Universal Framework Research Articles

A universal parallel simulation framework for energy pipeline networks on high-performance computers

Energy distribution networks represent crucial infrastructures for modern society, and various simulation tools have been widely used by energy suppliers to manage these intricate networks. However, simulation calculations include a large number of fluid control equations, and computational overhead limits the performance of simulation software. This paper proposes a universal parallel simulation framework for energy pipeline networks that takes advantages of data parallelism and computational independence between network elements. A non-pipe model of an energy supply network is optimized, and the input and output of the network model in the proposed framework are modified, which can reduce the development burden during the numerical computations of the pipeline network and weaken the computational correlation between different simulated components. In addition, independent computations can be performed concurrently through periodic data exchange procedures between component instances, improving the parallelism and efficiency of simulation computations. Further, a parallel water pipelines network simulation computing paradigm based on a heterogeneous computer hardware architecture is used to evaluate the proposed framework’s performance. A series of tests are conducted to verify the accuracy of the proposed framework, and simulation errors of less than 5% are achieved. The results of multi-threaded simulation experiments have demonstrated the feasibility of the proposed framework in a parallel computing approach. Moreover, an Advanced Micro Devices (AMD) Deep Computing Unit (DCU)-parallel program is implemented into a water supply network simulation system; the computational efficiency of this system is compared with that of its serial counterpart. The experimental results show that the proposed framework is appropriate for high-performance computer architectures, and the 18x speed-up ratio demonstrates that the parallel program based on the proposed universal framework outperforms the serial program. That provides the basis for the application of pipe network simulation on high-performance computers.

The effect of regime-switching transaction costs and cash dividends on liquidity premia

We investigate the effect of the regime-switching transaction costs and dividends on liquidity premium and investor’s optimal strategy. With reasonably calibrated parameters, we show that counter-cyclical transaction costs substantially raise liquidity premium while pro-cyclical dividends amplify this effect. More importantly, we observe that cash dividends can no longer play a role as a liquidity provider if the volatility of pro-cyclical dividends increases. Our model provides a universal framework in the liquidity contexts, so that we can examine the effect of regime switching on liquidity premium for all combinations of regimes in transaction costs and dividends.

A state-of-the-art universal machine learning framework for decoding suspect coded messages

This proposed framework aims to mitigate global terrorist threats, suspicious activities, and legal violations by introducing an innovative system that monitors encrypted short-text messages across instant messaging programs. In the current landscape, criminals, terrorists, and clandestine entities extensively employ encrypted messages for illicit endeavours on a global scale. Despite the escalating use of encrypted messages on social media platforms, a comprehensive solution for combating online crime is notably absent.The outlined framework addresses this gap by employing a cryptographic system that integrates semantic web ontology, a Predefined Dataset, and machine learning technology for encryption, decryption, and cryptanalysis. A key feature is the framework's ability to predict potential crimes by analyzing blogs for suspicious terminology conveyed through coded information. The identified information is then promptly communicated to law enforcement agencies, thereby alleviating the burden on numerous security entities and offering an efficient strategy against global crime.What sets this proposed framework apart is its unique utilization of encrypted text, a dimension that has remained unexplored in previous studies. The cohesive integration of its components expedites the process of anticipating crime types and significantly reduces the incidence of encrypted crimes on a global scale.

Automated Monitoring System for Suspended Photocatalytic Batch Reactions Based on Online Circulatory Spectrophotometry.

Employing an automated monitoring system (AMS) for data acquisition offers benefits, such as reducing the workload, in the kinetic study of suspended photocatalytic batch reactions. However, the current methods in this field tend to narrowly focus on the substrate and often overlook the optical characteristics of both the mixture and solid particles. To address this limitation, in this study, we propose a novel AMS based on online circulatory spectrophotometry (OCS) and incorporate debubbling, aeration, and segmented flow (DAS), named DAS-OCS-AMS. Initially, a debubbler is introduced to mitigate the issue of signal noise caused by bubbles (SNB). Subsequently, an aerated and segmented device is developed to address the issue of particle deposition on the inner wall of the pipeline (PDP) and on the windows of the flow cell (PDW). The proposed DAS-OCS-AMS is applied to monitor the kinetics of the photocatalytic degradation of Acid Orange Ⅱ by TiO2 (P25), and its results are compared with those obtained using the traditional OCS-AMS. The comparative analysis indicates that the proposed DAS-OCS-AMS effectively mitigates the influence of SNB, PDP, and PDW, yielding precise results both for the mixture and solid particles. The DAS-OCS-AMS provides a highly flexible universal framework for online circulatory automated monitoring and a robust hardware foundation for subsequent data processing research.

Protocol parameter extraction and centralization framework for comprehensive and in-depth CT protocol review and management.

CT protocol management is an arduous task that requires expertise from a variety of radiology professionals, including technologists, radiologists, radiology IT professionals, and medical physicists. Each CT vendor has unique, proprietary protocol file structures, some of which may vary by scanner model, making it difficult to develop a universal framework for distilling technical parameters to a human-readable file format. An ideal solution for CT protocol management is to minimize the work required for parameter extraction by introducing a data format into the workflow that is universal to all CT scanners. In this paper, we report a framework for CT protocol management that converts raw protocol files to an intermediary format before outputting them in a human-readable format for a variety of practical clinical applications, including routine protocol review, protocol version tracking, and cross-protocol comparisons. The framework was developed in Python 3. Technical parameters of interest were determined via collaborative effort between medical physicists and lead technologists. Protocol files were extracted and analyzed from a variety of scanners across our hospital-wide CT fleet, including various systems from Siemens and GE. Protocols were subcategorized based on relevant technical parameters into regular, dual-energy, and cardiac CT protocols. Backend code for technical parameter extraction from raw protocol files to a JavaScript Object Notation (JSON) format was performed on a per-system basis. Conversion from JSON to a readable output format (MS Excel) was performed identically for all scanners using the universal framework developed and presented in this work. Example results for Siemens and GE scanners are shown, including side-by-side comparisons for protocols with similar clinical indications. In conclusion, our CT protocol management framework may be deployed on any CT system to improve clinical efficiency in protocol review and upkeep.

Transparent AI-assisted chemical engineering process: Machine learning modeling and multi-objective optimization for integrating process data and molecular-level reaction mechanisms

Thoroughly utilizing the first principles of chemical processes, industrial big data, and artificial intelligence algorithms has been a deterministic trend in process modeling technology. However, the complexity of reaction networks triggers austere challenges to the deeper understanding of hybrid modeling techniques. Herein, this study explores a universal framework that integrates artificial intelligence algorithms and process mechanisms. The concept of “transparent AI-assisted chemical processes” is proposed. This study meticulously explains how to build a database integrating process big data and reaction network data. Based on the established machine learning framework incorporating process big data and mechanisms, multi-objective optimization algorithms are combined to achieve process optimization. The results indicate that the comprehensive dimensions of the optimized process's technical performance, economic performance, and environmental impact have energetically upgraded. Compared to the pre-optimization process, the optimized process's conversion rate and high-value product yield have increased by 4.44% and 4.25%, respectively. Moreover, the optimized process's non-renewable energy consumption and greenhouse gas emissions decreased by 6.23% and 12.60%, respectively.

[formula omitted]D-CS: A circularly shifting chaotic map generation method

Due to more complex behavior and larger number of control parameters, high-dimensional chaotic map generation methods may provide more satisfactory performance in various practical applications compared to low-dimensional counterparts. Designing n-dimensional (nD) chaotic map generation methods for generating chaotic maps in arbitrary dimensions with desired dynamics is an interesting but challenging task. In this paper, we propose a universal framework called the nD circularly shifting chaotic map generation method (nD-CS), which utilizes existing one-dimensional chaotic maps as seed maps to generate nD chaotic maps with complex and robust behaviors. Theoretical analysis proves that when specific criteria are met by its control parameters, our nD-CS can exhibit complex and robust hyperchaotic behavior in the sense of Lyapunov exponent. To demonstrate the effectiveness of our nD-CS, we first employ it to generate three examples of three-dimensional hyperchaotic maps. The results indicate high-performance indicators of these new chaotic maps. Furthermore, we compare the nD chaotic maps generated by our nD-CS to those produced by existing methods. The results demonstrate that our chaotic maps have a better overall performance.

Lambda3: homology search for protein, nucleotide, and bisulfite-converted sequences.

Local alignments of query sequences in large databases represent a core part of metagenomic studies and facilitate homology search. Following the development of NCBI Blast, many applications aimed to provide faster and equally sensitive local alignment frameworks. Most applications focus on protein alignments, while only few also facilitate DNA-based searches. None of the established programs allow searching DNA sequences from bisulfite sequencing experiments commonly used for DNA methylation profiling, for which specific alignment strategies need to be implemented. Here, we introduce Lambda3, a new version of the local alignment application Lambda. Lambda3 is the first solution that enables the search of protein, nucleotide as well as bisulfite-converted nucleotide query sequences. Its protein mode achieves comparable performance to that of the highly optimized protein alignment application Diamond, while the nucleotide mode consistently outperforms established local nucleotide aligners. Combined, Lambda3 presents a universal local alignment framework that enables fast and sensitive homology searches for a wide range of use-cases. Lambda3 is free and open-source software publicly available at https://github.com/seqan/lambda/.

Evaluatology: The science and engineering of evaluation

Evaluation is a crucial aspect of human existence and plays a vital role in each field. However, it is often approached in an empirical and ad-hoc manner, lacking consensus on universal concepts, terminologies, theories, and methodologies. This lack of agreement has significant consequences. This article aims to formally introduce the discipline of evaluatology, which encompasses the science and engineering of evaluation. We propose a universal framework for evaluation, encompassing concepts, terminologies, theories, and methodologies that can be applied across various disciplines, if not all disciplines.Our research reveals that the essence of evaluation lies in conducting experiments that intentionally apply a well-defined evaluation condition to individuals or systems under scrutiny, which we refer to as the subjects. This process allows for the creation of an evaluation system or model. By measuring and/or testing this evaluation system or model, we can infer the impact of different subjects. Derived from the essence of evaluation, we propose five axioms focusing on key aspects of evaluation outcomes as the foundational evaluation theory. These axioms serve as the bedrock upon which we build universal evaluation theories and methodologies. When evaluating a single subject, it is crucial to create evaluation conditions with different levels of equivalency. By applying these conditions to diverse subjects, we can establish reference evaluation models. These models allow us to alter a single independent variable at a time while keeping all other variables as controls. When evaluating complex scenarios, the key lies in establishing a series of evaluation models that maintain transitivity. Building upon the science of evaluation, we propose a formal definition of a benchmark as a simplified and sampled evaluation condition that guarantees different levels of equivalency. This concept serves as the cornerstone for a universal benchmark-based engineering approach to evaluation across various disciplines, which we refer to as benchmarkology.

VALUES DEVELOPMENT THROUGH GYMNASTIC EDUCATION IN PRESCHOOL CHILDREN

Physical education and sports are at the forefront of many schools of thought when it comes to values education since sport has the power to provide a universal framework for learning values. For this reason, the aim of this study is to examine the development of values in children aged four and five who start their gymnastics education in early childhood. In the experimentally planned study, the children's families filled the values scale as a pre-test and post-test, and the changes were examined. The population of the research group consisted of children aged four to five who attended the gymnastics course opened by a private club in the Akhisar district of Manisa during the summer period. The sample group consisted of 49 children whose consent was obtained after informing their families. The Preschool Values Scale Family Form (PVSFF), developed by Neslitürk and Çeliköz (2015), was used as a measurement tool. Although there was no significant difference between the pre-test and post-test mean scores of the Preschool Values Scale Family Form, a significant difference was found only in the responsibility sub-dimension of the scale. Therefore, it is understood that children can gain values, especially responsibility, through lifelong game-based sports. Considering that learning through game is important in early childhood, it is thought that emphasizing the concept of values in the movement or sports education programs to be structured and to inform the teachers, coaches or consultants working in this field is crucial.

Multikulturalisme dan Kebijakan Publik: Rekognisi dan Perlindungan Hak Asasi Masyarakat Adat di Indonesia dalam Perspektif Universalisme Interaktif Seyla Benhabib

This article discusses the issue of recognition and protection of community rights in Indonesia in the light of Seyla Benhabib's interactive universalism. The research aims to analyse the challenges and opportunities in pursuing recognition and protection of indigenous peoples within the framework of interactive universalism, by emphasising cultural diversity and the importance of the local context. Interactive universalism concerns about the importance of dialogue, public participation, and the adaptation of democratic principles in preserving indigenous peoples' rights. Here interactive universalism is used to review the state of indigenous peoples' rights in Indonesia. Various public policies have caused conflicts, ranging from land take over to human rights violation, that are detrimental to the existence of indigenous peoples. Public policy as a representation of the state should protect their rights. In this article public policies are reviewed from the perspective of interactive universalism. The review process will consider cultural diversity in order to prevent future problems and to recommend adequate and assured protection of the rights of indigenous peoples. This research is a qualitative research using a philosophical approach and literature review to analyse actual problems.

PHIR: A Platform Solution of Data-Driven Health Monitoring for Industrial Robots

The large-scale application of industrial robots has created a demand for more intelligent and efficient health monitoring, which is more efficiently met by data-driven methods due to the surge in data and the advancement of computing technology. However, applying deep learning methods to industrial robots presents critical challenges such as data collection, application packaging, and the need for customized algorithms. To overcome these difficulties, this paper introduces a Platform of data-driven Health monitoring for IRs (PHIR) that provides a universal framework for manufacturers to utilize deep-learning-based approaches with minimal coding. Real-time data from multiple IRs and sensors is collected through a cloud-edge system and undergoes unified pre-processing to facilitate model training with a large volume of data. To enable code-free development, containerization technology is used to convert algorithms into operators, and users are provided with a process orchestration interface. Furthermore, algorithm research both for sudden fault and long-term aging failure detection is conducted and applied to the platform for industrial robot health monitoring experiments, by which the superiority of the proposed platform, in reality, is proven through positive results.

Intelligent multi-objective optimization of 3D printing low-carbon concrete for multi-scenario requirements

This study presents, for the first time, a universal theoretical framework based on machine learning (ML) algorithms for the multi-objective optimization (MOO) design of 3D printing low-carbon concrete to meet the sustainability requirements of the construction industry. The study focuses on the simultaneous reduction of carbon emissions and costs during the design process. Specifically, the applicable scenarios are primarily classified into two categories: meet aesthetics requirements (printing layer height error-HE and printing layer width error-WE approaching 0) and strengths requirements (compressive strength-CS approaching 60 MPa and interlayer bonding strength-IBS as high as possible). Through a comparison with the traditional design method, 3D printing concrete based on the intelligent design method achieves a 44% reduction in costs and a 19% reduction in carbon emissions while meeting aesthetics requirements. Under strengths requirements, there is a 57% reduction in costs and a 22% reduction in carbon emissions. This demonstrates the indispensable and crucial role of the MOO method employed in this study in achieving the lowest costs and carbon emissions. Additionally, the results of experimental verification demonstrate that the models developed in this study have successfully achieved a close alignment between design values and experimental values within the allowable error range (The errors are 3.46%–9.83%). This not only holds the potential to drive the widespread application of 3D printing technology in the construction industry but also presents new possibilities for cleaner production, contributing to the achievement of sustainable development goals.

Assessing the quality of European Impact Assessments

Abstract This paper explores the possibility of developing a framework to assess the quality of Impact Assessments (IAs) by examining the common elements found in the existent academic literature around this concept, the stocktaking exercises carried out by the European institutions and the opinions of the Regulatory Scrutiny Board. At this intersection, we find that diversity in the interpretation and application of the guidelines is not only acceptable but also necessary in tailoring IAs to the needs they represent. Our findings are relevant because a universal framework that avoids focusing solely on assessing quality not only will provide much-needed coherence in this field but will also raise awareness about the normality of variability in the application of any European Union guidelines, thus reflecting the inherent nature of the IAs.

Auxiliary input-enhanced siamese neural network: A robust tool wear prediction framework with improved feature extraction and generalization ability

Tool wear monitoring is essential for automated and resilient manufacturing, as it can prevent catastrophic failures caused by severe wear on cutting edges during machining. The conventional tool wear monitoring approaches depend on features extracted from signals, which require sensitive signals and consistent tool wear. In practical scenarios, however, neither the sensitivity of collected signals to the tool wear status nor the consistency of the actual tool wear evolution is hard to meet the requirement of the tool condition monitoring algorithm, which greatly limit the wide spread of its industrial applications. To overcome this challenge, we propose an Auxiliary Input-enhanced Siamese Neural Network (AISNN) framework by incorporating a Siamese structure into the feature extraction part of a convolutional neural network (CNN), and introducing an auxiliary input to its nonlinear regression part. The Siamese structure, instead of extracting features directly from signals, distinguishes the difference between the features extracted from signals of the examined cut and the first cut, and uses this difference as the indicator of tool wear status. Moreover, the auxiliary input provides an additional feature that has heavy dependence on the tool wear, which enables the model learning the general wear evolution of the examined cutting tool. The effectiveness of the proposed AISNN framework is verified in a set of milling experiments where input signal is insensitive to the flank wear of cutting tool and different tools’ wear evolution exhibits obvious inconsistency. Compared to the traditional CNN, the proposed AISNN significantly improves the accuracy on the verification set from 63% to 95% and on the testing set from 50% to 81%. The results demonstrate that the AISNN framework achieves significant improvement in feature extraction and generalization ability. The proposed AISNN, as a universal framework, can empower most existing deep learning-based tool wear prediction methods, enhancing their robustness in handling insensitive signals and inconsistent wear evolution and thereby promoting more industrial applications.

A Review on Plant Tissue Culture

Plant tissue and cell culture involve the aseptic cultivation and propagation of plant cells, tissues, and organs in a meticulously controlled laboratory setting. This innovative method harnesses the potential of nutrient-rich media to efficiently replicate plant cells on a large scale, resulting in the rapid production of mature and disease-free plants. The cornerstone of commercial technology in this field is micropropagation, a process that achieves swift proliferation from minute plant cuttings, axillary buds, and, to a limited extent, from somatic embryos and cell clumps in suspension cultures and bioreactors. Micropropagation, a pivotal aspect of plant tissue and cell culture, holds immense value in generating high-quality and consistent planting materials. These materials find applications across diverse fields, including molecular genetic engineering, plant breeding, horticulture production, and environmental preservation. The process of micropopagation unfolds through several distinct stages, including propagation, subculture of explants for proliferation, shooting and rooting, and hardening. These stages collectively form a universal framework for large-scale multiplication of plants. This technique plays a critical role in overcoming the limitations of traditional plant propagation methods. By allowing for controlled and accelerated growth in a laboratory environment, plant tissue and cell culture contribute to the efficient production of disease-resistant and genetically uniform plant materials. This not only supports advancements in various scientific disciplines but also addresses practical needs in agriculture, horticulture, and environmental conservation. The continuous refinement and application of plant tissue and cell culture methods underscore their significance in meeting the growing demands for sustainable and high-quality plant materials across diverse sectors.

Mapping a pluralistic continuum of approaches to digital disconnection

The study of digital disconnection – the voluntary non-use of digital media – is a growing research domain characterized by increasingly pluralistic approaches. To map this diverse terrain, we offer an analytical heuristic: a continuum of approaches to digital disconnection. This tool proposes one primary dimension – viewing digital disconnection as individually functional to socially meaningful (ontology) – and two secondary, concomitant dimensions – holding post-positive to interpretivist perspectives on knowledge generation (epistemology) as well as applying quantitative to qualitative methods (methodology). We offer examples of scholarship along the continuum and advocate for the complementarity of different approaches. Although the continuum is not a universal framework, it provides a tool for scholars to review the domain and for digital disconnection researchers specifically to locate themselves on and build bridges across the continuum. We hope that this continuum supports and expands the pluralistic nature of digital disconnection scholarship into the future.

Entanglement Rényi entropies from ballistic fluctuation theory: The free fermionic case

The large-scale behaviour of entanglement entropy in finite-density states, in and out of equilibrium, can be understood using the physical picture of particle pairs. However, the full theoretical origin of this picture is not fully established yet. In this work, we clarify this picture by investigating entanglement entropy using its connection with the large-deviation theory for thermodynamic and hydrodynamic fluctuations. We apply the universal framework of Ballistic Fluctuation Theory (BFT), based the Euler hydrodynamics of the model, to correlation functions of branch-point twist fields, the starting point for computing Rényi entanglement entropies within the replica approach. Focusing on free fermionic systems in order to illustrate the ideas, we show that both the equilibrium behavior and the dynamics of Rényi entanglement entropies can be fully derived from the BFT. In particular, we emphasise that long-range correlations develop after quantum quenches, and accounting for these explain the structure of the entanglement growth. We further show that this growth is related to fluctuations of charge transport, generalising to quantum quenches the relation between charge fluctuations and entanglement observed earlier. The general ideas we introduce suggest that the large-scale behaviour of entanglement has its origin within hydrodynamic fluctuations.

High-efficiency reinforcement learning with hybrid architecture photonic integrated circuit

Reinforcement learning (RL) stands as one of the three fundamental paradigms within machine learning and has made a substantial leap to build general-purpose learning systems. However, using traditional electrical computers to simulate agent-environment interactions in RL models consumes tremendous computing resources, posing a significant challenge to the efficiency of RL. Here, we propose a universal framework that utilizes a photonic integrated circuit (PIC) to simulate the interactions in RL for improving the algorithm efficiency. High parallelism and precision on-chip optical interaction calculations are implemented with the assistance of link calibration in the hybrid architecture PIC. By introducing similarity information into the reward function of the RL model, PIC-RL successfully accomplishes perovskite materials synthesis task within a 3472-dimensional state space, resulting in a notable 56% improvement in efficiency. Our results validate the effectiveness of simulating RL algorithm interactions on the PIC platform, highlighting its potential to boost computing power in large-scale and sophisticated RL tasks.

Continually adapting pre-trained language model to universal annotation of single-cell RNA-seq data.

Cell-type annotation of single-cell RNA-sequencing (scRNA-seq) data is a hallmark of biomedical research and clinical application. Current annotation tools usually assume the simultaneous acquisition of well-annotated data, but without the ability to expand knowledge from new data. Yet, such tools are inconsistent with the continuous emergence of scRNA-seq data, calling for a continuous cell-type annotation model. In addition, by their powerful ability of information integration and model interpretability, transformer-based pre-trained language models have led to breakthroughs in single-cell biology research. Therefore, the systematic combining of continual learning and pre-trained language models for cell-type annotation tasks is inevitable. We herein propose a universal cell-type annotation tool, called CANAL, that continuously fine-tunes a pre-trained language model trained on a large amount of unlabeled scRNA-seq data, as new well-labeled data emerges. CANAL essentially alleviates the dilemma of catastrophic forgetting, both in terms of model inputs and outputs. For model inputs, we introduce an experience replay schema that repeatedly reviews previous vital examples in current training stages. This is achieved through a dynamic example bank with a fixed buffer size. The example bank is class-balanced and proficient in retaining cell-type-specific information, particularly facilitating the consolidation of patterns associated with rare cell types. For model outputs, we utilize representation knowledge distillation to regularize the divergence between previous and current models, resulting in the preservation of knowledge learned from past training stages. Moreover, our universal annotation framework considers the inclusion of new cell types throughout the fine-tuning and testing stages. We can continuously expand the cell-type annotation library by absorbing new cell types from newly arrived, well-annotated training datasets, as well as automatically identify novel cells in unlabeled datasets. Comprehensive experiments with data streams under various biological scenarios demonstrate the versatility and high model interpretability of CANAL. An implementation of CANAL is available from https://github.com/aster-ww/CANAL-torch. dengmh@pku.edu.cn. Supplementary data are available at Journal Name online.