Integral Feature Research Articles

“If your ox does not pull, what are you going to do?”: Persistent Violence in South Africa’s Deep-Level Gold Mines and Its Contribution to the 1922 Rand Rebellion

Abstract The 1922 Rand Rebellion was the only instance of worker protest in the twentieth century in which a modern state used tanks and military airplanes, as well as mounted infantry, to suppress striking workers. These circumstances were unprecedented in their own time and for most of the century. The compressed and intensely violent rebellion of twenty thousand white mineworkers in South Africa’s gold mines had several overlapping features. Within a matter of days—from 6 to 12 March—it went from a general strike to a racial pogrom and insurrection against the government of Prime Minister Jan Smuts. Throughout all these twists and turns, the battle standard remained, “Workers of the world unite and fight for a White South Africa!” Race and violence were integral features of South Africa’s industrial history, but they do not explain the moments when discrete groups of people chose to use them as weapons or bargaining tools. At the close of the First World War, for instance, South Africa’s white mine workers demanded a more comprehensive distribution of the privileges of white supremacy, but in a manner that was both violent and contentious. Consequently, South Africa’s immediate postwar period became one of the most violent moments in its history.

Deep intrusion net: an efficient framework for network intrusion detection using hybrid deep TCN and GRU with integral features

Deep intrusion net: an efficient framework for network intrusion detection using hybrid deep TCN and GRU with integral features

Analysis of the effectiveness of the implementation of investment projects projects of the eaeu regions

Relevance. The Eurasian Economic Union (EAEU) is the most important instrument of cooperation between Russia, Armenia, Belarus, Kazakhstan and Kyrgyzstan, as well as the most important strategic entity in the context of modern geopolitical realities. It is in such an unstable period that joint cooperation is an integral feature of the long-term development of the member states of the union. The article examines the processes of development and implementation of the largest construction projects within the EAEU, as well as examines the issues of financing these projects, and evaluates the effectiveness of their implementation for each participating country.The purpose of the work is to identify the role of the EAEU in the development of the participating countries, their points of contact and cooperation.Objectives: to analyze the volume of completed construction works of the EAEU member states, the number of housing squares commissioned on the territory of the EAEU; to estimate the amount of investments invested in the largest investment projects of the EAEU in the participating countries.Methodology. The basis of the study is the database of the official portal of the Eurasian Union and the Shanghai Cooperation Organization. The materials of Russian and foreign experts published in the official publication of the Eurasian Union "Bulletin of the EEA" were also used in this study. Methods of comparison, synthesis and analysis of information resources were used in the study. Results. The unified strategy for the economic development of the EAEU member states, taking into account internal and external challenges, is implemented mainly through the active development and development of the largest investment programs throughout the Union.Conclusions. The active work of the EAEU brings tangible benefits and advantages to each member country of the union through the exchange of technologies, production experience and resources, as well as through the development of transport infrastructure. At the same time, questions remain about the sufficiency of the contribution of specific States to joint development, since some of them do not have a sufficient level of development and a sufficient amount of natural resources.

DeepEnzyme: a robust deep learning model for improved enzyme turnover number prediction by utilizing features of protein 3D-structures.

Turnover numbers (kcat), which indicate an enzyme's catalytic efficiency, have a wide range of applications in fields including protein engineering and synthetic biology. Experimentally measuring the enzymes' kcat is always time-consuming. Recently, the prediction of kcat using deep learning models has mitigated this problem. However, the accuracy and robustness in kcat prediction still needs to be improved significantly, particularly when dealing with enzymes with low sequence similarity compared to those within the training dataset. Herein, we present DeepEnzyme, a cutting-edge deep learning model that combines the most recent Transformer and Graph Convolutional Network (GCN) to capture the information of both the sequence and 3D-structure of a protein. To improve the prediction accuracy, DeepEnzyme was trained by leveraging the integrated features from both sequences and 3D-structures. Consequently, DeepEnzyme exhibits remarkable robustness when processing enzymes with low sequence similarity compared to those in the training dataset by utilizing additional features from high-quality protein 3D-structures. DeepEnzyme also makes it possible to evaluate how point mutations affect the catalytic activity of the enzyme, which helps identify residue sites that are crucial for the catalytic function. In summary, DeepEnzyme represents a pioneering effort in predicting enzymes' kcat values with improved accuracy and robustness compared to previous algorithms. This advancement will significantly contribute to our comprehension of enzyme function and its evolutionary patterns across species.

Industry Image Classification Based on Stochastic Configuration Networks and Multi-Scale Feature Analysis.

For industry image data, this paper proposes an image classification method based on stochastic configuration networks and multi-scale feature extraction. The multi-scale features are extracted from images of different scales using deep 2DSCN, and the hidden features of multiple layers are also connected together to obtain more informational features. The integrated features are fed into SCNs to learn a classifier which improves the recognition rate for different categories. In the experiments, a handwritten digit database and an industry hot-rolled steel strip database are used, and the comparison results demonstrate the proposed method can effectively improve the classification accuracy.

Assembly of the bacterial ribosome with circularly permuted rRNA.

Co-transcriptional assembly is an integral feature of the formation of RNA-protein complexes that mediate translation. For ribosome synthesis, prior studies have indicated that the strict order of transcription of rRNA domains may not be obligatory during bacterial ribosome biogenesis, since a series of circularly permuted rRNAs are viable. In this work, we report the structural insights into assembly of the bacterial ribosome large subunit (LSU) based on cryo-EM density maps of intermediates that accumulate during in vitro ribosome synthesis using a set of circularly permuted (CiPer) rRNAs. The observed ensemble of 23 resolved ribosome large subunit intermediates reveals conserved assembly routes with an underlying hierarchy among cooperative assembly blocks. There are intricate interdependencies for the formation of key structural rRNA helices revealed from the circular permutation of rRNA. While the order of domain synthesis is not obligatory, the order of domain association does appear to proceed with a particular order, likely due to the strong evolutionary pressure on efficient ribosome synthesis. This work reinforces the robustness of the known assembly hierarchy of the bacterial large ribosomal subunit and offers a coherent view of how efficient assembly of CiPer rRNAs can be understood in that context.

Particle-soliton degeneracies from spontaneously broken non-invertible symmetry

We study non-invertible topological symmetry operators in massive quantum field theories in (1+1) dimensions. In phases where this symmetry is spontaneously broken we show that the particle spectrum often has degeneracies dictated by the non-invertible symmetry and we deduce a procedure to determine the allowed multiplets. These degeneracies are robust predictions and do not require integrability or other special features of renormalization group flows. We exhibit these conclusions in examples where the spectrum is known, recovering soliton and particle degeneracies. For instance, the Tricritical Ising model deformed by the subleading ℤ2 odd operator flows to a gapped phase with two degenerate vacua. This flow enjoys a Fibonacci fusion category symmetry which implies a threefold degeneracy of its particle states, relating the mass of solitons interpolating between vacua and particles supported in a single vacuum.

Natural and Regulative-Imperative in the Spatial Development of Russia

Organic spatial development is possible under the condition of a reasonable combination of naturalness and regulation of spatially conditioned processes and phenomena. In different periods, the ratio of these principles is different. In Soviet times, spatial development was absolutely dominated by comprehensive directive state planning and management. In the 1990s of post-Soviet reforms, there was a sharp turn towards immoderate and spontaneous decentralization of the spatial organization of life. In the 2000s, counter-reforms began a rapid return to the strengthening of state regulation in this area, which in many respects turned out to be excessive in terms of tasks and costs and unjustified in expectations. The meaning and content, tools and planned results of the current state policy of spatial development are concentrated in the “Strategy of Spatial Development of the Russian Federation for the Period up to 2025”, which claims to be a doctrinal document of modern regional policy. Its development and adoption were accompanied by a multitude of expert comments – from unconditionally apologetic to acutely critical. As the deadline for the end of the strategy approaches, a comprehensive assessment of its effectiveness and effectiveness becomes relevant, especially in connection with the preparation of a new strategy until 2030. The authors see the cornerstone drawback of the current strategy in ignoring the systemic nature of the spatial organization of society, which should be interpreted as an anthropogenic megasystem with its inherent systemic features of integrity, structural organization, direct and reverse connections, with a powerful potential for natural self-organization and selfdevelopment, supplemented in the special transitional conditions of post-Soviet spatial transformations by the regulatory influence on them from the outside State. The authors also emphasize that due to the unprecedented post-Soviet transitional spatial realities, it is impossible to count on diligent study and direct borrowing of foreign experience, due to its inadequacy of the modern Russian situation.

A multi-information fusion anomaly detection model based on convolutional neural networks and AutoEncoder

Network traffic anomaly detection, as an effective analysis method for network security, can identify differentiated traffic information and provide secure operation in complex and changing network environments. To avoid information loss caused when handling traffic data while improving the detection performance of traffic feature information, this paper proposes a multi-information fusion model based on a convolutional neural network and AutoEncoder. The model uses a convolutional neural network to extract features directly from the raw traffic data, and a AutoEncoder to encode the statistical features extracted from the raw traffic data, which are used to supplement the information loss due to cropping. These two features are combined to form a new integrated feature for network traffic, which has the load information from the original traffic data and the global information of the original traffic data obtained from the statistical features, thus providing a complete representation of the information contained in the network traffic and improving the detection performance of the model. The experiments show that the classification accuracy of network traffic anomaly detection using this model outperforms that of classical machine learning methods.

Rethinking AI code generation: a one-shot correction approach based on user feedback

Code generation has become an integral feature of modern IDEs, gathering significant attention. Notable approaches like GitHub Copilot and TabNine have been proposed to tackle this task. However, these tools may shift code writing tasks towards code reviewing, which involves modification from users. Despite the advantages of user feedback, their responses remain transient and lack persistence across interaction sessions. This is attributed to the inherent characteristics of generative AI models, which require explicit re-training for new data integration. Additionally, the non-deterministic and unpredictable nature of AI-powered models limits thorough examination of their unforeseen behaviors. We propose a methodology named One-shot Correction to mitigate these issues in natural language to code translation models with no additional re-training. We utilize decomposition techniques to break down code translation into sub-problems. The final code is constructed using code snippets of each query chunk, extracted from user feedback or selectively generated from a generative model. Our evaluation indicates comparable or improved performance compared to other models. Moreover, the methodology offers straightforward and interpretable approaches, which enable in-depth examination of unexpected results and facilitate insights for potential enhancements. We also illustrate that user feedback can substantially improve code translation models without re-training. Ultimately, we develop a preliminary GUI application to demonstrate the utility of our methodology in simplifying customization and assessment of suggested code for users.

Exploring the embodied experiences of social relationships in physical education

ABSTRACT There is a need for new pedagogies in physical education that emphasise embodied learning experiences and meaningfulness [Ennis, C. D. (2017). Educating students for a lifetime of physical activity: Enhancing mindfulness, motivation, and meaning. Research Quarterly for Exercise and Sport, 88(3), 241–250; Kretchmar, R. S. (2008). The increasing utility of elementary school physical education: A mixed blessing and unique challenge. The Elementary School Journal, 108(3), 161–170; Lambert, K. (2020). Re-conceptualizing embodied pedagogies in physical education by creating pre-text vignettes to trigger pleasure ‘in’ movement. Physical Education and Sport Pedagogy, 25(2), 154–173; Rintala, J. (2009). It's all about the –ing. Quest, 61(3), 278–288]. Social interaction is considered an integral feature of meaningful physical education [Beni, S., Fletcher, T., & Ní Chróinín, D. (2017). Meaningful experiences in physical education and youth sport: A review of the literature. Quest, 69(3), 291–312]. Previous research shows that various pedagogies of embodiment may contribute to enhancing positive social interaction [Azzarito, L., & Ennis, C. D. (2003). A sense of connection: Toward social constructivist physical education. Sport, Education and Society, 8(2), 179–197; Light, R. (2013). Positive Pedagogy for physical education and sport: Game Sense as an example. In R. Light, J. Quay, S. Harvey, & A. Mooney (Eds.), Contemporary developments in games teaching (pp. 29–42). Routledge]. This study explores pupils’ embodied experiences of social relationships in physical education. We aim to inform and further develop existing pedagogies of embodiment by expanding the knowledge about how intersubjectivity and sociality are experienced, and how these experiences affect the pupils’ movement exploration and enable them to value physical activity. By interpreting findings from a sensory ethnographic fieldwork in a tenth-grade class in Oslo, Norway, we show how pupils’ embodied experiences of social relationships revolve around establishing, confirming, maintaining, and questioning their sense of belonging to the social 'we'.

The Regional Ain Shams Telemedicine Network

Abstract With the deficiency in specialized care and low resources it is vital to come up with new healthcare delivery systems that utilize available resources in an efficient way. The hub-spoke model is not new but has recently been revisited as it aids in sharing of specialized services. A central hub is able to support attached spokes through physical and virtual healthcare services. To manage such a network a strong software platform with several integral features should be in place. Using available solutions may not satisfy all required features and may fragment the patient’s health records. Ain Shams University Virtual Hospital (AVH) dedicated to healthcare transformation in Egypt and the region, manages and connects its spokes through TREAT platform (The REgional Ainshams Telemedicine network) that was funded by the Science and Technology Development Fund. The software platform supports provider-to-provider (P2P) telemedicine but for the sake of continuity of care other telemedicine modules and physical services are present. It helps utilize available resources in an efficient way with the capacity to audit user and system performance. The patient module has a seperate entry point and integrates with the rest of the system. Inpatients and outpatients from inside the system and outside can be managed physically and remotely.

Enhanced interlayer interaction in sulfonated CONs membrane by amino-rich CONs enabling ultrafast proton transport

Covalent organic framework nanosheets (CONs) with porous crystalline features and ultrathin thickness are ideal candidates as membrane building blocks to form well-defined transfer nanochannels. The formidable challenge behind self-supporting CONs membrane lies in weak non-covalent interlayer interactions and thus loose stacking, insufficient strength and structure stabilities. Herein, we propose the fabrication of interlayer force-strengthened freestanding CONs membrane through the electrostatic attraction bridge effect of positively-charged amino-rich CONs (CON–NH2) to negatively-charged sulfonated CONs (CON–SO3H). Ultrathin and large lateral sized CON–SO3H and CON–NH2 are synthesized, followed by restacking to prepare freestanding CONs membrane with CON–SO3H as the membrane bulk. Benefiting from effective interlayer interconnection due to strong electrostatic interaction, the obtained CON–SO3H/CON–NH2 membrane displays features of ultrahigh integrity, dense stacking, eminent water/acid/base/organic solvents stabilities and mechanical strength (109 MPa). The shortened –SO3H distance contributes to construct site-continuous transfer pathways, and the deprotonated –SO3H and protonated –NH2 form acid–base pairs to decrease interfacial resistance, which impart membrane superior proton conductivity of 486 mS cm−1 (80 °C, 100% RH). This interlayer force enhancement strategy offers a promising perspective on achieving densely-stacked CONs membrane with ultrahigh mechanical property and conduction performance for fuel cell application.

Prediction method of human defecation based on informer audio data augmentation and improved residual network

Defecation care for disabled patients is a major challenge in health management. Traditional post-defecation treatment will bring physical injury and negative emotions to patients, while existing pre-defecation forecasting care methods are physically intrusive. On the basis of exploring the mechanism of defecation intention generation, and based on the characteristic analysis and clinical application of bowel sounds, it is found that the generation of desire to defecate and bowel sounds are correlated to a certain extent. Therefore, a deep learning-based bowel sound recognition method is proposed for human defecation prediction. The wavelet domain based Wiener filter is used to filter the bowel sound data to reduce other noise. Statistical analysis, fast Fourier transform and wavelet packet transform are used to extract the integrated features of bowel sound in time, frequency and time-frequency domain. In particular, an audio signal expansion data algorithm based on the Informer model is proposed to solve the problem of poor generalization of the training model caused by the difficulty of collecting bowel sound in reality. An improved one-dimensional residual network model (1D-IResNet) for defecation classification prediction is designed based on multi-domain features. The experimental results show that the proposed bowel sound augmentation strategy can effectively improve the data sample size and increase the sample diversity. Under the augmented dataset, the training speed of the 1D-IResNet model is accelerated, and the classification accuracy reaches 90.54 %, the F1 score reaches 83.88 %, which achieves a relatively good classification stability while maintaining a high classification index.

Real Estate Website

Abstract— This paper presents a novel real estate management system designed to address the inefficiencies faced in the contemporary property market. Developed using PHP and MySQL, the system offers a user-friendly web application that streamlines the processes for both property seekers and managers. Catering to distinct user groups, it empowers users with a comprehensive property database searchable by location and desired features. Additionally, users can leverage a built-in loan calculator (depending on implementation) for financial planning and potentially express interest in suitable properties. Admins, on the other hand, benefit from comprehensive management functionalities. These include user account management, property listing creation, editing, and availability control. The system also offers the potential to manage real estate agents, further enhancing its functionality for larger organizations. This user-centric approach prioritizes a user-friendly interface that simplifies searching, listing management, and overall interaction with the system. To ensure data security and integrity, the system implements robust role-based access control, restricting access to sensitive information based on user roles. Furthermore, a powerful search function facilitates efficient property discovery, allowing users to find their ideal properties with minimal effort. This paper delves into the details of the system, outlining its functionalities, the chosen development methodology (e.g., Agile), and the software and hardware requirements for running the application. It also acknowledges the system’s limitations in its initial phase, such as the potential need for future expansion of search filters or communication tools. Finally, the paper explores the exciting future scope of the system, including advanced search capabilities, integrated communication features, mobile application development, and potential integration with smart home technologies and machine learning for personalized recommendations. Keywords – Real Estate Management System, Property Management System, PHP,MySQL,User-Friendly Interface

Analysis of sports records evolution and limits based on integrated features

Sports records play a crucial role in understanding the limits of human achievement in sports. However, a thorough exploration of a comprehensive analysis of various sports records utilizing the existing statistical model has been lacking. This study introduces a framework for analyzing the integrated features and evolutionary trends of 23 sports records for men and women. It includes world records and intercontinental records from six continents, covering 6440 athletes from 2001 to 2020. Our findings indicate that human beings have not yet reached sports limits in athletic performance, suggesting a continuous improvement over time. Furthermore, we have investigated the contributions of our model’s parameters to the integrated features, emphasizing their robustness and convergence in handling data flow and information entropy. Additionally, our model underscores the significance of integrating various sports for ongoing advancement, in line with the Olympic motto “Together,” thereby promoting coordinated development.

Preserving Heritage Riverine Bridges: A Hydrological Approach to the Case Study of the Grau Bridge in Peru

Heritage bridges constitute an integral feature of the urban landscape in numerous cities. However, it is common for these structures to surpass their life cycle, rendering them ill-equipped to withstand the dynamic demands of users and extreme events, particularly hydrological occurrences. This research presents a methodology for the assessment of heritage riverine bridges, with a focus on the Grau Bridge in Peru as a case study. The investigation commences with an exhaustive literature review, complemented by a historical examination, followed by a preliminary diagnosis. Subsequently, hydrological and hydraulic studies are presented, encompassing drone surveys of the riverbed and the bridge, soil analyses, and the application of 1D and 2D models in HEC-RAS. The outcomes of this comprehensive analysis reveal the high vulnerability of the Grau Bridge. Finally, strategic interventions for its conservation are recommended.

Replay Attack Detection Using Integrated Glottal Excitation Based Group Delay Function and Cepstral Features

The automatic speaker verification system is susceptible to replay attacks. Recent literature has focused on score-level integration of multiple features, phase information-based features, high frequency-based features, and glottal excitation for the detection of replay attacks. This work presents glottal excitation-based all-pole group delay function (GAPGDF) features for replay attack detection. The essence of a group delay function based on the all-pole model is to exploit information from the speech signal phase spectrum in an effective manner. Further, the performance of integrated high-frequency-based CQCC features with cepstral features, subband spectral centroid-based features (SCFC and SCMC), APGDF, and LPC-based features is evaluated on the ASVspoof 2017 version 2.0 database. On the development set, an EER of 3.08% is achieved, and on the evaluation set, an EER of 9.86% is achieved. The proposed GAPGDF features provide an EER of 10.5% on the evaluation set. Finally, integrated GAPGDF and GCQCC features provide an EER of 8.80% on the evaluation set. The computation time required for the ASV systems based on various integrated features is compared to ensure symmetry between the integrated features and the classifier.

Enhanced multi-horizon occupancy prediction in smart buildings using cascaded Bi-LSTM models with integrated features

Accurate occupancy prediction in smart buildings is crucial for optimizing energy management, improving occupant comfort, and effectively controlling building systems, particularly for short- and long-term horizons. Recently, deep learning-based occupancy prediction methods have gained considerable attention. However, the full potential of these methods remains under explored in terms of model architecture variations and prediction horizons. This study introduces cascaded LSTM and cascaded Bi-LSTM models for multi-horizon predictions from 10 minutes to 24 hours, integrating a modified activation function, additional input features, and optimized hyper-parameters using OPTUNA. Traditional performance metrics and various other analyses were conducted to compare the models. Both models performed well for short- and long-term predictions, with minimal differences in the results. Nevertheless, analysis focusing on non-zero data errors (accounting for approximately 11% of occupied periods) and occupancy-wise errors showed a significant performance gap between the two models. The cascaded Bi-LSTM model demonstrated consistent performance across various prediction horizons and occupancy variations, with accuracy approximately 10-15% higher than the cascaded LSTM model, highlighting its superior capability in capturing complex dataset dynamics through a bidirectional process. This study highlights the importance of additional input features, data feature analysis, and multi-perspective result analysis to select the most suitable model for occupancy prediction, validated with pre- and post-modeling feature importance analysis.

The City as an Object of Legal Science: Problems and Prospects of Legal Regulation

One of the modern trends in the development of society and the State is total urbanization, which is embodied in the withering away of the rural way of life and the concentration of population and capital in megacities and surrounding agglomerations. The paper examines the historical patterns of urbanization, investigates the place of the city in the system of humanitarian knowledge, and explains the emergence of a new interdisciplinary field of urban research, namely, legal urbanology, the subject of which is the legal space of a modern city. Historically, cities have been centers of politics, trade, and education, and the spread of urban life has become one of the integral features of the capitalist mode of production that is the dominant economic system of production and distribution at the present stage. Nevertheless, the legal regulation of urban relations is associated with a number of problems that require resolution both at the theoretical level and at the legislative level. In this regard, the paper analyzes current Russian legislation, as well as subordinate and departmental rulemaking for compliance with the requirements of the time and evaluation of the effectiveness of regulating the foundations of the legal space of the city. To resolve the identified problems, it is proposed to adopt a systematic program act, the preparation of which can take into account the experience of foreign regulation of the sphere of urban relations.