Hasil untuk "Logic"

Patricia H. Thornton, W. Ocasio, M. Lounsbury

Marya L. Besharov, Wendy K. Smith

Jason Jay

S. Toulmin

D. Quadling, I. Lakatos, John Worral et al.

D. Harel

H. Levesque

R. Goldblatt

Parmveer Atwal, Ryley McWilliams, Ramani Ramaseshen et al.

Current diagnostic modalities for rheumatoid arthritis (RA), such as Magnetic Resonance Imaging (MRI) and ultrasound (US), excel at visualizing structural pathology but are either resource-intensive or often limited to morphological assessment. In this work, we present the design and technical validation of a low-cost continuous-wave near-infrared (NIR) dual-mode optical probe for functional monitoring of joint inflammation. Unlike superficial imaging, NIR light penetrates approximately 3–5 cm and is tissue and wavelength dependent, enabling trans-illumination of the synovial volume. The system combines reflectance and transmission geometries to resolve the ambiguity between disease presence and disease severity. To validate the diagnostic logic, we employed mcxyzn Monte Carlo (MC) simulations to model the optical signature of RA progression from early onset to EULAR-OMERACT grade 2 pannus hypertrophy on a simplified finger model, based on several tissue models in the literature and supported by physical measurements on a multilayer silicone phantom and in vivo signal verification on human volunteers. Our results demonstrate a distinct functional dichotomy: reflectance geometry serves as a binary discriminator of synovial turbidity onset, while transmission flux serves as a monotonic proxy for pannus volume, exhibiting a quantifiable signal decay consistent with the Beer–Lambert law. Signal verification on a subject with confirmed RA pathology demonstrated a significant increase in the effective attenuation coefficient (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>µ</mi></mrow><mrow><mi>e</mi><mi>f</mi><mi>f</mi></mrow></msub><mo> </mo><mo>~</mo><mo> </mo><mn>0.59</mn></mrow></semantics></math></inline-formula> mm⁻¹) compared to the healthy baseline (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>µ</mi></mrow><mrow><mi>e</mi><mi>f</mi><mi>f</mi></mrow></msub><mo> </mo><mo>~</mo><mo> </mo><mn>0.47</mn><mo> </mo></mrow></semantics></math></inline-formula> mm⁻¹). Furthermore, simulation analysis revealed a critical “metric inversion” in darker skin phenotypes (Fitzpatrick V–VI), where the standard beam-broadening signature of inflammation is artificially suppressed by epidermal absorption. We conclude that while transmission flux remains a robust grading metric across diverse skin tones, morphological beam-shape metrics are not robust, particularly in high-absorption populations. By targeting the hemodynamic precursors of structural damage, this dual-mode probe design offers a potential pathway for longitudinal, quantitative monitoring of disease activity at the point of care, while the systematic use of the Monte Carlo framework provides insight into the measurement geometry most suitable for a given clinical endpoint, whether that be detecting the presence or severity of rheumatoid arthritis.

Mohammad Hosein Sabzalian, Nurkhat Zhakiyev, Didar Yedilkhan et al.

This paper presents a novel intelligent control strategy for Unmanned Aerial Vehicles (UAVs) using Type-3 fuzzy-logic systems (T3-FLSs). UAVs are widely adopted in diverse fields due to their efficiency and versatility; however, they face significant control challenges under uncertain dynamics and environmental disturbances. Addressing these issues requires control strategies with high adaptability and robustness. T3-FLSs offer superior capabilities in modeling and managing uncertainties, making them well-suited for complex UAV environments. In this study, a robust T3-FLS-based nonlinear control framework is proposed. Novel online adaptation laws are developed to optimize the T3-FLS for dynamic modeling and to identify unknown uncertainty bounds. Using these estimations, a supplementary controller is designed to operate in parallel with the T3-FLS controller, enhancing overall robustness. Theoretical analysis confirms the system’s stability under the proposed adaptation mechanisms. The effectiveness and resilience of the control scheme are validated through extensive simulations, demonstrating strong performance under structured disturbances (modeled as sinusoidal disturbances) stochastic turbulence/measurement noise (modeled as random noise) and unknown nonlinear dynamics.

Omar Mar Cornelio, Barbara Bron Fonseca

This study addresses the challenge of uncertainty, variability, and indeterminacy in smart grid energy management by applying neutrosophic logic as a novel optimization and detection framework. A computational methodology was developed to integrate renewable energy generation, battery storage, and consumption under neutrosophic modeling, comparing its performance with classical, fuzzy, and machine learning approaches. The neutrosophic economic dispatch model enabled improved generation scheduling resilience, optimized battery degradation prediction, and optimized charging cycles. In parallel, a neutrosophic detection mechanism was designed to identify energy losses, anomalous consumption, and potential fraud in near real-time. The case study results demonstrate that the neutrosophic approach significantly reduced operating costs (USD 8,500 versus USD 15,000–20,000 for other models), maximized the economic benefit of actual detections (USD 940, the highest among the tested models), and yielded the most favorable net balance (−USD 7,560). These results indicate that neutrosophic models outperform traditional and fuzzy approaches in both cost efficiency and system reliability.

M. Ben-Ari, A. Pnueli, Z. Manna

Caera L. Grady, Elaine Murtagh, Maïté Verloigne et al.

Abstract Background Communication campaigns within multi-component school-based interventions could improve knowledge and awareness about physical activity (PA) behavior. Guidance to implement such communication campaigns is lacking. This paper presents the co-creation and evaluation processes that led to the development of the COMMUNICATE toolkit, which supports implementers to communicate PA messages. Methods Students and teachers from secondary schools enrolled in the Active School Flag (ASF) program were invited to participate. To provide a nuanced perspective on the communication of PA, ASF program implementers (i.e., coordinating teacher and adolescent peer leaders) and receivers (i.e., staff and students not involved in ASF delivery), together known as co-creators, engaged in three rounds of co-creation workshops to share ideas, provide feedback, and refine the toolkit. Workshop data were collected via activity recording sheets; written raw materials were photographed and later transcribed verbatim to generate a dataset. Inductive thematic analysis was conducted to organize and describe the toolkit components. A multi-stakeholder research steering group (n=7) was established to design, facilitate, and evaluate the co-creation process. The toolkit was refined between rounds of workshops. Throughout the co-creation process, the facilitator reflected after each workshop to improve its’ participatory nature. After the final workshop, co-creators completed a process evaluation questionnaire. Additional consultations with experts were held to bridge the gap in expertise. A logic model was developed to understand the theory of change behind the toolkit. Results Eight teachers and 38 students from four ASF schools participated in the co-creation workshops. All 14 aspects of the process evaluation were mainly positive (86.7-100%). Common reasons for negative responses included co-creators not engaging, too much moving around during workshops, teachers’ involvement, and working with strangers. The final version of the toolkit included resources for program implementers to i) raise awareness about PA and the program, ii) plan the promotion of PA, and iii) develop key communication skills. Conclusions The COMMUNICATE toolkit, informed by multi-stakeholder voices, emphasizes a multi-level, multi-stakeholder approach to communicating PA messages with adolescents in schools. It provides tools and resources for program implementers to improve communication efforts. The COMMUNICATE toolkit could be adapted to other peer-led school-based programs.

Yixuan Qiu, Zhongya Fan, Huiyun Feng et al.

Although remote sensing has become a common tool for monitoring mountainous reservoirs, studies on the detection of phytoplankton community compositions (PCCs) remain insufficient. Based on satellite and field data, we developed a mathematical model incorporating fuzzy logic and probabilistic methods to directly estimate the biomass of seven different phytoplankton species in Huating Lake. Water surface temperature (WST) and chlorophyll-a concentration ([Chl-a]) were selected as input parameters for this model. The WST data were processed using a single-channel algorithm that combined the brightness temperature conversion model and land surface emissivity algorithm. Inversion of [Chl-a] was conducted using an empirical approach to compare the four models developed for the two sensitive reflectance bands. The [Chl-a] values obtained from these models were significantly correlated with the field data (R > 0.8). The optimal model was selected based on validation results. After obtaining the inversion results for the WST and [Chl-a], we applied a fuzzy probabilistic model to determine the PCCs in Huating Lake from 2013 to 2023. A comparison with the measured data confirmed that this method reliably estimated PCC biomass (R > 0.65). However, the modeling accuracy was not particularly high for Bacillariophyta and Euglenophyta with high biomass. We analyzed the spatial and temporal distribution of PCCs in Huating Lake over 10 years from 2013 to 2023 and found that the results were reasonable. The results demonstrate that the fuzzy probabilistic approach offers a novel methodology for estimating the biomass of seven phytoplankton species. This method facilitates the expansion of remote-sensing technology for monitoring PCC changes in mountainous reservoirs and provides scientific data support for understanding algal bloom mechanisms and developing prevention strategies.

M. Fitting

The use of conventional classical logic is misleading for characterizing the behavior of logic programs because a logic program, when queried, will do one of three things: succeed with the query, fail with it, or not respond because it has fallen into infinite backtracking. In [7] Kleene proposed a three-valued logic for use in recursive function theory. The so-called third truth value was really undefined: truth value not determined. This logic is a useful tool in logic-program specification, and in particular, for describing models. (See [11].) Tarski showed that formal languages, like arithmetic, cannot contain their own truth predicate because one could then construct a paradoxical sentence that effectively asserts its own falsehood. Natural languages do allow the use of "is true", so by Tarski's argument a semantics for natural language must leave truth-value gaps: some sentences must fail to have a truth value. In [8] Kripke showed how a model having truth-value gaps, using Kleene's three-valued logic, could be specified. The mechanism he used is a famiUar one in program semantics: consider the least fixed point of a certain monotone operator. But that operator must be defined on a space involving three-valued logic, and for Kripke's application it will not be continuous. We apply techniques similar to Kripke's to logic programs. We associate with each program a monotone operator on a space of three-valued logic interpretations, or better partial interpretations. This space is not a complete lattice, and the operators are not, in general, continuous. But least and other fixed points do exist. These fixed points are shown to provide suitable three-valued program models. They relate closely to the least and greatest fixed points of the operators used in [1]. Because of the extra machinery involved, our treatment allows for a natural consideration of negation, and indeed, of the other prepositional connectives as well. And because of the elaborate structure of fixed points available, we are able to

K. Kunen

Abstract We define a semantics for negation as failure in logic programming. Our semantics may be viewed as a cross between the approaches of Clark [5] and Fitting [7]. As does [7], our semantics corresponds well with real PROLOG in the standard examples used in the literature to illustrate problems with [5]. Also, PROLOG and the common variants of it are sound but not complete for our semantics. Unlike [7], our semantics is constructive, in that the set of supported queries is recursively enumerable. Thus, a complete interpreter exists in theory, although we point out that there are serious difficulties in building one that works well in practice.

Louis Rustenholz, Maximiliano Klemen, Miguel Ángel Carreira-Perpiñán et al.

Automatic static cost analysis infers information about the resources used by programs without actually running them with concrete data, and presents such information as functions of input data sizes. Most of the analysis tools for logic programs (and many for other languages), as CiaoPP, are based on setting up recurrence relations representing (bounds on) the computational cost of predicates, and solving them to find closed-form functions. Such recurrence solving is a bottleneck in current tools: many of the recurrences that arise during the analysis cannot be solved with state-of-the-art solvers, including Computer Algebra Systems (CASs), so that specific methods for different classes of recurrences need to be developed. We address such a challenge by developing a novel, general approach for solving arbitrary, constrained recurrence relations, that uses machine-learning (sparse-linear and symbolic) regression techniques to guess a candidate closed-form function, and a combination of an SMT-solver and a CAS to check if it is actually a solution of the recurrence. Our prototype implementation and its experimental evaluation within the context of the CiaoPP system show quite promising results. Overall, for the considered benchmarks, our approach outperforms state-of-the-art cost analyzers and recurrence solvers, and solves recurrences that cannot be solved by them. Under consideration in Theory and Practice of Logic Programming (TPLP).

Dongmyoung Kim, Taesu Jeon, Insu Paek et al.

To compare and validate wind speed estimation algorithms applied to wind turbines, wind speed estimators were designed in this study, based on two methods presented in the literature, and their performance was validated using the NREL 5MW model. The first method for wind speed estimation involves a three-dimensional (3D) look-up table-based approach, constructed using drive train differential equations. The second method involves applying a continuous–discrete extended Kalman filter. To verify and compare the performance of the algorithms designed using these different methods, feed-forward control algorithms, available power estimation algorithms, and a linear quadratic regulator, based on fuzzy logic (LQRF) control algorithms, were selected and applied as verification means, using the estimated wind speed as the input. Based on the simulation results, the performance of the two methods was compared. The method using drive train differential equations demonstrated superior performance in terms of reductions in the standard deviations of rotor speed and electrical power, as well as in its prediction accuracy for the available power.

Jabal Yasir Nasution, Granita

Rumah produksi berkah bolu merupakan salah satu UMKM yang memproduksi kue bolu di Kota Pekanbaru. Pemilik usaha tersebut kesulitan dalam menentukan jumlah produksi kue karena hanya berdasarkan pada jumlah permintaan yang ada. Berdasarkan permasalahan tersebut, maka dipilihlah metode fuzzy Tsukamoto karena menggunakan penalaran monoton dalam setiap aturannya. Terdapat 4 tahapan yang dipakai dalam perhitungan metode Tsukamoto yaitu fuzzifikasi, inferensi, komposisi/Agregasi dan defuzzyfikasi. Hasil MAPE yang diperoleh dengan metode fuzzy Tsakomoto adalah 6,91% dan tingkat keakuratan sebesar 93,01%, yang memiliki arti bahwa metode fuzzy Tsukamoto sangat baik dalam memprediksi jumlah produksi, sehingga dapat digunakan sebagai sistem untuk mendukung keputusan dalam penentuan jumlah produksi kue bolu di rumah produksi Berkah Bolu.   The Berkah Bolu Production House is one of the Small and Medium Enterprise (SME) that produces bolu cakes in Pekanbaru City. The business owner has difficulty in determining the amount of cake production because it is only based on the number of existing requests. Based on these problems, the Tsukamoto fuzzy method was chosen because it uses monotonous reasoning in each rule. There are 4 stages used in the calculation of the Tsukamoto method, namely fuzzification, inference, composition or aggregation, and defuzzification. The MAPE result obtained by the fuzzy Tsukomoto method is 6.91% and the accuracy level is 93.01%, which means that the fuzzy Tsukamoto method is very good at predicting the amount of production, so it can be used as a decision support system in determining the amount of bolu cake production in the Berkah Bolu Production House.

E. Emerson, Joseph Y. Halpern