposes a wideband scalable model for assessing signal reliability and rationalizing 3-D integrated design in glass interposers. The established equivalent circuit model is derived for extracting the parasitic parameters of the signal transmission unit, and the relevant variables stem from the geometric dimension parameters. The result of comparative analysis uncovers that the simulation results are in excellent accordance with the analytical model. Furthermore, seven crosstalk shielding topologies are proposed, and their noise suppression ability is analyzed. Such the shielding structures can not only reach the insertion loss (IL) performance below 0.15 dB but also achieve the crosstalk suppression capability exceeding 60 dB even at 40 GHz. Feasible strategies are proposed to trade off IL, noise isolation (NI), and occupied area in order to match suitable application scenarios, which can provide a reference for layout design.

Measurement Criteria and Metrics of Retail Store Performance: An Empirical Study

In this work, we analyze the need for evolution in conventional decision support techniques while keeping in mind the complex and ever-changing requirements of the corporate environment. The goal is to make integrated performance assessment and management more crucial to improving the current decision-making tools, which are now dependent on historical data. It is regarded as a suitable application scenario due to the nature of the difficulties and developments in the retail sector. A framework is also suggested, and a case study is provided as evidence for our claim. Measurement of retailer productivity is necessary to comprehend performance entirely. There are far too many metrics that rely on accounting and financial metrics. Nevertheless, these metrics do not consider the underlying factors influencing bottom-line success. This article examines the idea of productivity concerning retail businesses, emphasizing input and output components and parametric and non-parametric productivity measurements. Furthermore, given are a research agenda and managerial implications.

Modeling the designs in terms of logistics service center placement: a case study

Suggesting the proper location for logistics facility can be considered as a decision making problem, wherein the final solution/decision is affected by multiple external or even internal circumstances. In order to address the decision making issues, various multi-criteria decision making (MCDM) techniques may be implemented; and hence, they can be applied even when making a decision about an adequate logistics service center (LSC) placement in an examined territory (i.e., national logistics network of the selected territory), which is an aim of this manuscript. Following the statements above, as for the individual instruments of MCDM to be implemented in terms of the crucial objective of this research, the definite decision making process will be carried out by applying the Analytic Hierarchy Process (AHP) followed by the Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS), on the basis of criteria weights defined by the Saaty pairwise comparison method. The methods used appear to be ideal instruments towards decision making on the most suitable location which is represented by the region in our case. Subsequently, these will be ordered from the most preferred to least one by using a preference ranking. As a result of the application of AHP and TOPSIS approaches, based on the conducted calculations in regard to decision making on identifying the proper LSC location out of eight selected regions, one specific region will be defined as the most suitable (so-called compromise) scenario. Individual tools allow for reducing the number of assigned criteria that are taken into account in searching process for individual solutions. In order to objectify the entire decision making procedure, ten topic-involved experts having practical experience with a subject of logistics object allocation will be asked to participate in the process. Preferences differ from one decision maker (expert) to another; hence, the outcome depends on who is making decisions and what their goals and preferences are.

Ray Tracing in Computer Graphics

The research is about the development of light and shadow in video games on the Internet, and also to compare the light and shadow effects displayed on video game screens using different techniques. Then, the types and history of ray tracing technology are explained, its working principle is studied, and its pros and cons are analyzed. After the research, this paper finally determined that a viable ray tracing technique in the current society is hybrid ray tracing, which is a combination of forward and backward ray tracing. Because forward ray tracing methods are too inefficient while backward ray tracing methods cannot calculate and render the light entering in front of the lens. By studying 3 main hybrid ray tracing, their advantages, disadvantages and suitable application scenarios are analyzed and determined in detail. Finally, the development and improvement of the technology in recent years are discussed. Currently, analyses of a particular ray tracing technology do exist in the market. However, there are still few articles that explore all available ray tracing technologies, taking the reader through the history and current state of ray tracing development and giving developers a more visual comparison to help them choose the most appropriate technology for the scenarios they want to develop.

Colloidal Quantum Dots: Synthesis, Composition, Structure, and Emerging Optoelectronic Applications

AbstractIn recent decades, quantum dots (QDs) with tunable bandgap, large absorption coefficient, high quantum yield, multiexciton effect, and easy solution processing have unparalleled advantages in photoelectric conversion. Optoelectronic devices based on QDs of different composition have made great progress. However, there is still a lack of reviews on comparing the optoelectronic application level of semiconductor quantum dots (SQDs), perovskite quantum dots (PQDs), and carbon quantum dots (CDs), and also rarely providing a comprehensive summary of photocatalysis. First, this review almost completely summarizes advantages and disadvantages of common synthesis methods for QDs, especially pointing out the importance of optimization strategies for preparing high‐quality QDs such as ligand engineering, ion exchange, and purification separation. Then, compositions of SQD, PQD, CD, and corresponding optoelectronic properties are introduced, respectively. Next, the strengths and weaknesses of the four QDs structures are compared in detail. Finally, the flourishing development in various optoelectronic applications is separately demonstrated, and development degree of SQDs, PQDs, and CDs is compared to discover the most suitable application scenarios for each. On these bases, bottlenecks and opportunities are also put forwarded, hoping to stimulate more breakthroughs in this field.