Optimal Function Research Articles

Integral Neuron: A New Concept for Nonlinear Neuron Modeling Using Weight Functions—Creation of XOR Neurons

In the present study, an extension of the idea of dynamic neurons is proposed by replacing the weights with a weight function that is applied simultaneously to all neuron inputs. A new type of artificial neuron called an integral neuron is modeled, in which the total signal is obtained as the integral of the weight function. The integral neuron enhances traditional neurons by allowing the signal shape to be linear and nonlinear. The training of the integral neuron involves finding the parameters of the weight function, where its functional values directly influence the total signal in the neuron’s body. This article presents theoretical and experimental evidence for the applicability and convergence of standard training methods such as gradient descent, Gauss–Newton, and Levenberg–Marquardt in searching for the optimal weight function of an integral neuron. The experimental part of the study demonstrates that a single integral neuron can be trained on the logical XOR function—something that is impossible for single classical neurons due to the linear nature of the summation in their bodies.

Closed trans-intersphincteric fistulotomy: a new modified sphincter-sparing technique for high transsphincteric anal fistula

BackgroundThe main goals of surgery for fistula-in-ano are to completely resolve the condition and maintain optimal anal function. Effective management of the internal opening during and proper postoperative drainage of the intersphincter plane are crucial for achieving successful outcomes. This study evaluated the clinical efficacy of a novel sphincter-sparing technique for treating high transsphincteric anal fistula (HTAF).MethodsThis prospective study included 55 patients with HTAF who underwent closed trans-intersphincteric fistulotomy (CTiF) between July 2021 and April 2022 at our institution. Preoperative anorectal magnetic resonance imaging was performed for all patients. The primary outcome measures assessed the rate of fistula healing while the secondary outcome measures evaluated healing time, Cleveland Clinic Florida fecal incontinence score (CCF-FIS), 11-point visual analog scale (VAS) pain score and postoperative complications.ResultsWe included 55 patients with HTAF in this study. During a mean follow-up period of 9.3 months, CTiF achieved a healing rate of 90.91% (50/55). The mean time to recovery was 7.09 ± 1.94 weeks. Four (7.27%) patients developed postoperative urinary retention. At the 6-month follow-up, the CCF-FIS and VAS score were 0 [(0,0) range, 0–3] and 0 [(0,1); range, 0–4], respectively. Two patients with recurrent HTAF recovered after treatment with a transanal opening of intersphincteric space procedure, and three recovered after seton placement.ConclusionsCTiF is a promising and effective sphincter-sparing technique for treating HTAF. To confirm long-term outcomes, larger sample size prospective randomized controlled trials are required.

Prediction of quaternary SnGeS2As4 monolayer as a promising photocatalyst in water splitting: A DFT study

Abstract The SnGeS2As4 monolayer was studied by first-principles calculations to estimate its potential for used as photocatalyst in water splitting process. The stability of this structure is confirmed based on the analysis of its phonon dispersion and AIDM simulation. The electronic features including density of state (DOS), band structure, and Bader charge were calculated. These data show the role of each constitute element in the formation of covalent π− and σ−bonds, which play important role in stabilizing the buckled honeycomb structure. Besides, the DOS and band structure also provide information regarding to the high light absorption rate α(ω) of 105–105 cm−1. The positions of valence band maximum (VBM) and conduction band minimum (CBM) are suitable for generation of hydrogen and oxygen gases. The SnGeS2As4 monolayer also has optimal work function 5.16 eV for water splitting process and a rather high solar-to-hydrogen efficiency ηSTH = 13.11%. It is worth to note that SnGeS2As4 monolayers with strains ranging from −2% to 6% are still capable to trigger redox reactions in the water splitting process. Moreover, the tensile trains slightly increase the light absorption rates. Such characteristics not only make SnGeS2As4 monolayer a suitable photocatalyst for water splitting but also ensure its performance in wider range of applications

An Entropy Dynamics Approach to Inferring Fractal-Order Complexity in the Electromagnetics of Solids

A fractal-order entropy dynamics model is developed to create a modified form of Maxwell’s time-dependent electromagnetic equations. The approach uses an information-theoretic method by combining Shannon’s entropy with fractional moment constraints in time and space. Optimization of the cost function leads to a time-dependent Bayesian posterior density that is used to homogenize the electromagnetic fields. Self-consistency between maximizing entropy, inference of Bayesian posterior densities, and a fractal-order version of Maxwell’s equations are developed. We first give a set of relationships for fractal derivative definitions and their relationship to divergence, curl, and Laplacian operators. The fractal-order entropy dynamic framework is then introduced to infer the Bayesian posterior and its application to modeling homogenized electromagnetic fields in solids. The results provide a methodology to help understand complexity from limited electromagnetic data using maximum entropy by formulating a fractal form of Maxwell’s electromagnetic equations.

Asynchronous parallel reinforcement learning for optimizing propulsive performance in fin ray control

AbstractFish fin rays constitute a sophisticated control system for ray-finned fish, facilitating versatile locomotion within complex fluid environments. Despite extensive research on the kinematics and hydrodynamics of fish locomotion, the intricate control strategies in fin-ray actuation remain largely unexplored. While deep reinforcement learning (DRL) has demonstrated potential in managing complex nonlinear dynamics; its trial-and-error nature limits its application to problems involving computationally demanding environmental interactions. This study introduces a cutting-edge off-policy DRL algorithm, interacting with a fluid–structure interaction (FSI) environment to acquire intricate fin-ray control strategies tailored for various propulsive performance objectives. To enhance training efficiency and enable scalable parallelism, an innovative asynchronous parallel training (APT) strategy is proposed, which fully decouples FSI environment interactions and policy/value network optimization. The results demonstrated the success of the proposed method in discovering optimal complex policies for fin-ray actuation control, resulting in a superior propulsive performance compared to the optimal sinusoidal actuation function identified through a parametric grid search. The merit and effectiveness of the APT approach are also showcased through comprehensive comparison with conventional DRL training strategies in numerical experiments of controlling nonlinear dynamics.

Is gratitude science inclusive? A scoping review on gratitude in individuals with diverse needs and exceptionalities

AbstractPast meta‐analytic, systematic and narrative reviews have painted a highly optimistic backdrop on the psychological, physical and social rewards associated with gratitude. However, as most of these reviews focused on how gratitude facilitates optimal psychological functioning in typically developing samples, question remains on the extent to which these findings are generalizable to individuals with diverse special needs or disabilities. This scoping review aims to map scholarly evidence on the psychological benefits of gratitude in individuals with various special needs and exceptionalities. Most studies relied on the dispositional gratitude model (~59%) and cross‐sectional designs (~75%) in investigating gratitude in people with diverse needs. Majority of empirical investigations also focused on studying gratitude in Western cultural contexts such as the United States. Further, prior studies showed that gratitude increased positively valenced well‐being outcomes. Future theoretical implications and scholarly directions are charted.

Prenatal Hemoglobin Concentration and Long-Term Child Neurocognitive Development.

Anemia in pregnancy, defined by a hemoglobin level (Hb) of less than 110 g/L, contributes to infant mortality and morbidity in sub-Saharan Africa. Maternal Hb changes physiologically and pathologically during pregnancy. However, the impact of these changes on long-term child neurocognitive function is unknown. This study therefore investigates the association between Hb at specific antenatal care visits and prenatal Hb trajectories during pregnancy and long-term child neurocognitive function. We analyzed data from a prospective cohort study that included 6-year-old singleton children born to women enrolled before 29 weeks of gestation into an antimalarial drug clinical trial. Hemoglobin level was analyzed from venous blood collected at least twice during pregnancy and at delivery. We used group-based trajectory modeling to identify distinct prenatal Hb trajectories. In total, 478 children (75.1% of eligible children) had assessment of cognitive and motor functions at 6 years of age. Three distinct Hb trajectories were identified: persistently anemic (Hb <110 g/L throughout the second and third trimesters), anemic to nonanemic (Hb <110 g/L at second trimester with increasing Hb toward the third trimester to Hb ≥110 g/L), and persistently nonanemic (Hb ≥110 g/L throughout the second and third trimesters). Children of women in the persistently anemic and anemic-to-nonanemic groups had significantly lower neurocognitive scores than children of women in the persistently nonanemic group (β = -6.8, 95% CI: -11.7 to -1.8; and β = -6.3, 95% CI: -10.4 to -2.2, respectively). The study shows that maintaining an elevation of Hb at or above 110 g/L from the second to third trimester of pregnancy may be associated with optimal long-term child neurocognitive function.

A retinex inspired deep image prior model for despeckling and deblurring of aerial and satellite images using proximal gradient method

ABSTRACT Unsupervised learning models, particularly in the remote sensing domain, have gained significant attention in recent years. Various degradations in the satellite images, primarily occurring during acquisition, pose a substantial hurdle in obtaining reliable ground truth and extensive training data. The Deep Image Prior model (DIP) addresses these issues by performing restoration tasks using a single image, relying on the implicit regularization inherent in the network architecture. In this paper, we propose a novel approach, integrating the DIP model within the retinex framework to restore aerial and satellite images from the Gamma distributed speckles and linear shift-invariant Gaussian blur along with contrast enhancement using the alternating proximal gradient descent ascent (PGDA) method. Our proposed methodology combines implicit regularization with explicit total variational (TV) regularization, incorporating automated estimation of local regularization parameters. The data-fidelity component in the optimization function is formulated using the Bayesian Maximum A posteriori (MAP) estimate, assuming the speckles follow the Gamma distribution. Demonstration of despeckling and deblurring alone and in addition as a combined task is carried out on aerial and Synthetic Aperture Radar (SAR) images with different resolutions and polarization from various sources. Results obtained are compared with various state-of-the-art despeckling and deblurring models using distinct image quality metrics such as Equivalent Number of Looks (ENL), Contrast to Noise Ratio (CNR), Edge Preserving Index (EPI), Entropy, Global Contrast Factor (GCF), Natural Image Quality Evaluator (NIQE), Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) and Bradley-Terry (B-T) score based on the various factors. The quality of restored images depicted superior performance of the proposed method over the existing models under study.

Prosthetic management in mayer-rokitansky-küster-hauser (MRKH) Syndrome: A case report highlighting vaginal reconstruction and its impact on quality of life

Mayer-Rokitansky-Küster-Hauser (MRKH) syndrome, a congenital condition affecting 1 in 4,500 female births, is marked by the absence or underdevelopment of the uterus and upper vagina. Vaginal reconstruction is vital for sexual function, with prosthetic stents playing a key role in maintaining neovaginal patency.Femal patient with MRKH syndrome were treated using custom-made hollow vaginal stents following the Abbe-McIndoe technique. Stents were fabricated from heat-cure acrylic resin based on MRI measurements, ensuring optimal fit and function. Follow-up confirmed maintained vaginal patency. This case report highlights the critical role of prosthodontists in managing MRKH syndrome. Custom prosthetic stent provided excellent outcomes, though challenges with retention and patient compliance was noted, underscoring the need for continued innovation. Personalized prosthetic vaginal stents are effective in managing MRKH syndrome, improving patient quality of life. These findings support their use as a standard treatment option.

Trends in the Evolution of the European City

The article examines the relevant trends in the development of a modern European city using socio-cultural methodology and the systematic approach. The study demonstrates that the evolution of the European urban space is now taking place holistically, affecting the unity of the social, natural and individual dimensions of urban life. The proposed system of trends in the evolution of the European city is considered in the article as an interdependent triad of creativity, smart development and inclusivity and at the same time an actual standard in the ongoing theoretical discussions about the “good city” and their practical applications. The foundation of the movement towards the optimal functioning of the European city rests on the creatively formulated goals of urbanisation. It is revealed that without creativity, neither movement towards sustainable urban development nor the productive evolution of inclusive urban strategies is possible. It is established that at this stage of the formation of the information society in Europe, it is the expansion of the inclusiveness of urban living spaces that is the most difficult problem to solve, both because of its complexity and the lack of a consensual theoretical foundation for its vision. It is demonstrated that theoretical research of this kind is largely positively correlated with the strategies recorded in official EU documents, as well as the participation of supranational structures in financing urbanisation programmes. In the second quarter of the XXI century in order to optimise this problematic urban scenario it will be necessary to adopt and then implement some absolutely non-trivial strategies, the content of which is not yet completely obvious.

Variance-based wave function optimization within the unrestricted doubly occupied configuration interaction framework: A half-projection treatment.

The energy-variance-based optimization procedures have proven to be useful tools to describe N-electron spectra. However, the resulting wave functions usually present spin-contaminant contributions. The goal of this work is to reduce the spin contamination of the results arising from the unrestricted doubly occupied configuration interaction method in its energy variance minimization version [Alcoba et al., J. Chem. Phys. 160, 164107 (2024)]. We propose to incorporate the half-projection technique, which allows removing the spin components with even or odd spin quantum number of an approximate N-electron wave function, into the framework of the unrestricted doubly occupied configuration interaction treatment. This implementation can be carried out following several possible ways, whose results are analyzed in detail, in order to show the behavior of each procedure. Numerical determinations performed on selected strongly correlated N-electron systems, in ground and excited states, allow us to assess the most suitable procedure.

Research on Function Optimization of Electric Vehicle Charging Stations Based on User Demand Analysis: An Empirical Study Using the Kano Model and AHP Method

With the widespread adoption of electric vehicles (EVs), the demand for reliable and user-friendly charging infrastructure has increased significantly. However, user dissatisfaction with public EV charging stations has also intensified, and the level of satisfaction with charging stations directly influences the development of the EV market. This study aimed to identify and prioritize user needs for EV charging stations to improve their design and functionality, ultimately enhancing user satisfaction and effectively promoting the sustainable development of the EV market. Using the KJ method, this study identified 23 key user needs and categorized them into must-be, one-dimensional, attractive, and indifferent requirements using the Kano model. The analytic hierarchy process (AHP) was subsequently applied to rank these requirements by their importance. The results indicate that, in the optimization of charging station functionality, the most critical user requirements include C1 charging gun stability (0.3176), C2 system stability (0.2822), C7 safety performance (0.0885), C15 payment convenience (0.0648), and C8 accurate feedback on charging station status (0.0501). This study provides valuable insights for designers and developers, offering a user-centered approach to optimizing public EV charging stations and improving the overall charging experience.

Optimized Evaluations of Irrigation Profits and Balance of Irrigation Water Demand and Supply Under Climate Change

Optimizing irrigation profit is essential for enhancing agricultural productivity and ensuring sustainable water resource management, especially under the uncertainties caused by climate variability. It signifies that maximizing irrigation profits has become the primary consideration of current agricultural irrigation systems. By discussing the change in irrigation profits with irrigation fraction , which represents the proportion of irrigated farmland per unit area of cultivated land, a new method of qualitative diagnosis of irrigation supply and demand balance was proposed by means of the optimal irrigation fraction corresponding to the objective function and the correlation coefficient between irrigation water demand anomalies and irrigation water withdrawal anomalies (). It will serve as a new attempt to optimize irrigation profits. We argue that the optimal objective function represents the state of equilibrium of irrigation supply and demand. This study proposes a reverse optimization idea, which includes two comparison methods, to estimate the comprehensively optimal irrigation fraction that maximizes irrigation profits and irrigation profit per unit area while minimizing risks associated with climatic anomalies. By demonstrating that performs exceptionally well in meeting the objectives of maximizing irrigation profits and minimizing risks across various climatic scenarios, we suggest a robust method for optimizing irrigation profits, which simply calculates because is the function of . Since could capture the optimal solution to the greatest extent. It can serve as a simple and effective solution to determine the optimal objective function and diagnose the state of balance between irrigation water demand and supply. This method offers practical implications for water resource management and agricultural planning in the face of climate change.

Pose-Invariant Face Recognition Using Optimized Hybrid Yolo Algorithm from Image

One application of computer vision is face recognition, which essentially involves the identification of visual patterns. Face recognition is a tool that we often employ for multimedia management, smart card applications, justice reform, and security. The goal of a face recognition system is to automatically identify faces in any image or video using a computer vision domain. There are several methods for the detection of faces from the video; still, the inaccurate detection and computational complexity degrades the recognition precision. Hence, an optimized hybrid deep learning is introduced for the recognition of pose-invariant faces from the image. The pose-invariant face recognition is employed using the proposed ResNet-152 integrated YOLO (Res-YOLONet), wherein the ResNet-152 and YOLOv5 are hybridized together to enhance the recognition accuracy with minimal computational complexity. Besides, the loss function optimization is devised using the proposed Enhanced Fennec Fox (EnFF) algorithm. The proposed EnFF algorithm is designed by integrating the adaptive weighting strategy within the conventional Fennec Fox algorithm for acquiring the global best solution. The loss function optimization using the EnFF enhances the recognition accuracy. The assessment of the proposed EnFF_Res-YOLONet based on Accuracy, Precision, Recall, and Specificity acquires the values of 96%, 94%, 97%, and 96.6%, respectively.

HybridGT: An Integration of Graph Transformer and LSTM for Effective Hyperspectral Band Selection

ABSTRACT Hyperspectral imagery has a high-dimensional curse due to numerous spectral bands. Band selection (BS) is crucial for efficiently reducing dimensionality, retaining only essential bands containing valuable information. However, deep learning-based techniques have gained more attention through trained networks for band selection. Recently, graph-based learning has been extensively used in hyperspectral imagery, revealing intrinsic data relationships. This article presents a novel hybrid approach for hyperspectral band selection, addressing the curse of dimensionality in hyperspectral imagery (HSI). Integrating Long Short Term Memory (LSTM) and Graph Transformer (GT), the method employs Bi-dimensional Empirical Mode Decomposition (BEMD) for spatial data enhancement. Using transfer learning, we explore a ResNet-50 deep network to identify optimal intrinsic mode functions (IMFs). The final band subset will be obtained by concatenating the features extracted from the graph transformer and LSTM networks from selected IMFs and residual IMF, respectively. The proposed HybridGT-BS technique surpasses state-of-the-art methods in classification accuracy across three well-known HSI datasets – IP-Indian Pines, SA-Salinas, and PU-PaviaU. With the support of experimental results, the proposed technique significantly outperforms the classification accuracy with the best bands of the HSIs.

Predicting Diameter Distributions in Mixed Forests in Southern Mexico

Understanding the diameter structure of a stand is crucial for making informed decisions regarding silviculture and forest management. This is achieved by collecting forest inventory data and applying them to probability density functions. The objective of this study was to identify the optimal probability density function for describing the diameter distribution in a mixed temperate forest in Oaxaca, Mexico. Circular sampling plots of 1000 m² were established, and the diameter at breast height (DBH) and total height of 16,863 trees of the 25 most important timber species were measured. Four probability density functions (Weibull 2P, Log-Normal, Log-Logistic, and Burr) were fitted by using maximum likelihood approach for different silvicultural treatments. The predictive ability of the functions was evaluated using bias, root mean square error, Kolmogorov-Smirnov statistic and graphical analyses. In terms of accuracy and parsimony, the Burr function demonstrated the best performance, followed by the Log-Normal and Log-Logistic functions. Consequently, the Burr function is recommended for modeling the diameter distribution of stands under different treatments in the study site.

Awareness of Phonetic Considerations in the Construction of Removable Dental Prostheses Among Dental Students: A Cross-Sectional Survey

ABSTRACT Background: The role of phonetics in the construction of removable dental prostheses is critical for ensuring patient satisfaction and optimal functionality. This study aims to evaluate the awareness and understanding of phonetic considerations among dental students during the fabrication of removable prostheses. Materials and Methods: A questionnaire-based cross-sectional study was conducted among 150 dental students. The questionnaire comprised 20 items assessing students’ knowledge, attitudes, and practices regarding the role of phonetics in removable dental prostheses. Data were analyzed using descriptive statistics and Chi-square tests to determine the significance of differences among various groups. Results: Out of 150 respondents, 120 (80%) were aware of the importance of phonetics in the construction of removable dental prostheses. Among them, 95 (63.3%) had received formal training on this topic, while 25 (16.7%) had learned through clinical experience. The majority (85%) of students recognized the impact of phonetic issues on patient comfort and satisfaction. However, only 60% of students routinely incorporated phonetic assessments during prosthesis construction. The Chi-square test revealed a significant difference in awareness levels between students in different years of study (P &lt; 0.05). Conclusion: This study highlights a moderate level of awareness among dental students regarding the role of phonetics in removable dental prostheses. While most students understand its importance, there is a gap in practical application, indicating a need for enhanced curriculum and training focused on phonetic considerations.

A robust and computationally efficient guided wave tomography of pipes

ABSTRACT Guided wave ultrasonic tomography of pipe usually uses the helical modes propagated between parallel transducer arrays to compensate for the missing incident angle . Nevertheless, separating mixed signals from various helical modes during inversion is challenging, and this method necessitates multiple replications of the two-dimensional unfolding area , leading to high computational costs. It also faces practical problems such as environmental noise, transducer differences, and poor coupling . To address these problems, this paper proposes a finite difference method that utilizes helical mode information without multiple replications or the need to separate signals. Additionally, an objective function based on zero-delay cross-correlation is employed to mitigate the effects of noise, transducer inconsistencies, and coupling issues that distort signal amplitudes. The proposed method reduces the inversion space of high-order helical modes of the pipeline to the area of a two-dimensional unfolding, and improves the reliability and accuracy of inversion through objective function optimisation. In numerical simulations, the algorithm achieves thickness loss imaging with errors less than 0.1 mm and demonstrates good performance in experimental tests .

An intelligent multi-element fault diagnosis method of rolling bearings considering damage degrees and sensor abnormity under small samples

Aiming at the intelligent fault diagnosis problem of rolling bearings, a novel diagnosis method considering damage degrees and sensor abnormity under small samples is proposed. A complex fault mode simulation scheme with a total of 18 states is designed for rolling bearings, including a single element fault, double elements fault, and all elements fault with damage degrees of slight and heavy and the loose threaded connection of the used sensor. The variational mode decomposition (VMD) is used to decompose the original vibration signals and reconstruct the denoised signals, the reconstructed signals are converted into the grayscale images, and then processed by local binary pattern (LBP) to enhance the image texture features. Under small samples, an improved deep convolutional generative adversarial network (DCGAN) through upsampling, activation function optimization, Dropout addition and model architecture adjustment is used to expand the grayscale texture image (GTI) samples. The improved DCGAN converges the fastest in all states, and the final MMD values are all below 0.5. For the different sample expansion ratios, the residual neural network (ResNet) as the fault diagnosis model is used to verify the effectiveness of DCGAN sample expansion method in improving the accuracy of fault diagnosis. The results show when the original number of samples is 100, the optimal expansion ratio is 1:1. And the fault diagnosis accuracy of ResNet with DCGAN sample expansion is increased by 6.81% from 85.97 to 92.78%, which proves that the proposed method can not only effectively distinguish the fault modes from a single element to all elements with different damage degrees of rolling bearings, but also identify the sensor abnormity with a high accuracy. This work provides an effective way for the intelligent diagnosis of complex fault modes of rolling bearings under small samples.

Mobile phone based laser scanning as a low-cost alternative for multidisciplinary data collection

Airborne and terrestrial laser scanners have traditionally been used as specialised toolsets for three-dimensional scene capture in engineering, providing highly accurate measurements with increasingly minimal human interaction. However, commercial or engineering-grade scanning instruments remain expensive and sensitive, requiring costly routine calibrations to ensure their optimum functionality. The recent inclusion of laser scanning sensors by mobile phone corporations such as Apple Computer Inc. is now analogous to the integration of Global Navigation Satellite Systems (GNSS) and cameras into smartphones as seen decades ago. Likely, these initial efforts to include the scanning sensor in mobile phones will see rapid improvements in the application and accuracy of the sensor to serve the growing need for scanning applications for transdisciplinary users. However, there is a limited amount of literature that benchmarks the emerging and low-cost scanning sensors to existing commercial ones to inform practice, thus prompting a need for researchers to evaluate and provide scientific evidence that can inform multidisciplinary scanning. It was noted that there was some absolute positional shift and scan drift in the iPhone Light Detection and Ranging (LiDAR) data. The researchers therefore investigated the extent to which the accuracy of laser scanning tools available within the iPhone 12 Pro compared to engineering-grade laser scanners. Outcomes from the study showed that iPhone scanners can deliver the required models, despite being unstable in dynamic environments when pitched against engineering-grade LiDAR scanners. The research recommends that stabilisers, such as stabilising gimbals or enhanced GNSS receivers, be used in practice to achieve improved accuracy from the mobile phone LiDAR.