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In the realm of edge AI systems where deep learning is paramount, protecting the intellectual property (IP) of multimodal neural network models is crucial. Current watermarking solutions often bypass the intricacies of multimodal models and the unique constraints of edge environments. Addressing this, a novel watermarking scheme specifically devised for multimodal neural networks is introduced, marking a significant stride in securing these models against IP theft and unauthorized use. A discrete watermark is ingeniously embedded within each modality of a multimodal model, synthesizing a comprehensive watermark that spans the entire model. This method ensures IP protection across varied data types without hampering the model's performance or imposing undue computational demands, making it ideal for resource-limited edge devices. By leveraging the redundancies inherent in multimodal data, watermarks are embedded efficiently, maintaining model integrity and operational effectiveness. A robust verification mechanism is implemented, accurately identifying watermark presence across modalities with minimal computational overhead. Empirical validation on a benchmark dataset demonstrates the method's efficacy in embedding watermarks discreetly while preserving the model's original task performance, showing a 1 to 4% increase in watermark detection rates and a 6 to 10% reduction in false positives compared to existing approaches. This positions the scheme as an effective strategy for IP protection in multimodal neural network models, especially suited for the computational economy required in edge AI systems. The work advances neural network watermarking and addresses the urgent need for scalable IP protection solutions in the evolving AI landscape. [ABSTRACT FROM AUTHOR]

In recent years, the Internet of Things (IoT) has become one of the most familiar names creating a benchmark and scaling new heights. IoT an indeed future of the communication that has transformed the objects (things) of the real world into smarter devices. With the advent of IoT technology, this decade is witnessing a transformation from traditional agriculture approaches to the most advanced ones. Limited research has been carried out in this direction. Thus, herein we present various technological aspects involved in IoT-based cultivation. The role and the key components of smart farming using IoT were examined, with a focus on network technologies, including layers, protocols, topologies, network architecture, etc. We also delve into the integration of relevant technologies such as cloud computing, big data analytics, and the integration of IoT-based cultivation. We explored various security issues in modern IoT cultivation and also emphasized the importance of safeguarding sensitive agricultural data. Additionally, a comprehensive list of applications based on sensors and mobile devices is provided, offering refined solutions for greenhouse management. The principles and regulations established by different countries for IoT-based cultivation systems are presented, demonstrating the global recognition of these technologies. Furthermore, a selection of successful use cases and real-world scenarios and applications were presented. Finally, the open research challenges and solutions in modern IoT-based cultivation were discussed. [ABSTRACT FROM AUTHOR]

In this paper, we further verify the validity of the quantum partial adiabatic search algorithm which was initialized in the previous related works by revisiting its quantum circuit model. The main results got here are as follows. When considering implementing quantum partial adiabatic evolution on a quantum circuit, a correction is given for the time slice estimation for the first stage during this approximation in the previous related works, new evidence is provided for a time complexity cost O (N / M) of quantum partial adiabatic algorithm is impossible, and the correct time complexity O (N / M) of it is emphasized once more according to its circuit correspondence, in which N is the total number of elements in the search problem of which M of them are the marked ones. The findings exposed are hopeful for revisiting quantum partial adiabatic evolution and its connection with the quantum circuit model. [ABSTRACT FROM AUTHOR]

In this paper, we proposed an efficient single-state three-party quantum key agreement (QKA) protocol. We proved that the protocol can resist potential outside attacks and inside attacks, and we generalized the efficient single-state three-party QKA scheme into the case of the N-party by substituting N-particle entangled state for three-particle entangled state as the resource quantum states. Compared with the previous QKA protocols, our scheme contains the good features of previous schemes, i.e., using one kind of maximally entangled states as the quantum resource, no requirements for pre-shared key between different participants, and no requirements for executing any unitary operations or quantum entanglement swapping. Furthermore, our scheme has significant improvements in terms of the times of quantum states transmission, the consumed qubits, and the qubit efficiency. In particular, as the number of participants increases, the number of qubits required by our scheme increases linearly rather than exponentially. [ABSTRACT FROM AUTHOR]

Large data volumes make manually keeping and preserving health records for future reference problematic. Most unusual is the coronavirus epidemic's overcrowding of hospitals. The Secure Pattern Electronic Healthcare Records (SPEHR) scheme provides a powerful and lightweight secure storage system for cloud-based Ehealthcare services that meets remote healthcare security criteria. So we proposed a neutrosophic model with a multicriterion decision-making (MCDM) method for the analysis and evaluation of these criteria. The neutrosophic model is used to deal with vague and uncertain data. Then we integrated the neutrosophic model with the AHP method to rank and compute the weights of the criteria. Offering stakeholders a secure interface and avoiding unwanted a ccess to cloud-stored data mitigates E-healthcare risks. The key derivation ensures end -to-end data. ciphering to prevent unauthorized use (KDF). This work provides robust security solutions for various environments. This work can meet security needs for people and secure-communicating organizations. We found the data privacy is the best criterion. [ABSTRACT FROM AUTHOR]

Intrusion detection systems (IDS) play a crucial role in securing networks and identifying malicious activity. This is a critical problem in cyber security. In recent years, metaheuristic optimization algorithms and deep learning techniques have been applied to IDS to improve their accuracy and efficiency. Generally, optimization algorithms can be used to boost the performance of IDS models. Deep learning methods, such as convolutional neural networks, have also been used to improve the ability of IDS to detect and classify intrusions. In this paper, we propose a new IDS model based on the combination of deep learning and optimization methods. First, a feature extraction method based on CNNs is developed. Then, a new feature selection method is used based on a modified version of Growth Optimizer (GO), called MGO. We use the Whale Optimization Algorithm (WOA) to boost the search process of the GO. Extensive evaluation and comparisons have been conducted to assess the quality of the suggested method using public datasets of cloud and Internet of Things (IoT) environments. The applied techniques have shown promising results in identifying previously unknown attacks with high accuracy rates. The MGO performed better than several previous methods in all experimental comparisons. [ABSTRACT FROM AUTHOR]

Multiparty quantum key agreement (MQKA) requires sharing a secure and fair key among participants. However, several malicious participants may collude together to steal the privacy of honest participants or determine the shared key, privately. In this work, we propose a tree-type MQKA protocol against collusive attacks, in which the entanglement swapping technology of multi-particle entangled states is used to construct the shared key. Compared with the previous MQKA protocols against collusive attacks, our scheme consumes fewer qubits and only needs to transmit quantum states once, which significantly reduces the consumption of quantum resources. [ABSTRACT FROM AUTHOR]

LiNi0.5Co0.2Mn0.3O2 (NCM523) has become one of the most popular cathode materials for current lithium-ion batteries due to its high-energy density and cost performance. However, the rapid capacity fading of NCM severely hinders its development and applications. Here, the single crystal NCM523 materials under different degradation states are characterized using scanning transmission electron microscopy (STEM). Then we developed a neural network model with a two-sequential attention block to recognize the crystal structure and locate defects in STEM images. The number of point defects in NCM523 is observed to experience a trend of increasing first and then decreasing in the degradation process. The space between the transition metal columns shrinks obviously, inducing dramatic capacity decay. This analysis sheds light on the defect evolution and chemical transformation correlated with layered material degradation. It also provides interesting hints for researchers to regenerate the electrochemical capacity and design better battery materials with longer life. [ABSTRACT FROM AUTHOR]

In this research, a modification of the Harris hawk optimization (HHO) is introduced and applied to feature selection. The proposed HHO variant is termed as hybrid multi-leader HHO with differential evolution (MLHHDE). To help hawks overcome the situation of falling into local optimum during the searching process, an improved memory structure is introduced, which makes the hawks learn simultaneously from the historical best and global best positions. Besides, the multi-leader mechanism is also introduced to make full use of the valuable information from global best memories (leaders), enhance the diversity of hawks' search mode, and improve the hawks' exploration capability. Furthermore, differential evolution operations are integrated into HHO to further improve the weak exploration phase. Our proposed MLHHDE algorithm integrates a V-shaped transfer function, which can convert continuous solutions to binary solutions. To validate the modification of MLHHDE, we compared its performance with the advanced optimization algorithms through three experiments. In the first experiment, the performance of MLHHDE to solve a set of problems from the CEC 2017 benchmark is evaluated. Meanwhile, the second experiment aims to apply the binary version of MLHHDE to tackle the feature selection task by applying it to a set of sixteen datasets from the UCI repository. In the third, we applied the proposed model as a quantitative structure-activity relationship method to predict the influenza viruses H1N1 as a real-world application. The performance of the proposed MLHHDE is assessed using a number of evaluation indicators. The experiment results prove the powerful capability of MLHHDE to find the optimal solution in the two experiments as well as it outperforms other methods (i.e., either global optimization or feature selection). In addition, the developed MLHHDE provides accuracy overall the UCI datasets nearly 84% with difference 5% between it and traditional HHO, also, it provides accuracy 92% with standard deviation when applied to predict H1N1. [ABSTRACT FROM AUTHOR]

Holding a lasting balance between the water resources and water demands has become a challenging task for water resources managers, especially in recent years with the looming global warming crisis and its resulting climatic change effects. This paper focuses on modeling the optimized operation of the Zayandehrud Reservoir, located in west-central Iran, under two fifth-generation climate change scenarios called RCP4.5 and RCP8.5. A novel variant of the gravitational search algorithm (GSA), named the adaptive accelerated GSA (AAGSA) is proposed and adopted as the optimizer of the reservoir operation in this paper. The major advancement of the AAGSA against the original GSA is its high exploration capability, allowing the proposal to effectively tackle a variety of difficulties any complex optimization problem can face. The goal of the optimization process is the maximization of the sustainability of supplying the downstream water demands by the reservoir. The optimal results obtained by the original GSA and the proposed AAGSA algorithms suggest that the AAGSA can achieve much more accurate results with much less computational runtime, such that the proposed AAGSA is able to achieve the reservoir operation sustainability index of 98.53% and 99.46%, under RCP4.5 and RCP8.5 scenarios, respectively. These figures are higher than those obtained by the original GSA by 23.5% and 16% under RCP4.5 and RCP8.5, respectively, while the runtime of the proposal is reduced by over 80% in both scenarios, as compared to the GSA, suggesting the high competence of the proposed AAGSA to solve such a high-dimensional and complex real-world engineering problem. [ABSTRACT FROM AUTHOR]

The design of complex mechanical components is a time-consuming process which involves many design variables with multiple interacted objectives and constraints. Traditionally, the design process of mechanical components is performed manually depending on the intuition and experience of the designer. In recent decades, automatic methods have been proposed to effectively search diverse and large parameter spaces. There is a growing interest in design optimization of mechanical systems using metaheuristic algorithms to improve the product lifecycle and performance and minimize the cost. Nowadays, there is a growing interest in design optimization of mechanical systems using metaheuristic algorithms to improve the product lifecycle and performance and minimize the cost. This review article demonstrates the applications of different metaheuristic algorithms in enhancing the design process of different mechanical systems. First, the basic concepts of common used metaheuristic algorithms are introduced. Then the applications of theses algorithms in optimization of different mechanical systems are discussed. [ABSTRACT FROM AUTHOR]

Deep neural networks (DNNs) have evolved as a beneficial machine learning method that has been successfully used in various applications. Currently, DNN is a superior technique of extracting information from massive sets of data in a self-organized method. DNNs have different structures and parameters, which are usually produced for particular applications. Nevertheless, the training procedures of DNNs can be protracted depending on the given application and the size of the training set. Further, determining the most precise and practical structure of a deep learning method in a reasonable time is a possible problem related to this procedure. Meta-heuristics techniques, such as swarm intelligence (SI) and evolutionary computing (EC), represent optimization frames with specific theories and objective functions. These methods are adjustable and have been demonstrated their effectiveness in various applications; hence, they can optimize the DNNs models. This paper presents a comprehensive survey of the recent optimization methods (i.e., SI and EC) employed to enhance DNNs performance on various tasks. This paper also analyzes the importance of optimization methods in generating the optimal hyper-parameters and structures of DNNs in taking into consideration massive-scale data. Finally, several potential directions that still need improvements and open problems in evolutionary DNNs are identified. [ABSTRACT FROM AUTHOR]

The Industrial Internet of Things (IIoT) has become a pivotal field of development that can increase the efficiency of real-time collection, recording, analysis, and control of the entire activities of various machines, and can actively enhance quality and reduce costs. The traditional IIoT depends on centralized architectures that are vulnerable to several kinds of cyber-attacks, such as bottlenecks and single points of failure. Blockchain technology has emerged to change these architectures to a decentralized form. In modern industrial settings, blockchain technology is utilized for its ability to provide high levels of security, low computational complexity, P2P communication, transparent logs, and decentralization. The present work proposes the use of a private blockchain mechanism for an industrial application in a cement factory, which offers low power consumption, scalability, and a lightweight security scheme; and which can play an efficient role in controlling access to valuable data generated by sensors and actuators. A low-power ARM Cortex-M processor is utilized due to its efficiency in terms of processing cryptographic algorithms, and this plays an important part in improving the computational execution of the proposed architecture. In addition, instead of proof of work (PoW), our blockchain network uses proof of authentication (PoAh) as a consensus mechanism to ensure secure authentication, scalability, speed, and energy efficiency. Our experimental results show that the proposed framework achieves high levels of security, scalability and ideal performance for smart industrial environments. Moreover, we successfully realized the integration of blockchain technology with the industrial internet of things devices, which provides the blockchain technology features and efficient resistance to common cyber-security attacks. [ABSTRACT FROM AUTHOR]

Battery lifetime prediction is a promising direction for the development of next-generation smart energy storage systems. However, complicated degradation mechanisms, different assembly processes, and various operation conditions of the batteries bring tremendous challenges to battery life prediction. In this work, charge/discharge data of 12 solid-state lithium polymer batteries were collected with cycle lives ranging from 71 to 213 cycles. The remaining useful life of these batteries was predicted by using a machine learning algorithm, called symbolic regression. After populations of breed, mutation, and evolution training, the test accuracy of the quantitative prediction of cycle life reached 87.9%. This study shows the great prospect of a data-driven machine learning algorithm in the prediction of solid-state battery lifetimes, and it provides a new approach for the batch classification, echelon utilization, and recycling of batteries. [ABSTRACT FROM AUTHOR]

Polymers have been widely used in energy storage, construction, medicine, aerospace, and so on. However, the complexity of chemical composition and morphology of polymers has brought challenges to their development. Thanks to the integration of machine learning algorithms and large data resources, the data‐driven methods have opened up a new road for the development of polymer science and engineering. The emerging polymer informatics attempts to accelerate the performance prediction and process optimization of new polymers by using machine learning models based on reliable data. With the gradual supplement of currently available databases, the emergence of new databases and the continuous improvement of machine learning algorithms, the research paradigm of polymer informatics will be more efficient and widely used. Based on these points, this paper reviews the development trends of machine learning assisted polymer informatics and provides a simple introduction for researchers in materials, artificial intelligence, and other fields. [ABSTRACT FROM AUTHOR]

Multilevel thresholding image segmentation has received considerable attention in several image processing applications. However, the process of determining the optimal threshold values (as the preprocessing step) is time-consuming when traditional methods are used. Although these limitations can be addressed by applying metaheuristic methods, such approaches may be idle with a local solution. This study proposed an alternative multilevel thresholding image segmentation method called VPLWOA, which is an improved version of the volleyball premier league (VPL) algorithm using the whale optimization algorithm (WOA). In VPLWOA, the WOA is used as a local search system to improve the learning phase of the VPL algorithm. A set of experimental series is performed using two different image datasets to assess the performance of the VPLWOA in determining the values that may be optimal threshold, and the performance of this algorithm is compared with other approaches. Experimental results show that the proposed VPLWOA outperforms the other approaches in terms of several performance measures, such as signal-to-noise ratio and structural similarity index. [ABSTRACT FROM AUTHOR]

Coronavirus pandemic (COVID-19) has infected more than ten million persons worldwide. Therefore, researchers are trying to address various aspects that may help in diagnosis this pneumonia. Image segmentation is a necessary pr-processing step that implemented in image analysis and classification applications. Therefore, in this study, our goal is to present an efficient image segmentation method for COVID-19 Computed Tomography (CT) images. The proposed image segmentation method depends on improving the density peaks clustering (DPC) using generalized extreme value (GEV) distribution. The DPC is faster than other clustering methods, and it provides more stable results. However, it is difficult to determine the optimal number of clustering centers automatically without visualization. So, GEV is used to determine the suitable threshold value to find the optimal number of clustering centers that lead to improving the segmentation process. The proposed model is applied for a set of twelve COVID-19 CT images. Also, it was compared with traditional k-means and DPC algorithms, and it has better performance using several measures, such as PSNR, SSIM, and Entropy. [ABSTRACT FROM AUTHOR]

We present the analog analogue of Grover's problem as an example of the time-independent Hamiltonian for applying the speed limit of the imaginary-time Schrödinger equation derived by Okuyama and Ohzeki and the new class of energy-time uncertainty relation proposed by Kieu. It is found that the computational time of the imaginary-time quantum annealing of this Grover search can be exponentially small, while the counterpart of the quantum evolution driven by the real-time Schrödinger equation could only provide square root speedup, compared with classic search. The present results are consistent with the cases of the time-dependent quantum evolution of the natural Grover problem in previous works. We once again emphasize that the logarithm and square root algorithmic performances are generic in imaginary-time quantum annealing and quantum evolution driven by real-time Schrödinger equation, respectively. Also, we provide evidences to search deep reasons why the imaginary-time quantum annealing can lead to exponential speedup and the real-time quantum annealing can make square root speedup. [ABSTRACT FROM AUTHOR]

Recently, Kieu derived a new class of time–energy uncertainty relations, which is not only formal but also suitable for evaluating the speed limit of quantum dynamics. However, when this time–energy uncertainty relation was applied to study the quantum adiabatic Grover's search, some approximation had been done for reaching the right conclusion. That is, the final state being orthogonal to the initial state in this problem was assumed which may cause confusion, while the truth is that the overlap between these two states is indeed very small when the problem size is very big but not equal to zero exactly. In this paper, we modify the time–energy uncertainty relation of Kieu applied to two quantum evolutions of Grover's problem, and find that the resulting computational complexities match well with the previous works on this topic but without the need of making the assumption like that in the work of Kieu. [ABSTRACT FROM AUTHOR]

COVID-19 is a worldwide epidemic, as announced by the World Health Organization (WHO) in March 2020. Machine learning (ML) methods can play vital roles in identifying COVID-19 patients by visually analyzing their chest x-ray images. In this paper, a new ML-method proposed to classify the chest x-ray images into two classes, COVID-19 patient or non-COVID-19 person. The features extracted from the chest x-ray images using new Fractional Multichannel Exponent Moments (FrMEMs). A parallel multi-core computational framework utilized to accelerate the computational process. Then, a modified Manta-Ray Foraging Optimization based on differential evolution used to select the most significant features. The proposed method evaluated using two COVID-19 x-ray datasets. The proposed method achieved accuracy rates of 96.09% and 98.09% for the first and second datasets, respectively. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) has received widespread attention over the last few decades due to its potential to increase automation and accelerate productivity. In recent years, a large number of training data, improved computing power, and advanced deep learning algorithms are conducive to the wide application of AI, including material research. The traditional trial‐and‐error method is inefficient and time‐consuming to study materials. Therefore, AI, especially machine learning, can accelerate the process by learning rules from datasets and building models to predict. This is completely different from computational chemistry where a computer is only a calculator, using hard‐coded formulas provided by human experts. Herein, the application of AI in material innovation is reviewed, including material design, performance prediction, and synthesis. The realization details of AI techniques and advantages over conventional methods are emphasized in these applications. Finally, the future development direction of AI is expounded from both algorithm and infrastructure aspects. [ABSTRACT FROM AUTHOR]

We propose a homomorphic search protocol based on quantum homomorphic encryption, in which a client Alice with limited quantum ability can give her encrypted data to a powerful but untrusted quantum server and let the server search for her without decryption. By outsourcing the interactive key-update process to a trusted key center, Alice only needs to prepare and encrypt her original data and to decrypt the ciphered search result in linear time. Besides, we also present a compact and perfectly secure quantum homomorphic evaluation protocol for Clifford circuits, where the decryption key can be calculated by Alice with polynomial overhead with respect to the key length. [ABSTRACT FROM AUTHOR]

Recently, significant attention has been devoted to vaccine-derived poliovirus (VDPV) surveillance due to its severe consequences. Prediction of the outbreak incidence of VDPF requires an accurate analysis of the alarming data. The overarching aim to this study is to develop a novel hybrid machine learning approach to identify the key parameters that dominate the outbreak incidence of VDPV. The proposed method is based on the integration of random vector functional link (RVFL) networks with a robust optimization algorithm called whale optimization algorithm (WOA). WOA is applied to improve the accuracy of the RVFL network by finding the suitable parameter configurations for the algorithm. The classification performance of the WOA-RVFL method is successfully validated using a number of datasets from the UCI machine learning repository. Thereafter, the method is implemented to track the VDPV outbreak incidences recently occurred in several provinces in Lao People's Democratic Republic. The results demonstrate the accuracy and efficiency of the WOA-RVFL algorithm in detecting the VDPV outbreak incidences, as well as its superior performance to the traditional RVFL method. [ABSTRACT FROM AUTHOR]

The viscosity of bitumen is extremely high at ambient temperature and in order to produce and transport the bitumen, it is necessary to reduce its viscosity. In this regard various n-alkane with different surface tension dilutes bitumen. This study features the application of artificial neural network for prediction of surface tension of branched alkanes. Modeling results showed that the both models are accurate and effective in prediction of surface tension of branched alkanes. However, the results were compared with experimental data and it was found that the estimated surface tension by ANN-MLP is more accurate than radial basis function network. [ABSTRACT FROM AUTHOR]

In machine learning and data mining, feature selection (FS) is one of the most important tasks required to select the most relevant instances from a dataset. In other words, FS is used to reduce the amount of information, creating a subset that represents the entire pool of data. The accuracy of the FS is reflected in a good classification of the information. This article presents an improved version of the social spider optimization (SSO) algorithm. The SSO tends to fail in local optima during the iterative process and is not possible to avoid this situation in the standard form. The proposed version avoids selecting the irrelevant features that demerit the performance of the FS. To achieve this goal, the opposition-based learning is used, in which there is a rule used to increase the exploration of the search space and the prominent zones in a determined neighborhood. The proposed algorithm is called opposition-based social spider optimization (OBSSO), and it has been tested over different mathematical problems. Moreover, the OBSSO, also, has been tested and compared with similar approaches using different datasets with specific information selected from UCI repository. The experimental results provide the evidence of the capabilities of the OBSSO for solving complex optimization problems. [ABSTRACT FROM AUTHOR]

Feature selection (FS) is a machine learning process commonly used to reduce the high dimensionality problems of datasets. This task permits to extract the most representative information of high sized pools of data, reducing the computational effort in other tasks as classification. This article presents a hybrid optimization method for the FS problem; it combines the slap swarm algorithm (SSA) with the particle swarm optimization. The hybridization between both approaches creates an algorithm called SSAPSO, in which the efficacy of the exploration and the exploitation steps is improved. To verify the performance of the proposed algorithm, it is tested over two experimental series, in the first one, it is compared with other similar approaches using benchmark functions. Meanwhile, in the second set of experiments, the SSAPSO is used to determine the best set of features using different UCI datasets. Where the redundant or the confusing features are removed from the original dataset while keeping or yielding a better accuracy. The experimental results provide the evidence of the enhancement in the SSAPSO regarding the performance and the accuracy without affecting the computational effort. [ABSTRACT FROM AUTHOR]

In this paper [J. Sun, S. Lu and F. Liu, Open Syst. Info. Dyn.23(3) (2016) 1650016], we have studied a general class of adiabatic evolutions in quantum adiabatic search type problem, and found another limitation on it, besides those reported in our early work. In this paper, we extend this study by considering adding an extra driving scheme to the system Hamiltonian of this type of quantum adiabatic evolution. It will be shown that, like the previous study in [J. Sun, S. Lu and F. Liu, Open Syst. Info. Dyn.23(3) (2016) 1650016], the limitation above can also possibly penetrate into the search type quantum-adiabatic computation even by this more general family of system Hamiltonian, which may shed light on what we should have in mind when solving practical problems by means of quantum adiabatic evolution. [ABSTRACT FROM AUTHOR]

It is known that when the initial state has no overlap with the final state in an adiabatic computation, an extra assisted driving Hamiltonian has to be used to avoid the failure of quantum adiabatic algorithm. This assisted Hamiltonian has a relatively fixed form, which can be seen in the related literature but the mechanism behind it is still not clear enough. In this paper, we discuss the usefulness of a generalized form of assisted driving Hamiltonian in adiabatic evolution which may shed light on this problem. [ABSTRACT FROM AUTHOR]

Pregnant women are a unique group undergoing profound structural modifications in uterus, breast, adipose tissue and extracellular fluids. Amino acid metabolic stress is a unique physical process that occurs during pregnancy. Metals constitute a fundamental part of the maternal body and have a universal effect on amino acid metabolism. However, the exact interaction between metals and amino acid metabolism during pregnancy is unknown. The aim of the present study was to determine the correlations of metals with amino acid metabolic intermediates in the urine of 232 healthy pregnant women in their first, second and third trimesters during normal pregnancy. Sixteen metals in the urine of 232 healthy pregnant women in their first, second and third trimesters were quantified using inductively coupled plasma mass spectrometry (ICP-MS). An ultra-performance liquid chromatography quadrupole time-of-flight mass spectrometer (UPLC-QTOFMS)-based metabolomics approach was conducted to detect intermediate products involved in amino acid metabolism during the entire pregnancy period. A panel regression model was established to investigate the relationship between urine metals and amino acid metabolism. Seven metals—cadmium, cobalt, copper, cesium, manganese, thallium and vanadium—showed significant association with amino acid metabolic intermediates, including 2-oxoarginine, 3-indoleacetonitrile, indole, indole-5,6-quinone, N2-succinyl- L -glutamic acid 5-semialdehyde, N-methyltryptamine and N-succinyl- L , L -2,6-diaminopimelate, in the healthy pregnant women. These findings indicated that exposure to cadmium, cobalt, copper, cesium, manganese, thallium and vanadium significantly affected the metabolic status of tryptophan, arginine, proline, tyrosine and lysine metabolism in the maternal body during normal pregnancy. [ABSTRACT FROM AUTHOR]

It has been known that when an assisted driving item is added to the Hamiltonian of the main system, the efficiency of the resultant adiabatic evolution can be significantly improved. In some special cases, it can be seen that, only by adding an assisted driving Hamiltonian can the resultant adiabatic evolution work. So it seems that, the additional driving Hamiltonian plays an important role in adiabatic computing. In this paper, we show five kinds of possible forms of the driving Hamiltonian, and analyses the performance of the algorithms. We find that the former fourth kinds of driving Hamiltonian may still cause the adiabatic algorithm lose their efficiency when the overlap of the initial and final states equals some special value. More importantly, we find that only the final kinds of the driving Hamiltonian can be extended. By modifying its interpolating functions, the final kind of adiabatic algorithm can suit any fidelity of the systems to keep the adiabatic evolution always be valid. Finally, the time complexity of the extended algorithm is better than the original adiabatic algorithm, which owns a constant running time complexity. Although the time complexity is shifted to the complexity of implementing the driving Hamiltonian added. [ABSTRACT FROM AUTHOR]

Quantum steganography can solve some problems that are considered inefficient in image information concealing. It researches on Quantum image information concealing to have been widely exploited in recent years. Quantum image information concealing can be categorized into quantum image digital blocking, quantum image stereography, anonymity and other branches. Least significant bit (LSB) information concealing plays vital roles in the classical world because many image information concealing algorithms are designed based on it. Firstly, based on the novel enhanced quantum representation (NEQR), image uniform blocks clustering around the concrete the least significant Qu-block (LSQB) information concealing algorithm for quantum image steganography is presented. Secondly, a clustering algorithm is proposed to optimize the concealment of important data. Finally, we used Con-Steg algorithm to conceal the clustered image blocks. Information concealing located on the Fourier domain of an image can achieve the security of image information, thus we further discuss the Fourier domain LSQu-block information concealing algorithm for quantum image based on Quantum Fourier Transforms. In our algorithms, the corresponding unitary Transformations are designed to realize the aim of concealing the secret information to the least significant Qu-block representing color of the quantum cover image. Finally, the procedures of extracting the secret information are illustrated. Quantum image LSQu-block image information concealing algorithm can be applied in many fields according to different needs. [ABSTRACT FROM AUTHOR]

In this paper, we study the role of prior probability on the efficiency of quantum local adiabatic search algorithm. The following aspects for prior probability are found here: firstly, only the probabilities of marked states affect the running time of the adiabatic evolution; secondly, the prior probability can be used for improving the efficiency of the adiabatic algorithm; thirdly, like the usual quantum adiabatic evolution, the running time for the case of multiple solution states where the number of marked elements are smaller enough than the size of the set assigned that contains them can be significantly bigger than that of the case where the assigned set only contains all the marked states. [ABSTRACT FROM AUTHOR]

Local adiabatic search has been implemented on the quantum circuit model, and the time steps necessary to guarantee the approximation precision are of the same order as the time complexity of the algorithm. However, it is easy to verify that this conclusion doesn't hold for global adiabatic search. But we know that adiabatic computing is equivalent to the circuit model of quantum computation. Therefore, there must exist a corresponding quantum circuit that correctly implements such a global adiabatic search. In this Letter, we show that this indeed could be true. [ABSTRACT FROM AUTHOR]

In this paper, we propose a modified construction of the quantum adiabatic algorithm for Boolean functions studied by M. Andrecut et al. [13, 14]. Our algorithm has the time complexity for the evaluation of Boolean functions, without additional computational cost of implementing the driving Hamiltonian, which is required by the adiabatic evolution described in [13, 14]. [ABSTRACT FROM AUTHOR]

Wei et al. have shown an accurate relation between the running time and the distance of the initial state and the final state of a local adiabatic evolution, that is, the adiabatic running time can be completely determined by the distance between the states. They also asked whether more complicated adiabatic evolutions have similar simple relation. We show that the adiabatic evolution with a general class of models of interpolation path proposed by Das et al. is just such another kind of adiabatic evolution which enjoys the relation discussed by Wei et al. [ABSTRACT FROM AUTHOR]

In this paper, we propose a quantum version of the differential cryptanalysis which offers a quadratic speedup over the existing classical one and show the quantum circuit implementing it. The quantum differential cryptanalysis is based on the quantum minimum/maximum-finding algorithm, where the values to be compared and filtered are obtained by calling the quantum counting algorithm. Any cipher which is vulnerable to the classical differential cryptanalysis based on counting procedures can be cracked more quickly under this quantum differential attack. [ABSTRACT FROM AUTHOR]

In this paper, we first uncover a fact that a partial adiabatic quantum search with $$O(\sqrt{N/M})$$ time complexity is in fact optimal, in which $$N$$ is the total number of elements in an unstructured database, and $$M$$ ( $$M\ge 1$$ ) of them are the marked ones(one) $$(N\gg M)$$ . We then discuss how to implement a partial adiabatic search algorithm on the quantum circuit model. From the implementing procedure on the circuit model, we can find out that the approximating steps needed are always in the same order of the time complexity of the adiabatic algorithm. [ABSTRACT FROM AUTHOR]

Due to the intrinsic similarity between partial adiabatic evolution and global adiabatic evolution, we generalize the partial adiabatic evolution proposed recently to its local adiabatic algorithm version. However, unlike that the local adiabatic evolution can speed up the global adiabatic algorithm quadratically, we prove that this new quantum algorithm presented here just has the same time complexity as the original partial adiabatic evolution. This may imply the optimality of the original partial adiabatic evolution or its generalized version. Additionally, a concrete example is given to further support our conclusion. [ABSTRACT FROM AUTHOR]

Deutsch-Jozsa algorithm has been implemented via quantum adiabatic evolutions by Das et al. (Phys Rev A 65:062310, ) and Wei et al. (Phys Lett A 354:271, ). In the latter literature, the authors have shown a modified version of the adiabatic evolution which can improve the performance of the algorithm of S. Das et al's to constant time. In this paper, we also improve the algorithm of S. Das et al's in a constant time but by using a different construction of adiabatic evolution, i.e., adding ancillary qubits. The algorithm in this paper provides an alternative option to potential users. [ABSTRACT FROM AUTHOR]

We study the quantum version of a decision tree classifier to fill the gap between quantum computation and machine learning. The quantum entropy impurity criterion which is used to determine which node should be split is presented in the paper. By using the quantum fidelity measure between two quantum states, we cluster the training data into subclasses so that the quantum decision tree can manipulate quantum states. We also propose algorithms constructing the quantum decision tree and searching for a target class over the tree for a new quantum object. [ABSTRACT FROM AUTHOR]

We consider the problem of finding one or more desired items out of an unsorted database. Patel has shown that if the database permits quantum queries, then mere digitization is sufficient for efficient search for one desired item. The algorithm, called factorized quantum search algorithm, presented by him can locate the desired item in an unsorted database using O( $$log_4N$$) queries to factorized oracles. But the algorithm requires that all the attribute values must be distinct from each other. In this paper, we discuss how to make a database satisfy the requirements, and present a quantum search engine based on the algorithm. Our goal is achieved by introducing auxiliary files for the attribute values that are not distinct, and converting every complex query request into a sequence of calls to factorized quantum search algorithm. The query complexity of our algorithm is O( $$log_4N$$) for most cases. [ABSTRACT FROM AUTHOR]

In this paper, we again discuss quantum search by partial adiabatic evolution, which was first proposed by Zhang et al. In contrast to previous conclusions, we show that partial adiabatic search does not improve the time complexity of a local adiabatic algorithm. Firstly, we show a variant of this algorithm and find that it is equivalent to the original partial adiabatic algorithm, in the sense of the same time complexity. But we give two alternate viewpoints on this 'new' adiabatic algorithm-'global' adiabatic evolution and local adiabatic evolution approaches, respectively. Then, we discuss how global and local adiabatic quantum search can be recast in the framework of partial adiabatic search algorithm. It is found here that the former two algorithms could be considered as special cases of the later one when appropriately tuning the evolution interval of it. Also this implies the flexibility of quantum search based on partial adiabatic evolution. [ABSTRACT FROM AUTHOR]

In this context, we study three different strategies to improve the time complexity of the widely used adiabatic evolution algorithms when solving a particular class of quantum search problems where both the initial and final Hamiltonians are one-dimensional projector Hamiltonians on the corresponding ground state. After some simple analysis, we find the time complexity improvement is always accompanied by the increase of some other 'complexities' that should be considered. But this just gives the implication that more feasibilities can be achieved in adiabatic evolution based quantum algorithms over the circuit model, even though the equivalence between the two has been shown. In addition, we also give a rough comparison between these different models for the speedup of the problem. [ABSTRACT FROM AUTHOR]