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Monocrystalline silicon is still very interesting material for solar cells fabrication due to its quality and external efficiency. Nevertheless during a tailoring of eligible silicon wafers, some inhomogeneities or irregularities emerge and provide defects which give trouble to good operation of solar panels. Generally, there are two classes of defects in silicon wafer: material defects due to imperfections or irregularity in crystal structure (point, line, square or volume defects), and defects induced by wafer processing. To avoid a use of damaged cells, macroscopic and microscopic measurement techniques must be applied. In this paper we present a microscopic method combining electrical noise measurements with scanning probe localization of luminous micro-spots defects. The paper brings experimental results showing local electric and optical investigations of defects in etched monocrystalline silicon solar cells and a use of cold field emission tungsten electrode as a local probe for apertureless scanning near-field optical microscope.

The purpose of this paper is to examine the digitalization of operational processes and activities in lean manufacturing firms and explore the associated learning implications through the lens of cumulative capability theory.

Adopting a multiple-case design, we examine four cases of digitalization initiatives within lean manufacturing firms. We collected data through semi-structured interviews and direct observations during site visits.

The study uncovers the development of learning capabilities as a result of integrating lean and digitalization. We find that digitalization in lean manufacturing firms contributes to the development of both routinized and evolutionary learning capabilities in a cumulative fashion.

The study adds nuance to the limited theoretical understanding of the integration of lean and digitalization by showing how it cumulatively develops the learning capabilities of lean manufacturing firms. As such, the study supports the robustness of cumulative capability theory. We further contribute to research by offering empirical support for the cumulative nature of learning.

The purpose of the present study is to empirically investigate the impact of the newly established entrepreneurial activity on economic development of the Czech NUTS 3 regions during the period of years 2003-2015.

An econometric approach was used to validate the stated hypotheses assuming a positive relationship between the new entrepreneurial activity and regional economic growth and a negative relationship between the new entrepreneurial activity and unemployment rate. For the methods, regression models with fixed effects were estimated on the panel that included 13 Czech regions, covering the period of years 2003-2015. The new entrepreneurial activity was classified into two forms – rate of newly established self-employed set-ups per capita and rate of newly established business companies and partnership set-ups per capita.

Different impacts of newly established business companies and the self-employed were found on real gross domestic product (GDP) per capita. Only the higher rates of newly established business companies and partnership were associated with higher levels of GDP per capita in the Czech regions, and no impact was found for the rate of new self-employed set-ups. Nevertheless, both forms of newly established entrepreneurial activity were associated with lower unemployment rates in the Czech regions; however, the impact of newly established business companies was significantly higher. The obtained results have several policy implications, which are discussed in the present paper.

Support of entrepreneurship in the Czech regions may improve the situation on the local labour markets and may deliver new job opportunities through the newly established enterprises. The Czech entrepreneurship policies focused on the growth of GDP and economic boom should be oriented more on the support of high-growth enterprises (unicorns).

The empirical analysis was conducted on the basis of the research gap in the studies related to the impact of the newly established entrepreneurial activity on the economic development of the Czech regions. Obtained results have several policy implications, which are discussed in the present paper.

The purpose of this paper is to introduce new implementations for parallel processing applications using bijective systolic networks and the corresponding carbon-based field emission controlled switching. The developed implementations are performed in the reversible domain to perform the required bijective parallel computing, where the implementations for parallel computations that utilize the presented field-emission controlled switching and their corresponding m-ary (many-valued) extensions for the use in nano systolic networks are introduced. The first part of the paper presents important fundamentals with regards to systolic computing and carbon-based field emission that will be utilized in the implementations within the second part of the paper.

The introduced systolic systems utilize recent findings in field emission and nano applications to implement the functionality of the basic bijective systolic network. This includes many-valued systolic computing via field emission techniques using carbon-based nanotubes and nanotips. The realization of bijective logic circuits in current and emerging technologies can be very important for various reasons. The reduction of power consumption is a major requirement for the circuit design in future technologies, and thus, the new nano systolic circuits can play an important role in the design of circuits that consume minimal power for future applications such as in low-power signal processing. In addition, the implemented bijective systems can be utilized to implement massive parallel processing and thus obtaining very high processing performance, where the implementation will also utilize the significant size reduction within the nano domain. The extensions of implementations to field emission-based many-valued systolic networks using the introduced bijective nano systolic architectures are also presented.

Novel bijective systolic architectures using nano-based field emission implementations are introduced in this paper, and the implementation using the general scheme of many-valued computing is presented. The carbon-based field emission implementation of nano systolic networks is also introduced. This is accomplished using the introduced field emission carbon-based devices, where field emission from carbon nanotubes and nano-apex carbon fibers is utilized. The implementations of the many-valued bijective systolic networks utilizing the introduced nano-based architectures are also presented.

The introduced bijective systolic implementations form new important directions in the systolic realizations using the newly emerging nano-based technologies. The 2-to-1 multiplexer is a basic building block in “switch logic,” where in switch logic, a logic circuit is realized as a combination of switches rather than a combination of logic gates as in the gate logic, which proves to be less costly in synthesizing multiplexer-based wide variety of modern circuits and systems since nano implementations exist in very compact space where carbon-based devices switch reliably using much less power than silicon-based devices. The introduced implementations for nano systolic computation are new and interesting for the design in future nanotechnologies that require optimal design specifications of minimum power consumption and minimum size layout such as in low-power control of autonomous robots and in the adiabatic low-power very-large-scale-integration circuit design for signal processing applications.

Learning through formal and informal experiences is critical for building content knowledge, pedagogical skills, and self-efficacy/confidence for preservice teachers. teachHOUSTON offers numerous teacher enhancement opportunities outside the teacher education courses which allows preservice teachers to connect to the real world which includes being able to relate to a diverse population of students and to understand how the course content can be related to them, their families, and communities in their everyday experiences. Through formal and informal experiences such as professional development workshops, discipline specific courses, research experiences, and internships, preservice teachers have the opportunity to engage in hands-on science activities they can use with their students, develop lessons, and gain knowledge on how to deliver this content while managing their classroom. This chapter will give an overview of the formal and informal experiences offered through teachHOUSTON with a highlight on the structure and content of the six week Noyce Internship Program which engaged interns as counselors and teaching assistants in a summer STEM camp for underserved middle school students and introduces the interns to interactive sessions that model promising practices for teaching.

The aim of this study is to propose a governance model and key performance indicators on how policymakers can contribute to a more accessible, inclusive and sustainable mobility within and across smart cities to examine sustainable urban mobility grounded on the rational management of public transportation infrastructure.

This study employed desk research methodology grounded on secondary data from existing documents and previous research to develop a sustainable mobility governance model that explores key factors that influence future urban policy development. The collected secondary data was descriptively analyzed to provide initiatives and elements needed to achieve sustainable mobility services in smart cities.

Findings from this study provide evidence on how cities can benefit from the application of data from different sources to provide value-added services to promote integrated and sustainable mobility. Additionally, findings from this study discuss the role of smart mobility for sustainable services and the application for data-driven initiatives toward sustainable smart cities to enhance mobility interconnectivity, accessibility and multimodality. Findings from this study identify technical and non-technical factors that impact the sustainable mobility transition.

Practically, this study advocates for the use of smart mobility and data-driven services in smart cities to improve commuters' behavior aimed at long-term behavior change toward sustainable mobility by creating awareness on the society and supporting policymakers for informed decisions. Implications from this study provide information that supports policymakers and municipalities to implement data-driven mobility services.

This study provides implications toward behavioral change of individuals to adopt a more sustainable mode of travels, increase citizens’ quality of life, improve economic viability of business involved in providing mobility-related services and support decision-making for municipalities and policymakers during urban planning and design by incorporating the sustainability dimension into their present and future developments.

This paper explores how urban transportation can greatly reduce greenhouse gas emissions and provides implications for cities to improve accessibility and sustainability of public transportation, while simultaneously promoting the adoption of more environmentally friendly means of mobility within and across cities. Besides, this study provides a detailed discussion focusing on the potential opportunities and challenges faced in urban environment in achieving sustainable mobility. The governance model developed in this study can also be utilized by technology startups and transportation companies to assess the factors that they need to put in place or improve for the provision of sustainable mobility services.

Differential pressure is an important technological parameter, one urgent task of which is control and measurement. To date, the lion’s share of research in this area has focused on the development and improvement of differential pressure sensors. The purpose of this paper is to develop a smart differential pressure sensor with improved operational and metrological characteristics.

The operating principle of the developed pressure sensor is based on the capacitive measurement principle. The measuring unit of the developed pressure sensor is based on a differential capacitive sensitive element. Programmable system-on-chip (PSoC) technology has been used to develop the electronics unit.

The use of a differential capacitive sensitive element allows the unit to compensate for the influence of interference (for example, temperature) on the measurement result. With the use of PSoC technology, it is also possible to increase the noise immunity of the developed smart differential pressure sensor and provide an unparalleled combination of flexibility and integration of analog and digital functionality.

The use of PSoC technology in the developed smart differential pressure sensor has many indisputable advantages, as the size of the entire circuit can be minimized. As a result, the circuit has improved noise immunity. Accordingly, the procedure for debugging and changing the software of the electronics unit is simplified. These features make development and manufacturing cost effective.

The purpose of this paper is to introduce new non‐classical implementations of neural networks (NNs). The developed implementations are performed in the quantum, nano, and optical domains to perform the required neural computing. The various implementations of the new NNs utilizing the introduced architectures are presented, and their extensions for the utilization in the non‐classical neural‐systolic networks are also introduced.

The introduced neural circuits utilize recent findings in the quantum, nano, and optical fields to implement the functionality of the basic NN. This includes the techniques of many‐valued quantum computing (MVQC), carbon nanotubes (CNT), and linear optics. The extensions of implementations to non‐classical neural‐systolic networks using the introduced neural‐systolic architectures are also presented.

Novel NN implementations are introduced in this paper. NN implementation using the general scheme of MVQC is presented. The proposed method uses the many‐valued quantum orthonormal computational basis states to implement such computations. Physical implementation of quantum computing (QC) is performed by controlling the potential to yield specific wavefunction as a result of solving the Schrödinger equation that governs the dynamics in the quantum domain. The CNT‐based implementation of logic NNs is also introduced. New implementations of logic NNs are also introduced that utilize new linear optical circuits which use coherent light beams to perform the functionality of the basic logic multiplexer by utilizing the properties of frequency, polarization, and incident angle. The implementations of non‐classical neural‐systolic networks using the introduced quantum, nano, and optical neural architectures are also presented.

The introduced NN implementations form new important directions in the NN realizations using the newly emerging technologies. Since the new quantum and optical implementations have the advantages of very high‐speed and low‐power consumption, and the nano implementation exists in very compact space where CNT‐based field effect transistor switches reliably using much less power than a silicon‐based device, the introduced implementations for non‐classical neural computation are new and interesting for the design in future technologies that require the optimal design specifications of super‐high speed, minimum power consumption, and minimum size, such as in low‐power control of autonomous robots, adiabatic low‐power very‐large‐scale integration circuit design for signal processing applications, QC, and nanotechnology.

The capability to predict and evaluate various configurations’ performance during the conceptual design phase using multidisciplinary design analysis and optimization can significantly increase the preliminary design process’s efficiency and reduce design and development costs. This research paper aims to perform multidisciplinary design and optimization for an expendable microsatellite launch vehicle (MSLV) comprising three solid-propellant stages, capable of delivering micro-payloads in the low earth orbit. The methodology’s primary purpose is to increase the conceptual and preliminary design process’s efficiency by reducing both the design and development costs.

Multidiscipline feasible architecture is applied for the multidisciplinary design and optimization of an expendable MSLV at the conceptual level to accommodate interdisciplinary interactions during the optimization process. The multidisciplinary design and optimization framework developed and implemented in this research effort encompasses coupled analysis disciplines of vehicle geometry, mass calculations, aerodynamics, propulsion and trajectory. Nineteen design variables were selected to optimize expendable MSLV to launch a 100 kg satellite at an altitude of 600 km in the low earth orbit. Modern heuristic optimization methods such as genetic algorithm (GA), particle swarm optimization (PSO) and SA are applied and compared to obtain the optimal configurations. The initial population is created by passing the upper and lower bounds of design variables to the optimizer. The optimizer then searches for the best possible combination of design variables to obtain the objective function while satisfying the constraints.

All of the applied heuristic methods were able to optimize the design problem. Optimized design variables from these methods lie within the lower and upper bounds. This research successfully achieves the desired altitude and final injection velocity while satisfying all the constraints. In this research effort, multiple runs of heuristic algorithms reduce the fundamental stochastic error.

The use of multiple heuristics optimization methods such as GA, PSO and SA in the conceptual design phase owing to the exclusivity of their search approach provides a unique opportunity for exploration of the feasible design space and helps in obtaining alternative configurations capable of meeting the mission objectives, which is not possible when using any of the single optimization algorithm.

The optimized configurations can be further used as baseline configurations in the microsatellite launch missions’ conceptual and preliminary design phases.

Satellite launch vehicle design and optimization is a complex multidisciplinary problem, and it is dealt with effectively in the multidisciplinary design and optimization domain. It integrates several interlinked disciplines and gives the optimum result that satisfies these disciplines’ requirements. This research effort provides the multidisciplinary design and optimization-based simulation framework to predict and evaluate various expendable satellite launch vehicle configurations’ performance. This framework significantly increases the conceptual and preliminary design process’s efficiency by reducing design and development costs.

The purpose of this paper is to propose a methodology for the enrichment and tailoring of a knowledge organization system (KOS), in order to support the information extraction (IE) task for the analysis of documents in the tourism domain. In particular, the KOS is used to develop a named entity recognition (NER) system.

A method to improve and customize an available thesaurus by leveraging documents related to the tourism in Italy is firstly presented. Then, the obtained thesaurus is used to create an annotated NER corpus, exploiting both distant supervision, deep learning and a light human supervision.

The study shows that a customized KOS can effectively support IE tasks when applied to documents belonging to the same domains and types used for its construction. Moreover, it is very useful to support and ease the annotation task using the proposed methodology, allowing to annotate a corpus with a fraction of the effort required for a manual annotation.

The paper explores an alternative use of a KOS, proposing an innovative NER corpus annotation methodology. Moreover, the KOS and the annotated NER data set will be made publicly available.