Driver at 10 MJ and 1 shot/30 min for inertial confinement fusion at high gain:Efficient,compact,low-cost,low laser-plasma instabilities,beam color selectable from 2ω/3ω/4ω,applicable to multiple laser fusion schemes

The achievement of ignition at the National Ignition Facility(NIF)has prompted a global wave of further research on inertial fusion energy(IFE).However,IFE requires a target gain G of 30-100,and it is hard to achieve fusion at such high gain with the energy,configuration,and technical approach of the NIF.Here,we present a conceptual design for a next-generation laser driver that is applicable to multiple laser fusion schemes and provides 10 MJ,2-3 PW at 3ω(or 2ω,in which case the energy and power can be higher),and one shot per 30 min,with the aim of achieving G>30.It is also efficient,compact,and low in cost,and it has low susceptibility to laser-plasma instabilities.

A Survey on Type-3 Fuzzy Logic Systems and Their Control Applications

In this paper,we offer a review of type-3 fuzzy logic systems and their applications in control.The main objective of this work is to observe and analyze in detail the applications in the control area using type-3 fuzzy logic systems.In this case,we review their most important applications in control and other related topics with type-3 fuzzy systems.Intelligent algorithms have been receiving increasing attention in control and for this reason a review in this area is important.This paper reviews the main applications that make use of Intelligent Computing methods.Specifically,type-3 fuzzy logic systems.The aim of this research is to be able to appreciate,in detail,the applications in control systems and to point out the scientific trends in the use of Intelligent Computing techniques.This is done with the construction and visualization of bibliometric networks,developed with VosViewer Software,which it is a free Java-based program,mainly intended to be used for analyzing and visualizing bibliometric networks.With this tool,we can create maps of publications,authors,or journals based on a co-citation network or construct maps of keywords,countries based on a co-occurrence networks,research groups,etc.

A multi-feature-based intelligent redundancy elimination scheme for cloud-assisted health systems

Redundancy elimination techniques are extensively investigated to reduce storage overheads for cloud-assisted health systems.Deduplication eliminates the redundancy of duplicate blocks by storing one physical instance referenced by multiple duplicates.Delta compression is usually regarded as a complementary technique to deduplication to further remove the redundancy of similar blocks,but our observations indicate that this is disobedient when data have sparse duplicate blocks.In addition,there are many overlapped deltas in the resemblance detection process of post-deduplication delta compression,which hinders the efficiency of delta compression and the index phase of resemblance detection inquires abundant non-similar blocks,resulting in inefficient system throughput.Therefore,a multi-feature-based redundancy elimination scheme,called MFRE,is proposed to solve these problems.The similarity feature and temporal locality feature are excavated to assist redundancy elimination where the similarity feature well expresses the duplicate attribute.Then,similarity-based dynamic post-deduplication delta compression and temporal locality-based dynamic delta compression discover more similar base blocks to minimise overlapped deltas and improve compression ratios.Moreover,the clustering method based on block-relationship and the feature index strategy based on bloom filters reduce IO overheads and improve system throughput.Experiments demonstrate that the proposed method,compared to the state-of-the-art method,improves the compression ratio and system throughput by 9.68%and 50%,respectively.

Federated Learning on Internet of Things:Extensive and Systematic Review

The proliferation of IoT devices requires innovative approaches to gaining insights while preserving privacy and resources amid unprecedented data generation.However,FL development for IoT is still in its infancy and needs to be explored in various areas to understand the key challenges for deployment in real-world scenarios.The paper systematically reviewed the available literature using the PRISMA guiding principle.The study aims to provide a detailed overview of the increasing use of FL in IoT networks,including the architecture and challenges.A systematic review approach is used to collect,categorize and analyze FL-IoT-based articles.Asearch was performed in the IEEE,Elsevier,Arxiv,ACM,and WOS databases and 92 articles were finally examined.Inclusion measures were published in English and with the keywords“FL”and“IoT”.The methodology begins with an overview of recent advances in FL and the IoT,followed by a discussion of how these two technologies can be integrated.To be more specific,we examine and evaluate the capabilities of FL by talking about communication protocols,frameworks and architecture.We then present a comprehensive analysis of the use of FL in a number of key IoT applications,including smart healthcare,smart transportation,smart cities,smart industry,smart finance,and smart agriculture.The key findings from this analysis of FL IoT services and applications are also presented.Finally,we performed a comparative analysis with FL IID(independent and identical data)and non-ID,traditional centralized deep learning(DL)approaches.We concluded that FL has better performance,especially in terms of privacy protection and resource utilization.FL is excellent for preserving privacy becausemodel training takes place on individual devices or edge nodes,eliminating the need for centralized data aggregation,which poses significant privacy risks.To facilitate development in this rapidly evolving field,the insights presented are intended to help practitioners and researchers navigate the complex terrain of FL and IoT.

The Contribution of AI-Powered Mobile Apps to Smart City Ecosystems

In the groundbreaking study “The Contribution of AI-powered Mobile Apps to Smart City Ecosystems,” authored by Zaki Ali Bayashot, the transformative role of artificial intelligence (AI) in urban development is meticulously examined. This comprehensive research delineates the multifaceted ways in which AI-powered mobile applications can significantly enhance the efficiency, sustainability, and livability of urban environments, marking a pivotal step towards the realization of smart cities globally. Bayashot meticulously outlines the critical areas where AI-powered apps offer unprecedented advantages, including urban mobility, public safety, energy management, and environmental monitoring. By leveraging AI’s capabilities, these applications not only streamline city operations but also foster a more sustainable interaction between city dwellers and their environment. The paper emphasizes the importance of data-driven decision-making in urban planning, showcasing how AI analytics can predict and mitigate traffic congestion, optimize energy consumption, and enhance emergency response strategies. The author also explores the social implications of AI in urban settings, highlighting the potential for these technologies to bridge the gap between government entities and citizens. Through engaging case studies, Bayashot demonstrates how participatory governance models, enabled by AI apps, can promote transparency, accountability, and citizen engagement in urban management. A significant contribution of this research is its focus on the challenges and opportunities presented by the integration of AI into smart city ecosystems. Bayashot discusses the technical, ethical, and privacy concerns associated with AI applications, advocating for a balanced approach that ensures technological advancements do not come at the expense of civil liberties. The study calls for robust regulatory frameworks to govern the use of AI in public spaces, emphasizing the need for ethical AI practices that respect privacy and promote inclusivity. Furthermore, Bayashot’s research underscores the necessity of cross-disciplinary collaboration in the development and implementation of AI technologies in urban contexts. By bringing together experts from information technology, urban planning, environmental science, and social sciences, the author argues for a holistic approach to smart city development. This interdisciplinary strategy ensures that AI applications are not only technologically sound but also socially and environmentally responsible. The paper concludes with a visionary outlook on the future of smart cities, posited on the seamless integration of AI technologies. Bayashot envisions a world where AI-powered mobile apps not only facilitate smoother urban operations but also empower citizens to actively participate in the shaping of their urban environments. This research serves as a critical call to action for policymakers, technologists, and urban planners to embrace AI as a tool for creating more sustainable, efficient, and inclusive cities. By presenting a detailed analysis of the current state of AI in urban development, coupled with practical insights and forward-looking recommendations, “The Contribution of AI-powered Mobile Apps to Smart City Ecosystems” stands as a seminal work that is poised to inspire and guide the evolution of urban landscapes worldwide. Its comprehensive exploration of the subject matter, combined with its impactful conclusions, make it a must-read for anyone involved in the field of smart city development, AI technology, or urban policy-making.

Energy Storage Systems Technologies, Evolution and Applications

Energy in its varied forms and applications has become the main driver of today’s modern society. However, recent changes in power demand and climatic changes (decarbonization policy) has awakened the need to rethink through the current energy generating and distribution system. This led to the exploration of other energy sources of which renewable energy (like thermal, solar and wind energy) is fast becoming an integral part of most energy system. However, this innovative and promising energy source is highly unreliable in maintaining a constant peak power that matches demand. Energy storage systems have thus been highlighted as a solution in managing such imbalances and maintaining the stability of supply. Energy storage technologies absorb and store energy, and release it on demand. This includes gravitational potential energy (pumped hydroelectric), chemical energy (batteries), kinetic energy (flywheels or compressed air), and energy in the form of electrical (capacitors) and magnetic fields. This paper provides a detailed and comprehensive overview of some of the state-of-the-art energy storage technologies, its evolution, classification, and comparison along with various area of applications. Also highlighted in this paper is a plethora of power electronic Interface technologies that plays a significant role in enabling optimum performance and utilization of energy storage systems in different areas of application.

Design of Intelligent Window Automatic Monitoring System Based on Microcontroller Control

To ensure the safety of residents’lives and property by using automatic opening and closing of ordinary windows,this article designs an intelligent window automatic monitoring system.The article proposes a software and hardware design scheme for the system,which comprises a microcontroller control module,temperature and humidity detection module,harmful gas detection module,rainfall detection module,human thermal radiation induction module,Organic Light-Emitting Diode(OLED)display module,stepper motor drive module,Wi-Fi communication module,etc.Users use this system to monitor environmental data such as temperature,humidity,rainfall,harmful gas concentrations,and human health.Users can control the opening and closing of windows through manual,microcontroller,and mobile application(app)remote methods,providing users with a more convenient,comfortable,and safe living environment.

The personal protective equipment(PPE)based on individual combat:A systematic review and trend analysis

With the development of ordnance technology,the survival and safety of individual combatants in hightech warfare are under serious threat,and the Personal Protective Equipment(PPE),as an important guarantee to reduce casualties and maintain military combat effectiveness,is widely developed.This paper systematically reviewed various PPE based on individual combat through literature research and comprehensive discussion,and introduced in detail the latest application progress of PPE in terms of material and technology from three aspects:individual integrated protection system,traditional protection equipment,and intelligent protection equipment,respectively,and discussed in depth the functional improvement and optimization status brought by advanced technology for PPE,focusing on the achievements of individual equipment technology application.Finally,the problems and technical bottlenecks in the development of PPE were analyzed and summarized,and the development trend of PPE were pointed out.The results of the review will provide a forward-looking reference for the current development of individual PPE,and are important guidance for the design and technological innovation of advanced equipment based on the future technological battlefield.

Role of tumor-associated macrophages in common digestive system malignant tumors

Many digestive system malignant tumors are characterized by high incidence and mortality rate.Increasing evidence has revealed that the tumor microenvironment(TME)is involved in cancer initiation and tumor progression.Tumor-associated macrophages(TAMs)are a predominant constituent of the TME,and participate in the regulation of various biological behaviors and influence the prognosis of digestive system cancer.TAMs can be mainly classified into the antitumor M1 phenotype and protumor M2 phenotype.The latter especially are crucial drivers of tumor invasion,growth,angiogenesis,metastasis,immunosuppression,and resistance to therapy.TAMs are of importance in the occurrence,development,diagnosis,prognosis,and treatment of common digestive system malignant tumors.In this review,we summarize the role of TAMs in common digestive system malignant tumors,including esophageal,gastric,colorectal,pancreatic and liver cancers.How TAMs promote the development of tumors,and how they act as potential therapeutic targets and their clinical applications are also described.

Recent progress on two-dimensional ferroelectrics:Material systems and device applications

Ferroelectrics are a type of material with a polar structure and their polarization direction can be inverted reversibly by applying an electric field.They have attracted tremendous attention for their extensive applications in non-volatile memory,sensors and neuromorphic computing.However,conventional ferroelectric materials face insulating and interfacial issues in the commercialization process.In contrast,two-dimensional(2D)ferroelectric materials usually have excellent semiconductor performance,clean van der Waals interfaces and robust ferroelectric order in atom-thick layers,and hold greater promise for constructing multifunctional ferroelectric optoelectronic devices and nondestructive ultra-high-density memory.Recently,2D ferroelectrics have obtained impressive breakthroughs,showing overwhelming superiority.Herein,firstly,the progress of experimental research on 2D ferroelectric materials is reviewed.Then,the preparation of 2D ferroelectric devices and their applications are discussed.Finally,the future development trend of 2D ferroelectrics is looked at.

Developing microwave-induced thermoacoustic tomography:System,application,and reconstruction

A microwave-induced thermoacoustic imaging(MITAT)system is a non-destructive physical medical imaging method that combines the advantages of the high contrast of microwave imaging and the high resolution of ultrasound imaging.It uses the microwave as the excitation source and ultrasound as the information carrier.When different kinds of biological tissue absorb electromagnetic energy,it results in localized temperature rises.The thermal expansion will induce ultrasonic signals(i.e.,thermoacoustic signals),known as the thermoacoustic effect.The microwave absorption image of the sample can be reconstructed by algorithm processing.The MITAT contrast depends on different dielectric parameters of different kinds of tissue.We introduce the developed system and its application.In addition,the challenges and prospects of MITAT for further development are discussed.

Integrated Electrode-Electrolyte Optimization to Manufacture a Real-Life Applicable Aqueous Supercapacitor with Record-Breaking Lifespan

Aqueous supercapacitors(SCs)have been regarded as a promising candidate for commercial energy storage device due to their superior safety,low cost,and environmental benignity.Unfortunately,an age-old challenge of achieving both long electrode lifespan and qualified energy-storage property blocks their practical application.Herein,we develop an electrode-electrolyte integrated optimization strategy to fulfill the real-life device requirements.Electrode optimization simultaneously regulates the nanomorphology and surface chemistry of the tungsten oxide anode,resulting in superior electrochemical performance given by an ideal“bird-nest”structure with optimal oxygen vacancy status;the anodes interact with and are protected from dissolution and structural collapse by the rationally designed hybrid electrolyte with optimized pH and facilitated cation desorption behavior.Collaboratively,a record-breaking durability of no capacitive decay after 250000 cycles is achieved.On the basis of this integrated optimization,the first aqueous pouch SCs with real-life practicability were manufactured by a soft-package encapsulation technique,which can steadily power commercial 3 C products such as tablets and smartphones and maintain safely working against extreme conditions.This work demonstrates the possibility of using aqueous energy storage devices with enhanced safety and lower cost to replace the commercial organic counterparts for wide range of daily applications.

An Enhanced Intelligent Intrusion Detection System to Secure E-Commerce Communication Systems

Information and communication technologies are spreading rapidly due to their fast proliferation in many fields.The number of Internet users has led to a spike in cyber-attack incidents.E-commerce applications,such as online banking,marketing,trading,and other online businesses,play an integral role in our lives.Network Intrusion Detection System(NIDS)is essential to protect the network from unauthorized access and against other cyber-attacks.The existing NIDS systems are based on the Backward Oracle Matching(BOM)algorithm,which minimizes the false alarm rate and causes of high packet drop ratio.This paper discussed the existing NIDS systems and different used pattern-matching techniques regarding their weaknesses and limitations.To address the existing system issues,this paper proposes an enhanced version of the BOM algorithm by using multiple pattern-matching methods for the NIDS system to improve the network performance.The proposed solution is tested in simulation with existing solutions using the Snort and NSL-KDD datasets.The experimental results indicated that the proposed solution performed better than the existing solutions and achieved a 5.17%detection rate and a 0.22%lower false alarm rate than the existing solution.

Lightweight Network Ensemble Architecture for Environmental Perception on the Autonomous System

It is important for the autonomous system to understand environmental information.For the autonomous system,it is desirable to have a strong generalization ability to deal with different complex environmental information,as well as have high accuracy and quick inference speed.Network ensemble architecture is a good choice to improve network performance.However,it is unsuitable for real-time applications on the autonomous system.To tackle this problem,a new neural network ensemble named partial-shared ensemble network(PSENet)is presented.PSENet changes network ensemble architecture from parallel architecture to scatter architecture and merges multiple component networks together to accelerate the inference speed.To make component networks independent of each other,a training method is designed to train the network ensemble architecture.Experiments on Camvid and CIFAR-10 reveal that PSENet achieves quick inference speed while maintaining the ability of ensemble learning.In the real world,PSENet is deployed on the unmanned system and deals with vision tasks such as semantic segmentation and environmental prediction in different fields.

CRISPR/Cas9 systems:Delivery technologies and biomedical applications

The emergence of the clustered regularly interspaced short palindromic repeats(CRISPR)/CRISPR-associated protein 9(Cas9)genome-editing system has brought about a significant revolution in the realm of managing human diseases,establishing animal models,and so on.To fully harness the potential of this potent gene-editing tool,ensuring efficient and secure delivery to the target site is paramount.Consequently,developing effective delivery methods for the CRISPR/Cas9 system has become a critical area of research.In this review,we present a comprehensive outline of delivery strategies and discuss their biomedical applications in the CRISPR/Cas9 system.We also provide an indepth analysis of physical,viral vector,and non-viral vector delivery strategies,including plasmid-,mRNA-and protein-based approach.In addition,we illustrate the biomedical applications of the CRISPR/Cas9 system.This review highlights the key factors affecting the delivery process and the current challenges facing the CRISPR/Cas9 system,while also delineating future directions and prospects that could inspire innovative delivery strategies.This review aims to provide new insights and ideas for advancing CRISPR/Cas9-based delivery strategies and to facilitate breakthroughs in biomedical research and therapeutic applications.

Smart stimuli-responsive drug delivery systems in spotlight of COVID-19

The world has been dealing with a novel severe acute respiratory syndrome(SARS-CoV-2)since the end of 2019,which threatens the lives of many peopleworldwide.COVID-19 causes respiratory infection with different symptoms,from sneezing and coughing to pneumonia and sometimes gastric symptoms.Researchers worldwide are actively developing novel drug delivery systems(DDSs),such as stimuli-responsive DDSs.The ability of these carriers to respond to external/internal and even multiple stimuli is essential in creating“smart”DDS that can effectively control dosage,sustained release,individual variations,and targeted delivery.To conduct a comprehensive literature survey for this article,the terms“Stimuli-responsive”,“COVID-19”and“Drug delivery”were searched on databases/search engines like“Google Scholar”,“NCBI”,“PubMed”,and“Science Direct”.Many different types of DDSs have been proposed,including those responsive to various exogenous(light,heat,ultrasound andmagnetic field)or endogenous(microenvironmental changes in pH,ROS and enzymes)stimuli.Despite significant progress in DDS research,several challenging issues must be addressed to fill the gaps in the literature.Therefore,this study reviews the drug release mechanisms and applications of endogenous/exogenous stimuli-responsive DDSs while also exploring their potential with respect to COVID-19.

Investigation on the Folding Mode and Deployment Process of an Inflatable Boarding Platform for the Marine Emergency Evacuation System

Monopiles are the most common foundation form of offshore wind turbines,which bear the vertical load,lateral load and bending moment.It remains uncertain whether the applied vertical load increases the lateral deflection of the pile.This paper investigated the influence of vertical load on the behaviour of monopiles installed in the sand under combined load using three-dimensional numerical methods.The commercial software PLAXIS was used for simulations in this paper.Monopiles were modelled as a structure incorporating linear elastic material behaviour and soil was modelled using the Hardening-Soil(HS)constitutive model.The monopiles under vertical load,lateral load and combined vertical and lateral loads were respectively studied taking into account the sequence of load application and pile slenderness ratio(L/D;L and D are the length and diameter of the pile).Results suggest that the sequence of load application plays a major role in how vertical load affects the deflection behaviour of the pile.Specifically,when L/D ratios obtained by lengthening the pile while keeping its diameter constant are 3,5 and 8,the relationships between lateral load and the deflection behaviour of the pile under the effect of vertical load demonstrate a similar trend.Furthermore,the cause of increased lateral capacity of the pile under the action of applied vertical load in the common practical application case and in the VPL case was analyzed by studying the variation law of soil stress along the pile embedment.Results confirm that the confining effect of vertical load increases means effective stress of the soil around the pile,thus increasing soil stiffness and pile capacity.

Identifying the critical phosphorus balance for optimizing phosphorus input and regulating soil phosphorus effectiveness in a typical winter wheat-summer maize rotation system in North China

Phosphorus(P)is a nonrenewable resource and a critical element for plant growth that plays an important role in improving crop yield.Excessive P fertilizer application is widespread in agricultural production,which not only wastes phosphate resources but also causes P accumulation and groundwater pollution.Here,we hypothesized that the apparent P balance of a crop system could be used as an indicator for identifying the critical P input in order to obtain a high yield with high phosphorus use efficiency(PUE).A 12-year field experiment with P fertilization rates of 0,45,90,135,180,and 225 kg P_(2)O_(5)ha^(-1)was conducted to determine the crop yield,PUE,and soil Olsen-P value response to P balance,and to optimize the P input.Annual yield stagnation occurred when the P fertilizer application exceeded a certain level,and high yield and PUE levels were achieved with annual P fertilizer application rates of 90-135 kg P_(2)O_(5)ha^(-1).A critical P balance range of 2.15-4.45 kg P ha^(-1)was recommended to achieve optimum yield with minimal environmental risk.The critical P input range estimated from the P balance was 95.7-101 kg P_(2)O_(5)ha^(-1),which improved relative yield(>90%)and PUE(90.0-94.9%).In addition,the P input-output balance helps in assessing future changes in Olsen-P values,which increased by 4.07 mg kg^(-1)of P for every 100 kg of P surplus.Overall,the P balance can be used as a critical indicator for P management in agriculture,providing a robust reference for limiting P excess and developing a more productive,efficient and environmentally friendly P fertilizer management strategy.

Use of new technologies for the promotion of physical activity in patients with mental illness:A systematic review

BACKGROUND Physical exercise is an underutilized tool for the management of mental disorders.New technologies have made a breakthrough in health care,and one of its possible applications(apps)could be that of customizing exercise programs for special populations,such as patients with mental disorders.However,the app of the so-called e-health to mental health care is still limited.AIM To know the efficacy of apps to promote physical activity in patients with mental disorders.METHODS We conducted a systematic review of the PubMed and Embase databases with the aim of exploring the use of new technologies for the enhancement of physical exercise in patients with a psychiatric illness.Following the selection process,10 articles were included in the review.RESULTS The most commonly used devices in this type of intervention are wearable devices and web platforms.Good results in terms of effectiveness and acceptability were obtained in most of the studies.CONCLUSION Our findings suggest that the use of new technologies in mental health represents a feasible strategy with great potential in clinical practice.

Proposed Biometric Security System Based on Deep Learning and Chaos Algorithms

Nowadays,there is tremendous growth in biometric authentication and cybersecurity applications.Thus,the efficient way of storing and securing personal biometric patterns is mandatory in most governmental and private sectors.Therefore,designing and implementing robust security algorithms for users’biometrics is still a hot research area to be investigated.This work presents a powerful biometric security system(BSS)to protect different biometric modalities such as faces,iris,and fingerprints.The proposed BSSmodel is based on hybridizing auto-encoder(AE)network and a chaos-based ciphering algorithm to cipher the details of the stored biometric patterns and ensures their secrecy.The employed AE network is unsupervised deep learning(DL)structure used in the proposed BSS model to extract main biometric features.These obtained features are utilized to generate two random chaos matrices.The first random chaos matrix is used to permute the pixels of biometric images.In contrast,the second random matrix is used to further cipher and confuse the resulting permuted biometric pixels using a two-dimensional(2D)chaotic logisticmap(CLM)algorithm.To assess the efficiency of the proposed BSS,(1)different standardized color and grayscale images of the examined fingerprint,faces,and iris biometrics were used(2)comprehensive security and recognition evaluation metrics were measured.The assessment results have proven the authentication and robustness superiority of the proposed BSSmodel compared to other existing BSSmodels.For example,the proposed BSS succeeds in getting a high area under the receiver operating characteristic(AROC)value that reached 99.97%and low rates of 0.00137,0.00148,and 3516 CMC,2023,vol.74,no.20.00157 for equal error rate(EER),false reject rate(FRR),and a false accept rate(FAR),respectively.