Laboratory or Department?Exploration and Creation in Computer Science and Technology

In the very beginning,the Computer Laboratory of the University of Cambridge was founded to provide computing service for different disciplines across the university.As computer science developed as a discipline in its own right,boundaries necessarily arose between it and other disciplines,in a way that is now often detrimental to progress.Therefore,it is necessary to reinvigorate the relationship between computer science and other academic disciplines and celebrate exploration and creativity in research.To do this,the structures of the academic department have to act as supporting scaffolding rather than barriers.Some examples are given that show the efforts being made at the University of Cambridge to approach this problem.

Panel Discussion on“Development Trends of Computer Science in the New Era”

At the panel session of the 3rd Global Forum on the Development of Computer Science,attendees had an opportunity to deliberate recent issues affecting computer science departments as a result of the recent growth in the field.6 heads of university computer science departments participated in the discussions,including the moderator,Professor Andrew Yao.The first issue was how universities are managing the growing number of applicants in addition to swelling class sizes.Several approaches were suggested,including increasing faculty hiring,implementing scalable teaching tools,and working closer with other departments through degree programs that integrate computer science with other fields.The second issue was about the position and role of computer science within broader science.Participants generally agreed that all fields are increasingly relying on computer science techniques,and that effectively disseminating these techniques to others is a key to unlocking broader scientific progress.

Design and Analysis of Power and Transmission System of Downhole Pure Electric Command Vehicle

Electric vehicles use electric motors, which turn electrical energy into mechanical energy. As electric motors are conventionally used in all the industry, it is an established development site. It’s a mature technology with ideal power and torque curves for vehicular operation. Conventional vehicles use oil and gas as fuel or energy storage. Although they also have an excellent economic impact, the continuous use of oil and gas threatened the world’s reservation of total oil and gas. Also, they emit carbon dioxide and some toxic ingredients through the vehicle’s tailpipe, which causes the greenhouse effect and seriously impacts the environment. So, as an alternative, electric car refers to a green technology of decarbonization with zero emission of greenhouse gases through the tailpipe. So, they can remove the problem of greenhouse gas emissions and solve the world’s remaining non-renewable energy storage problem. Pure electric vehicles (PEV) can be applied in all spheres, but their special implementation can only be seen in downhole operations. They are used for low noise and less pollution in the downhole process. In this study, the basic structure of the pure electric command vehicle is studied, the main components of the command vehicle power system, namely the selection of the drive motor and the power battery, are analyzed, and the main parameters of the drive motor and the power battery are designed and calculated. The checking calculation results show that the power and transmission system developed in this paper meets the design requirements, and the design scheme is feasible and reasonable.

Optimization and Deployment of Memory-Intensive Operations in Deep Learning Model on Edge

As a large amount of data is increasingly generated from edge devices,such as smart homes,mobile phones,and wearable devices,it becomes crucial for many applications to deploy machine learning modes across edge devices.The execution speed of the deployed model is a key element to ensure service quality.Considering a highly heterogeneous edge deployment scenario,deep learning compiling is a novel approach that aims to solve this problem.It defines models using certain DSLs and generates efficient code implementations on different hardware devices.However,there are still two aspects that are not yet thoroughly investigated yet.The first is the optimization of memory-intensive operations,and the second problem is the heterogeneity of the deployment target.To that end,in this work,we propose a system solution that optimizes memory-intensive operation,optimizes the subgraph distribution,and enables the compiling and deployment of DNN models on multiple targets.The evaluation results show the performance of our proposed system.

Design and Optimization of Actuator for HT-25 Multifunctional Loader

To break down the development interaction of the working gadget of the multi-practical wheel loader and to compute the heap of each part, the Denavit-Hartenberg strategy was applied to build up the kinematics of the instrument model. Also, all the while, set up the elements model of dynamic framework. A multi-body element programming MSC, ADAMS and its active module were applied to assemble component power through a pressure framework reenactment model. An entirety working cycle interaction of the functioning gadget of the wheel loader was mimicked, and the investigation results thoroughly show the development interaction of the functional device and the stacked state of each part, and check the mechanical properties of the working gadget and dynamic execution water-driven framework effectively.

Developing Lexicons for Enhanced Sentiment Analysis in Software Engineering:An Innovative Multilingual Approach for Social Media Reviews

Sentiment analysis is becoming increasingly important in today’s digital age, with social media being a significantsource of user-generated content. The development of sentiment lexicons that can support languages other thanEnglish is a challenging task, especially for analyzing sentiment analysis in social media reviews. Most existingsentiment analysis systems focus on English, leaving a significant research gap in other languages due to limitedresources and tools. This research aims to address this gap by building a sentiment lexicon for local languages,which is then used with a machine learning algorithm for efficient sentiment analysis. In the first step, a lexiconis developed that includes five languages: Urdu, Roman Urdu, Pashto, Roman Pashto, and English. The sentimentscores from SentiWordNet are associated with each word in the lexicon to produce an effective sentiment score. Inthe second step, a naive Bayesian algorithm is applied to the developed lexicon for efficient sentiment analysis ofRoman Pashto. Both the sentiment lexicon and sentiment analysis steps were evaluated using information retrievalmetrics, with an accuracy score of 0.89 for the sentiment lexicon and 0.83 for the sentiment analysis. The resultsshowcase the potential for improving software engineering tasks related to user feedback analysis and productdevelopment.

Machine learning algorithms able to predict the prognosis of gastric cancer patients treated with immune checkpoint inhibitors

BACKGROUND Although immune checkpoint inhibitors(ICIs)have demonstrated significant survival benefits in some patients diagnosed with gastric cancer(GC),existing prognostic markers are not universally applicable to all patients with advanced GC.AIM To investigate biomarkers that predict prognosis in GC patients treated with ICIs and develop accurate predictive models.METHODS Data from 273 patients diagnosed with GC and distant metastasis,who un-derwent≥1 cycle(s)of ICIs therapy were included in this study.Patients were randomly divided into training and test sets at a ratio of 7:3.Training set data were used to develop the machine learning models,and the test set was used to validate their predictive ability.Shapley additive explanations were used to provide insights into the best model.RESULTS Among the 273 patients with GC treated with ICIs in this study,112 died within 1 year,and 129 progressed within the same timeframe.Five features related to overall survival and 4 related to progression-free survival were identified and used to construct eXtreme Gradient Boosting(XGBoost),logistic regression,and decision tree.After comprehensive evaluation,XGBoost demonstrated good accuracy in predicting overall survival and progression-free survival.CONCLUSION The XGBoost model aided in identifying patients with GC who were more likely to benefit from ICIs therapy.Patient nutritional status may,to some extent,reflect prognosis.

Rule acquisition of three-way semi-concept lattices in formal decision context

Three-way concept analysis is an important tool for information processing,and rule acquisition is one of the research hotspots of three-way concept analysis.However,compared with three-way concept lattices,three-way semi-concept lattices have three-way operators with weaker constraints,which can generate more concepts.In this article,the problem of rule acquisition for three-way semi-concept lattices is discussed in general.The authors construct the finer relation of three-way semi-concept lattices,and propose a method of rule acquisition for three-way semi-concept lattices.The authors also discuss the set of decision rules and the relationships of decision rules among object-induced three-way semi-concept lattices,object-induced three-way concept lattices,classical concept lattices and semi-concept lattices.Finally,examples are provided to illustrate the validity of our conclusions.

S^(2)ANet:Combining local spectral and spatial point grouping for point cloud processing

Background Despite the recent progress in 3D point cloud processing using deep convolutional neural networks,the inability to extract local features remains a challenging problem.In addition,existing methods consider only the spatial domain in the feature extraction process.Methods In this paper,we propose a spectral and spatial aggregation convolutional network(S^(2)ANet),which combines spectral and spatial features for point cloud processing.First,we calculate the local frequency of the point cloud in the spectral domain.Then,we use the local frequency to group points and provide a spectral aggregation convolution module to extract the features of the points grouped by the local frequency.We simultaneously extract the local features in the spatial domain to supplement the final features.Results S^(2)ANet was applied in several point cloud analysis tasks;it achieved stateof-the-art classification accuracies of 93.8%,88.0%,and 83.1%on the ModelNet40,ShapeNetCore,and ScanObjectNN datasets,respectively.For indoor scene segmentation,training and testing were performed on the S3DIS dataset,and the mean intersection over union was 62.4%.Conclusions The proposed S^(2)ANet can effectively capture the local geometric information of point clouds,thereby improving accuracy on various tasks.

Clinical significance of upregulated Rho GTPase activating protein 12 causing resistance to tyrosine kinase inhibitors in hepatocellular carcinoma

BACKGROUND Hepatocellular carcinoma(HCC)is a major health challenge with high incidence and poor survival rates in China.Systemic therapies,particularly tyrosine kinase inhibitors(TKIs),are the first-line treatment for advanced HCC,but resistance is common.The Rho GTPase family member Rho GTPase activating protein 12(ARHGAP12),which regulates cell adhesion and invasion,is a potential therapeutic target for overcoming TKI resistance in HCC.However,no studies on the expression of ARHGAP12 in HCC and its role in resistance to TKIs have been reported.AIM To unveil the expression of ARHGAP12 in HCC,its role in TKI resistance and its potential associated pathways.METHODS This study used single-cell RNA sequencing(scRNA-seq)to evaluate ARHGAP12 mRNA levels and explored its mechanisms through enrichment analysis.CellChat was used to investigate focal adhesion(FA)pathway regulation.We integrated bulk RNA data(RNA-seq and microarray),immunohistochemistry and proteomics to analyze ARHGAP12 mRNA and protein levels,correlating with clinical outcomes.We assessed ARHGAP12 expression in TKI-resistant HCC,integrated conventional HCC to explore its mechanism,identified intersecting FA pathway genes with scRNA-seq data and evaluated its response to TKI and immunotherapy.RESULTS ARHGAP12 mRNA was found to be highly expressed in malignant hepatocytes and to regulate FA.In malignant hepatocytes in high-score FA groups,MDK-[integrin alpha 6(ITGA6)+integrinβ-1(ITGB1)]showed specificity in ligand-receptor interactions.ARHGAP12 mRNA and protein were upregulated in bulk RNA,immunohistochemistry and proteomics,and higher expression was associated with a worse prognosis.ARHGAP12 was also found to be a TKI resistance gene that regulated the FA pathway.ITGB1 was identified as a crossover gene in the FA pathway in both scRNA-seq and bulk RNA.High expression of ARHGAP12 was associated with adverse reactions to sorafenib,cabozantinib and regorafenib,but not to immunotherapy.CONCLUSION ARHGAP12 expression is elevated in HCC and TKI-resistant HCC,and its regulatory role in FA may underlie the TKI-resistant phenotype.

Overview of the Third Global Forum

Computer science continues to grow at a rapid pace,raising the issue of how universities can best adapt to this trend.At the third Global Forum on the Development of Computer Science(GFDCS),five heads of departments of computer science from Asia,Europe,and North America came together to exchange ideas under the theme Challenges and Opportunities of Computer Science in the New Era.Through the discussions,a number of new challenges were explored,including how to meet the growing demand for computer science education,how to manage increased teaching loads,how to foster collaboration between computer science and other disciplines,how to raise ethical awareness,and how to support new“transdisciplinary”modes of education and research.At the same time,there was a consensus on the need to strengthen the role of computer science in other departments,the importance of industrial collaboration,and the need for more scalable approaches to teaching.The evolving role of computer science within the context of broader science was also discussed.

Neurological rehabilitation of stroke patients via motor imaginary-based brain-computer interface technology

The present study utilized motor imaginary-based brain-computer interface technology combined with rehabilitation training in 20 stroke patients. Results from the Berg Balance Scale and the Holden Walking Classification were significantly greater at 4 weeks after treatment （P 〈 0.01）, which suggested that motor imaginary-based brain-computer interface technology improved balance and walking in stroke patients.

Analysis and Research for the Technology of Remote Transaction Mirror Image

The technology of remote transaction mirror image is a feasible, low-investment and well-effect disaster backup scheme in finance business system. The basic idea, construction, working principles and characteristic of remote transaction mirror image are presented in this paper. We analyze and compare similarities and differences among this disaster backup scheme and others usually used. The technology of remote transaction mirror image have the advantages such as less requiring of software and hardware system platform, low-investment, being able to control and restore lost data, insuring the data consistency and integrity.

Gene expression and metabolite profiles of cotton fiber during cell elongation and secondary cell wall synthesis

Cotton fibers elongate rapidly after initiation of elongation, eventually leading to the deposit of a large amount of cellulose. To reveal features of cotton fiber cells at the fast elongation and the secondary cell wall synthesis stages, we compared the respective transcriptomes and metabolite profiles. Comparative analysis of transcriptomes by cDNA array identified 633 genes that were differentially regulated during fiber development. Principal component analysis （PCA） using expressed genes as variables divided fiber samples into four groups, which are diagnostic of developmental stages. Similar grouping results are also found if we use non-polar or polar metabolites as variables for PCA of developing fibers. Auxin signaling, wall-loosening and lipid metabolism are highly active during fiber elongation, whereas cellulose biosynthesis is predominant and many other metabolic pathways are downregulated at the secondary cell wall synthesis stage. Transcript and metabolite profiles and enzyme activities are consistent in demonstrating a specialization process of cotton fiber development toward cellulose synthesis. These data demonstrate that cotton fiber cell at a certain stage has its own unique feature, and developmental stages of cotton fiber cells can be distinguished by their transcript and metabolite profiles. During the secondary cell wall synthesis stage, metabolic pathways are streamed into cellulose synthesis.

Assessing the impacts of ecological-living-productive land changes on eco-environmental quality in Xining City on Qinghai-Tibet Plateau, China

The Ecological-living-productive land(ELPL)classification system was proposed in an effort to steer China's land pattern to an ecological-centered path,with the development model shifting from a single function into more integrated multifunction land use.The focus is coordinating the man-land contradictions and developing an intensive,efficient and sustainable land use policy in an increasingly tense relationship between humans and nature.Driven by socioeconomic change and rapid population growth,many cities are undergoing urban sprawl,which involves the consumption of cropland and ecological land and threatens the ecological balance.This paper aims to quantitatively analyze the critical effects of ELPL changes on eco-environmental quality according to land use classification based on leading function of ecology,living and production from 1990 to 2015 with a case study of Xining City.Also,four future land use scenarios were simulated for 2030 using the Future Land Use Simulation(FLUS)model that couples human and natural effects.Our results show a decrease in productive land(PL)and an increase in ecological land(EL)and living land(LL)in Xining City.Forestry ecological land(FEL)covered the top largest proportion;agriculture productive land(APL)showed the greatest reduction and urban and rural living land(U-RLL)presented a dramatic increase.The eco-environmental quality improved in 1990-2010,mainly affected by the conversion of APL to FEL and GEL.However,the encroachment of U-RLL into APL,other ecological land(OEL)and FEL was the main contributor to the decline in eco-environmental quality in 2010-2015 as well as the primary reason for the increase area of lower-quality.The Harmonious Development(HD)-Scenario,characterized by a rational allocation of LL and PL and a better eco-environment,would have implications for planning and monitoring future management of ELPL,and may represent a valuable reference for local policy-makers.

A Review of Smart Materials for the Boost of Soft Actuators,Soft Sensors,and Robotics Applications

With the advance of smart material science,robotics is evolving from rigid robots to soft robots.Compared to rigid robots,soft robots can safely interact with the environment,easily navigate in unstructured fields,and be minimized to operate in narrow spaces,owning to the new actuation and sensing technologies developed by the smart materials.In the review,different actuation and sensing technologies based on different smart materials are analyzed and summarized.According to the driving or feedback signals,actuators are categorized into electrically responsive actuators,thermally responsive actuators,magnetically responsive actuators,and photoresponsive actuators;sensors are categorized into resistive sensors,capacitive sensors,magnetic sensors,and optical waveguide sensors.After introducing the principle and several robotic prototypes of some typical materials in each category of the actuators and sensors.The advantages and disadvantages of the actuators and sensors are compared based on the categories,and their potential applications in robotics are also presented.

Genomic Analysis of MicroRNA Promoters and Their Cis-Acting Elements in Soybean

MicroRNAs （miRNAs） are derived from distinct loci in the genome and play crucial roles in RNA-mediated gene silencing mechanisms that regulate cellular processes during development and stress responses of plants. The miRNAs are approximately 21 nucleotides long and code for the complementary strand to a larger genic mRNA. They are often found within the complementary primary transcript （pri-miRNAs）. In the past few years, a growing number of soybean miRNAs have been discovered, however, little is known about the transcriptional regulation of these miRNAs. In this study, promoters and cis-acting elements of soybean miRNAs were analyzed using the genomic data for the first time. A total of 82 miRNAs were located among 122 loci in genome, some were present as double or multiple copies. Five clusters that included ten miRNAs were found in genome, and only one cluster share the same promoter. A total of 191 promoters from 122 loci of the soybean miRNA sequences were found and further analyzed. The results indicated that the conserved soybean miRNA genes had a greater proportion of promoters than that of non-conserved ones, and the distribution of the transcript start sites （TSSs） and TATA-boxes found had different motif styles between conserved and non-conserved miRNA genes. Furthermore, the cis-acting elements 5＇ of the TSSs were analyzed to obtain potential function and spatiotemporal expression pattern of miRNAs. The data obtained here may lead to the identification of specific sequences upstream of pre-miRNAs and the functional annotation of miRNAs in soybean.

Research of Marine Sensor Web Based on SOA and EDA

A great deal of ocean sensor observation data exists, for a wide range of marine disciplines, derived from in situ and remote observing platforms, in real-time, near-real-time and delayed mode. Ocean monitoring is routinely completed using sensors and instruments. Standardization is the key requirement for exchanging information about ocean sensors and sensor data and for comparing and combining information from different sensor networks. One or more sensors are often physically integrated into a single ocean ‘instrument' device, which often brings in many challenges related to diverse sensor data formats, parameters units, different spatiotemporal resolution, application domains, data quality and sensors protocols. To face these challenges requires the standardization efforts aiming at facilitating the so-called Sensor Web, which making it easy to provide public access to sensor data and metadata information. In this paper, a Marine Sensor Web, based on SOA and EDA and integrating the MBARI's PUCK protocol, IEEE 1451 and OGC SWE 2.0, is illustrated with a five-layer architecture. The Web Service layer and Event Process layer are illustrated in detail with an actual example. The demo study has demonstrated that a standard-based system can be built to access sensors and marine instruments distributed globally using common Web browsers for monitoring the environment and oceanic conditions besides marine sensor data on the Web, this framework of Marine Sensor Web can also play an important role in many other domains' information integration.

Terrain Rendering LOD Algorithm Based on Improved Restrictive Quadtree Segmentation and Variation Coefficient of Elevation

Aiming to deal with the difficult issues of terrain data model simplification and crack disposal,the paper proposed an improved level of detail(LOD)terrain rendering algorithm,in which a variation coefficient of elevation is introduced to express the undulation of topography.Then the coefficient is used to construct a node evaluation function in the terrain data model simplification step.Furthermore,an edge reduction strategy is combined with the improved restrictive quadtree segmentation to handle the crack problem.The experiment results demonstrated that the proposed method can reduce the amount of rendering triangles and enhance the rendering speed on the premise of ensuring the rendering effect compared with a traditional LOD algorithm.

Identification of metastasis-associated genes in colorectal cancer through an integrated genomic and transcriptomic analysis

Objective： Identification of colorectal cancer （CRC） metastasis genes is one of the most important issues in CRC research. For the purpose of mining CRC metastasis-associated genes, an integrated analysis of mJcroarray data was presented, by combined with evidence acquired from comparative genornic hybridization （CGH） data. Methods： Gene expression profile data of CRC samples were obtained at Gene Expression Omnibus （GEO） website. The 15 important chromosomal aberration sites detected by using CGH technology were used for integrated genomic and transcriptomic analysis. Significant Analysis of Microarray （SAM） was used to detect significantly differentially expressed genes across the whole genome. The overlapping genes were selected in their corresponding chromosomal aberration regions, and analyzed by using the Database for Annotation, Visualization and Integrated Discovery （DAVID）. Finally, SVM-T-RFE gene selection algorithm was applied to identify ted genes in CRC. Results： A minimum gene set was obtained with the minimum number [14] of genes, and the highest classification accuracy （100%） in both PRI and META datasets. A fraction of selected genes are associated with CRC or its metastasis. Conclusions- Our results demonstrated that integration analysis is an effective strategy for mining cancer- associated genes.