Rule-based Research Articles

Continuous and low-carbon production of biomass flash graphene

Flash Joule heating (FJH) is an emerging and profitable technology for converting inexhaustible biomass into flash graphene (FG). However, it is challenging to produce biomass FG continuously due to the lack of an integrated device. Furthermore, the high-carbon footprint induced by both excessive energy allocation for massive pyrolytic volatiles release and carbon black utilization in alternating current-FJH (AC-FJH) reaction exacerbates this challenge. Here, we create an integrated automatic system with energy requirement-oriented allocation to achieve continuous biomass FG production with a much lower carbon footprint. The programmable logic controller flexibly coordinated the FJH modular components to realize the turnover of biomass FG production. Furthermore, we propose pyrolysis-FJH nexus to achieve biomass FG production. Initially, we utilize pyrolysis to release biomass pyrolytic volatiles, and subsequently carry out the FJH reaction to focus on optimizing the FG structure. Importantly, biochar with appropriate resistance is self-sufficient to initiate the FJH reaction. Accordingly, the medium-temperature biochar-based FG production without carbon black utilization exhibited low carbon emission (1.9 g CO2-eq g−1 graphene), equivalent to a reduction of up to ~86.1% compared to biomass-based FG production. Undoubtedly, this integrated automatic system assisted by pyrolysis-FJH nexus can facilitate biomass FG into a broad spectrum of applications.

Gene expression programs in mammalian spermatogenesis.

Mammalian spermatogenesis, probably the most complex of all cellular developmental processes, is an ideal model both for studying the specific mechanism of gametogenesis and for understanding the basic rules governing all developmental processes, as it entails both cell type-specific and housekeeping molecular processes. Spermatogenesis can be viewed as a mission with many tasks to accomplish, and its success is genetically programmed and ensured by the collaboration of a large number of genes. Here, I present an overview of mammalian spermatogenesis and the mechanisms underlying each step in the process, covering the cellular and molecular activities that occur at each developmental stage and emphasizing their gene regulation in light of recent studies.

POLITICAL CRITICISM OF SECULARISM AS THE MAIN LINE OF WESTERN MODERNITY IN THE JOURNALISM OF A.I. SOLZHENITSYN

This article raises questions related to the political interpretation of A.I. Solzhenitsyn. It is demonstrated that Solzhenitsyn’s work in this aspect can be described as a political and philosophical legitimation of the project of “conservative modernity”. The main plot dominant in Solzhenitsyn’s reflections is the political and philosophical criticism of Western secularism and its political consequences. Secularism is described by Solzhenitsyn as a “perversion” of the Christian idea, in this respect Bolshevism and Western liberalism are equated with each other, embodying the radical features of the Western modernist project. The main question that worries Solzhenitsyn: how is modernity possible, in which the basic Christian principles and rules are preserved and developed? The solution to this issue is connected with the political implementation of this project - the construction of a conservative version of democracy, in which the observance of human rights and the construction of a rule of law state is based on Christian, not secular values.

Evaluation of Multimedia Teaching Platform for Piano Accompainment Using Logic Pro

The role of education in a country's progress is crucial. It ensures the intellectual and human resources a country needs to grow. There has been a growing consensus in China that gaining a thorough understanding of advanced foreign theories and effective teaching is crucial to keeping up with the country's rapid advancement and growth of its higher education system and the continuous intensifying and improved performance of the reform of education and teaching activities. College and university music programs often include piano teaching (PT) as a required curriculum component. As a result, the job it does in the classroom is crucial to developing modern musical literacy and understanding.. This article shows how to regulate the classroom tempo by utilizing multimedia instructional tools, how to pique students' interest in learning, and how to use multimedia tools to improve the quality of piano instruction. We used a logic programming system to design a piano teaching platform.it is used to analyze the knowledge of piano learners and manipulate their interference. By gathering user behavior from different Internet distribution platforms and evaluating the feature vector by the function space created by the pertinent evaluation metrics, the analysis system is also utilized to assess some instructional techniques' online public thought assessment. The system's accuracy is 96%, indicating a successful teaching assessment. The system's value evaluation paradigm makes it possible to comprehend user behaviors precisely and apply them to other disciplines.

A Lithium Battery Health Evaluation Method Based on Considering Disturbance Belief Rule Base

Lithium-ion batteries are widely used in modern society as important energy storage devices due to their high energy density, rechargeable performance, and light weight. However, the capacity and performance of lithium-ion batteries gradually degrade with the number of charge or discharge cycles and environmental conditions, which can affect the reliability and lifetime of the batteries, so it is necessary to accurately evaluate their health. The belief rule base (BRB) model is an evaluation model constructed based on rules that can handle uncertainties in the operation of lithium-ion batteries. However, lithium-ion batteries may be affected by disturbances from internal or external sources during operation, which may affect the evaluation results. To prevent this problem, this paper proposes a disturbance-considering BRB modeling approach that considers the possible effects of disturbances on the battery in the operating environment and quantifies the disturbance-considering capability of the assessment model in combination with expert knowledge. Second, robustness and interpretability constraints are added in this paper, and an improved optimization algorithm is constructed that maintains or possibly improves the resistance of the model to disturbance. Finally, using the lithium-ion batteries provided by the National Aeronautics and Space Administration (NASA) Prediction Centre of Excellence and the University of Maryland as a case study, this paper verifies that the proposed modeling approach is capable of constructing robust models and demonstrates the effectiveness of the improved optimization algorithm.

Design methodology for the control logic of flexible production systems and related logistics

The ever-shorter product life cycle entails a shortening of the life cycle of the production site and production systems as well. One of the biggest advantages of flexible production systems is that new products can be introduced to them continuously, so that their life cycle can be significantly extended. Flexible manufacturing systems, on the other hand, also require flexible control, the logic of which must be defined already in the design phase. The main elements of the design methodology of the production and logistics layout that make up the research are the information relationship chart and the related decision tables, which are suitable for speeding up the design and later for facilitating the creation of a digital twin and control logic programming.

Treatment ideas of acupuncture and moxibustion for adenomyosis based on "etiology, location, nature and development of disease".

Focusing on the syndrome/pattern differentiation to determine treatment, the approaches to the diagnosis and treatment of acupuncture and moxibustion for adenomyosis are explored by identifying the etiology, location, nature and development of disease. The syndromes/patterns of adenomyosis are differentiated in view of both zangfu and meridian theories. The treatment is delivered complying with the menstrual cycle and the basic rule of treatment, "treating the symptoms in the acute stage, while the root causes in the recovery stage". During menstrual period, stopping pain and eliminating stasis are dominant; while during the other days of menstrual cycle, regulating zangfu dysfunction (excess or deficiency) is emphasized. In general, the functions of the thoroughfare vessel and the conception vessel should be specially considered and adjusted, and the principles of treatment include strengthening the spleen, regulating the kidney and soothing the liver. Acupoints are selected mainly from the spleen meridian of foot-taiyin, the kidney meridian of foot-shaoyin and the conception vessel. Ciliao (BL 32), Shiqizhui (EX-B 8), Zigong (EX-CA 1), Diji (SP 8) and four-gate points (bilateral Hegu [LI 4] and Taichong [LR 3]) are used in menstrual period; Zusanli (ST 36), Sanyinjiao (SP 6) and Taixi (KI 3) in postmenstrual phase; Guanyuan (CV 4), Luanchao (Ovary, Extra) and Qihai (CV 6) in intermenstrual phase; while, Guanyuan (CV 4), Qihai (CV 6) and Shenque (CV 8), combined with Gongsun (SP 4), Neiguan (PC 6) and Jianshi (PC 5) in premenstrual phase. According to the dynamic development of patient's conditions, the reinforcing or reducing techniques of acupuncture and moxibustion are feasibly applied in treatment of adenomyosis.

A Compositional Theory of Linearizability

Compositionality is at the core of programming languages research and has become an important goal toward scalable verification of large systems. Despite that, there is no compositional account of linearizability , the gold standard of correctness for concurrent objects. In this article, we develop a compositional semantics for linearizable concurrent objects. We start by showcasing a common issue, which is independent of linearizability, in the construction of compositional models of concurrent computation: interaction with the neutral element for composition can lead to emergent behaviors, a hindrance to compositionality. Category theory provides a solution for the issue in the form of the Karoubi envelope. Surprisingly, and this is the main discovery of our work, this abstract construction is deeply related to linearizability and leads to a novel formulation of it. Notably, this new formulation neither relies on atomicity nor directly upon happens-before ordering and is only possible because of compositionality, revealing that linearizability and compositionality are intrinsically related to each other. We use this new, and compositional, understanding of linearizability to revisit much of the theory of linearizability, providing novel, simple, algebraic proofs of the locality property and of an analogue of the equivalence with observational refinement . We show our techniques can be used in practice by connecting our semantics with a simple program logic that is nonetheless sound concerning this generalized linearizability.

Design of highly efficient far-field beam shapers with irregular maskless microlens arrays

Regular tandem microlens arrays are well described and widely used for beam shaping and homogenization. Applying absorbing slides between the entrance and exit lenslets and channel-wise variation of the slides’ shape and size allows flexible control of the beam’s intensity profile and silhouette. The downside of absorbing slides is a significant transmission loss, limiting the achievable level of system efficiency. This work describes a more efficient method for micro-optical beam shaping with maskless irregular microlens arrays (iMLA). The iMLAs are completely absorption-free elements, enhancing the overall efficiency of the optical system. We describe basic design rules for iMLAs, including stray-light suppression, tolerancing, and modeling under consideration of manufacturing imperfections.

Domain‐specific translation tool from structured text to C source code with code readability enhancement in programmable logic controllers

AbstractThe Industrial Internet has emerged as a key technology in the field of industrial automation, revolutionizing traditional manufacturing processes and enabling advanced control systems by integrating machines, sensors, and software systems through network connectivity, allowing for real‐time data exchange, analysis, and decision‐making in industrial environments. Programmable Logic Controllers (PLCs) play a critical role in industrial automation systems, which are specialized digital computers designed to control various manufacturing processes and machinery. As a promising language, Structured Text (ST) is of paramount importance for industrial Internet, and it is essential to translate ST into C, which is widely used in various industries for implementing control systems. However, due to differences in syntax, data types, and programming paradigms between the two languages, the translation process is facing several challenges. In this paper, we present a domain‐specific translation tool that automates the process of converting ST code to C code. This tool incorporates advanced AI techniques, particularly leveraging the capabilities of NL‐PL LLMs, to facilitate accurate and efficient translation while generating comprehensive documentation to assist developers. Finally, the evaluation and case studies conducted validate the effectiveness of the tool, demonstrating its practical applicability and benefits in real‐world industrial settings.

Photo-controlled programmable logic gate via self-powered Cu2O/BiFeO3/TiO2 ferroelectric heterojunction photodetectors

The increased demands for high integration, fast computation, and a wide range of data processing in digital logic circuits have triggered an interest in programmable logic gates that can run different logic operations. Photodetectors (PDs) based on ferroelectric materials can obtain different ferroelectric polarization states utilizing pre-biased voltage regulation to achieve programmable logic functions, but low photoelectric conversion efficiency and slow photoresponse speed are still key problems to be addressed for programmable logic gate devices. Herein, the Cu2O/BiFeO3(BFO)/TiO2 ferroelectric heterojunction PDs were constructed by introducing a p-type hole transport layer Cu2O on the BFO/TiO2 ferroelectric heterojunction films, which not only broadens the photoresponse spectra range to the red light, but also forms a stepped energy band structure at the heterojunction interface, and the interfacial electric field promotes the separation and transport of photogenerated carriers, improving the self-powered photodetection performance of the devices. The Cu2O/BFO/TiO2 PDs demonstrate a responsivity of 3.0 mA/W and a photoresponse rise time/decay time of 2.3/23.9 ms at 420 nm light illumination. The Cu2O/BFO/TiO2 PD can be polarized in different states by pre-biased voltage and outputs different electrical signal values under the ultraviolet (365 nm) and visible (420 nm) monochromatic light or the mixed light irradiation, thus the device can realize the "AND" gate output in the positive bias polarization state and the "OR" gate output in the negative bias polarization state, which shows that the ferroelectric heterojunction PDs has a great potential application in the field of photo-controlled programmable logic gate circuits.

A cloud-based infrastructure to deploy supervisory forecast models for predictive coagulant dosing control

Abstract Advanced optimal dosing control based on multiple online sensor data is operational in several treatment facilities in Norway. The benefits of the dosing control system in maintaining stable phosphate/solids removal and saving coagulant usage are documented in the literature. The dosing algorithm is currently implemented in a programmable logic controller (PLC) connected to the treatment plant's Supervisory Control and Data Acquisition System (SCADA) system. The PLC receives online sensor data from the plant's SCADA, calculates the optimal dosing values, and transmits optimal dosage values back to the SCADA system. The dosing algorithm is frequently updated to keep in sync with the process and equipment upgrades of the treatment plant and advances in control algorithm schemes. The upgrades include new regulatory feedback loops structural changes to the dose equation, and the addition of conditional setpoints. Each maintenance and upgrade routine entails operational downtime where the dosing algorithm is set to a sub-optimal flow-proportional dose. This paper presents a non-intrusive Internet of Things (IoT) infrastructure to implement a predictive/forecast component to an existing dosing control algorithm. The benefits of the new cloud-based system in improving nutrient removal, increasing operational flexibility, and reducing maintenance downtime are presented in this work.

SIHQR model with time delay for worm spread analysis in IIoT-enabled PLC network

Industrial Internet of Things (IIoT) is a product of deep integration between Internet of Things (IoT) and industrial control networks. As an important component of IIoT, Programmable Logic Controller (PLC) has become a springboard for many hackers to disrupt industrial networks by spreading viruses. It is known that worm can cause enormous loss. Therefore, revealing the rules of worm spread in PLC networks and exploring methods to inhibit worm spread are increasingly important. To this end, we propose a new worm spread model named SIHQR with time delay. Additionally, we establish a game model between susceptible and infected nodes in order to express the infection rate by several game parameters. Through analysis, we discuss the conditions for the emergence of three scenarios: disease-free equilibrium, endemic equilibrium and Hopf bifurcation. We study the stability at the equilibrium points and obtain an expression for a threshold value which determines whether the system exhibits endemic equilibrium or Hopf bifurcation. In the experimental section, we analyze the impact of initial values of node states and the magnitude of game parameters on worm spread in the three aforementioned scenarios. Finally, we propose a method to mitigate or even eliminate the Hopf bifurcation. The experimental results show that the proposed model has welldone performance.

A novel method for post-earthquake functional evaluation of city building portfolios

Enhancing the seismic resilience of city building portfolios is crucial to mitigating the impact of seismic disasters. A novel method is proposed to evaluate the post-earthquake functionality of city building portfolios, which can serve as the foundation for assessing the seismic resilience of cities. This method incorporates multiple dimensions of functional evaluation, ranging from structural and nonstructural components to city building portfolios, elucidating the mechanism of functional assemblies from lower to higher dimensions. The calculation of the physical functionality of individual buildings relies on the functionalities of their structural and nonstructural components, which are determined using the functional mapping method. The impacts of infrastructure failures on the functionality of individual buildings are explored using the hierarchical belief rule base inference method based on the evidential reasoning algorithm (BRBI-ER). A technique that combines Point of Interest analysis and visual interpretation is utilized to classify building portfolios into three types: single-function building portfolios, multi-function building portfolios, and integrated-function building portfolios. A cumulative functional correlation model and an integrated functional correlation model using BRBI-ER are established to evaluate the functionality of these types of building portfolios. The results can contribute to predicting the risk of functional losses before earthquakes, guiding the recovery process after earthquakes, and taking proactive measures to improve the seismic resilience of cities.

Learning explanatory logical rules in non-linear domains: a neuro-symbolic approach

AbstractDeep neural networks, despite their capabilities, are constrained by the need for large-scale training data, and often fall short in generalisation and interpretability. Inductive logic programming (ILP) presents an intriguing solution with its data-efficient learning of first-order logic rules. However, ILP grapples with challenges, notably the handling of non-linearity in continuous domains. With the ascent of neuro-symbolic ILP, there’s a drive to mitigate these challenges, synergising deep learning with relational ILP models to enhance interpretability and create logical decision boundaries. In this research, we introduce a neuro-symbolic ILP framework, grounded on differentiable Neural Logic networks, tailored for non-linear rule extraction in mixed discrete-continuous spaces. Our methodology consists of a neuro-symbolic approach, emphasising the extraction of non-linear functions from mixed domain data. Our preliminary findings showcase our architecture’s capability to identify non-linear functions from continuous data, offering a new perspective in neural-symbolic research and underlining the adaptability of ILP-based frameworks for regression challenges in continuous scenarios.

Short-Term Electrical Energy Demand Forecasting Using Fuzzy Logic Technique

Minimization of electrical energy wastage and provision of its regular supply make electrical load forecasting an important aspect of the power infrastructure. An accurate load forecasting is crucial for the reduction of the cost of electrical energy generation and spinning reserve capacity. Therefore, in this study, fuzzy logic (FL) technique was employed for the projection of electrical energy demand on short-term basis. The FL model was trained using the six months hourly load and temperature data respectively obtained from 132/33 kV Ikeja West Transmission Station, Ayobo and Nigerian Meteorological Agency, Oshodi, Lagos State, Nigeria. Triangular and trapezoidal membership functions (MFs) were used in the training of the model with Mamdani fuzzy inference system. The time, load and temperature inputs were fuzzified into six, four and three MFs respectively while the fuzzification process used 25 fuzzy rule bases. The centroid method of defuzzification was used to change the results into readable values. The adequacy of the FL model was determined using five metrics including mean square error (MSE), mean absolute error (MAE), mean absolute percentage error (MAPE), chi square (χ2) and F- test. The obtained results revealed that the FL model developed excelled in all the five tests of adequacy considered with MSE, MAE, MAPE, χ2 and F-test values of 4.17, 6.74, 11.51%, 7.93 and 1.27 respectively and hence, performed satisfactorily. This work established that the fuzzy logic approach is appropriate for electrical load projection on short-term basis.

TORT LIABILITY AND UNAWARENESS

AbstractWe explore the implications of unawareness for tort law. We study cases where injurers and victims initially are unaware that some acts can yield harmful consequences, or that some acts or harmful consequences are even possible, but later become aware. We model unawareness by Reverse Bayesianism. We compare the two basic liability rules of Anglo‐American tort law, negligence and strict liability, and argue that negligence has an important advantage over strict liability in a world with unawareness—negligence, through the stipulation of due care standards, spreads awareness about the updated probability of harm.

ZYNQ-Based Visible Light Defogging System Design Realization.

Under a foggy environment, the air contains a large number of suspended particles, which lead to the loss of image information and decline of contrast collected by the vision system. This makes subsequent processing and analysis difficult. At the same time, the current stage of the defogging system has problems such as high hardware cost and poor real-time processing. In this article, an image defogging system is designed based on the ZYNQ platform. First of all, on the basis of the traditional dark-channel defogging algorithm, an algorithm for segmenting the sky is proposed, and in this way, the image distortion caused by the sky region is avoided, and the atmospheric light value and transmittance are estimated more accurately. Then color balancing is performed after image defogging to improve the quality of the final output image. The parallel computing advantage and logic resources of the PL (Programmable Logic) part (FPGA) of ZYNQ are fully utilized through instruction constraints and logic optimization. Finally, the visible light detector is used as the input to build a real-time video processing experiment platform. The experimental results show that the system has a good defogging effect and meet the real-time requirements.

Joint contrastive learning and belief rule base for named entity recognition in cybersecurity

Named Entity Recognition (NER) in cybersecurity is crucial for mining information during cybersecurity incidents. Current methods rely on pre-trained models for rich semantic text embeddings, but the challenge of anisotropy may affect subsequent encoding quality. Additionally, existing models may struggle with noise detection. To address these issues, we propose JCLB, a novel model that Joins Contrastive Learning and Belief rule base for NER in cybersecurity. JCLB utilizes contrastive learning to enhance similarity in the vector space between token sequence representations of entities in the same category. A Belief Rule Base (BRB) is developed using regexes to ensure accurate entity identification, particularly for fixed-format phrases lacking semantics. Moreover, a Distributed Constraint Covariance Matrix Adaptation Evolution Strategy (D-CMA-ES) algorithm is introduced for BRB parameter optimization. Experimental results demonstrate that JCLB, with the D-CMA-ES algorithm, significantly improves NER accuracy in cybersecurity.

Patients torn to pieces.

Dissecting bodies is a common practice in many cultures. But in "big data medicine", the art of dissecting the human body has become an obsession. Indeed, modern biotechnology allows us to see and measure the molecular components of every single cell. But how can we put this immense number of bits and pieces back together again and see the patient as a whole? The first turning point is that proposed by René Descartes, who, inspired by dreams and visions, conceived the idea of unifying all scientific disciplines through the pervasive application of mathematics. Descartes formulates four basic rules, the second (top-down method) and third (bottom-up method) of which become crucial in modern data analysis. An instructive case study considered here is that of pulmonary tuberculosis, where the Cartesian approach of decomposing problems into smaller and smaller "pieces" - from organism to organ and from cellular lesion to the microscopic level - has led to the cure of the disease through antibiotics. This success story inspired Paul Ehrlich who, with the concept of the "magic bullet", defined modern pharmacology. However, this paradigm is being challenged today by multifactorial diseases and big data medicine, where the enormous availability of clinical and molecular data must be integrated to arrive at a therapeutic decision. The Cartesian approach shows its limitations today, as witnessed by the similar difficulty in fields other than medicine, illustrated here by the case of choosing to produce a successful television series based on user profiling. The take-home message is that the amount of data collected does not automatically guarantee success but that, instead of being data-driven, a collective "human" overview and assessment is inevitable. That is, close collaboration between clinicians and data analysts, integrating expertise, is needed to address challenges in the diagnosis and treatment of complex diseases through imagination and not mere extrapolation.