General System Research Articles

General Aqueous System Simulation through an AI-Embedded Metaverse Chemistry Laboratory.

Recent decades have witnessed the rapid development of autonomous laboratories and artificial intelligence, where experiments can be automatically run and optimized. Although human work is reduced, the total time of experimental optimization is still consuming due to limitations of the current ab metaverse framework, which accurately predicts the future state of the system by receiving and analyzing in situ experimental data. To substitute for traditional simulation methods, we designed a physically endorsed deep learning model to predict the future system picture ranging from atomic image to bulk appearance, intensively using the correlations between properties of the system. Through this framework, we studied the general aqueous system, covering 100+ common ionic solutions. We can accurately simulate properties for a general aqueous system as well as predict the time of solvation of ionic compounds ahead of real experiments. In this way, the experiments can be optimized more efficiently without waiting for the end of a bad iteration. We hope our work offers a fresh direction for the digitization of chemical information, enhancing access to and use of experimental data in advancing the field of physical chemistry.

Synthesis and characterization of cellulose Grewia optiva yarn and woven mat using an alkali solution

PurposeThe production of commodities from renewable organic material has gained enormous interest due to the rising public understanding of conscious development. Natural fibers are accessible in environment, and their parameters depend on their physical traits and chemically engineered makeup. Grewia optiva (GO) comes under the category of bast fibers, also known as Bhimal. This study aims to realize the consequences of alkali treatment on the characterization of natural fibers, their developed yarn and then the woven mat.Design/methodology/approachRaw fiber was treated with 20% alkali solution. After treatment, it was evaluated for changes in the general laboratory system. This treatment is known as mercerization, through which the parameters of cellulose fibers can be enhanced. After this process, the content of cellulose increases due to the removal of the hydroxyl group. The removal of microfibrils, hemicellulose and the introduction of hydrogen bonds increase the interactive capacity of fiber. The job was divided into different sections, including acquiring fiber, developing yarn and creating a mat.FindingsThe quality of the surface of the fiber was enhanced after the treatment. Its diameter was reduced to 54.72 microns. In the developed woven mat, it was densified using water treatment and attained grams per square meter (GSM) of 389.7.Originality/valueThe usage of mercerization softens the fiber to twist into yarn, which is finally woven into a mat. Treatment of fabric or mat makes it denser to impart better strength. A woven mat of natural fibers provides maximum strength to the composites.

Verifying Hierarchic Multipartite and Network Nonlocalities with a Unified Method

AbstractThe multipartite nonlocality provides deep insights into the fundamental feature of quantum mechanics and guarantees different degrees of cryptography security for potential applications in the quantum internet. Verifying multipartite nonlocal correlations is a difficult task. A unified approach is proposed that encompasses all the quantum characteristics of the multipartite correlated system beyond from fully separable to biseparable no‐signaling correlations. A straightforward method is offered to verify general systems by lifting partial nonlocal correlations. This allows to construct a chained Bell inequality, facilitating the unified verification of hierarchic multipartite nonlocalities. The lifting method is finally applied to verify the correlations derived from quantum networks.

Decentralized adaptive control of large‐scale time‐varying stochastic nonlinear systems with unknown covariance

AbstractIn this paper, a novel decentralized adaptive state‐feedback design method is proposed for large‐scale stochastic nonlinear systems. Different from the existing results, we consider more practical and more general systems, that is, systems with both unknown covariance and time‐varying powers. When dealing with the unknown time‐varying covariance, we do not need to know its bound, but use the estimator to design an adaptive law function. Then, a new adaptive controller is constructed with the backstepping method to ensure that the closed‐loop system is globally stable in probability, and that the states are regulated to the origin almost surely. Finally, a simulation example is given to illustrate the effectiveness of the design scheme.

Scikit-ANFIS: A Scikit-Learn Compatible Python Implementation for Adaptive Neuro-Fuzzy Inference System

AbstractThe Adaptative neuro-fuzzy inference system (ANFIS) has shown great potential in processing practical data from control, prediction, and inference applications, reflecting advantages in both high performance and system interpretability as a result of the hybridization of neural networks and fuzzy systems. Matlab has been a prevalent platform that allows to utilize and deploy ANFIS conveniently. On the other hand, due to the recent popularity of machine learning and deep learning, which are predominantly Python-based, implementations of ANFIS in Python have attracted recent attention. Although there are a few Python-based ANFIS implementations, none of them are directly compatible with scikit-learn, one of the most frequently used libraries in machine learning. As such, this paper proposes Scikit-ANFIS, a novel scikit-learn compatible Python implementation for ANFIS by adopting a uniform format such as fit() and predict() functions to provide the same interface as scikit-learn. Our Scikit-ANFIS is designed in a user-friendly way to not only manually generate a general fuzzy system and train it with the ANFIS method but also to automatically create an ANFIS fuzzy system. We also provide four kinds of representative cases to show that Scikit-ANFIS represents a valuable addition to the scikit-learn compatible Python software that supports ANFIS fuzzy reasoning. Experimental results on four datasets show that our Scikit-ANFIS outperforms recent Python-based implementations while achieving parallel performance to ANFIS in Matlab, a standard implementation officially realized by Matlab, which indicates the performance advantages and application convenience of our software.

Input‐output linearization and decoupling of mechanical control systems

AbstractIn this article, we present the problem of simultaneous input‐output feedback linearization and decoupling (noninteracting) for mechanical control systems with outputs. We show that the natural requirement of preserving mechanical structure of the system and of transformations imposes supplementary conditions when compared to the classical solution of the same problem for general control systems. These conditions can be expressed using objects on the configuration space only. We illustrate our results with several examples of mechanical control systems.

Secular Dynamics of Compact Three-planet Systems

The secular Laplace–Lagrange orbital solution, decomposing eccentricities into a set of uniformly precessing eigenmodes, is a classical result that is typically solved numerically. However, in the limit where orbits are closely spaced, several simplifications make it possible to make analytical progress. We derive simple expressions for the eccentricity eigenmodes in a coplanar three-planet system where the middle planet is much less massive than its neighbors, and we show that these approximate the true eigenmodes of more general systems with three massive planets in various limits. These results provide intuition for the secular dynamics of real systems, and have applications for understanding the stability boundary for compact multiplanet systems.

Telephone consulting for ‘Personalised Care and Support Planning’ with people with long-term conditions: a qualitative study of healthcare professionals’ experiences during COVID-19 restrictions and beyond

BackgroundPersonalised Care and Support Planning (PCSP) replaces conventional annual reviews for people with long-term conditions. It is designed to help healthcare professionals (HCPs) and patients engage in conversations as equals and collaboratively plan actions oriented to each patient’s priorities, alongside biomedical concerns. Little is known about how the shift to remote consulting initiated with COVID-19 restrictions has impacted PCSP.AimTo investigate HCPs’ experiences of conducting PCSP conversations remotely and consider implications for the fulfilment of PCSP ambitions as remote consulting continues beyond COVID-19 restrictions.Methods19 semi-structured interviews with HCPs in England and Scotland; interpretive analysis.ResultsHCPs’ accounts made clear that COVID-19 restrictions impacted multiple aspects of PCSP delivery, not just the mode of conversation. Broader disruption to general practice systems for gathering and sharing information ahead of PCSP conversations, and moves to ‘wide window’ appointment times, made it harder for patients to be prepared for PCSP conversations. This constrained scope to achieve PCSP ambitions even with the best professional communication skills. Most remote PCSP conversations were conducted by telephone. In the absence of visual communication with patients, it was sometimes harder to achieve the ambitions of PCSP conversations, including to balance patient and professional agendas, fulfil key planning activities, and foster a relational ethos of equal, collaborative partnership. The challenges were particularly severe when working with new patients and people with complex clinical and social problems. Although options for telephone appointments now offer valued flexibility, sustained experience of struggling to achieve PCSP ambitions via remote consulting led some HCPs to lower their standards for judging a “good” PCSP conversation, and to diminished professional satisfaction.ConclusionsThere are significant challenges to fulfilling the ambitions of PCSP via telephone, especially when preparatory support is limited. This study provides grounds for scepticism about how compatible telephone appointments can be with this person-centred model of working, especially for people who are socially disadvantaged and live with complex health conditions. These threats to the provision of person-centred support for people with long-term conditions warrant careful attention going forward if the PCSP model and its benefits are to be sustained.

Switching sliding mode control for systems with disturbance and applications to multi-agent systems

A novel exponential switching sliding mode reaching law is proposed to address the stability issues of discrete-time linear systems subject to external disturbances in this paper. This approach adopts a reference trajectory strategy. By utilising the designed exponential reaching law, it is demonstrated that, compared with traditional methods, both the sliding mode variable and the steps required to enter the quasi-sliding mode domain can be adaptively adjusted based on the system's state and parameters. Subsequently, a disturbance-dependent reaching law, built upon the exponential model, is developed to ensure the Lyapunov stability of the system and to attenuate its ultimate bandwidth. Furthermore, an analysis is conducted on the stability condition between the exponential and disturbance-dependent reaching laws, illustrating that the designed referred sliding phase optimises the ultimate bandwidth. This methodology is then applied to multi-agent systems to validate its effectiveness, where quasi-leader-following consensus and error boundedness are explored. Finally, the results are corroborated through numerical simulations, considering both a general discrete-time system and a multi-agent systems.

A New Hybrid Approach for Clustering, Classification, and Prediction of World Development Indicators Combining General Type-2 Fuzzy Systems and Neural Networks

A New Hybrid Approach for Clustering, Classification, and Prediction of World Development Indicators Combining General Type-2 Fuzzy Systems and Neural Networks

Charakter prawny wniosku o świadczenie emerytalno- rentowe powszechnego systemu ubezpieczeń

The legislator assumed that an application should be submitted for retirement and disability benefits from the general insurance system. When determining the form and content of the application, the legislator did not directly specify its legal nature, which is important for both the applicant and the pension authority. The specific nature of the benefit application implies legal consequences in terms of legal, factual and technical actions undertaken by the pension authority after submitting the benefit application. By analyzing the existing legal norms of a procedural nature, it is possible to determine these effects and, through their prism, determine the nature of an application for a pension benefit as an important element of a legal transaction.

An Extensible Evaluation Framework Applied to Clinical Text Deidentification Natural Language Processing Tools: Multisystem and Multicorpus Study.

Clinical natural language processing (NLP) researchers need access to directly comparable evaluation results for applications such as text deidentification across a range of corpus types and the means to easily test new systems or corpora within the same framework. Current systems, reported metrics, and the personally identifiable information (PII) categories evaluated are not easily comparable. This study presents an open-source and extensible end-to-end framework for comparing clinical NLP system performance across corpora even when the annotation categories do not align. As a use case for this framework, we use 6 off-the-shelf text deidentification systems (ie, CliniDeID, deid from PhysioNet, MITRE Identity Scrubber Toolkit [MIST], NeuroNER, National Library of Medicine [NLM] Scrubber, and Philter) across 3 standard clinical text corpora for the task (2 of which are publicly available) and 1 private corpus (all in English), with annotation categories that are not directly analogous. The framework is built on shell scripts that can be extended to include new systems, corpora, and performance metrics. We present this open tool, multiple means for aligning PII categories during evaluation, and our initial timing and performance metric findings. Code for running this framework with all settings needed to run all pairs are available via Codeberg and GitHub. From this case study, we found large differences in processing speed between systems. The fastest system (ie, MIST) processed an average of 24.57 (SD 26.23) notes per second, while the slowest (ie, CliniDeID) processed an average of 1.00 notes per second. No system uniformly outperformed the others at identifying PII across corpora and categories. Instead, a rich tapestry of performance trade-offs emerged for PII categories. CliniDeID and Philter prioritize recall over precision (with an average recall 6.9 and 11.2 points higher, respectively, for partially matching spans of text matching any PII category), while the other 4 systems consistently have higher precision (with MIST's precision scoring 20.2 points higher, NLM Scrubber scoring 4.4 points higher, NeuroNER scoring 7.2 points higher, and deid scoring 17.1 points higher). The macroaverage recall across corpora for identifying names, one of the more sensitive PII categories, included deid (48.8%) and MIST (66.9%) at the low end and NeuroNER (84.1%), NLM Scrubber (88.1%), and CliniDeID (95.9%) at the high end. A variety of metrics across categories and corpora are reported with a wider variety (eg, F2-score) available via the tool. NLP systems in general and deidentification systems and corpora in our use case tend to be evaluated in stand-alone research articles that only include a limited set of comparators. We hold that a single evaluation pipeline across multiple systems and corpora allows for more nuanced comparisons. Our open pipeline should reduce barriers to evaluation and system advancement.

Residents' willingness to engage in carbon generalized system of preferences - A personality insight study based on the extended theory of planned behavior

In the context of China's dual carbon target, reducing personal carbon emissions has been identified as a crucial strategy to achieve the target. The 2022 Digital China Development Report emphasizes the significance of implementing the Carbon Generalized System of Preferences (CGSP) in driving individual carbon reduction efforts in China. However, the psychological factors influencing public participation in CGSP are still unknown. Based on the Extended Theory of Planned Behavior (TPB), this study explored the psychological factors of different personality trait groups' participation in the CGSP and categorized 712 respondents into Compatible, Positive, Responsible, and Susceptible based on the MINI-IPIP scale and the K-means method. The results show that the strength of willingness to engage (WTE) in CGSP was ranked as: Compatible > Positive > Responsible > Susceptible and the WTE of compatible groups is more influenced by attitude, while Perceived Behavioral Control (PBC) plays a more crucial role in other groups. Personal Norms (PN) and Policy Awareness (PA) were significant for all specific personality groups except the Susceptible group. Surprisingly subjective norms had little to do with WTE. We believe that policymakers should consider the impact of PBC on WTE when formulating policies and raise the expectation of residents in terms of the value they can obtain from participating in CGSP. Additionally, promotional activities related to PN and PA in connection with CGSP should be conducted. These efforts may help individuals better engage in CGSP.

The place of managers in the corporate governance architecture

ABSTRACT This article discusses deficiencies in existing models of the company. It outlines three immutable layers of corporate ontology and argues for a mental model of the company based on general systems theory. The model reveals that each company has at its heart a managerial hierarchy which controls corporate actions through direct and indirect means. The most important participants in the everyday life of the company are its senior and middle managers. Consequently, greater emphasis needs to be placed upon managers’ corporate governance/compliance responsibilities. The legislature should provide for an obligation on companies to engage in risk-mapping and an obligation on managers to ensure, within their areas of responsibility, compliance with the law.

Data stream solution for decision-making processes: a general and adaptive system for decision support

ABSTRACT With the rise of new data generation processes and availability rates, new challenges appear linked with the information and pattern extraction for decisional processes. In these contexts, decision support systems (DSS) need to incrementally integrate the data flows to provide readable, understandable and meaningful conclusions. This research presents a novel adaptive DSS that offers updated and explainable knowledge for decision makers, especially in uncertain decision scenarios. Also, this work explores applications of this technology in some social fields, aiming to analyse the potential contribution of machine learning, combined with adaptive decision systems, in society.

Counting atypical black hole microstates from entanglement wedges

Disentangled black hole microstates are atypical states in holographic CFTs whose gravity duals do not have smooth horizons. If there exist sufficiently many disentangled microstates to account for the entire black hole entropy, then any black hole microstate can be written as a superposition of states without smooth horizons. We show that there exist sufficiently many disentangled microstates to account for almost the entire black hole entropy of a large AdS black hole at the semiclassical limit GN → 0. In addition, we also argue that in generic quantum many-body systems with short-ranged interactions, there exist sufficiently many area law states in the microcanonical subspace to account for almost the entire thermodynamic entropy in the standard thermodynamic limit. Area law states are atypical since a typical state should contain volume law entanglement. Furthermore, we also present an explicit way to construct such a set of area law states, and argue that the same construction may also be used to construct disentangled states.

Онтологизация познания: уровни, направления и средства реализации

The article investigates the problem of ontologization of cognition, i.e. increasing the level of conformity between the knowledge system and being. Various levels of ontologization of knowledge are examined depending on the nature of the reflection of reality in the system of knowledge. Four levels of ontology are distinguished: the first level corresponds to the injective and bijective mappings of reality onto the knowledge system; the second level involves surjective mapping; the third level encompasses knowledge formed through isomorphic mapping of relationships between elements from reality onto the knowledge system; the fourth level involves knowledge formed through isomorphic mapping of relationships between elements within the knowledge system. Prospects for the ontologization of cognition are related to the expansion of metaphysical and consequent knowledge, the elevation to the fourth level of knowledge domains that lack ontologicality, and the expansion of knowledge linking secondary laws and properties of objects to patterns. The main tool for ontologizing cognition should be a general theory of systems, extended with a metaphysical component and supplemented with extensive collections of patterns, primitives, secondary properties and laws. The author is working on such a general systems theory, referred to as the “empirico-metaphysical general theory of systems”. In conclusion, development of an empirico-metaphysical theory of systems promises to provide a robust framework for addressing the challenges of cognition representation, synthesis, and application across diverse domains. As researchers continue to delve into the nuances of ontological alignment, the proposed framework offers a promising avenue for fostering interdisciplinary collaboration and advancing our understanding of the fundamental nature of cognition.

Potential Impact of Withdrawal of Generalized System of Preferences for India by European Union

Trade between India and Europe has been growing at strong pace for the last two decades. India’s goods exports to Europe have increased from USD 12.3 Bn in 2002 to USD 68.9 Bn in 2002. During the same time period India’s export products eligible for Europe Generalized System of Preferences (GSP) has also risen in absolute terms from USD 5.8 Bn in 2002 to USD 19.7 Bn in 2022. While the absolute value of GSP covered products has increased, the percentage coverage of India’s export products eligible for Europe GSP has fallen from 47% in 2002 to just 29% in 2022. Since, the end of 2022, India is graduating out of many sectors covered under the GSP regime. While, this is a major concern for policymakers, and exporters in general, the potential impact has not been quantified. This article quantifies this impact applying CGE modelling through GTAP. The results show that, while India’s overall exports are affected only to a minimum extent by the removal of GSP, it is mainly due to the growth in non-GSP sectors and services exports. However, the negative shock on some labour-intensive sectors will have an impact on the country’s overall GDP.

Optimal parameter estimation for uncertain structural systems under unknown random excitations

An optimal parameter estimation method for uncertain structural systems under unknown random excitations is proposed, which combines the Bayesian inference and stochastic dynamics using geometrically averaged likelihood and optimal response estimation. The general description of optimal parameter estimation problems for uncertain dynamic systems under only stochastic responses measured is presented. The posterior probability density conditional on measured responses is expressed by the likelihood function conditional on system parameters based on Bayes’ theorem. For finite time processes, the parameter estimation problem as the probability integral of conditional means is converted into the optimization problem expressed as maximizing the posterior probability density. A geometrically averaged likelihood function is defined and used for calculating the logarithmic posterior probability density. This estimation can avoid the numerical singularity of the likelihood function and reduce the effects of incomplete posterior probability density and inaccurate prior statistics of unknown random excitations, and then it will be more reasonable and effective. Furthermore, the differential equations for system response means and covariances are derived and solved based on stochastic dynamics theory. The means and covariances conditional on responses at the present instant are expressed by the previous statistics based on optimal response estimation. By combining two results, the analytical expressions of the averaged likelihood function and logarithmic posterior probability density are obtained which will be more reliable and accurate. The proposed optimal estimation method is verified by numerical results for a five-storey frame structure under base random excitation. The estimated results are not affected by the prior statistics errors of random excitation as a factor. For noisy observation, the Kalman filtering is incorporated in the estimation method and hence the estimation is more accurate and robust even for low signal noise ratios. The optimal estimation method has the potential for application to general uncertain systems.

TFormer: A time–frequency Transformer with batch normalization for driver fatigue recognition

Within the framework of the advanced human-cybernetic interfaces (HCI), Cross-subject electroencephalogram (EEG)-based driver fatigue recognition is emerging as a pivotal application in the paradigm of Industry 5.0. Recognizing the importance of ensuring driver safety through proactive monitoring, it is essential to offer a general EEG decoding system to improve road safety. This work investigated the use of Transformers for the challenging cross-subject EEG decoding task due to the great success the Transformers have achieved in various applications. Previous research focused on using Transformers to capture global temporal information, but less work targeted global frequency-domain patterns. Furthermore, in order to leverage a standard Transformer architecture grounded in natural language processing for EEG decoding, it is imperative to account for inherent characteristics in EEG and make pertinent adjustments accordingly. In this work, we proposed a time–frequency Transformer (TFormer) that can automatically learn the global time–frequency patterns from raw EEG data. TFormer consisted of three components: convolutional stems for input embedding, time–frequency multi-head cross-attention (TF-MCA) for integrating time-domain patterns into frequency points, and self-attention to further learn global time-frequency patterns. Moreover, we analyzed TFormer’s internal settings and found batch normalization (BN) more suitable for cross-subject EEG decoding than layer normalization (LN). The experiment results demonstrated the superiority of the proposed model compared to existing methods. Overall, our work contributes to the development of Transformer models in EEG decoding and illustrates a different way to leverage Transformers for decoding raw EEG data.