Urban areas are increasingly affected by intense rainstorm-induced flooding, posing serious risks to human life and property. To reduce the impact of such disasters and promote urban safety and sustainable development, a systematic assessment of Urban Flood Disaster Risk (UFDR), along with appropriate management strategies, is essential. Due to the high cost and complexity of acquiring and processing numerous potential indicators, identifying the most influential predictive variables is critical. This study integrates adaptive fuzzy logic with machine learning techniques to predict flood probabilities and develop evidence-based mitigation protocols. The proposed framework incorporates 13 carefully selected evaluation indicators, categorized into three dimensions: hazard triggers, environmental susceptibility, and community vulnerability. Indicator weights are determined through a combination of subjective (Analytic Hierarchy Process) and objective (CRITIC) weighting methods. Dynamic risk assessment is conducted using the Variable Fuzzy Pattern Evaluation (VFPE) model, while temporal features are automatically extracted using one-dimensional convolutional neural networks (1D-CNN). Flood probability is predicted using several machine learning algorithms, including Random Forest (RF), Decision Tree (DT), Extreme Gradient Boosting (XGBoost), and Support Vector Machine (SVM). The contribution of each input variable is assessed using feature importance scores derived from the RF model, averaged over a leave-one-out cross-validation (LOOCV) process. Results indicate that the SVM model achieves the highest accuracy and reliability for the multi-class classification task, particularly in identifying high-risk events. RF and XGBoost also demonstrate strong performance, offering a balance between predictive accuracy and model interpretability. Overall, the proposed methodology provides an effective and data-driven approach to support urban flood risk assessment and disaster mitigation planning.
LI Miaohui, GU Junhao, HUANG Ting, QIAO Yu, CHEN Bing
Objective To construct a causal artificial intelligence (AI) framework model embedded with medical knowledge for AI-assisted diagnosis of bone marrow morphology in acute leukemia based on medical diagnostic logic. Methods A stage-by-stage causal AI diagnostic framework guided by medical knowledge was constructed using a dataset of 20 089 microscopic images of bone marrow and peripheral blood smears of 93 cases(69 patients with other diseases exhibiting normal bone marrow and peripheral blood; 24 patients with hematological diseases, including 14 cases of acute leukemia ) from Ruijin Hospital (2020-2023). Multiple specialized modules including bone marrow hyperplasia assessment module(M1), bone marrow cell classification module(M2), AKP score calculation module(M3), POX-stained leukaemic cell feature extraction module(M4), and CE -stained leukaemic cell feature extraction module(M5), were trained and tested in a 7∶3 ratio to extract key morphological features, including bone marrow hyperplasia status, the constituent ratio of bone marrow cells, and cytochemical staining patterns in both bone marrow and peripheral blood. Based on morphological characteristics of the peripheral blood and bone marrow of hematological diseases, the aforementioned specific AI recognition models were combined step by step according to medical diagnostic logic, integrating multi-dimensional morphological characteristics of bone marrow cells, and a two-stage process sequential diagnosis and prediction was carried(stage 1: integrating M1, M2, and M3;stage2: integrating modules 4 and 5). The first stage involved distinguishing between normal and abnormal bone marrow cases, while simultaneously identifying chronic myeloid leukemia. The second stage focused on the diagnosis of acute leukemia and the differentiation of its subtypes. An end-to-end AI diagnostic framework model was also established as a control and compared with the medical knowledge-embedded causal AI diagnostic framework model in real-case diagnostic testing. Results The medical knowledge-embedded causal AI diagnostic framework model achieved higher accuracy than the end-to-end AI framework model in real-case diagnostic testing at both stages (Stage 1 accuracy: 90.00% vs. 70.00%; Stage 2 accuracy: 83.33% vs. 66.67%). Conclusions Medical expertise and diagnostic logic play a crucial role in AI-assisted diagnosis of bone marrow cell morphology for hematological diseases. The application of the medical knowledge-embedded causal AI diagnostic framework model improves the accuracy and interpretability of AI-assisted diagnosis, providing a novel and generalizable paradigm for intelligent diagnosis of hematological diseases.
In these notes we propose a new, simpler proof system for first-order matching logic with application and definedness. The new proof system is inspired by Tarski's axiomatization for first order-logic with equality (simplified by Kalish and Montague), that does not involve the notions of a free variable and free substitution. We give also a proof system for first-order matching logic with application, obtained by adapting to matching logic Gödel's proof system for first-order intuitionistic logic.
We introduce the $L_!^S$-calculus, a linear lambda-calculus extended with scalar multiplication and term addition, that acts as a proof language for intuitionistic linear logic (ILL). These algebraic operations enable the direct expression of linearity at the syntactic level, a property not typically available in standard proof-term calculi. Building upon previous work, we develop the $L_!^S$-calculus as an extension of the $L^S$-calculus with the $!$ modality. We prove key meta-theoretical properties--subject reduction, confluence, strong normalisation, and an introduction property--as well as preserve the expressiveness of the original $L^S$-calculus, including the encoding of vectors and matrices, and the correspondence between proof-terms and linear functions. A denotational semantics is provided in the framework of linear categories with biproducts, ensuring a sound and adequate interpretation of the calculus. This work is part of a broader programme aiming to build a measurement-free quantum programming language grounded in linear logic.
Objective Digitization and intelligence (D&I) is essential for rail transit high-quality development. Currently, there is a lack of top-level design for high-speed maglev D&I, necessitating research to meet the requirements for the high-quality construction, operation, and sustainable development of high-speed maglev system. Method The progress of D&I advancements for railway and urban rail transit in China is introduced, and the characteristics of current D&I development in rail transit sector are summarized. Considering its unique technological characteristics, the practical foundation for high-speed maglev D&I are analyzed from aspects such as technological basis, time window, and social needs. The connotations of high-speed maglev D&I are identified from four levels of technology, business, management, and society. A basic evolution path for high-speed maglev D&I is proposed, elaborating on five key aspects: top-level design, technology-based construction, intelligent application deepening, organizational management optimization, and intelligent cycle establishment. Based on system engineering methods, the overall framework for high-speed maglev D&I is designed, abstracting the entire system into seven sub-frameworks: service, function, logic, technology, physics, data, and security. Result & Conclusion High-speed maglev D&I is not a simple technical concept, but a continuous process of development and evolution. Revolving around data as the main thread, the basic path is divided into two dimensions: evolution and implementation. Its overall framework is open, flexible, multi-dimensional, and multi-level, including four aspects: system engineering method, system abstraction, data system, and smart cycling. The conceptual connotation, basic path, and overall framework collectively constitute the foundation for high-speed maglev D&I top-level design.
High-dimensional quantum units of information, or qudits, can carry more than one quantum bit of information in a single degree of freedom and can, therefore, be used to boost the performance of quantum communication and quantum computation protocols. A photon in a superposition of 2N time bins—a time-bin qudit—contains as much information as N qubits. Here, we show that N-qubit states encoded in a single time-bin qudit can be arbitrarily and deterministically generated, manipulated, and measured using a number of linear optics elements that scale linearly with N, as opposed to prior proposals of single-qudit implementation of N-qubit logic, which typically requires O(2N) elements. The simple and cost-effective implementation we propose can be used as a small-scale quantum processor. We then demonstrate a path toward scalability by interfacing distinct qudit processors to a matter qubit (atom or quantum dot spin) in an optical resonator. Such a cavity quantum electrodynamics system allows for more advanced functionalities, such as single-qubit nondemolition measurement and two-qubit gates between distinct qudits. It could also enable quantum interfaces with other matter quantum nodes in the context of quantum networks and distributed quantum computing.
Atomic physics. Constitution and properties of matter
On the wave of intensive development of cognitive sciences we see the intensive physiology, psychology and philosophy studies in the Russian tradition. Philosophy discusses the analytical think and the problem of free will, logic, epistemology and enactivism; the orient studies with attention to experience are very rarely. Against this background, psychiatric neurophenomenology as one of the most important field for science and practice is completely forgotten. The paper reconstructs continuity between the “old” philosophical theories in psychiatry in the 20th century beginning and the “new” interpretations in neuroscience. The author demonstrates the most characteristic parallels of this renewal: neurophenomenology problematizes the problem of description and understanding methodology, as K. Jaspers, accents the prelogical experience, as E. Strauss, tunes away from the subject-object split to immediate experience, as L. Binswanger, emphasizes the embodiment and totalization of communication, as R. Laing. Reliance on the analysis of actual modern discussions paper shows how traditional problems transforms in recent years, and what perspectives these transformations open for philosophy and interdisciplinary practice. The author studies psychiatric phenomenology of experience of J. Parnas, G. Stanghellini, L. Sass, D. Zahavi, K. Mundt, T. Fuchs and others, analyses the interdisciplinary potential of modern research and diagnostic programs. It is concluded that, by overcoming the biological and psychological dichotomy, by turning to the field of experience as a field of general interest, neurophenomenology creates a research field in which the philosophy of psychiatry overcomes its antinomianism.
Wilquer Silvano de Souza Ferreira, Gláucia Maria Vasconcellos Vale, Patrícia Bernardes
Purpose – The aim of this article is to test the hypothesis that peer-to-peer technology platforms (Uber) are associated with disruption in the institutional environment, affecting beliefs, norms and users' ways of thinking and acting. Design/methodology/approach – Probability sample comprising 843 users (446 passengers; 397 drivers) in the city of Belo Horizonte, Brazil, using a set of indicators was specifically designed for this study. Findings – Uber triggers significant changes in the systems of rewards and sanctions, in social preferences, and in entrepreneurial structure and governance, and promotes the coexistence of an institutional logic, hitherto dominant, with new believes, rules, norms and regulatory systems. Originality/value – This is a pioneer study that associates institutional approach's elements with technology platforms; the authors also elaborated and utilized an analysis model consisting of a set of completely original indicators capable of mapping and measuring different dimensions of the phenomenon under analysis.
Fernando León-Saavedra, María del Carmen Listán-García, María del Pilar Romero de la Rosa
Abstract We correct a logic mistake in our paper “On statistical convergence and strong Cesàro convergence by moduli for double sequences” (León-Saavedra et al. in J. Inequal. Appl. 2022:62, 2022).
Mohammad Reza Zare Dehabadi, Babak Ziyae, Seyed Mojtaba Sajadi
Introduction: A review of the research backgrounds in the field of business accelerators shows that many studies have considered the effects of this process on investment, ecosystems, entrepreneurship and innovation in general. However, none of these studies focus on specific industries such as telecommunications. Also, there has been no centralized information and communication technology, so there is a research gap. It was found that there is no model to express the accelerating components of technology businesses in this field. Due to the role of advanced technology in the telecommunications industry, the increasing development in the field of silent technologies and the transfer of advanced technologies in the industry, the need for accelerators of innovation in the industry is very urgent. They need to be able to come up with innovative ideas regarding feasibility studies, testing, funding and marketing after commercialization; Therefore, it is absolutely necessary to fill the gap between the presence of a scientific model in a technological accelerator in Iranian communication services companies, most of whose technology is imported from China, Korea, etc. This is the innovative aspect of this research for the first time in Iran and in the telecommunications industry. One of the main reasons for the failure of accelerators is the lack of experience and competency of some entrepreneurs, which would promote different learning achievements and skills in a variety of fields. Therefore, the main purpose of this study is to design an accelerator model of technological businesses in the field of communication service companies.Methodology: The present research is applied in terms of purpose and qualitative in terms of data collection. Due to the nature of the research problem, the researcher has solved it with an interpretive paradigm through a qualitative approach and inductive logic. So, the question to answer is how the accelerator model of technology businesses is formed in communication service companies.In the first part of this research, the texts related to the concepts of accelerator and technological businesses have been extracted from library sources, and the criteria are set with regard to the keywords of accelerator, technological business and their derivatives. In the second part of the study, interviews were conducted with 19 managers of Iran's telecommunications technology in the summer of 2020. The statistical population of the study included the experts from the academic sector who had at least two articles in the field of technology business and research projects in this field. In the executive sector, there were people with a university degree and at least four years of work in the field of technological business in the field of telecommunications technology. The sampling was performed purposefully and continued until the researcher reached theoretical saturation. In this study, the interviews were semi-structured and the 5w1h technique was used to answer some questions such as how, why, what, where, who and when. The codes were extracted for 19 interviews that became saturated, and no new code was added to the previous codes during further interviews. This number of samples was enough for the interview section. In order to analyze the data, encoding was performed using the Atlas T software version 8. In this research, three methods were used to realize the validity of the research tool, including using two tools for writing questions, correcting the questions by experts, and finally reviewing the questions by conducting two preliminary interviews. This was done up to sample 19. The reliability coefficient based on the agreement between the two coders was 66%. The strategy of the present study was to better solve the problem of the data theory of the Strauss and Corbin Foundation, in which the researcher follows a process theory and describes the actions and interactions that occur over time.Results and Discussion: In the present study, the researcher reached theoretical saturation after nineteen interviews. The interviews were briefly conducted with the question of "designing an accelerator model of technology businesses in communication service companies" (open interview). Also, two stages of open and central coding were used. The interview questions were ‘What are the most important infrastructures for creating accelerators in the field of ICT?’, ‘What factors will accelerate the commercialization of ICT startup products in accelerators?’, ‘What will be the consequences of accelerating the creation of technology businesses in the field of ICT?’, and ‘What facilitators are involved in existing startups in the ICT accelerator?’The research led to the identification of 11 categories and 52 selected codes. The strategic factors included organizational factors and technology development and transfer, causal factors including financing and opportunity recognition, intervening factors including entrepreneurship component, and contextual factors including managerial components, which are environmental, cultural and economic. Finally, the consequences include technological entrepreneurship and coaching.Conclusion: The results show that the current situation in the country and its role in technology businesses to create employment and generate new opportunities in the field of economic activities are appropriate. By recognizing the accelerating areas of technology businesses in communication services, companies can create new opportunities for entrepreneurs. It can cause the economic and social progress of the society.
Abstract Yang & Song (2022, https://doi.org/10.1029/2022GL098393) first claimed existence of Earth's inner core differential rotation based on the waveform similarity of two neighboring stations AAK and KZA across an earthquake doublet and then postulated a local velocity gradient at the top of the inner core based on the difference of PKiKP‐PKIKP differential times between the stations and inferred inner core differential rotation rate. In this comment, we collectively analyze the seismic data in the region and add the data of another nearby station HORS into analysis. HORS and KZA, located in an opposite direction away from AAK, consistently exhibit high waveform similarity. Collective analysis of seismic data demonstrates the invalidity of both their logic of claiming existence of inner core differential rotation and their postulation of “a local inner core gradient” to infer differential rotation. Localized and episodic inner core surface change provides a physically consistent explanation to the seismic data.
Makmur Saini, Muhammad Ruswandi Djalal, Muhammad Azhar
et al.
Solar Home System is one of the technologies for utilizing solar power at home. To optimize the performance of PLTS, it is necessary to regulate the use of electrical energy. In this research, an effort is made to control the load using fuzzy logic to regulate the power consumption used by the load so that energy can be utilized effectively. The fuzzy logic method works based on the input given so that the desired results can be as expected. To test the effectiveness of the fuzzy logic method, this study was tested with two types of loads: lighting and fan loads. For lighting loads, it uses two light sensor inputs, and for air conditioning, it uses two temperature sensor inputs and a PIR sensor. The test results show that in the experiment of setting the light load using fuzzy logic, the average power usage at the load is 11.31 watts. In contrast, without fuzzy logic, the average power usage value for the load is 14.29 watts. In the fan load control experiment using fuzzy logic, the energy consumption setting was obtained according to the room temperature input and the number of people in the room. The test results received power usage without using fuzzy logic, where the average fan power usage value is 4.32 Watts, while without fuzzy logic, the average power usage value is 2.97 Watts. For one sensor input, the average power usage value of the average fan is 3.02 Watts; without fuzzy logic, the average power usage value is 2.93 Watts for two sensor inputs.
Manuel Casal-Guisande, Jorge Cerqueiro-Pequeño, José-Benito Bouza-Rodríguez
et al.
The use of intelligent systems in clinical diagnostics has evolved, integrating statistical learning and knowledge-based representation models. Two recent works propose the identification of risk factors for the diagnosis of obstructive sleep apnea (OSA). The first uses statistical learning to identify indicators associated with different levels of the apnea-hypopnea index (AHI). The second paper combines statistical and symbolic inference approaches to obtain risk indicators (<i>Statistical Risk</i> and <i>Symbolic Risk</i>) for a given AHI level. Based on this, in this paper we propose a new intelligent system that considers different AHI levels and generates risk pairs for each level. A learning-based model generates <i>Statistical Risks</i> based on objective patient data, while a cascade of fuzzy expert systems determines a <i>Symbolic Risk</i> using symptom data from patient interviews. The aggregation of risk pairs at each level involves a fuzzy expert system with automatically generated fuzzy rules using the Wang-Mendel algorithm. This aggregation produces an <i>Apnea Risk</i> indicator for each AHI level, allowing discrimination between OSA and non-OSA cases, along with appropriate recommendations. This approach improves variability, usefulness, and interpretability, increasing the reliability of the system. Initial tests on data from 4400 patients yielded AUC values of 0.74–0.88, demonstrating the potential benefits of the proposed intelligent system architecture.
Abstract In the information explosion society, information security is highly demanded in the practical application, which raised a surge of interest in designing secure and reliable information transmission channels based on the inherent properties of emerging devices. Here, an innovative strategy to achieve the data encryption and reading during the data confidential transmission based on VO2 device is proposed. Owing to the specific insulator‐to‐metal transition property of VO2, the phase transitions between the insulator and metallic states are modulated by the combination of electric field, temperature, and light radiation. These external stimulus‐induced phase diagram is directly associated with the defined VO2 device, which are applicable for control the “0” or “1” electrical logic state for the information encryption. A prototype device is fabricated on an epitaxial VO2 film, which displayed a unique data encryption function with excellent stability. The current study not only demonstrated a multiphysical field‐modulated VO2 device for information encryption, but also supplied some clues for functional devices applications in other correlated oxide materials.
Primal logic arose in access control; it has a remarkably efficient (linear time) decision procedure for its entailment problem. But primal logic is a general logic of information. In the realm of arbitrary items of information (infons), conjunction, disjunction, and implication may seem to correspond (set-theoretically) to union, intersection, and relative complementation. But, while infons are closed under union, they are not closed under intersection or relative complementation. It turns out that there is a systematic transformation of propositional intuitionistic calculi to the original (propositional) primal calculi; we call it Flatting. We extend Flatting to quantifier rules, obtaining arguably the right quantified primal logic, QPL. The QPL entailment problem is exponential-time complete, but it is polynomial-time complete in the case, of importance to applications (at least to access control), where the number of quantifiers is bounded.
Mohd Shareduwan Mohd Kasihmuddin, Siti Zulaikha Mohd Jamaludin, Mohd. Asyraf Mansor
et al.
Creating optimal logic mining is strongly dependent on how the learning data are structured. Without optimal data structure, intelligence systems integrated into logic mining, such as an artificial neural network, tend to converge to suboptimal solution. This paper proposed a novel logic mining that integrates supervised learning via association analysis to identify the most optimal arrangement with respect to the given logical rule. By utilizing Hopfield neural network as an associative memory to store information of the logical rule, the optimal logical rule from the correlation analysis will be learned and the corresponding optimal induced logical rule can be obtained. In other words, the optimal logical rule increases the chances for the logic mining to locate the optimal induced logic that generalize the datasets. The proposed work is extensively tested on a variety of benchmark datasets with various performance metrics. Based on the experimental results, the proposed supervised logic mining demonstrated superiority and the least competitiveness compared to the existing method.
We study the expressive powers of SO-HORN$^{*}$, SO-HORN$^{r}$ and SO-HORN$^{*r}$ on all finite structures. We show that SO-HORN$^{r}$, SO-HORN$^{*r}$, FO(LFP) coincide with each other and SO-HORN$^{*}$ is proper sublogic of SO-HORN$^{r}$. To prove this result, we introduce the notions of DATALOG$^{*}$ program, DATALOG$^{r}$ program and their stratified versions, S-DATALOG$^{*}$ program and S-DATALOG$^{r}$ program. It is shown that, on all structures, DATALOG$^{r}$ and S-DATALOG$^{r}$ are equivalent and DATALOG$^{*}$ is a proper sublogic of DATALOG$^{r}$. SO-HORN$^{*}$ and SO-HORN$^{r}$ can be treated as the negations of DATALOG$^{*}$ and DATALOG$^{r}$, respectively. We also show that SO-EHORN$^{r}$ logic which is an extended version of SO-HORN captures co-NP on all finite structures.