Logic System Research Articles

Protecting women’s rights under the current criminal law of Vietnam

Enhancing women’s rights is a crucial matter globally and has been addressed in various legal documents. Vietnam has actively collaborated with the international community to bolster the safeguarding of women’s rights within its criminal justice framework. The 2015 Criminal Code of Vietnam underwent significant revisions to ensure the well-being and rights of women. However, certain provisions still require improvement to ensure comprehensive protection. This article thoroughly examines the shortcomings in safeguarding women’s rights within Vietnam’s criminal law system and proposes measures to enhance the relevant provisions in the 2015 Criminal Code. By refining and expanding regulations, providing stronger support and protection for victims, improving monitoring mechanisms, and ensuring effective enforcement, we can bolster the protection of women’s rights in the 2015 Criminal Code. A robust and logical legal system is essential for women to attain maximum rights and safety within society.

An integrative sensor of body states: how the mushroom body modulates behavior depending on physiological context.

The brain constantly compares past and present experiences to predict the future, thereby enabling instantaneous and future behavioral adjustments. Integration of external information with the animal's current internal needs and behavioral state represents a key challenge of the nervous system. Recent advancements in dissecting the function of the Drosophila mushroom body (MB) at the single-cell level have uncovered its three-layered logic and parallel systems conveying positive and negative values during associative learning. This review explores a lesser-known role of the MB in detecting and integrating body states such as hunger, thirst, and sleep, ultimately modulating motivation and sensory-driven decisions based on the physiological state of the fly. State-dependent signals predominantly affect the activity of modulatory MB input neurons (dopaminergic, serotoninergic, and octopaminergic), but also induce plastic changes directly at the level of the MB intrinsic and output neurons. Thus, the MB emerges as a tightly regulated relay station in the insect brain, orchestrating neuroadaptations due to current internal and behavioral states leading to short- but also long-lasting changes in behavior. While these adaptations are crucial to ensure fitness and survival, recent findings also underscore how circuit motifs in the MB may reflect fundamental design principles that contribute to maladaptive behaviors such as addiction or depression-like symptoms.

Deep learning approaches to the game of Connect6

Especially with the rise of artificial intelligence, people need more ability to think logically and make judgments than before. Board games can be used as a tool for training logic, which has a positive effect on the sustainable development of society. Connect6 is a very popular board game because it is a fair and highly complex game with simple rules. This research aims to address the technical challenges of building a game-based logical learning system with a deep neural network for Connect6. We used heuristic knowledge to establish the knowledge base. Our novel algorithm, based on residual deep convolutional neural networks (DCNNs), outperforms conventional approaches. We compare neural network architectures, demonstrating the superiority of residual DCNNs.Our AI program, “Kavalan,” achieves remarkable search performance. Furthermore, the introduced approaches lend themselves to application in other sudden-death board games, such as Gomoku, the endgame of Chess and Shogi.

Android Application for Waste Collection Management

Abstract: The value and rate of logical waste discarding will remain to rise as long as mortal conditioning remains. The result of the scurrility of logical waste, which is a great peril to the world and to mortal health, calls for the want to amend the situation before it can get out of phase. This leads us to exercise the stylish ways to take logical waste collection following the rearmost trends in movable technology. This study developed a waste collection model with the end of intending a movable waste operation operation model that will support ameliorate quality operation of logical waste collection and better environmental jolt reduction. The model places waste creators in the middle ground of the waste collection system as they play a crucial part in producing logical waste, aiding waste companies during waste collection and providing an operative device for external councils to epitomise this appreciation. all conditioning related to logical waste collection and directing their opinions. In this study we have handed all the necessary and over- to- assignation movable technology and the model can work to achieve an operative logical waste collection system at home.

Why Does China Implement the "Dual Reduction Policy" to Restrict Educational Industry? An Explanation Based on Marxist Political Economy

Karl Marx and Friedrich Engels proposed in "The Communist Manifesto" the viewpoint of the inevitable demise of capitalism, Marxist political economy serves as an explanation and justification of this viewpoint. Marxist political economy forms a complete logical system, which elucidates the developmental path of capitalist collapse through three stages: surplus value, free competition, and monopoly capitalism. The theory of surplus value explains the intrinsic essence of capitalist society, wherein capitalists profit by exploiting labor, while free market competition and monopoly capitalism are external manifestations of the operational laws of capitalist society. In recent years, the disorderly expansion of educational capital in China has shown clear monopolistic tendencies. At this juncture, China promptly implemented the "dual reduction policy" to restrict the expansion of educational capital, which is a practical application of Marxist political economy. From the perspective of Marxist political economy theory, China's restrictions on educational capital serve three purposes: firstly, to prevent capital from excessively depriving laborers of surplus value; secondly, to avoid cyclical economic crises brought about by unregulated capital competition; thirdly, to prevent monopolies and thereby avert both economic crises and the social crises brought about by monopolies.

Fuzzy functional observer‐based sliding mode control for T‐S fuzzy cyber‐physical systems subject to disturbances and deception attacks

AbstractThis paper is concerned with the fuzzy functional observer‐based sliding mode control for T‐S fuzzy cyber‐physical systems subject to disturbances and deception attacks. First, the deception attack is modeled by an exogenous system with state dependent nonlinearity. Second, a fuzzy learning functional observer with fuzzy logic systems learning unknown nonlinear function, whose parameters can be directly found, is designed to estimate the partly unavailable states, deception attacks and lumped disturbances simultaneously. Then, a fuzzy sliding mode control is designed to compensate the effect of deception attack and reject the disturbances. Furthermore, some sufficient conditions are given to guarantee that the closed‐loop fuzzy system is exponential convergent. Finally, a numerical example is given to illustrate the validity of the proposed method.

Decentralized fuzzy DSC for uncertain large-scale interconnected switched systems in non-strict feedback form with input saturation

This paper demonstrates a fuzzy decentralized dynamic surface control (DSC) scheme for switched large-scale interconnected nonlinear systems under arbitrary switching, which contains non-strict feedback form and unknown input saturation uncertainties. An auxiliary design system is established to handled input saturation. Uncertainties of non-strict feedback form are learned by fuzzy logic systems (FLSs) approximators, DSC method is designed to conquer “explosion of complexity” inherented by repeated differential of virtute controller in backstepping approach. Ii is shown that based on common Lyapunov function (CLF) design and analysis scheme, all the closed-loop systems signals are uniformly ultimately bounded (UUB), simulation results are provided to demonstrate the effectiveness of this proposed strategy.

Perancangan Perangkat Modular Kendali Suhu dan Aliran Gas Elpiji Berbasis Fuzzy Logic

AbstractTemperature is very significant in every stage in the heating processes and will affect the quality of the resulting product. The inaccuracy of temperature control causes the production process to change from time to time, in addition to the increasingly inconsistent quality of the resulting product. In this study, the use of a closed loop system based on fuzzy logic will be applied to control the temperature and flow of fuel. Temperature control is connected to gas flow control because this equipment is specifically for gas (LPG) fueled equipment. By the fuzzy logic system, it is expected to produce a control equipment that can control temperature accuratly and can save fuel. This control equipment is modular in nature so that it can be applied to all bakery & cake oven equipment, coffee roasters, tea leaf wiping machines, fryers and other small scale industrial machines without having to change and modify the original design. The temperature settling point can be adjusted from 80oC to 300oC as desired in the heating process. The test results show that temperature control using fuzzy logic control system on coffee roasters and bread ovens obtains a maximum overshoot value which is still within the tolerance of 1.3% and fuel savings of around 13%

Adaptive event-triggered tracking control for nonlinear systems with prescribed performance: A time-domain mapping approach

This paper proposed an event-triggered adaptive fuzzy prescribed time control strategy for a class of nonlinear systems with uncertain external disturbances and unknown virtual control coefficients under prescribed performance. A novel time-domain mapping technique is proposed to transform the prescribed time tracking control problem into an asymptotic convergence problem. An observer is designed to estimate for the external disturbance and fuzzy approximation error of fuzzy logic systems (FLSs). The problem of “explosion of complexity” is addressed by applying dynamic surface control (DSC) technique to backstepping. An adaptive fuzzy event-triggered strategy is designed using the Lyapunov stability theorem. This strategy ensures that, after time-domain mapping, the tracking error asymptotically converges to zero. Additionally, there is no Zeno behavior and all system signals are bounded. Finally, the effectiveness of the proposed scheme is verified through three simulation examples.

Leveraging ANFIS with Adam and PSO optimizers for Parkinson's disease

Parkinson's disease (PD) is an age-related neurodegenerative disorder characterized by motor deficits, including tremor, rigidity, bradykinesia, and postural instability. According to the World Health Organization, about 1 % of the global population has been diagnosed with PD, and this figure is expected to double by 2040. Early and accurate diagnosis of PD is critical to slowing down the progression of the disease and reducing long-term disability. Due to the complexity of the disease, it is difficult to accurately diagnose it using traditional clinical tests. Therefore, it has become necessary to develop intelligent diagnostic models that can accurately detect PD. This article introduces a novel hybrid approach for accurate prediction of PD using an ANFIS with two optimizers, namely Adam and PSO. ANFIS is a type of fuzzy logic system used for nonlinear function approximation and classification, while Adam optimizer has the ability to adaptively adjust the learning rate of each individual parameter in an ANFIS at each training step, which helps the model find a better solution more quickly. PSO is a metaheuristic approach inspired by the behavior of social animals such as birds. Combining these two methods has potential to provide improved accuracy and robustness in PD diagnosis compared to existing methods. The proposed method utilized the advantages of both optimization techniques and applied them on the developed ANFIS model to maximize its prediction accuracy. This system was developed by using an open access clinical and demographic data. The chosen parameters for the ANFIS were selected through a comparative experimental analysis to optimize the model considering the number of fuzzy membership functions, number of epochs of ANFIS, and number of particles of PSO. The performance of the two ANFIS models: ANFIS (Adam) and ANFIS (PSO) focusing at ANFIS parameters and various evaluation metrics are further analyzed in detail and presented, The experimental results showed that the proposed ANFIS (PSO) shows better results in terms of loss and precision, whereas, the ANFIS (Adam) showed the better results in terms of accuracy, f1-score and recall. Thus, this adaptive neural-fuzzy algorithm provides a promising strategy for the diagnosis of PD, and show that the proposed models show their suitability for many other practical applications.

PH-Controllable Redox Responsive Amphiphilic Viologens for Switchable Emulsions.

A pH and redox dual responsive amphiphilic viologen is synthesized, which can be reversibly transformed among the zwitterionic (SVa), monovalent anionic (SV+), and divalent anionic (SVH2+) forms upon pH variation, exhibiting pH-controllable redox responsive properties. Switchable Pickering emulsions with different droplet size and viscosity are prepared by the mixture of hydrophilic silica nanoparticles and the viologens (SV+ or SVH2+) at acidic conditions, while such combination yielded an oil-in-dispersion emulsion at neutral pH value. Not only can rapid reversible demulsification/stabilization of the Pickering emulsions be achieved by redox reactions, but the rate of redox-demulsification can also be controlled by pH trigger. The dual-responsive amphiphilic viologens have potential applications in developing intelligent colloid materials and molecular logic systems.

Output feedback command filtered fuzzy controller design for uncertain strict-feedback nonlinear systems with unmodeled dynamics and event-triggered strategy

An adaptive control problem is considered for a class of nth-order uncertain nonlinear systems with unmodeled dynamics. Compared with the traditional backstepping method, this article improves the systems performance by introducing command filtered and event-triggered. The former primarily enhances the flexibility of controller design, while the latter employs a variable threshold triggering mechanism to achieve savings in communication resources. Simultaneously, the estimation error is compensated by the introduced hyperbolic tangent function. In addition, fuzzy logic system (FLS) is primarily used for observer design and approximation of unknown functions. The conclusion that the closed-loop signals are bounded is obtained through stability analysis. Finally, the numerical simulation results provide a comparative validation of the effectiveness of the proposed control scheme.

Some New Proof System for Propositional Modal Logic

A method of constructing some proof system for propositional modal logic is described. Earlier for unmodal logics introduced notions of determinative conjunct and determinative disjunctive normal, as well as the elimination rule, are generalized for propositional modal logic and on the base of them, the proof system Emod is constructed. For some sequences of tautologies, lower exponential bounds for the number of proof lines in the described system are easily obtained. Նկարագրված է արտածման որոշակի համակարգի կառուցման եղանակը ասույթային մոդալ տրամաբանության համար: Ընդհանրացված են նախկինում ոչ մոդալ տրամաբանությունների համար ներմուծված որոշիչ կոնյունկտի և որոշիչ դիզյունկ-տիվ նորմալ ձևի, ինչպես նաև բացառման կանոնը, գաղափարները, և դրանց հիման վրա կառուցված է Emod արտածման համակարգը: Նույնաբանությունների որոշակի հաջորդականությունների համար հեշտորեն ստացված է ցուցչային կարգի ստորին գնահատական նկարագրված համակարգում արտածման նվազագույն քայլերի քանակի համար: Описан метод построения некоторой пропозициональной системы выводов для модальной логики. Введенные ранее для немодальных логик понятия определяющего конъюнктa и определяющей дизъюнктивной нормальной формы, а также правила элиминации обобщены для пропозициональной модальной логики, и на их основе построена система Emod. Для некоторых последовательностей тавтологий легко получены нижние экспоненциальные оценки минимального количествa шагов выводов в описываемой системе.

Framework for Understanding Factors Influencing Clothing Selection

Clothing selection is a product of social construction that defines what people choose to wear. The choice of what, how, and when to select what individuals, wear is based on the perception that surrounds their reality. For the purpose of this research work, factors influencing clothing selection have been organized into a framework to facilitate easier understanding. The factors influencing clothing selection have been arranged into a logical system of concepts grouped into four categories: Social/Psychological, Economic, Environmental, and Physiological Factors. A detailed deconstruction of each of these factors has been further conceptualized into smaller concepts and discussed in a nuanced context. Under the Social/Psychological factors are concepts such as culture, gender, occupation, personality, religion, socio-status, and fashion. Economic factors evaluated are concepts such as the Economic well-being of a Nation (GDP), the Economic status of individuals, Economic environment (availability of technology and raw materials and buying behavior). Environmental factors such as Location (Urban/Rural), Climatic conditions, protection from extreme weather conditions, and unconventional places like deep seas, and space were examined. Lastly, the physiological factors such as age, health condition, and figure were also evaluated. Developing a conceptual framework for understanding clothing selection is apt in filling the gap in Literature since most research works on factors influencing clothing selection may not have been organized into a framework for clothing selection, whether intrinsically or extrinsically motivated.

Logic Signal Amplification System for Sensitive Electrochemiluminescence Detection and Subtype Identification of Cancer Cells.

Achieving sensitive detection and accurate identification of cancer cells is vital for diagnosing and treating the disease. Here, we developed a logic signal amplification system using DNA tetrahedron-mediated three-dimensional (3D) DNA nanonetworks for sensitive electrochemiluminescence (ECL) detection and subtype identification of cancer cells. Specially designed hairpins were integrated into DNA tetrahedral nanostructures (DTNs) to perform a catalytic hairpin assembly (CHA) reaction in the presence of target microRNA, forming hyperbranched 3D nanonetworks. Benefiting from the "spatial confinement effect," the DNA tetrahedron-mediated catalytic hairpin assembly (DTCHA) reaction displayed significantly faster kinetics and greater cycle conversion efficiency than traditional CHA. The resulting 3D nanonetworks could load a large amount of Ru(phen)32+, significantly enhancing its ECL signal, and exhibit detection limits for both miR-21 and miR-141 at the femtomolar level. The biosensor based on modular logic gates facilitated the distinction and quantification of cancer cells and normal cells based on miR-21 levels, combined with miR-141 levels, to further identify different subtypes of breast cancer cells. Overall, this study provides potential applications in miRNA-related clinical diagnostics.

Multi-CNN based logical reasoning system for facial expression recognition on small-sample datasets

Facial expression recognition has long been an area of great interest across a wide range of fields. Deep learning is commonly employed in facial expression recognition and demonstrates excellent performance in large-sample classification tasks. However, deep learning models often encounter challenges when confronted with small-sample expression classification problems, as they struggle to extract sufficient relevant features from limited data, resulting in subpar performance. This paper presents a novel approach called the Multi-CNN Logical Reasoning System, which is based on local area recognition and logical reasoning. It initiates the process by partitioning facial expression images into two distinct components: eye action and mouth action. Subsequently, it utilizes logical reasoning based on the inherent relationship between local actions and global expressions to facilitate facial expression recognition. Throughout the reasoning process, it not only incorporates manually curated knowledge but also acquires hidden knowledge from the raw data. Experimental results conducted on two small-sample datasets derived from the KDEF and RaFD datasets demonstrate that the proposed approach exhibits faster convergence and higher prediction accuracy when compared to classical deep learning-based algorithms.

Advancing Fuzzy Logic: A Hierarchical Fuzzy System Approach

Fuzzy logic systems (FLS) are widely used in various engineering, medical, and scientific applications for modelling complex and uncertain systems. However, traditional FLS has limitations in handling complex and hierarchical structures due to their lack of scalability and interpretability. This paper proposes an approach to hierarchical fuzzy systems (HFS) that enhances the traditional FLS by providing a hierarchical structure with multiple levels of fuzzy rules. The main contribution of this paper is the proposal of HFS, which improves interpretability, scalability, and accuracy compared to traditional FLS, particularly for real-world applications. However, the question arises, "How can the FLS be converted into the HFS?" In this paper, the approach to HFS architecture will comprise two levels of FLS, where the first level determines the overall behaviour of the system, and the second level refines the output by considering the local behaviour. The proposed approach has been validated through experimental results on a case studies, such as the Iris flower classification. The results demonstrate that HFS provides more efficient and reliable solutions and can be applied to various complex and hierarchical systems in different domains, such as manufacturing, robotics, and decision-making.

Big data-assisted student’s English learning ability appraisal model using fuzzy logic system

The ability, interest, and prior accomplishments of students with varying proficiency levels all impact how they learn English. Exact validation is essential for facilitating efficient evaluation and training models. The research’s innovative significance resides in incorporating personal attributes, progressive appraisal, and Fuzzy Logic-based appraisal in English language learning. The PA2M model, which addresses the shortcomings of existing models, offers a thorough and accurate assessment, enabling personalized recommendations and enhanced teaching tactics for students with varied skill levels. This research proposes the Fuzzy Logic System (FLS)-based Persistent Appraisal Assessment Model (PA2M). Based on the students’ evolving performance and accumulated data, this model evaluates the students’ English learning capabilities. The model assesses the student’s ability using fuzzification approaches to reduce variations in appraisal verification by linking personal attributes with performance. Mamdani FIS offers a clear and thorough evaluation of student’s English learning capacity within the framework of the appraisal methodology. The inputs are updated utilizing performance and accumulated ability data to improve validation consistently and reduce converge errors. During the fuzzification process, pre-convergence from unavailable appraisal sequences is eliminated. The PA2M approach determines precise improvements and evaluations depending on student ability by merging prior and current data. Several appraisal validations and verifications result in clear fresh suggestions. According to experimental data, the suggested model enhances 9.79% of recommendation rates, 8.79% of appraisal verification, 8.25% of convergence factor, 12.56% error ratio, and verification time with 8.77% over a range of inputs. The PA2M model provides a fresh and useful way to evaluate English learning potential, filling in some gaps in the body of knowledge and practice.

Fuzzy logical system for personalized vocal music instruction and psychological awareness in colleges using big data

Traditional psychological awareness relating to vocal musical instruction often disregards the impact of earlier experiences on music learning could result in a gap in meeting the needs of individual students. Conventional learning techniques of music related to psychological awareness for each individual has been focused on and addressed in this research. Technological upgrades in Fuzzy Logic (FL) and Big Data (BD) related to Artificial Intelligence (AI) are provided as a solution for the existing challenges and provide enhancement in personalized music education. The combined approach of BD-assisted Radial Basis Function is added with the Takagi Sugeno (RBF-TS) inference system, able to give personalized vocal music instruction recommendations and indulge psychological awareness among students. Applying Mel-Frequency Cepstral Coefficients (MFCC) is beneficial in capturing variant vocal characteristics as a feature extraction technique. The BD-assisted RBF can identify the accuracy of pitch differences and quality of tone, understand choices from students, and stimulate psychological awareness. The uncertainties are addressed by using the TS fuzzy inference system and delivering personalized vocal training depending on different student preference factors. With the use of multimodal data, the proposed RBF-TS approach can establish a fuzzy rule base in accordance with the personalized emotional elements, enhancing self-awareness and psychological well-being. Validation of the proposed approach using an Instruction Resource Utilization Rate (IRUR) gives significant improvements in engaging students, analyzing the pitching accuracy, frequency distribution of vocal music instruction, and loss function called Mean Square Error(MSE). The proposed research algorithm pioneers a novel solution using advanced AI algorithms addressing the research challenges in existing personalized vocal music education. It promises better student outcomes in the field of music education.

Application of ANOVA in interval type-2 fuzzy logic systems for modeling the process of ceramic coating preparation in the foundry industry

Application of ANOVA in interval type-2 fuzzy logic systems for modeling the process of ceramic coating preparation in the foundry industry