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In this work the experimental effect of a slow decay of the photoluminescence is studied theoretically in the case of quantum dots with an indirect energy band gap. The slow decay of the photoluminescence is considered as decay in time of the luminescence intensity, following the excitation of the quantum dot sample electronic system by a short optical pulse. In the presented theoretical treatment the process is studied as a single dot property. The inter-valley deformation potential interaction of the excited conduction band electrons with lattice vibrations is considered in the self-consistent Born approximation to the electronic self-energy. The theory is built on the non-equilibrium electronic quantum transport theory. The time dependence of the photoluminescence decay is estimated upon using a simple effective mass model. The numerical calculation of the considered model shows the power-law time characteristics of the photoluminescence decay in the long-time limit of the decay. We demonstrate that the nonadiabatic influence of the interaction of the conduction band electrons with the lattice vibrations provides a mechanism giving us the power-law time dependence of the photoluminescence intensity signal. This theoretical result emphasizes the role of the electron-phonon interaction in the nanostructures.

Through many years of study, we have found that cold quantum is the most important force in nature. Under the pressure of coldness on hotness, various materials are formed. Under the pressure of cold quantum, these materials are provided with gravity, and celestial bodies start to move. The pressure of cold quantum exists in space and materials. It is the pressure of cold quantum that huge changes between the four seasons on the earth begin to appear. The whirlpool, produced from the cold quantum pressure, pushes all the celestial bodies making them turn and change. The coldness converts frozen water into ice, which could not be achieved by any other force. The extreme and powerful strength of cold quantum has been well‐known. Therefore, we claim that the cold quantum pressure is the greatest force which ever existed in the universe.

In nano-scale-based very large scale integration technology, quantum-dot cellular automata (QCA) is considered as a strong and capable technology to replace the well-known complementary metal oxide semiconductor technology. In QCA technique, rotated majority gate (RMG) design is not explored greatly, and therefore, its advantages compared to original majority gate are unnoticed. This paper aims to provide a thorough observation at RMG gate with its capability to build robust circuits.

This paper presents a new methodology for structuring reliable 2n-bit full adder (FA) circuit design in QCA utilizing RMG. Mathematical proof is provided for RMG gate structure. A new 1-bit FA circuit design is projected here, which is constructed with RMG gate and clock-zone-based crossover approach in its configuration.

A new structure of a FA is projected in this paper. The proposed design uses only 50 number of QCA cells in its implementation with a latency of 3 clock zones. The proposed 1-bit FA design conception has been checked for its structure robustness by designing various 2, 4, 8, 16, 32 and 64-bit FA designs. The proposed FA designs save power from 46.87% to 25.55% at maximum energy dissipation of circuit level, 39.05% to 23.36% at average energy dissipation of circuit-level and 42.03% to 37.18% at average switching energy dissipation of circuit level.

This paper fulfills the gape of focused research for RMG with its detailed mathematical modeling analysis.

The purpose of this paper is to propose an alternative approach to approximate reasoning by DNA computing, thereby adding a new dimension to the existing approximate reasoning method by bringing it down to nanoscale computing. The logical aspect of approximate reasoning is replaced by DNA chemistry.

To achieve this goal, first the synthetic DNA sequence fuzzified by quantum dot, which is a recent advancement of nanotechnology. Thus with the help of fuzzy DNA, which holds the vague concept of human reasoning, the basic method of approximate reasoning on a DNA chip is realized. This approach avoids the tedious choice of a suitable implication operator (for a particular application) necessary for existing approximate reasoning based on fuzzy logic. The inferred consequences obtained from DNA computing‐based approximate reasoning is ultimately hybridized with appropriate complementary sequence probed on a DNA‐chip to confirm the result of inference.

The present approach is suitable for reasoning under vague and uncertain environment and does not require any subject choice of any individual expert, which is essential for existing approximate reasoning method.

This new tool for approximate reasoning based on DNA computing is applicable to several problems of science and engineering; namely pattern classification, control theory, weather forecasting, atmospheric science, etc.

This information will be useful in the selection of materials and technology for the detection and removal of mercury ions at a low cost and with high sensitivity and selectivity. The purpose of this study is to provide the useful information for selection of materials and technology to detect and remove the mercury ions from water with high sensitivity and selectivity. The purpose of this study is to provide the useful information for selection of materials and technology to detect and remove the mercury ions from water with high sensitivity and selectivity.

Different nano- and bio-materials allowed for the development of a variety of biosensors – colorimetric, chemiluminescent, electrochemical, whole-cell and aptasensors – are described. The materials used for their development also make it possible to use them in removing heavy metals, which are toxic contaminants, from environmental water samples.

This review focuses on different technologies, tools and materials for mercury (heavy metals) detection and remediation to environmental samples.

This review gives up-to-date and systemic information on modern nanotechnology methods for heavy metal detection. Different recognition molecules and nanomaterials have been discussed for remediation to water samples. The present review may provide valuable information to researchers regarding novel mercury ions detection sensors and encourage them for further research/development.

Whilst at the Department of Computer Science at Columbia University, US, Michael Lebowitz, currently at the Analytical Proprietary Trading Unit of Morgan Stanley and Company, New York, researched into a variety of areas in natural language processing and machine learning. In particular his UNIMEM learning program has been applied to a wide range of domains including census data, software evaluation and congressional voting records. In a recent research contribution, “The Use of Memory in Text Processing”, Communications of the ACM, Vol. 31 No. 12, 1988, pp. 1483–1505, he describes how RESEARCHER, a program that reads, remembers and generalises from patent abstracts, makes use of its automatically generated memory to assist low‐level text processing. This, he says, involves disambiguation that could be accomplished in no other way.

The purpse of this study is to examine sustainable technology development (STD) during the “Valley of Death” phase encountered by university startups undertaking intellectual property rights (IPR) commercialisation.

A comprehensive literature review was conducted after searching for relevant documents across multiple databases. Semi-structured interviews with university startup founders were also conducted as part of a qualitative case study.

This study resulted in two significant findings. First, the Valley of Death has been redefined in the specific context of IPR commercialisation by university startups. Second, the sustainable technology development framework (STDF) has been conceptualised to enhance the success rate of IPR commercialisation by university startups. The authors also identified three essential components of STD in the context of university startups: market development, technical efficiency and business sustainability.

This exploratory research involved a thorough literature analysis. Given that only one qualitative case study was conducted, data saturation was not achieved. Further empirical research is needed to validate the conceptualised STDF.

The validated STDF will be a useful tool for enhancing the success of IPR commercialisation by university startups.

While others have focused on innovating business models, this study focused on an underexplored area: the sustainability of technology development during the commercialisation of IPR by university startups during the Valley of Death phase.

The involvement of wear, friction and lubrication in engineering systems and industrial applications makes it imperative to study the various aspects of tribology in relation with advanced technologies and concepts. The concept of Industry 4.0 and its implementation further faces a lot of barriers, particularly in developing economies. Real-time and reliable data is an important enabler for the implementation of the concept of Industry 4.0. For availability of reliable and real-time data about various tribological systems is crucial in applying the various concepts of Industry 4.0. This paper aims to attempt to highlight the role of sensors related to friction, wear and lubrication in implementing Industry 4.0 in various tribology-related industries and equipment.

A through literature review has been done to study the interrelationships between the availability of tribology-related data and implementation of Industry 4.0 are also discussed. Relevant and recent research papers from prominent databases have been included. A detailed overview about the various types of sensors used in generating tribological data is also presented. Some studies related to the application of machine learning and artificial intelligence (AI) are also included in the paper. A discussion on fault diagnosis and cyber physical systems in connection with tribology has also been included.

Industry 4.0 and tribology are interconnected through various means and the various pillars of Industry 4.0 such as big data, AI can effectively be implemented in various tribological systems. Data is an important parameter in the effective application of concepts of Industry 4.0 in the tribological environment. Sensors have a vital role to play in the implementation of Industry 4.0 in tribological systems. Determining the machine health, carrying out maintenance in off-shore and remote mechanical systems is possible by applying online-real-time data acquisition.

The paper tries to relate the pillars of Industry 4.0 with various aspects of tribology. The paper is a first of its kind wherein the interdisciplinary field of tribology has been linked with Industry 4.0. The paper also highlights the role of sensors in generating tribological data related to the critical parameters, such as wear rate, coefficient of friction, surface roughness which is critical in implementing the various pillars of Industry 4.0.

This paper aims to discuss the new applications enabled by printed sensors.

The paper discusses how silicon‐based sensors are manufactured using the time‐consuming, expensive, and complicated fabrication process of traditional semiconductor devices and shows what is needed in order to produce such new devices with the advantages of printed sensors.

With new materials, new processing technologies and a new manufacturing process, thin, flexible, lightweight, cost‐effective sensors are made possible through the power of printed semiconductors.

This paper should be of value in terms of understanding the pros of printed semiconductors and the resulting sensors which have a number of unique mechanical, electrical, and optical properties.