Strategy Analytics：谷歌主宰VR头戴设备出货量,三星成为营收冠军

2016年推出的一系列新的VR平台竞相争夺消费者、开发者和企业的关注。Strategy Analytics发布的最新研究报告《2016年VR头戴设备平台市场份额》估算,2016年VR头戴设备出货量超过3 000万,在不断碎片化的市场中被六大主要VR平台瓜分。

Strategy Analytics:服务机器人销量将在2022年达到3400万

家用机器人和商用机器人将会在未来十年迅速发展。在五年后的2022年，个人服务机器人的销量，包括吸尘器机器人和个人陪护机器人将会增长280％，而专业服务机器人，如送货和清洁机器人，增长将会超过600％。

Energy Driven by Internet of Things Analytics and Artificial Intelligence

Developing an integrated and intelligent approach to securing the ITE(information technology environment)is an emergent and evolving concern for every organization and consumer entity during the last few decades.Major topics of concern include“SI”(security intelligence),“D-DA”(data-driven analytics),“PE”(proven expertise),and“R-TD”(real-time defense)capabilities.“DRBTs”(dynamic response behavior types)include“incident response”,“endpoint management”,“threat intelligence”,“network security”,and“fraud protection”.The consumer demand for electricity as essential public access and service is indexed to population growth estimates.Consumer-driven economies continue to add electrical consumption to their grids even though improvements in lower-power consumption and higher design efficiencies are present in new electric-powered products.Dependence on the production of electrical energy has no peer replacement technology and creates a societal vulnerability to targeted public electrical grid interruptions.When access to,or production of,electrical power is interrupted,the result is a“Mass Effect”every consumer feels with equal distribution.Electric grid security falls directly into the levels of authorized,and unauthorized,access via the“IoT”(Internet of Things)concepts,and the“IoM2M”(Internet of Machine-to-Machine)integration.Electrical grid operations that include production and network management augment each other in order to support the demand for electricity every day either in peak or off-peak,thus cybersecurity plays a big role in the protection of such assets at our disposal.With help from AI(artificial intelligence)integrated into the IoT a resilient system can be built to protect the electric grid system nationwide and will be able to detect and preempt Smart Malware attacks.

Strategy Analytics:2010年移动行业大趋势展望

通信、内容及互联网行业有史以来最艰难的一年已经结束，Strategy Analytics认为，2010年，全球移动生态系统形势依然严峻，但前景堪称乐观：终端市场的复苏要比服务市场的复苏更强劲；运营商之间的整合会加剧；利润会从纯终端厂商继续流向整合终端和服务的厂商；3G会在更多的新兴市场推出，而新兴市场会继续主导手机出货量；2010年全球无线服务／应用、终端，及网络设备市场总规模将会有不到3％的小幅增长。

准噶尔盆地南缘四棵树凹陷超深层构造演化与油气成藏过程

准噶尔盆地南缘下组合近年取得了重要勘探突破,随着勘探持续深化,在工业油气流圈闭附近的构造却钻探失利,下组合区域差异较大,成藏过程复杂。通过对高泉地区构造进行平衡地质剖面恢复,结合流体包裹体、埋藏史、热史和油气运聚数值模拟研究,揭示了高泉地区下组合的构造演化与成藏过程,并分析了部分井失利的原因。结果表明,高泉构造在前侏罗纪已经存在,为一宽缓古隆起,新生代早期受喜马拉雅造山运动影响,分割为若干断块构造,分割后的圈闭成藏过程具有差异性。高泉构造总体经历了3期油气充注和晚期的调整改造:第一期为中新统塔西河组沉积时期(约16 Ma)的低熟原油充注,对应包裹体荧光为黄色;第二期为独山子组沉积中期的成熟原油充注(约7 Ma),对应的是石英颗粒愈合缝内蓝色、蓝白色荧光油包裹体;第三期为上新世(约3 Ma)以来的天然气充注,天然气来源主要为四棵树凹陷,高泉地区本地烃源岩主体并未进入生气阶段。四棵树凹陷近端和远端圈闭成藏潜力和勘探远景具有较大差别,凹陷周边圈闭晚期多经历改造和调整,近端圈闭具有距离油源近、供烃充足等优势,应作为优先勘探目标,远端圈闭改造后油气源补充不足,具有较大勘探风险。

Data analytics and optimization for smart industry

Industrial intelligence is a core technology in the upgrading of the production processes and management modes of traditional industries.Motivated by the major development strategies and needs of industrial intellectualization in China,this study presents an innovative fusion structure that encompasses the theoretical foundation and technological innovation of data analytics and optimization,as well as their application to smart industrial engineering.First,this study describes a general methodology for the fusion of data analytics and optimization.Then,it identifies some data analytics and system optimization technologies to handle key issues in smart manufacturing.Finally,it provides a four-level framework for smart industry based on the theoretical and technological research on the fusion of data analytics and optimization.The framework uses data analytics to perceive and analyze industrial production and logistics processes.It also demonstrates the intelligent capability of planning,scheduling,operation optimization,and optimal control.Data analytics and system optimization technologies are employed in the four-level framework to overcome some critical issues commonly faced by manufacturing,resources and materials,energy,and logistics systems,such as high energy consumption,high costs,low energy efficiency,low resource utilization,and serious environmental pollution.The fusion of data analytics and optimization allows enterprises to enhance the prediction and control of unknown areas and discover hidden knowledge to improve decision-making efficiency。Therefore,industrial intelligence has great importance in China’s industrial upgrading and transformation into a true industrial power.

Astrocytes dynamically regulate the blood-brain barrier in the healthy brain

The blood-brain barrier(BBB)(discovered and defined by Max Lewandowsky and Lina Stern,and not,as it is universally,and yet erroneously believed,by Paul Ehrlich(Verkhratsky and Pivoriunas,2023))that separates the nervous system from the circulation is evolutionarily conserved from arthropods to man.The primeval BBB of the invertebrates and some early vertebrates was made solely by glial cells and secured(in invertebrates)by septate junctions.

An Interactive Visual Analytics Framework for Multi-Field Data in a Geo-Spatial Context

Climate research produces a wealth of multivariate data. These data often have a geospatial reference and so it is of interest to show them within their geospatial context. One can consider this configuration as a multifield visualization problem, where the geo-space provides the expanse of the field. However, there is a limit on the amount of multivariate information that can be fit within a certain spatial location, and the use of linked multivariate information displays has previously been devised to bridge this gap. In this paper we focus on the interactions in the geographical display, present an implementation that uses Google Earth, and demonstrate it within a tightly linked parallel coordinates display. Several other visual representations, such as pie and bar charts are integrated into the Google Earth display and can be interactively manipulated. Further, we also demonstrate new brushing and visualization techniques for parallel coordinates, such as fixed-window brushing and correlation-enhanced display. We conceived our system with a team of climate researchers, who already made a few important discoveries using it. This demonstrates our system＇s great potential to enable scientific discoveries, possibly also in other domains where data have a geospatial reference.

Radiative Blood-Based Hybrid Copper-Graphene Nanoliquid Flows along a Source-Heated Leaning Cylinder

Variant graphene,graphene oxides(GO),and graphene nanoplatelets(GNP)dispersed in blood-based copper(Cu)nanoliquids over a leaning permeable cylinder are the focus of this study.These forms of graphene are highly beneficial in the biological and medical fields for cancer therapy,anti-infection measures,and drug delivery.The non-Newtonian Sutterby(blood-based)hybrid nanoliquid flows are generalized within the context of the Tiwari-Das model to simulate the effects of radiation and heating sources.The governing partial differential equations are reformulated into a nonlinear set of ordinary differential equations using similar transformational expressions.These equations are then transformed into boundary value problems through a shooting technique,followed by the implementation of the bvp4c tool in MATLAB.The influences of various parameters on the model’s nondimensional velocity and temperature profiles,reduced skin friction,and reduced Nusselt number are presented for detailed discussions.The results indicated that Cu-GNP/blood and Cu-GO/blood hybrid nanofluids exhibit the lowest and highest velocity distributions,respectively,for increased nanoparticles volume fraction,curvature parameter,Sutterby fluid parameter,Hartmann number,and wall permeability parameter.Conversely,opposite trends are observed for the temperature distribution for all considered parameters,except the mixed convection parameter.Increases in the reduced skin friction magnitude and the reduced Nusselt number with higher values of graphene/GO/GNP nanoparticle volume fraction are also reported.Finally,GNP is identified as the superior heat conductor,with an average increase of approximately 5%and a peak of 7.8%in the reduced Nusselt number compared to graphene and GO nanoparticles in the Cu/blood nanofluids.

Effect of Rosmarinus officinalis and Origanum majorana extracts on stability of sunflower oil during storage and repeated heating

Both rosemary(Rosmarinus officinalis)and marjoram(Origanum majorana)are abundant in phenolic compounds,exhibiting exceptional antioxidant activity.This study aims to assess the impact of rosemary and marjoram extracts on the stability of sunflower oil during storage and repeated heating.Sunflower oil supplemented with herbal extracts or butylated hydroxytoluene(BHA)at a concentration of 200 ppm was stored for six months under light and dark conditions at room temperature.Peroxide value(PV),p-anisidine value(An-V),and total oxidation(TOTOX)value were measured to monitor lipid oxidation progression.A significant difference(P<0.05)was observed between light and dark storage for all studied samples regarding oxidation parameters.The ethanolic extract of rosemary exhibited higher antioxidant activity compared to BHA and other extracts.Furthermore,sunflower oil supplemented with the ethanolic extract of rosemary underwent weekly treatment at 100℃for 30 min over four consecutive weeks.Although all oxidation indicators increased during repeated heating,the addition of rosemary and marjoram extracts as well as BHA significantly reduced these indicators.These findings demonstrate that both rosemary extracts and marjoram extracts can serve as natural antioxidants in edible oils.

Blockchain-Enabled Cybersecurity Provision for Scalable Heterogeneous Network:A Comprehensive Survey

Blockchain-enabled cybersecurity system to ensure and strengthen decentralized digital transaction is gradually gaining popularity in the digital era for various areas like finance,transportation,healthcare,education,and supply chain management.Blockchain interactions in the heterogeneous network have fascinated more attention due to the authentication of their digital application exchanges.However,the exponential development of storage space capabilities across the blockchain-based heterogeneous network has become an important issue in preventing blockchain distribution and the extension of blockchain nodes.There is the biggest challenge of data integrity and scalability,including significant computing complexity and inapplicable latency on regional network diversity,operating system diversity,bandwidth diversity,node diversity,etc.,for decision-making of data transactions across blockchain-based heterogeneous networks.Data security and privacy have also become the main concerns across the heterogeneous network to build smart IoT ecosystems.To address these issues,today’s researchers have explored the potential solutions of the capability of heterogeneous network devices to perform data transactions where the system stimulates their integration reliably and securely with blockchain.The key goal of this paper is to conduct a state-of-the-art and comprehensive survey on cybersecurity enhancement using blockchain in the heterogeneous network.This paper proposes a full-fledged taxonomy to identify the main obstacles,research gaps,future research directions,effective solutions,andmost relevant blockchain-enabled cybersecurity systems.In addition,Blockchain based heterogeneous network framework with cybersecurity is proposed in this paper tomeet the goal of maintaining optimal performance data transactions among organizations.Overall,this paper provides an in-depth description based on the critical analysis to overcome the existing work gaps for future research where it presents a potential cybersecurity design with key requirements of blockchain across a heterogeneous network.

An Evidence-Based CoCoSo Framework with Double Hierarchy Linguistic Data for Viable Selection of Hydrogen Storage Methods

Hydrogen is the new age alternative energy source to combat energy demand and climate change.Storage of hydrogen is vital for a nation’s growth.Works of literature provide different methods for storing the produced hydrogen,and the rational selection of a viable method is crucial for promoting sustainability and green practices.Typically,hydrogen storage is associated with diverse sustainable and circular economy(SCE)criteria.As a result,the authors consider the situation a multi-criteria decision-making(MCDM)problem.Studies infer that previous models for hydrogen storage method(HSM)selection(i)do not consider preferences in the natural language form;(ii)weights of experts are not methodically determined;(iii)hesitation of experts during criteria weight assessment is not effectively explored;and(iv)three-stage solution of a suitable selection of HSM is unexplored.Driven by these gaps,in this paper,authors put forward a new integrated framework,which considers double hierarchy linguistic information for rating,criteria importance through inter-criteria correlation(CRITIC)for expert weight calculation,evidence-based Bayesian method for criteria weight estimation,and combined compromise solution(CoCoSo)for ranking HSMs.The applicability of the developed framework is testified by using a case example of HSM selection in India.Sensitivity and comparative analysis reveal the merits and limitations of the developed framework.

Non-linear dynamic state-space network modeling for decoding neurodegeneration

Neurodegenerative disorders represent a pervasive global health challenge,yet therapeutic options remain conspicuously limited.These disorders are inherently dynamic processes within the central nervous system,unfolding across distinct sub-stages:initial structural neuronal alterations(sub-stage 1),functional impairment(sub-stage 2),and culminating in neuronal death(sub-stage 3).

The Influence of Tartaric Acid in the Silver Nanoparticle Synthesis Using Response Surface Methodology

Silver nanoparticles(AgNPs)synthesized using tartaric acid as a capping agent have a great impact on the reaction kinetics and contribute significantly to the stability of AgNPs.The protective layer formed by tartaric acid is an important factor that protects the silver surface and reduces potential cytotoxicity problems.These attributes are critical for assessing the compatibility of AgNPs with biological systems and making them suitable for drug delivery applications.The aim of this research is to conduct a comprehensive study of the effect of tartaric acid concentration,sonication time and temperature on the formation of silver nanoparticles.Using Response Surface Methodology(RSM)with Face-Centered Central Composite Design(FCCD),the optimization process identifies the most favorable synthesis conditions.UV-Vis spectrum regression analysis shows that AgNPs stabilized with tartaric acid are more stable than AgNPs without tartaric acid.This highlights the increased stability that tartaric acid provides in AgNP ssssynthesis.Particle size distribution analysis showed a multimodal distribution for AgNPs with tartaric acid and showed the smallest size peak with an average size of 20.53 nm.The second peak with increasing intensity shows a dominant average size of 108.8 nm accompanied by one standard deviation of 4.225 nm and a zeta potential of−11.08 mV.In contrast,AgNPs synthesized with polyvinylpyrrolidone(PVP)showed a unimodal particle distribution with an average particle size of 81.62 nm and a zeta potential of−2.96 mV.The more negative zeta potential of AgNP-tartaric acid indicates its increased stability.Evaluation of antibacterial activity showed that AgNPs stabilized with tartaric acid showed better performance against E.coli and B.subtilis bacteria compared with AgNPs-PVP.In summary,this study highlights the potential of tartaric acid in AgNP synthesis and suggests an avenue for the development of stable AgNPs with versatile applications.

Electricity Carbon Quota Trading Scheme based on Certificateless Signature and Blockchain

The carbon tradingmarket can promote“carbon peaking”and“carbon neutrality”at low cost,but carbon emission quotas face attacks such as data forgery,tampering,counterfeiting,and replay in the electricity trading market.Certificateless signatures are a new cryptographic technology that can address traditional cryptography’s general essential certificate requirements and avoid the problem of crucial escrowbased on identity cryptography.However,most certificateless signatures still suffer fromvarious security flaws.We present a secure and efficient certificateless signing scheme by examining the security of existing certificateless signature schemes.To ensure the integrity and verifiability of electricity carbon quota trading,we propose an electricity carbon quota trading scheme based on a certificateless signature and blockchain.Our scheme utilizes certificateless signatures to ensure the validity and nonrepudiation of transactions and adopts blockchain technology to achieve immutability and traceability in electricity carbon quota transactions.In addition,validating electricity carbon quota transactions does not require time-consuming bilinear pairing operations.The results of the analysis indicate that our scheme meets existential unforgeability under adaptive selective message attacks,offers conditional identity privacy protection,resists replay attacks,and demonstrates high computing and communication performance.

Antigen epitopes of animal coronaviruses:a mini-review

Coronaviruses are widespread in nature and can infect mammals and poultry,making them a public health concern.Globally,prevention and control of emerging and re-emerging animal coronaviruses is a great challenge.The mecha-nisms of virus-mediated immune responses have important implications for research on virus prevention and control.The antigenic epitope is a chemical group capable of stimulating the production of antibodies or sensitized lympho-cytes,playing an important role in antiviral immune responses.Thus,it can shed light on the development of diagnos-tic methods and novel vaccines.Here,we have reviewed advances in animal coronavirus antigenic epitope research,aiming to provide a reference for the prevention and control of animal and human coronaviruses.

Outsmarting Android Malware with Cutting-Edge Feature Engineering and Machine Learning Techniques

The growing usage of Android smartphones has led to a significant rise in incidents of Android malware andprivacy breaches.This escalating security concern necessitates the development of advanced technologies capableof automatically detecting andmitigatingmalicious activities in Android applications(apps).Such technologies arecrucial for safeguarding user data and maintaining the integrity of mobile devices in an increasingly digital world.Current methods employed to detect sensitive data leaks in Android apps are hampered by two major limitationsthey require substantial computational resources and are prone to a high frequency of false positives.This meansthat while attempting to identify security breaches,these methods often consume considerable processing powerand mistakenly flag benign activities as malicious,leading to inefficiencies and reduced reliability in malwaredetection.The proposed approach includes a data preprocessing step that removes duplicate samples,managesunbalanced datasets,corrects inconsistencies,and imputes missing values to ensure data accuracy.The Minimaxmethod is then used to normalize numerical data,followed by feature vector extraction using the Gain ratio andChi-squared test to identify and extract the most significant characteristics using an appropriate prediction model.This study focuses on extracting a subset of attributes best suited for the task and recommending a predictivemodel based on domain expert opinion.The proposed method is evaluated using Drebin and TUANDROMDdatasets containing 15,036 and 4,464 benign and malicious samples,respectively.The empirical result shows thatthe RandomForest(RF)and Support VectorMachine(SVC)classifiers achieved impressive accuracy rates of 98.9%and 98.8%,respectively,in detecting unknown Androidmalware.A sensitivity analysis experiment was also carriedout on all three ML-based classifiers based on MAE,MSE,R2,and sensitivity parameters,resulting in a flawlessperformance for both datasets.This approach has substantial potential for real-world applications and can serve asa valuable tool for preventing the spread of Androidmalware and enhancing mobile device security.

Postmortem ^(7)Li NMR analysis for assessing the reversibility of lithium metal electrodes in lithium metal batteries

Despite the proficiency of lithium(Li)-7 NMR spectroscopy in delineating the physical and chemical states of Li metal electrodes,challenges in specimen preparation and interpretation impede its progress.In this study,we conducted a comprehensive postmortem analysis utilizing ^(7)Li NMR,employing a stan-dard magic angle spinning probe to examine protective-layer coated Li metal electrodes and LiAg alloy electrodes against bare Li metal electrodes within Li metal batteries(LMBs).Our investigation explores the effects of sample burrs,alignment with the magnetic field,the existence of liquid electrolytes,and precycling on the ^(7)Li NMR signals.Through contrasting NMR spectra before and after cycling,we identi-fied alterations in Li^(0) and Li^(+) signals attributable to the degradation of the Li metal electrode.Our NMR analyses decisively demonstrate the efficacy of the protective layer in mitigating dendrite and solid elec-trolyte interphase formation.Moreover,we noted that Li*ions near the Li metal surface exhibit magnetic susceptibility anisotropy,revealing a novel approach to studying diamagnetic species on Li metal elec-trodes in LMBs.This study provides valuable insights and practical guidelines for characterizing distinct lithium states within LMBs.

Image Fusion Using Wavelet Transformation and XGboost Algorithm

Recently,there have been several uses for digital image processing.Image fusion has become a prominent application in the domain of imaging processing.To create one final image that provesmore informative and helpful compared to the original input images,image fusion merges two or more initial images of the same item.Image fusion aims to produce,enhance,and transform significant elements of the source images into combined images for the sake of human visual perception.Image fusion is commonly employed for feature extraction in smart robots,clinical imaging,audiovisual camera integration,manufacturing process monitoring,electronic circuit design,advanced device diagnostics,and intelligent assembly line robots,with image quality varying depending on application.The research paper presents various methods for merging images in spatial and frequency domains,including a blend of stable and curvelet transformations,everageMax-Min,weighted principal component analysis(PCA),HIS(Hue,Intensity,Saturation),wavelet transform,discrete cosine transform(DCT),dual-tree Complex Wavelet Transform(CWT),and multiple wavelet transform.Image fusion methods integrate data from several source images of an identical target,thereby enhancing information in an extremely efficient manner.More precisely,in imaging techniques,the depth of field constraint precludes images from focusing on every object,leading to the exclusion of certain characteristics.To tackle thess challanges,a very efficient multi-focus wavelet decomposition and recompositionmethod is proposed.The use of these wavelet decomposition and recomposition techniques enables this method to make use of existing optimized wavelet code and filter choice.The simulated outcomes provide evidence that the suggested approach initially extracts particular characteristics from images in order to accurately reflect the level of clarity portrayed in the original images.This study enhances the performance of the eXtreme Gradient Boosting(XGBoost)algorithm in detecting brain malignancies with greater precision through the integration of computational image analysis and feature selection.The performance of images is improved by segmenting them employing the K-Means algorithm.The segmentation method aids in identifying specific regions of interest,using Particle Swarm Optimization(PCA)for trait selection and XGBoost for data classification.Extensive trials confirm the model’s exceptional visual performance,achieving an accuracy of up to 97.067%and providing good objective indicators.

Progress and prospects of Mg-based amorphous alloys in azo dye wastewater treatment

Mg-based amorphous alloys exhibit efficient catalytic performance and excellent biocompatibility with a promising application probability,specifically in the field of azo dye wastewater degradation.However,the problems like difficulty in preparation and poor cycling stability need to be solved.At present,Mg-based amorphous alloys applied in wastewater degradation are available in powder and ribbon.The amorphous alloy powder fabricated by ball milling has a high specific surface area,and its reactivity is thousands of times better than that of gas atomized alloy powder.But the development is limited due to the high energy consumption,difficult and costly process of powder recycling.The single roller melt-spinning method is a new manufacturing process of amorphous alloy ribbon.Compared to amorphous powder,the specific surface area of amorphous ribbon is relatively lower,therefore,it is necessary to carry out surface modification to enhance it.Dealloying is a way that can form a pore structure on the surface of the amorphous alloys,increasing the specific surface area and providing more reactive sites,which all contribute to the catalytic performance.Exploring the optimal conditions for Mg-based amorphous alloys in wastewater degradation by adjusting amorphous alloy composition,choosing suitable method to preparation and surface modification,reducing cost,expanding the pH range will advance the steps to put Mg-based amorphous alloys in industrial environments into practice.