打造“汽车里的好莱坞” 专访比亚迪董事长王传福、DTS高层

在北京举行的2012年北京国际汽车博览会期间,DTS这家全球知名的高清音频技术公司,宣布其与比亚迪公司正式签约。作为中国快速发展的汽车企业以及绿色能源科技公司,比亚迪在激烈的市场竞争中独辟蹊径,计划在中国市场推出整合了DTS环绕声技术的一系列车型,打造＂汽车上的家庭影院＂这一独特卖点。这使得比亚迪成为了中国首家使用DTS顶级环绕声技术的汽车制造商,也意味着比亚迪的驾乘者将能获得发烧友级别的车载音效体验。

Biofabrication of nanocomposite-based scaffolds containing human bone extracellularmatrix for the differentiation of skeletal stem and progenitor cells

Autograft or metal implants are routinely used in skeletal repair.However,they fail to provide long-term clinical resolution,necessitating a functional biomimetic tissue engineering alternative.The use of native human bone tissue for synthesizing a biomimeticmaterial inkfor three-dimensional(3D)bioprintingof skeletal tissueis anattractivestrategyfor tissueregeneration.Thus,human bone extracellular matrix(bone-ECM)offers an exciting potential for the development of an appropriate microenvironment for human bone marrow stromal cells(HBMSCs)to proliferate and differentiate along the osteogenic lineage.In this study,we engineered a novel material ink(LAB)by blending human bone-ECM(B)with nanoclay(L,Laponite®)and alginate(A)polymers using extrusion-based deposition.The inclusion of the nanofiller and polymeric material increased the rheology,printability,and drug retention properties and,critically,the preservation of HBMSCs viability upon printing.The composite of human bone-ECM-based 3D constructs containing vascular endothelial growth factor(VEGF)enhanced vascularization after implantation in an ex vivo chick chorioallantoic membrane(CAM)model.The inclusion of bone morphogenetic protein-2(BMP-2)with the HBMSCs further enhanced vascularization and mineralization after only seven days.This study demonstrates the synergistic combination of nanoclay with biomimetic materials(alginate and bone-ECM)to support the formation of osteogenic tissue both in vitro and ex vivo and offers a promising novel 3D bioprinting approach to personalized skeletal tissue repair.

让更多消费者体验到DTS的技术优势

环绕声技术发展到今天,引入声音对象（Audio Object）方式来实现环绕声效果可说是一种未来的发展趋势,正如4K UHD、HDR等视频技术,它们一直都在推动整个行业的发展。对于三维音效技术,它的目的就是还原出接近真实的声音效果,跟以前的环绕声系统对比起来,它增加了顶部声道扬声器（Overhead Channel Speaker）部分,这样对于声音的高度感以及原本属于天空或者上方部分的各种声音效果，声音工程师都可以通过项部声道扬声器实现真实还原。

武汉社区足球场空间布局及高质量发展的规划研究

基础设施的高质量发展是国家现代化的内在要求,社区足球场作为基础设施的一部分,其空间布局规划是影响高质量发展的关键因素。本文面向高质量基础设施建设提出了社区足球场发展的研究框架,以武汉社区足球场为例,探讨了武汉城市社区足球场地设施试点方案前、后足球场的空间格局和空间可达性,并提出了经过总体规划的社区足球场建设方法。结果表明:①在社区足球场数量及分布方面,中心城区和远郊区呈现明显的空间差异,核密度值由中心城区向外辐射递减;社区足球场在规划试点后,数量增加,平均最近邻指数小于试点前的,集聚程度提升;与试点前相比,空间分布重心向东北方向移动,标准差椭圆面积变大,表明社区足球场呈现出空间扩张的态势。②规划试点前社区足球场可达性呈不规则点状分布格局;规划试点后社区足球场可达性形成“一轴一带、多中心”的空间结构,高可达性足球场主要沿长江沿线密集分布。③从场地布局模式、组织管理、资金投入及政策法规四方面分析了社区足球场地建设的新模式。

CEMA-LSTM:Enhancing Contextual Feature Correlation for Radar Extrapolation Using Fine-Grained Echo Datasets

Accurate precipitation nowcasting can provide great convenience to the public so they can conduct corresponding arrangements in advance to deal with the possible impact of upcoming heavy rain.Recent relevant research activities have shown their concerns on various deep learning models for radar echo extrapolation,where radar echo maps were used to predict their consequent moment,so as to recognize potential severe convective weather events.However,these approaches suffer from an inaccurate prediction of echo dynamics and unreliable depiction of echo aggregation or dissipation,due to the size limitation of convolution filter,lack of global feature,and less attention to features from previous states.To address the problems,this paper proposes a CEMA-LSTM recurrent unit,which is embedded with a Contextual Feature Correlation Enhancement Block(CEB)and a Multi-Attention Mechanism Block(MAB).The CEB enhances contextual feature correlation and supports its model to memorize significant features for near-future prediction;the MAB uses a position and channel attention mechanism to capture global features of radar echoes.Two practical radar echo datasets were used involving the FREM and CIKM 2017 datasets.Both quantification and visualization of comparative experimental results have demonstrated outperformance of the proposed CEMA-LSTMover recentmodels,e.g.,PhyDNet,MIM and PredRNN++,etc.In particular,compared with the second-rankedmodel,its average POD,FAR and CSI have been improved by 3.87%,1.65%and 1.79%,respectively on the FREM,and by 1.42%,5.60%and 3.16%,respectively on the CIKM 2017.

Cryptanalysis of 2D-SCMCI Hyperchaotic Map Based Image Encryption Algorithm

Chaos-based cryptosystems are considered a secure mode of communication due to their reliability.Chaotic maps are associated with the other domains to construct robust encryption algorithms.There exist numerous encryption schemes in the literature based on chaotic maps.This work aims to propose an attack on a recently proposed hyper-chaotic map-based cryptosystem.The core notion of the original algorithm was based on permutation and diffusion.A bitlevel permutation approach was used to do the permutation row-and column-wise.The diffusion was executed in the forward and backward directions.The statistical strength of the cryptosystem has been demonstrated by extensive testing conducted by the author of the cryptosystem.This cryptanalysis article investigates the robustness of this cryptosystem against a chosen-plaintext attack.The secret keys of the cryptosystem were retrieved by the proposed attack with 258 chosen-plain images.The results in this manuscript suggest that,in addition to standard statistical evaluations,thorough cryptanalysis of each newly suggested cryptosystem is necessary before it can be used in practical application.Moreover,the data retrieved is also passed through some statistical analysis to compare the quality of the original and retrieved data.The results of the performance analysis indicate the exact recovery of the original data.To make the cryptosystem useful for applications requiring secure data exchange,a few further improvement recommendations are also suggested.

A Novel Internet of Medical Thing Cryptosystem Based on Jigsaw Transformation and Ikeda Chaotic Map

Image encryption has attracted much interest as a robust security solution for preventing unauthorized access to critical image data.Medical picture encryption is a crucial step in many cloud-based and healthcare applications.In this study,a strong cryptosystem based on a 2D chaotic map and Jigsaw transformation is presented for the encryption of medical photos in private Internet of Medical Things(IoMT)and cloud storage.A disorganized three-dimensional map is the foundation of the proposed cipher.The dispersion of pixel values and the permutation of their places in this map are accomplished using a nonlinear encoding process.The suggested cryptosystem enhances the security of the delivered medical images by performing many operations.To validate the efficiency of the recommended cryptosystem,various medical image kinds are used,each with its unique characteristics.Several measures are used to evaluate the proposed cryptosystem,which all support its robust security.The simulation results confirm the supplied cryptosystem’s secrecy.Furthermore,it provides strong robustness and suggested protection standards for cloud service applications,healthcare,and IoMT.It is seen that the proposed 3D chaotic cryptosystem obtains an average entropy of 7.9998,which is near its most excellent value of 8,and a typical NPCR value of 99.62%,which is also near its extreme value of 99.60%.Moreover,the recommended cryptosystem outperforms conventional security systems across the test assessment criteria.

Prediction of the SARS-CoV-2 Derived T-Cell Epitopes’Response Against COVID Variants

TheCOVID-19 outbreak began in December 2019 andwas declared a global health emergency by the World Health Organization.The four most dominating variants are Beta,Gamma,Delta,and Omicron.After the administration of vaccine doses,an eminent decline in new cases has been observed.The COVID-19 vaccine induces neutralizing antibodies and T-cells in our bodies.However,strong variants likeDelta and Omicron tend to escape these neutralizing antibodies elicited by COVID-19 vaccination.Therefore,it is indispensable to study,analyze and most importantly,predict the response of SARS-CoV-2-derived t-cell epitopes against Covid variants in vaccinated and unvaccinated persons.In this regard,machine learning can be effectively utilized for predicting the response of COVID-derived t-cell epitopes.In this study,prediction of T-cells Epitopes’response was conducted for vaccinated and unvaccinated people for Beta,Gamma,Delta,and Omicron variants.The dataset was divided into two classes,i.e.,vaccinated and unvaccinated,and the predicted response of T-cell Epitopes was divided into three categories,i.e.,Strong,Impaired,and Over-activated.For the aforementioned prediction purposes,a self-proposed Bayesian neural network has been designed by combining variational inference and flow normalization optimizers.Furthermore,the Hidden Markov Model has also been trained on the same dataset to compare the results of the self-proposed Bayesian neural network with this state-of-the-art statistical approach.Extensive experimentation and results demonstrate the efficacy of the proposed network in terms of accurate prediction and reduced error.

七成消费者因质量好而续费会员制超市需稳住价格和质量

2023年7月底,盒马推出“移山价”,打响会员制超市价格战第一枪。之后两个月,盒马“移山价”从北京、上海延展到全国15座城市,山姆榴莲千层也从128元降到85元,这场朴实无华的商战愈战愈凶。前段时间,“DT商业观察”发起了一次关于会员制超市的调研,共回收到1147份有效问卷。参与调研的读者中,95后、90后和85后占比超过7成,女性占比超6成,超8成人生活在一线或新一线城市。逛会员制超市的人多吗?大家更偏好哪个超市?都在买些什么?透过这次调查,我们或许可以得到答案。

A Joint Optimization Algorithm for Renewable Energy System

Energy sustainability is a hot topic in both scientific and political cir-cles.To date,two alternative approaches to this issue are being taken.Some peo-ple believe that increasing power consumption is necessary for countries’economic and social progress,while others are more concerned with maintaining carbon consumption under set limitations.To establish a secure,sustainable,and economical energy system while mitigating the consequences of climate change,most governments are currently pushing renewable growth policies.Energy mar-kets are meant to provide consumers with dependable electricity at the lowest pos-sible cost.A profit-maximization optimal decision model is created in the electric power market with the combined wind,solar units,loads,and energy storage sys-tems,based on the bidding mechanism in the electricity market and operational principles.This model utterly considers the technological limits of new energy units and storages,as well as the involvement of new energy and electric vehicles in market bidding through power generation strategy and the output arrangement of the virtual power plant’s coordinated operation.The accuracy and validity of the optimal decision-making model of combined wind,solar units,loads,and energy storage systems are validated using numerical examples.Under multi-operating scenarios,the effects of renewable energy output changes on joint sys-tem bidding techniques are compared.

An Automated Classification Technique for COVID-19 Using Optimized Deep Learning Features

In 2020,COVID-19 started spreading throughout the world.This deadly infection was identified as a virus that may affect the lungs and,in severe cases,could be the cause of death.The polymerase chain reaction(PCR)test is commonly used to detect this virus through the nasal passage or throat.However,the PCR test exposes health workers to this deadly virus.To limit human exposure while detecting COVID-19,image processing techniques using deep learning have been successfully applied.In this paper,a strategy based on deep learning is employed to classify the COVID-19 virus.To extract features,two deep learning models have been used,the DenseNet201 and the SqueezeNet.Transfer learning is used in feature extraction,and models are fine-tuned.A publicly available computerized tomography(CT)scan dataset has been used in this study.The extracted features from the deep learning models are optimized using the Ant Colony Optimization algorithm.The proposed technique is validated through multiple evaluation parameters.Several classifiers have been employed to classify the optimized features.The cubic support vector machine(Cubic SVM)classifier shows superiority over other commonly used classifiers and attained an accuracy of 98.72%.The proposed technique achieves state-of-the-art accuracy,a sensitivity of 98.80%,and a specificity of 96.64%.

An Efficient Automated Technique for Classification of Breast Cancer Using Deep Ensemble Model

Breast cancer is one of the leading cancers among women.It has the second-highest mortality rate in women after lung cancer.Timely detection,especially in the early stages,can help increase survival rates.However,manual diagnosis of breast cancer is a tedious and time-consuming process,and the accuracy of detection is reliant on the quality of the images and the radiologist’s experience.However,computer-aided medical diagnosis has recently shown promising results,leading to the need to develop an efficient system that can aid radiologists in diagnosing breast cancer in its early stages.The research presented in this paper is focused on the multi-class classification of breast cancer.The deep transfer learning approach has been utilized to train the deep learning models,and a pre-processing technique has been used to improve the quality of the ultrasound dataset.The proposed technique utilizes two deep learning models,Mobile-NetV2 and DenseNet201,for the composition of the deep ensemble model.Deep learning models are fine-tuned along with hyperparameter tuning to achieve better results.Subsequently,entropy-based feature selection is used.Breast cancer identification using the proposed classification approach was found to attain an accuracy of 97.04%,while the sensitivity and F1 score were 96.87%and 96.76%,respectively.The performance of the proposed model is very effective and outperforms other state-of-the-art techniques presented in the literature.

A New S-Box Design System for Data Encryption Using Artificial Bee Colony Algorithm

Securing digital image data is a key concern in today’s information-driven society.Effective encryption techniques are required to protect sensitive image data,with the Substitution-box(S-box)often playing a pivotal role in many symmetric encryption systems.This study introduces an innovative approach to creating S-boxes for encryption algorithms.The proposed S-boxes are tested for validity and non-linearity by incorporating them into an image encryption scheme.The nonlinearity measure of the proposed S-boxes is 112.These qualities significantly enhance its resistance to common cryptographic attacks,ensuring high image data security.Furthermore,to assess the robustness of the S-boxes,an encryption system has also been proposed and the proposed S-boxes have been integrated into the designed encryption system.To validate the effectiveness of the proposed encryption system,a comprehensive security analysis including brute force attack and histogram analysis has been performed.In addition,to determine the level of security during the transmission and storage of digital content,the encryption system’s Number of Pixel Change Rate(NPCR),and Unified Averaged Changed Intensity(UACI)are calculated.The results indicate a 99.71%NPCR and 33.51%UACI.These results demonstrate that the proposed S-boxes offer a significant level of security for digital content throughout its transmission and storage.

An Immutable Framework for Smart Healthcare Using Blockchain Technology

The advancements in sensing technologies,information processing,and communication schemes have revolutionized the healthcare sector.Electronic Healthcare Records(EHR)facilitate the patients,doctors,hospitals,and other stakeholders to maintain valuable data and medical records.The traditional EHRs are based on cloud-based architectures and are susceptible to multiple cyberattacks.A single attempt of a successful Denial of Service(DoS)attack can compromise the complete healthcare system.This article introduces a secure and immutable blockchain-based framework for the Internet of Medical Things(IoMT)to address the stated challenges.The proposed architecture is on the idea of a lightweight private blockchain-based network that facilitates the users and hospitals to perform multiple healthcare-related operations in a secure and trustworthy manner.The efficacy of the proposed framework is evaluated in the context of service execution time and throughput.The experimental outcomes indicate that the proposed design attained lower service execution time and higher throughput under different control parameters.

A Novel Parallel Computing Confidentiality Scheme Based on Hindmarsh-Rose Model

Due to the inherent insecure nature of the Internet,it is crucial to ensure the secure transmission of image data over this network.Additionally,given the limitations of computers,it becomes evenmore important to employ efficient and fast image encryption techniques.While 1D chaotic maps offer a practical approach to real-time image encryption,their limited flexibility and increased vulnerability restrict their practical application.In this research,we have utilized a 3DHindmarsh-Rosemodel to construct a secure cryptosystem.The randomness of the chaotic map is assessed through standard analysis.The proposed system enhances security by incorporating an increased number of system parameters and a wide range of chaotic parameters,as well as ensuring a uniformdistribution of chaotic signals across the entire value space.Additionally,a fast image encryption technique utilizing the new chaotic system is proposed.The novelty of the approach is confirmed through time complexity analysis.To further strengthen the resistance against cryptanalysis attacks and differential attacks,the SHA-256 algorithm is employed for secure key generation.Experimental results through a number of parameters demonstrate the strong cryptographic performance of the proposed image encryption approach,highlighting its exceptional suitability for secure communication.Moreover,the security of the proposed scheme has been compared with stateof-the-art image encryption schemes,and all comparison metrics indicate the superior performance of the proposed scheme.

杨梅汁内花色苷热降解动力学研究

对杨梅汁内花色苷的热稳定性进行了研究 .结果表明 ,充氮条件下 ,p H值为 1.0、3.2、4 .5的热降解活化能 (E0 )分别为 386 .90 2、317.837和 2 83.0 2 8k J/ mol.在有氧条件下相应的 E0 分别为343.4 6 4、2 90 .970和 2 5 8.2 5 1k J/ mol.说明充氮处理可以明显提高杨梅汁内花色苷的稳定性 ,同时也可看出 ,不管是在有氧还是充氮条件下 ,随着 p H值的增高 ,E0 值变小 ,说明低值下花色苷的热稳定性较好 .

针对混合极性的并行表格技术的遗传算法

针对混合极性的最佳极性优化问题,提出一种基于并行表格技术的遗传算法.在3n混合极性搜索过程中,采用并行表格技术计算遗传算法中种群的适应度函数;并行表格技术不按变量顺序产生on-set项,克服了在传统表格技术中顺序产生相关项造成数据相关性问题,有效地提高了CPU利用率.实验结果表明文中算法在保证最优结果的同时,可平均缩短8%的处理时间.

自主智能制造:社会—信息—物理交互、参考体系架构与运作机制

走向自主是智能制造未来发展的必然趋势,但并不意味着将人排除在制造系统之外,自主制造仍需要以人为中心进行设计。从系统科学的角度出发,在分析物理回路中人机交互和工业4.0自主性的基础上,探讨社会—信息—物理交互的自主智能制造系统的基本构成、元素交互和参考体系架构以及人在其中的角色,进而将人在回路里、回路上和回路外理念分别置于智慧制造的物理空间、信息空间和社会空间,形成三位一体的自主社会信息物理生产系统,最后以混合人工智能方法驱动的按订单生产作业调度问题为例对其运作机制加以展示说明。

双容神经网络解耦控制的在线实现

主要进行基于神经网络的解耦控制研究。针对罗克韦尔实验室中的双容液位对象强耦合的特点,将模糊自适应PID算法控制和神经网络引入控制系统的设计中。提出一种神经网络在线解耦算法并用神经网络对双容液位系统进行建模。将该算法用于双溶液位控制系统的设计中,用来实现对被控对象的解耦控制。通过对设计方案的仿真研究和现场实时控制,结果表明:该设计方案具有良好的解耦效果;控制系统的调节品质令人满意。

细胞角蛋白19-mRNA定量检测

目的基于水母发光蛋白生物发光免疫分析技术，建立一种细胞角蛋白19-mRNA（CK19-mRNA）定量检测技术。方法用生物发光分析技术检测淋巴细胞的CK19-mRNA。利用RT—PCR对CK19-mRNA进行放大,PCR引物的5′端用生物素标记，然后用交联了地高辛寡核苷酸探针与目标生物素化的DNA模板杂交。杂交复合物与包被在微孔板上的链亲合素结合，加入标记了水母发光蛋白的地高辛抗体与地高辛特异结合，再加入钙离子激发水母发光蛋白发光，发光强度正比予CK19-mRNA的量。结果该技术的线性范围可达5个数量级，标准曲线的直线回归系数r=0．98～0．99，探测灵敏度可达22amol／L的CK19-mRNA产物。重复性检测中，批间变异系数〈70％，批内变异系数〈6％。结论生物发光分析技术是一种灵敏可靠、非放射性的微量CK19-mRNA定量方法。