Structural development defects essentially refer to code structure that violates object-oriented design principles. They make program maintenance challenging and deteriorate software quality over time. Various detecti...Structural development defects essentially refer to code structure that violates object-oriented design principles. They make program maintenance challenging and deteriorate software quality over time. Various detection approaches, ranging from traditional heuristic algorithms to machine learning methods, are used to identify these defects. Ensemble learning methods have strengthened the detection of these defects. However, existing approaches do not simultaneously exploit the capabilities of extracting relevant features from pre-trained models and the performance of neural networks for the classification task. Therefore, our goal has been to design a model that combines a pre-trained model to extract relevant features from code excerpts through transfer learning and a bagging method with a base estimator, a dense neural network, for defect classification. To achieve this, we composed multiple samples of the same size with replacements from the imbalanced dataset MLCQ1. For all the samples, we used the CodeT5-small variant to extract features and trained a bagging method with the neural network Roberta Classification Head to classify defects based on these features. We then compared this model to RandomForest, one of the ensemble methods that yields good results. Our experiments showed that the number of base estimators to use for bagging depends on the defect to be detected. Next, we observed that it was not necessary to use a data balancing technique with our model when the imbalance rate was 23%. Finally, for blob detection, RandomForest had a median MCC value of 0.36 compared to 0.12 for our method. However, our method was predominant in Long Method detection with a median MCC value of 0.53 compared to 0.42 for RandomForest. These results suggest that the performance of ensemble methods in detecting structural development defects is dependent on specific defects.展开更多
From a perspective of theoretical study, there are some faults in the models of the existing object-oriented programming languages. For example, C# does not support metaclasses, the primitive types of Java and C# are ...From a perspective of theoretical study, there are some faults in the models of the existing object-oriented programming languages. For example, C# does not support metaclasses, the primitive types of Java and C# are not objects, etc. So, this paper designs a programming language, Shrek, which integrates many language features and constructions in a compact and consistent model. The Shrek language is a class-based purely object-oriented language. It has a dynamical strong type system, and adopts a single-inheritance mechanism with Mixin as its complement. It has a consistent class instantiation and inheritance structure, and the ability of intercessive structural computational reflection, which enables it to support safe metaclass programming. It also supports multi-thread programming and automatic garbage collection, and enforces its expressive power by adopting a native method mechanism. The prototype system of the Shrek language is implemented and anticipated design goals are achieved.展开更多
Detecting well-known design patterns in object-oriented program source code can help maintainers understand the design of a program. Through the detection, the understandability, maintainability, and reusability of ob...Detecting well-known design patterns in object-oriented program source code can help maintainers understand the design of a program. Through the detection, the understandability, maintainability, and reusability of object-oriented programs can be improved. There are automated detection techniques;however, many existing techniques are based on static analysis and use strict conditions composed on class structure data. Hence, it is difficult for them to detect and distinguish design patterns in which the class structures are similar. Moreover, it is difficult for them to deal with diversity in design pattern applications. To solve these problems in existing techniques, we propose a design pattern detection technique using source code metrics and machine learning. Our technique judges candidates for the roles that compose design patterns by using machine learning and measurements of several metrics, and it detects design patterns by analyzing the relations between candidates. It suppresses false negatives and distinguishes patterns in which the class structures are similar. As a result of experimental evaluations with a set of programs, we confirmed that our technique is more accurate than two conventional techniques.展开更多
Object-oriented programming divides the crop production into subsystems and simulates their behaviors. Many classes were designed to simulate the behaviors of different parts or different physiological processes in cr...Object-oriented programming divides the crop production into subsystems and simulates their behaviors. Many classes were designed to simulate the behaviors of different parts or different physiological processes in crop production system. At the same time, many classes have to be employed for bettering user's interface. But how to manage these classes on a higher level to cooperate them into a perfect system is another problem to study. The Rice Growth Models (RGM) system represents an effort to define and implement a framework to manage these classes. In RGM system, the classes were organized into the model-document-view architecture to separate the domain models, data management and user interface. A single document with multiple views interface frame window was adopted in RGM. In the architectures, the simulation models only exchange data with documents while documents act as intermediacies between simulation models and interfaces. Views get data from documents and show the results to users. The classes for the different functions can be grouped into different architectures. Different architectures communicate with each other through documents. The classes for the different functions can be grouped into different architectures. By using the architecture, communication between classes is more efficient. Modeler can add classes in architectures or other architectures to extend the system without having to change system structure, which is useful for construction and maintenance of agricultural system models.展开更多
Object-oriented model possesses inherent concurrency. Integration of concurrency and object-orientation is a promising new field. MPI is a message-passing standard and has been adopted by more and more systems. This p...Object-oriented model possesses inherent concurrency. Integration of concurrency and object-orientation is a promising new field. MPI is a message-passing standard and has been adopted by more and more systems. This paper proposes a novel approach to realize concurrent object-oriented programming based on Message-passing interface(MPI) in which future method communication is adopted between concurrent objects. A state behavior set is proposed to solve inheritance anomaly, and a bounded buffer is taken as an example to illustrate this proposal. The definition of ParaMPI class, which is the most important class in the concurrent class library, and implementation issues are briefly described.展开更多
Incorporation of explainability features in the decision-making web-based systems is considered a primary concern to enhance accountability,transparency,and trust in the community.Multi-domain Sentiment Analysis is a ...Incorporation of explainability features in the decision-making web-based systems is considered a primary concern to enhance accountability,transparency,and trust in the community.Multi-domain Sentiment Analysis is a significant web-based system where the explainability feature is essential for achieving user satisfaction.Conventional design methodologies such as object-oriented design methodology(OODM)have been proposed for web-based application development,which facilitates code reuse,quantification,and security at the design level.However,OODM did not provide the feature of explainability in web-based decision-making systems.X-OODM modifies the OODM with added explainable models to introduce the explainability feature for such systems.This research introduces an explainable model leveraging X-OODM for designing transparent applications for multidomain sentiment analysis.The proposed design is evaluated using the design quality metrics defined for the evaluation of the X-OODM explainable model under user context.The design quality metrics,transferability,simulatability,informativeness,and decomposability were introduced one after another over time to the evaluation of the X-OODM user context.Auxiliary metrics of accessibility and algorithmic transparency were added to increase the degree of explainability for the design.The study results reveal that introducing such explainability parameters with X-OODM appropriately increases system transparency,trustworthiness,and user understanding.The experimental results validate the enhancement of decision-making for multi-domain sentiment analysis with integration at the design level of explainability.Future work can be built in this direction by extending this work to apply the proposed X-OODM framework over different datasets and sentiment analysis applications to further scrutinize its effectiveness in real-world scenarios.展开更多
The design of finite element analysis program using object-oriented programming (OOP) techniques is presented. The objects, classes and the subclasses used in the programming are explained. The system of classes libra...The design of finite element analysis program using object-oriented programming (OOP) techniques is presented. The objects, classes and the subclasses used in the programming are explained. The system of classes library of finite element analysis program and Windows-type Graphical User Interfaces by VC + + and its MFC are developed. The reliability, reusability and extensibility of program are enhanced. It is a reference to develop the large-scale, versatile and powerful systems of object-oriented finite element software.展开更多
Due to the small size,variety,and high degree of mixing of herbaceous vegetation,remote sensing-based identification of grassland types primarily focuses on extracting major grassland categories,lacking detailed depic...Due to the small size,variety,and high degree of mixing of herbaceous vegetation,remote sensing-based identification of grassland types primarily focuses on extracting major grassland categories,lacking detailed depiction.This limitation significantly hampers the development of effective evaluation and fine supervision for the rational utilization of grassland resources.To address this issue,this study concentrates on the representative grassland of Zhenglan Banner in Inner Mongolia as the study area.It integrates the strengths of Sentinel-1 and Sentinel-2 active-passive synergistic observations and introduces innovative object-oriented techniques for grassland type classification,thereby enhancing the accuracy and refinement of grassland classification.The results demonstrate the following:(1)To meet the supervision requirements of grassland resources,we propose a grassland type classification system based on remote sensing and the vegetation-habitat classification method,specifically applicable to natural grasslands in northern China.(2)By utilizing the high-spatial-resolution Normalized Difference Vegetation Index(NDVI)synthesized through the Spatial and Temporal Non-Local Filter-based Fusion Model(STNLFFM),we are able to capture the NDVI time profiles of grassland types,accurately extract vegetation phenological information within the year,and further enhance the temporal resolution.(3)The integration of multi-seasonal spectral,polarization,and phenological characteristics significantly improves the classification accuracy of grassland types.The overall accuracy reaches 82.61%,with a kappa coefficient of 0.79.Compared to using only multi-seasonal spectral features,the accuracy and kappa coefficient have improved by 15.94%and 0.19,respectively.Notably,the accuracy improvement of the gently sloping steppe is the highest,exceeding 38%.(4)Sandy grassland is the most widespread in the study area,and the growth season of grassland vegetation mainly occurs from May to September.The sandy meadow exhibits a longer growing season compared with typical grassland and meadow,and the distinct differences in phenological characteristics contribute to the accurate identification of various grassland types.展开更多
We present a graph-based model of a generic type system for an OO language. The type system supports the features of recursive types, generics and interfaces, which are commonly found in modern OO languages such as Ja...We present a graph-based model of a generic type system for an OO language. The type system supports the features of recursive types, generics and interfaces, which are commonly found in modern OO languages such as Java. In the classical graph theory, we define type graphs, instantia- tion graphs and conjunction graphs that naturally iIlustrate the relations among types, generics and interfaces within complex OO programs. The model employs a combination of nominal and anonymous nodes to represent respectively types that are identified by names and structures, and de- fines graph-based relations and operations on types including equivalence, subtyping, conjunction and instantiation. Algo- rithms based on the graph structures are designed for the im- plementation of the type system. We believe that this type system is important for the development of a graph-based logical foundation of a formal method for verification of and reasoning about OO programs.展开更多
As one of the main geographical elements in urban areas,buildings are closely related to the development of the city.Therefore,how to quickly and accurately extract building information from remote sensing images is o...As one of the main geographical elements in urban areas,buildings are closely related to the development of the city.Therefore,how to quickly and accurately extract building information from remote sensing images is of great significance for urban map updating,urban planning and construction,etc.Extracting building information around power facilities,especially obtaining this information from high-resolution images,has become one of the current hot topics in remote sensing technology research.This study made full use of the characteristics of GF-2 satellite remote sensing images,adopted an object-oriented classification method,combined with multi-scale segmentation technology and CART classification algorithm,and successfully extracted the buildings in the study area.The research results showed that the overall classification accuracy reached 89.5%and the Kappa coefficient was 0.86.Using the object-oriented CART classification algorithm for building extraction could be closer to actual ground objects and had higher accuracy.The extraction of buildings in the city contributed to urban development planning and provided decision support for management.展开更多
BACKGROUND Hepatocellular carcinoma(HCC)is an inflammation-associated tumor with a dismal prognosis.Immunotherapy has become an important treatment strategy for HCC,as immunity is closely related to inflammation in th...BACKGROUND Hepatocellular carcinoma(HCC)is an inflammation-associated tumor with a dismal prognosis.Immunotherapy has become an important treatment strategy for HCC,as immunity is closely related to inflammation in the tumor microenvir-onment.Inflammation regulates the expression of programmed death ligand-1(PD-L1)in the immunosuppressive tumor microenvironment and affects im-munotherapy efficacy.Interleukin-17A(IL-17A)is involved in the remodeling of the tumor microenvironment and plays a protumor or antitumor role in different tumors.We hypothesized that IL-17A participates in tumor progression by affe-cting the level of immune checkpoint molecules in HCC.The upregulation of PD-L1 expression in HCC cells by IL-17A was assessed by reverse transcription PCR,western blotting,and flow cytometry.Mechanistic studies were conducted with gene knockout models and pathway inhibitors.The function of IL-17A in immune evasion was explored through coculture of T cells and HCC cells.The effects of IL-17A on the malignant biological behaviors of HCC cells were evaluated in vitro,and the antitumor effects of an IL-17A inhibitor and its synergistic effects with a PD-L1 inhibitor were studied in vivo.RESULTS IL-17A upregulated PD-L1 expression in HCC cells in a dose-dependent manner,whereas IL-17A receptor knockout or treatment with a small mothers against decapentaplegic 2 inhibitor diminished the PD-L1 expression induced by IL-17A.IL-17A enhanced the survival of HCC cells in the coculture system.IL-17A increased the viability,G2/M ratio,and migration of HCC cells and decreased the apoptotic index.Cyclin D1,VEGF,MMP9,and Bcl-1 expression increased after IL-17A treatment,whereas BAX expression decreased.The combination of IL-17A and PD-L1 inhibitors showed synergistic antitumor efficacy and increased cluster of differentiation 8+T lymphocyte infiltration in an HCC mouse model.CONCLUSION IL-17A upregulates PD-L1 expression via the IL-17A receptor/phosphorylation-small mothers against decapenta-plegic 2 signaling pathway in HCC cells.Blocking IL-17A enhances the therapeutic efficacy of PD-L1 antibodies in HCC in vivo.展开更多
UML Class diagram generation from textual requirements is an important task in object-oriented design and programing course.This study proposes a method for automatically generating class diagrams from Chinese textual...UML Class diagram generation from textual requirements is an important task in object-oriented design and programing course.This study proposes a method for automatically generating class diagrams from Chinese textual requirements on the basis of Natural Language Processing(NLP)and mapping rules for sentence pattern matching.First,classes are identified through entity recognition rules and candidate class pruning rules using NLP from requirements.Second,class attributes and relationships between classes are extracted using mapping rules for sentence pattern matching on the basis of NLP.Third,we developed an assistant tool integrated into a precision micro classroom system for automatic generation of class diagram,to effectively assist the teaching of object-oriented design and programing course.Results are evaluated with precision,accuracy and recall from eight requirements of object-oriented design and programing course using truth values created by teachers.Our research should benefit beginners of object-oriented design and programing course,who may be students or software developers.It helps them to create correct domain models represented in the UML class diagram.展开更多
BACKGROUND Gastric cancer(GC)is one of the most common malignancies worldwide,and Helicobacter pylori(HP)infection is a well-established risk factor for its development.Programmed death-ligand 1(PD-L1)expression is a ...BACKGROUND Gastric cancer(GC)is one of the most common malignancies worldwide,and Helicobacter pylori(HP)infection is a well-established risk factor for its development.Programmed death-ligand 1(PD-L1)expression is a crucial biomarker for predicting the efficacy of immune checkpoint inhibitors in cancer treatment.While HP infection and PD-L1 expression in GC may be linked,the relationship between them remains unclear,in part because there have been conflicting results reported from various studies.AIM To perform a meta-analysis to assess the relationship between HP and PD-L1 expression in patients with GC.METHODS A systematic literature review was conducted using PubMed,Embase,Cochrane Library,and Web of Science databases.Observational studies that examined the association between HP infection and PD-L1 expression in patients with GC were included.Odds ratios and 95%confidence intervals were calculated to estimate the association.Heterogeneity was assessed using Cochrane’s Q test and I²statistic.A random-effects model was used due to significant heterogeneity across studies.RESULTS Fourteen studies involving a total of 3069 patients with GC were included.The pooled analysis showed a significant association between HP infection and increased PD-L1 expression in GC tissues(odd ratio=1.69,95%confidence interval:1.24-2.29,P<0.001,I^(2)=59%).Sensitivity analyses confirmed the robustness of these findings.Subgroup analyses did not show significant variation based on geographic region,sample size,or method of PD-L1 assessment.Publication bias was minimal,as shown by funnel plots and Egger’s regression test.CONCLUSION HP infection is associated with increased PD-L1 expression in GC,suggesting that HP status may influence the response to programmed cell death protein 1/PD-L1 blockade therapy.展开更多
Over the last two decades,the dogma that cell fate is immutable has been increasingly challenged,with important implications for regenerative medicine.The brea kth rough discovery that induced pluripotent stem cells c...Over the last two decades,the dogma that cell fate is immutable has been increasingly challenged,with important implications for regenerative medicine.The brea kth rough discovery that induced pluripotent stem cells could be generated from adult mouse fibroblasts is powerful proof that cell fate can be changed.An exciting extension of the discovery of cell fate impermanence is the direct cellular reprogram ming hypothesis-that terminally differentiated cells can be reprogrammed into other adult cell fates without first passing through a stem cell state.展开更多
Presents an object-oriented NBO(node-block-object)data model for hypermedia system.It takes advantage of object-oriented method,encapsulates all multimedia information as well as link functions in one unit,It has succ...Presents an object-oriented NBO(node-block-object)data model for hypermedia system.It takes advantage of object-oriented method,encapsulates all multimedia information as well as link functions in one unit,It has successfully achieved cross link to offer much better flexibility and two-way link to realize forward and backward searching in hypermedia system navigation.A conditional relation on links has also been realized,that is very helpful for time sensitive multimedia information processing and multimedia object cooperation.展开更多
The brain's extracellular matrix(ECM),which is comprised of protein and glycosaminoglycan(GAG)scaffolds,constitutes 20%-40% of the human brain and is considered one of the largest influencers on brain cell functio...The brain's extracellular matrix(ECM),which is comprised of protein and glycosaminoglycan(GAG)scaffolds,constitutes 20%-40% of the human brain and is considered one of the largest influencers on brain cell functioning(Soles et al.,2023).Synthesized by neural and glial cells,the brain's ECM regulates a myriad of homeostatic cellular processes,including neuronal plasticity and firing(Miyata et al.,2012),cation buffering(Moraws ki et al.,2015),and glia-neuron interactions(Anderson et al.,2016).Considering the diversity of functions,dynamic remodeling of the brain's ECM indicates that this understudied medium is an active participant in both normal physiology and neurological diseases.展开更多
BACKGROUND Breast cancer(BC)continues to occupy a leading position in terms of morbidity and mortality from malignant neoplasms among the female population.One of the promising markers associated with BC progression i...BACKGROUND Breast cancer(BC)continues to occupy a leading position in terms of morbidity and mortality from malignant neoplasms among the female population.One of the promising markers associated with BC progression is programmed death ligand 1(PD-L1).Previously,we investigated PD-L1 expression in BC via a new antibody against programmed cell death protein 1 ligand 1(PDCD1 LG1)and reported that high PDCD1 LG1 expression in tumor cells is an independent factor for a high risk of regional metastasis in patients with BC.However,the prognostic significance of PDCD1 LG1 expression in BC stromal cells has not been adequately studied.AIM To study the features of PDCD1 LG1 expression in BC stromal cells and its relationship with BC clinicopathological characteristics.METHODS In a prospective single-center observational study,tumor samples from 148 patients with newly diagnosed BC were examined.The tumor sections were immunohistochemically stained with antibodies against PDCD1 LG1.In the tumor samples,the PDCD1 LG1-positive lymphocyte(PDCD1 LG1+LF)score,presence of nuclear PDCD1 LG1 expression in the LFs,PDCD1 LG1 expression in polymorphic cell infiltrates(PDCD1 LG1+polymorphic cell infiltrates[PCIs]),and cells of the fibroblastic stroma and endothelial cells of the tumor microvessels were assessed.Statistical analyses were performed using Statistica 10.0 software.RESULTS A PDCD1 LG1+LF score≥3 was detected more often at stages N0 and N3 than at N1 and N2(P=0.03).Moderate and pronounced PDCD1 LG1+PCIs and the presence of PDCD1 LG1+fibroblastic stroma were associated with negative estrogen receptor status(P=0.0008 and P=0.03,respectively),human epidermal growth factor receptor 2-positive(HER2+)BC(P<0.00001 and P=0.0005),and luminal B HER2+,non-luminal HER2+and triple-negative BC(P<0.00001 and P=0.004).The risk of metastasis to regional lymph nodes(RLNs)depend on lymphovascular invasion(LVI)and the PDCD1 LG1+LF score.In the absence of LVI and a PDCD1 LG1+LF score<3 or≥3,metastases in RLNs were absent in 66.6%and 93.9%of patients with BC,respectively.In the presence of LVI and a PDCD1 LG1+LF score<3 or≥3,metastases in RLNs were detected in 82.6%and 92.7%of patients with BC,respectively.CONCLUSION The results indicated that the combined assessment of the PDCD1 LG1+LF score and LVI can improve the accuracy of predicting the risk of metastasis to RLNs in patients with BC.展开更多
Generating dynamically feasible trajectory for fixed-wing Unmanned Aerial Vehicles(UAVs)in dense obstacle environments remains computationally intractable.This paper proposes a Safe Flight Corridor constrained Sequent...Generating dynamically feasible trajectory for fixed-wing Unmanned Aerial Vehicles(UAVs)in dense obstacle environments remains computationally intractable.This paper proposes a Safe Flight Corridor constrained Sequential Convex Programming(SFC-SCP)to improve the computation efficiency and reliability of trajectory generation.SFC-SCP combines the front-end convex polyhedron SFC construction and back-end SCP-based trajectory optimization.A Sparse A^(*)Search(SAS)driven SFC construction method is designed to efficiently generate polyhedron SFC according to the geometric relation among obstacles and collision-free waypoints.Via transforming the nonconvex obstacle-avoidance constraints to linear inequality constraints,SFC can mitigate infeasibility of trajectory planning and reduce computation complexity.Then,SCP casts the nonlinear trajectory optimization subject to SFC into convex programming subproblems to decrease the problem complexity.In addition,a convex optimizer based on interior point method is customized,where the search direction is calculated via successive elimination to further improve efficiency.Simulation experiments on dense obstacle scenarios show that SFC-SCP can generate dynamically feasible safe trajectory rapidly.Comparative studies with state-of-the-art SCP-based methods demonstrate the efficiency and reliability merits of SFC-SCP.Besides,the customized convex optimizer outperforms off-the-shelf optimizers in terms of computation time.展开更多
Deep-time Earth research plays a pivotal role in deciphering the rates,patterns,and mechanisms of Earth's evolutionary processes throughout geological history,providing essential scientific foundations for climate...Deep-time Earth research plays a pivotal role in deciphering the rates,patterns,and mechanisms of Earth's evolutionary processes throughout geological history,providing essential scientific foundations for climate prediction,natural resource exploration,and sustainable planetary stewardship.To advance Deep-time Earth research in the era of big data and artificial intelligence,the International Union of Geological Sciences initiated the“Deeptime Digital Earth International Big Science Program”(DDE)in 2019.At the core of this ambitious program lies the development of geoscience knowledge graphs,serving as a transformative knowledge infrastructure that enables the integration,sharing,mining,and analysis of heterogeneous geoscience big data.The DDE knowledge graph initiative has made significant strides in three critical dimensions:(1)establishing a unified knowledge structure across geoscience disciplines that ensures consistent representation of geological entities and their interrelationships through standardized ontologies and semantic frameworks;(2)developing a robust and scalable software infrastructure capable of supporting both expert-driven and machine-assisted knowledge engineering for large-scale graph construction and management;(3)implementing a comprehensive three-tiered architecture encompassing basic,discipline-specific,and application-oriented knowledge graphs,spanning approximately 20 geoscience disciplines.Through its open knowledge framework and international collaborative network,this initiative has fostered multinational research collaborations,establishing a robust foundation for next-generation geoscience research while propelling the discipline toward FAIR(Findable,Accessible,Interoperable,Reusable)data practices in deep-time Earth systems research.展开更多
文摘Structural development defects essentially refer to code structure that violates object-oriented design principles. They make program maintenance challenging and deteriorate software quality over time. Various detection approaches, ranging from traditional heuristic algorithms to machine learning methods, are used to identify these defects. Ensemble learning methods have strengthened the detection of these defects. However, existing approaches do not simultaneously exploit the capabilities of extracting relevant features from pre-trained models and the performance of neural networks for the classification task. Therefore, our goal has been to design a model that combines a pre-trained model to extract relevant features from code excerpts through transfer learning and a bagging method with a base estimator, a dense neural network, for defect classification. To achieve this, we composed multiple samples of the same size with replacements from the imbalanced dataset MLCQ1. For all the samples, we used the CodeT5-small variant to extract features and trained a bagging method with the neural network Roberta Classification Head to classify defects based on these features. We then compared this model to RandomForest, one of the ensemble methods that yields good results. Our experiments showed that the number of base estimators to use for bagging depends on the defect to be detected. Next, we observed that it was not necessary to use a data balancing technique with our model when the imbalance rate was 23%. Finally, for blob detection, RandomForest had a median MCC value of 0.36 compared to 0.12 for our method. However, our method was predominant in Long Method detection with a median MCC value of 0.53 compared to 0.42 for RandomForest. These results suggest that the performance of ensemble methods in detecting structural development defects is dependent on specific defects.
基金The National Science Fund for Distinguished Young Scholars (No.60425206)the National Natural Science Foundation of China (No.60633010)the Natural Science Foundation of Jiangsu Province(No.BK2006094)
文摘From a perspective of theoretical study, there are some faults in the models of the existing object-oriented programming languages. For example, C# does not support metaclasses, the primitive types of Java and C# are not objects, etc. So, this paper designs a programming language, Shrek, which integrates many language features and constructions in a compact and consistent model. The Shrek language is a class-based purely object-oriented language. It has a dynamical strong type system, and adopts a single-inheritance mechanism with Mixin as its complement. It has a consistent class instantiation and inheritance structure, and the ability of intercessive structural computational reflection, which enables it to support safe metaclass programming. It also supports multi-thread programming and automatic garbage collection, and enforces its expressive power by adopting a native method mechanism. The prototype system of the Shrek language is implemented and anticipated design goals are achieved.
文摘Detecting well-known design patterns in object-oriented program source code can help maintainers understand the design of a program. Through the detection, the understandability, maintainability, and reusability of object-oriented programs can be improved. There are automated detection techniques;however, many existing techniques are based on static analysis and use strict conditions composed on class structure data. Hence, it is difficult for them to detect and distinguish design patterns in which the class structures are similar. Moreover, it is difficult for them to deal with diversity in design pattern applications. To solve these problems in existing techniques, we propose a design pattern detection technique using source code metrics and machine learning. Our technique judges candidates for the roles that compose design patterns by using machine learning and measurements of several metrics, and it detects design patterns by analyzing the relations between candidates. It suppresses false negatives and distinguishes patterns in which the class structures are similar. As a result of experimental evaluations with a set of programs, we confirmed that our technique is more accurate than two conventional techniques.
文摘Object-oriented programming divides the crop production into subsystems and simulates their behaviors. Many classes were designed to simulate the behaviors of different parts or different physiological processes in crop production system. At the same time, many classes have to be employed for bettering user's interface. But how to manage these classes on a higher level to cooperate them into a perfect system is another problem to study. The Rice Growth Models (RGM) system represents an effort to define and implement a framework to manage these classes. In RGM system, the classes were organized into the model-document-view architecture to separate the domain models, data management and user interface. A single document with multiple views interface frame window was adopted in RGM. In the architectures, the simulation models only exchange data with documents while documents act as intermediacies between simulation models and interfaces. Views get data from documents and show the results to users. The classes for the different functions can be grouped into different architectures. Different architectures communicate with each other through documents. The classes for the different functions can be grouped into different architectures. By using the architecture, communication between classes is more efficient. Modeler can add classes in architectures or other architectures to extend the system without having to change system structure, which is useful for construction and maintenance of agricultural system models.
文摘Object-oriented model possesses inherent concurrency. Integration of concurrency and object-orientation is a promising new field. MPI is a message-passing standard and has been adopted by more and more systems. This paper proposes a novel approach to realize concurrent object-oriented programming based on Message-passing interface(MPI) in which future method communication is adopted between concurrent objects. A state behavior set is proposed to solve inheritance anomaly, and a bounded buffer is taken as an example to illustrate this proposal. The definition of ParaMPI class, which is the most important class in the concurrent class library, and implementation issues are briefly described.
基金support of the Deanship of Research and Graduate Studies at Ajman University under Projects 2024-IRG-ENiT-36 and 2024-IRG-ENIT-29.
文摘Incorporation of explainability features in the decision-making web-based systems is considered a primary concern to enhance accountability,transparency,and trust in the community.Multi-domain Sentiment Analysis is a significant web-based system where the explainability feature is essential for achieving user satisfaction.Conventional design methodologies such as object-oriented design methodology(OODM)have been proposed for web-based application development,which facilitates code reuse,quantification,and security at the design level.However,OODM did not provide the feature of explainability in web-based decision-making systems.X-OODM modifies the OODM with added explainable models to introduce the explainability feature for such systems.This research introduces an explainable model leveraging X-OODM for designing transparent applications for multidomain sentiment analysis.The proposed design is evaluated using the design quality metrics defined for the evaluation of the X-OODM explainable model under user context.The design quality metrics,transferability,simulatability,informativeness,and decomposability were introduced one after another over time to the evaluation of the X-OODM user context.Auxiliary metrics of accessibility and algorithmic transparency were added to increase the degree of explainability for the design.The study results reveal that introducing such explainability parameters with X-OODM appropriately increases system transparency,trustworthiness,and user understanding.The experimental results validate the enhancement of decision-making for multi-domain sentiment analysis with integration at the design level of explainability.Future work can be built in this direction by extending this work to apply the proposed X-OODM framework over different datasets and sentiment analysis applications to further scrutinize its effectiveness in real-world scenarios.
文摘The design of finite element analysis program using object-oriented programming (OOP) techniques is presented. The objects, classes and the subclasses used in the programming are explained. The system of classes library of finite element analysis program and Windows-type Graphical User Interfaces by VC + + and its MFC are developed. The reliability, reusability and extensibility of program are enhanced. It is a reference to develop the large-scale, versatile and powerful systems of object-oriented finite element software.
基金supported by the National Natural Science Foundation of China[grant number 42001386,42271407]within the ESA-MOST China Dragon 5 Cooperation(ID:59313).
文摘Due to the small size,variety,and high degree of mixing of herbaceous vegetation,remote sensing-based identification of grassland types primarily focuses on extracting major grassland categories,lacking detailed depiction.This limitation significantly hampers the development of effective evaluation and fine supervision for the rational utilization of grassland resources.To address this issue,this study concentrates on the representative grassland of Zhenglan Banner in Inner Mongolia as the study area.It integrates the strengths of Sentinel-1 and Sentinel-2 active-passive synergistic observations and introduces innovative object-oriented techniques for grassland type classification,thereby enhancing the accuracy and refinement of grassland classification.The results demonstrate the following:(1)To meet the supervision requirements of grassland resources,we propose a grassland type classification system based on remote sensing and the vegetation-habitat classification method,specifically applicable to natural grasslands in northern China.(2)By utilizing the high-spatial-resolution Normalized Difference Vegetation Index(NDVI)synthesized through the Spatial and Temporal Non-Local Filter-based Fusion Model(STNLFFM),we are able to capture the NDVI time profiles of grassland types,accurately extract vegetation phenological information within the year,and further enhance the temporal resolution.(3)The integration of multi-seasonal spectral,polarization,and phenological characteristics significantly improves the classification accuracy of grassland types.The overall accuracy reaches 82.61%,with a kappa coefficient of 0.79.Compared to using only multi-seasonal spectral features,the accuracy and kappa coefficient have improved by 15.94%and 0.19,respectively.Notably,the accuracy improvement of the gently sloping steppe is the highest,exceeding 38%.(4)Sandy grassland is the most widespread in the study area,and the growth season of grassland vegetation mainly occurs from May to September.The sandy meadow exhibits a longer growing season compared with typical grassland and meadow,and the distinct differences in phenological characteristics contribute to the accurate identification of various grassland types.
文摘We present a graph-based model of a generic type system for an OO language. The type system supports the features of recursive types, generics and interfaces, which are commonly found in modern OO languages such as Java. In the classical graph theory, we define type graphs, instantia- tion graphs and conjunction graphs that naturally iIlustrate the relations among types, generics and interfaces within complex OO programs. The model employs a combination of nominal and anonymous nodes to represent respectively types that are identified by names and structures, and de- fines graph-based relations and operations on types including equivalence, subtyping, conjunction and instantiation. Algo- rithms based on the graph structures are designed for the im- plementation of the type system. We believe that this type system is important for the development of a graph-based logical foundation of a formal method for verification of and reasoning about OO programs.
基金Research on Algorithm Model for Monitoring and Evaluating Typical Disaster Situations of Electric Power Equipment Based on Remote Sensing Imaging Technology of Heaven and Earth,South Grid Guangxi Power Grid Company Science and Technology Project(GXKJXM20222160).
文摘As one of the main geographical elements in urban areas,buildings are closely related to the development of the city.Therefore,how to quickly and accurately extract building information from remote sensing images is of great significance for urban map updating,urban planning and construction,etc.Extracting building information around power facilities,especially obtaining this information from high-resolution images,has become one of the current hot topics in remote sensing technology research.This study made full use of the characteristics of GF-2 satellite remote sensing images,adopted an object-oriented classification method,combined with multi-scale segmentation technology and CART classification algorithm,and successfully extracted the buildings in the study area.The research results showed that the overall classification accuracy reached 89.5%and the Kappa coefficient was 0.86.Using the object-oriented CART classification algorithm for building extraction could be closer to actual ground objects and had higher accuracy.The extraction of buildings in the city contributed to urban development planning and provided decision support for management.
基金Supported by the Natural Science Foundation of Gansu Province,No.21JR7RA373 and No.24JRRA295.
文摘BACKGROUND Hepatocellular carcinoma(HCC)is an inflammation-associated tumor with a dismal prognosis.Immunotherapy has become an important treatment strategy for HCC,as immunity is closely related to inflammation in the tumor microenvir-onment.Inflammation regulates the expression of programmed death ligand-1(PD-L1)in the immunosuppressive tumor microenvironment and affects im-munotherapy efficacy.Interleukin-17A(IL-17A)is involved in the remodeling of the tumor microenvironment and plays a protumor or antitumor role in different tumors.We hypothesized that IL-17A participates in tumor progression by affe-cting the level of immune checkpoint molecules in HCC.The upregulation of PD-L1 expression in HCC cells by IL-17A was assessed by reverse transcription PCR,western blotting,and flow cytometry.Mechanistic studies were conducted with gene knockout models and pathway inhibitors.The function of IL-17A in immune evasion was explored through coculture of T cells and HCC cells.The effects of IL-17A on the malignant biological behaviors of HCC cells were evaluated in vitro,and the antitumor effects of an IL-17A inhibitor and its synergistic effects with a PD-L1 inhibitor were studied in vivo.RESULTS IL-17A upregulated PD-L1 expression in HCC cells in a dose-dependent manner,whereas IL-17A receptor knockout or treatment with a small mothers against decapentaplegic 2 inhibitor diminished the PD-L1 expression induced by IL-17A.IL-17A enhanced the survival of HCC cells in the coculture system.IL-17A increased the viability,G2/M ratio,and migration of HCC cells and decreased the apoptotic index.Cyclin D1,VEGF,MMP9,and Bcl-1 expression increased after IL-17A treatment,whereas BAX expression decreased.The combination of IL-17A and PD-L1 inhibitors showed synergistic antitumor efficacy and increased cluster of differentiation 8+T lymphocyte infiltration in an HCC mouse model.CONCLUSION IL-17A upregulates PD-L1 expression via the IL-17A receptor/phosphorylation-small mothers against decapenta-plegic 2 signaling pathway in HCC cells.Blocking IL-17A enhances the therapeutic efficacy of PD-L1 antibodies in HCC in vivo.
基金This work is supported by the Collaborative education project of QST Innovation Technology Group Co.,Ltd and the Ministry of Education of PRC(NO.201801243022).
文摘UML Class diagram generation from textual requirements is an important task in object-oriented design and programing course.This study proposes a method for automatically generating class diagrams from Chinese textual requirements on the basis of Natural Language Processing(NLP)and mapping rules for sentence pattern matching.First,classes are identified through entity recognition rules and candidate class pruning rules using NLP from requirements.Second,class attributes and relationships between classes are extracted using mapping rules for sentence pattern matching on the basis of NLP.Third,we developed an assistant tool integrated into a precision micro classroom system for automatic generation of class diagram,to effectively assist the teaching of object-oriented design and programing course.Results are evaluated with precision,accuracy and recall from eight requirements of object-oriented design and programing course using truth values created by teachers.Our research should benefit beginners of object-oriented design and programing course,who may be students or software developers.It helps them to create correct domain models represented in the UML class diagram.
文摘BACKGROUND Gastric cancer(GC)is one of the most common malignancies worldwide,and Helicobacter pylori(HP)infection is a well-established risk factor for its development.Programmed death-ligand 1(PD-L1)expression is a crucial biomarker for predicting the efficacy of immune checkpoint inhibitors in cancer treatment.While HP infection and PD-L1 expression in GC may be linked,the relationship between them remains unclear,in part because there have been conflicting results reported from various studies.AIM To perform a meta-analysis to assess the relationship between HP and PD-L1 expression in patients with GC.METHODS A systematic literature review was conducted using PubMed,Embase,Cochrane Library,and Web of Science databases.Observational studies that examined the association between HP infection and PD-L1 expression in patients with GC were included.Odds ratios and 95%confidence intervals were calculated to estimate the association.Heterogeneity was assessed using Cochrane’s Q test and I²statistic.A random-effects model was used due to significant heterogeneity across studies.RESULTS Fourteen studies involving a total of 3069 patients with GC were included.The pooled analysis showed a significant association between HP infection and increased PD-L1 expression in GC tissues(odd ratio=1.69,95%confidence interval:1.24-2.29,P<0.001,I^(2)=59%).Sensitivity analyses confirmed the robustness of these findings.Subgroup analyses did not show significant variation based on geographic region,sample size,or method of PD-L1 assessment.Publication bias was minimal,as shown by funnel plots and Egger’s regression test.CONCLUSION HP infection is associated with increased PD-L1 expression in GC,suggesting that HP status may influence the response to programmed cell death protein 1/PD-L1 blockade therapy.
基金supported by Canada First Research Excellence Fund,Medicine by Design(to CMM)。
文摘Over the last two decades,the dogma that cell fate is immutable has been increasingly challenged,with important implications for regenerative medicine.The brea kth rough discovery that induced pluripotent stem cells could be generated from adult mouse fibroblasts is powerful proof that cell fate can be changed.An exciting extension of the discovery of cell fate impermanence is the direct cellular reprogram ming hypothesis-that terminally differentiated cells can be reprogrammed into other adult cell fates without first passing through a stem cell state.
文摘Presents an object-oriented NBO(node-block-object)data model for hypermedia system.It takes advantage of object-oriented method,encapsulates all multimedia information as well as link functions in one unit,It has successfully achieved cross link to offer much better flexibility and two-way link to realize forward and backward searching in hypermedia system navigation.A conditional relation on links has also been realized,that is very helpful for time sensitive multimedia information processing and multimedia object cooperation.
基金supported by National Institute on Aging(NIH-NIA)R21 AG074152(to KMA)National Institute of Allergy and Infectious Diseases(NIAID)grant DP2 AI171150(to KMA)Department of Defense(DoD)grant AZ210089(to KMA)。
文摘The brain's extracellular matrix(ECM),which is comprised of protein and glycosaminoglycan(GAG)scaffolds,constitutes 20%-40% of the human brain and is considered one of the largest influencers on brain cell functioning(Soles et al.,2023).Synthesized by neural and glial cells,the brain's ECM regulates a myriad of homeostatic cellular processes,including neuronal plasticity and firing(Miyata et al.,2012),cation buffering(Moraws ki et al.,2015),and glia-neuron interactions(Anderson et al.,2016).Considering the diversity of functions,dynamic remodeling of the brain's ECM indicates that this understudied medium is an active participant in both normal physiology and neurological diseases.
基金Supported by Russian Science Foundation,No.23-25-00183.
文摘BACKGROUND Breast cancer(BC)continues to occupy a leading position in terms of morbidity and mortality from malignant neoplasms among the female population.One of the promising markers associated with BC progression is programmed death ligand 1(PD-L1).Previously,we investigated PD-L1 expression in BC via a new antibody against programmed cell death protein 1 ligand 1(PDCD1 LG1)and reported that high PDCD1 LG1 expression in tumor cells is an independent factor for a high risk of regional metastasis in patients with BC.However,the prognostic significance of PDCD1 LG1 expression in BC stromal cells has not been adequately studied.AIM To study the features of PDCD1 LG1 expression in BC stromal cells and its relationship with BC clinicopathological characteristics.METHODS In a prospective single-center observational study,tumor samples from 148 patients with newly diagnosed BC were examined.The tumor sections were immunohistochemically stained with antibodies against PDCD1 LG1.In the tumor samples,the PDCD1 LG1-positive lymphocyte(PDCD1 LG1+LF)score,presence of nuclear PDCD1 LG1 expression in the LFs,PDCD1 LG1 expression in polymorphic cell infiltrates(PDCD1 LG1+polymorphic cell infiltrates[PCIs]),and cells of the fibroblastic stroma and endothelial cells of the tumor microvessels were assessed.Statistical analyses were performed using Statistica 10.0 software.RESULTS A PDCD1 LG1+LF score≥3 was detected more often at stages N0 and N3 than at N1 and N2(P=0.03).Moderate and pronounced PDCD1 LG1+PCIs and the presence of PDCD1 LG1+fibroblastic stroma were associated with negative estrogen receptor status(P=0.0008 and P=0.03,respectively),human epidermal growth factor receptor 2-positive(HER2+)BC(P<0.00001 and P=0.0005),and luminal B HER2+,non-luminal HER2+and triple-negative BC(P<0.00001 and P=0.004).The risk of metastasis to regional lymph nodes(RLNs)depend on lymphovascular invasion(LVI)and the PDCD1 LG1+LF score.In the absence of LVI and a PDCD1 LG1+LF score<3 or≥3,metastases in RLNs were absent in 66.6%and 93.9%of patients with BC,respectively.In the presence of LVI and a PDCD1 LG1+LF score<3 or≥3,metastases in RLNs were detected in 82.6%and 92.7%of patients with BC,respectively.CONCLUSION The results indicated that the combined assessment of the PDCD1 LG1+LF score and LVI can improve the accuracy of predicting the risk of metastasis to RLNs in patients with BC.
基金supported by the National Natural Science Foundation of China(No.62203256)。
文摘Generating dynamically feasible trajectory for fixed-wing Unmanned Aerial Vehicles(UAVs)in dense obstacle environments remains computationally intractable.This paper proposes a Safe Flight Corridor constrained Sequential Convex Programming(SFC-SCP)to improve the computation efficiency and reliability of trajectory generation.SFC-SCP combines the front-end convex polyhedron SFC construction and back-end SCP-based trajectory optimization.A Sparse A^(*)Search(SAS)driven SFC construction method is designed to efficiently generate polyhedron SFC according to the geometric relation among obstacles and collision-free waypoints.Via transforming the nonconvex obstacle-avoidance constraints to linear inequality constraints,SFC can mitigate infeasibility of trajectory planning and reduce computation complexity.Then,SCP casts the nonlinear trajectory optimization subject to SFC into convex programming subproblems to decrease the problem complexity.In addition,a convex optimizer based on interior point method is customized,where the search direction is calculated via successive elimination to further improve efficiency.Simulation experiments on dense obstacle scenarios show that SFC-SCP can generate dynamically feasible safe trajectory rapidly.Comparative studies with state-of-the-art SCP-based methods demonstrate the efficiency and reliability merits of SFC-SCP.Besides,the customized convex optimizer outperforms off-the-shelf optimizers in terms of computation time.
基金Strategic Priority Research Program of the Chinese Academy of Sciences,No.XDB0740000National Key Research and Development Program of China,No.2022YFB3904200,No.2022YFF0711601+1 种基金Key Project of Innovation LREIS,No.PI009National Natural Science Foundation of China,No.42471503。
文摘Deep-time Earth research plays a pivotal role in deciphering the rates,patterns,and mechanisms of Earth's evolutionary processes throughout geological history,providing essential scientific foundations for climate prediction,natural resource exploration,and sustainable planetary stewardship.To advance Deep-time Earth research in the era of big data and artificial intelligence,the International Union of Geological Sciences initiated the“Deeptime Digital Earth International Big Science Program”(DDE)in 2019.At the core of this ambitious program lies the development of geoscience knowledge graphs,serving as a transformative knowledge infrastructure that enables the integration,sharing,mining,and analysis of heterogeneous geoscience big data.The DDE knowledge graph initiative has made significant strides in three critical dimensions:(1)establishing a unified knowledge structure across geoscience disciplines that ensures consistent representation of geological entities and their interrelationships through standardized ontologies and semantic frameworks;(2)developing a robust and scalable software infrastructure capable of supporting both expert-driven and machine-assisted knowledge engineering for large-scale graph construction and management;(3)implementing a comprehensive three-tiered architecture encompassing basic,discipline-specific,and application-oriented knowledge graphs,spanning approximately 20 geoscience disciplines.Through its open knowledge framework and international collaborative network,this initiative has fostered multinational research collaborations,establishing a robust foundation for next-generation geoscience research while propelling the discipline toward FAIR(Findable,Accessible,Interoperable,Reusable)data practices in deep-time Earth systems research.
