Sigmoid Function Research Articles

Empirical models and artificial intelligence for estimating hourly diffuse solar radiation in the state of Alagoas, Northeastern Brazil

Diffuse solar irradiation (HD) data are essential for the design and management of photovoltaic solar systems, biosphere-atmosphere modeling, and other applications. However, HD observations are scarce in several locations, especially in tropical regions. Employing hourly diffuse solar irradiation (HDh) and global solar irradiation (HGh) data collected between 2002─2003 and 2007─2008 in Alagoas State, Northeast Brazil, this study assesses various modeling techniques. Empirical models, including third-degree polynomial, logistic, sigmoidal, and rational functions, were compared with AI methods such as artificial neural networks (ANN), support vector machine (SVM), and adaptive neuro-fuzzy inference system (ANFIS). Additionally, it explores how solarimetric and meteorological variables impact the performance of these models. The empirical models showed similar performance in estimating KDh(=HDh/HGh) (r2 > 0.726, modified Willmott – dm > 0.704, and RMSD < 0.103), with the third-degree polynomial model standing out. The empirical models had difficulty estimating KDh for hourly atmospheric transmittance (KTh) > 0.80, which indicated that they are not able to adequately simulate clear sky conditions, mostly due to surface reflections and clouds at the end of the day. ANN (r2 > 0.718, dm > 0.702, and RMSD < 0.105) showed better precision and accuracy of estimates for a greater number of schemes in relation to SVM and ANFIS (r2 > 0.704, dm > 0.699, RMSD < 0.108) and to empirical models. AI methods were able to represent the complexity of these conditions, with overall performance in estimating KDh superior or equivalent to empirical models. This study underscores the significance of exploring diverse methods for HD estimation, demonstrating promising potential for accurate and reliable estimation of hourly diffuse solar irradiation.

The approximation capabilities of Durrmeyer-type neural network operators

AbstractIn this paper, a new family of neural network (NN) operators is introduced. The idea is to consider a Durrmeyer-type version of the widely studied discrete NN operators by Costarelli and Spigler (Neural Netw 44:101–106, 2013). Such operators are constructed using special density functions generated from suitable sigmoidal functions, while the reconstruction coefficients are based on a convolution between a general kernel function $$\chi $$ χ and the function being reconstructed, f. Here, we investigate their approximation capabilities, establishing both pointwise and uniform convergence theorems for continuous functions. We also provide quantitative estimates for the approximation order thanks to the use of the modulus of continuity of f; this turns out to be strongly influenced by the asymptotic behaviour of the sigmoidal function $$\sigma $$ σ . Our study also shows that the estimates we provide are, under suitable assumptions, the best possible. Finally, $$L^p$$ L p -approximation is also established. At the end of the paper, examples of activation functions are discussed.

Computing with magnetic tunnel junction based sigmoidal activation functions

Computing with magnetic tunnel junction based sigmoidal activation functions

Modeling the Effect of Vegetation Coverage on Unmanned Aerial Vehicles-Based Object Detection: A Study in the Minefield Environment

An important consideration for UAV-based (unmanned aerial vehicle) object detection in the natural environment is vegetation height and foliar cover, which can visually obscure the items a machine learning model is trained to detect. Hence, the accuracy of aerial detection of objects such as surface landmines and UXO (unexploded ordnance) is highly dependent on the height and density of vegetation in a given area. In this study, we develop a model that estimates the detection accuracy (recall) of a YOLOv8 object’s detection implementation as a function of occlusion due to vegetation coverage. To solve this function, we developed an algorithm to extract vegetation height and coverage of the UAV imagery from a digital surface model generated using structure-from-motion (SfM) photogrammetry. We find the relationship between recall and percent occlusion is well modeled by a sigmoid function using the PFM-1 landmine test case. Applying the sigmoid recall-occlusion relationship in conjunction with our vegetation cover algorithm to solve for percent occlusion, we mapped the uncertainty in detection rate due to vegetation in UAV-based SfM orthomosaics in eight different minefield environments. This methodology and model have significant implications for determining the optimal location and time of year for UAV-based object detection tasks and quantifying the uncertainty of deep learning object detection models in the natural environment.

LGC-DBP: the method of DNA-binding protein identification based on PSSM and deep learning.

The recognition of DNA Binding Proteins (DBPs) plays a crucial role in understanding biological functions such as replication, transcription, and repair. Although current sequence-based methods have shown some effectiveness, they often fail to fully utilize the potential of deep learning in capturing complex patterns. This study introduces a novel model, LGC-DBP, which integrates Long Short-Term Memory (LSTM), Gated Inception Convolution, and Improved Channel Attention mechanisms to enhance the prediction of DBPs. Initially, the model transforms protein sequences into Position Specific Scoring Matrices (PSSM), then processed through our deep learning framework. Within this framework, Gated Inception Convolution merges the concepts of gating units with the advantages of Graph Convolutional Network (GCN) and Dilated Convolution, significantly surpassing traditional convolution methods. The Improved Channel Attention mechanism substantially enhances the model's responsiveness and accuracy by shifting from a single input to three inputs and integrating three sigmoid functions along with an additional layer output. These innovative combinations have significantly improved model performance, enabling LGC-DBP to recognize and interpret the complex relationships within DBP features more accurately. The evaluation results show that LGC-DBP achieves an accuracy of 88.26% and a Matthews correlation coefficient of 0.701, both surpassing existing methods. These achievements demonstrate the model's strong capability in integrating and analyzing multi-dimensional data and mark a significant advancement over traditional methods by capturing deeper, nonlinear interactions within the data.

Simplifications and approximations in a single-gene circuit modeling

The absence of detailed knowledge about regulatory interactions makes the use of phenomenological assumptions mandatory in cell biology modeling. Furthermore, the challenges associated with the analysis of these models compel the implementation of mathematical approximations. However, the constraints these methods introduce to biological interpretation are sometimes neglected. Consequently, understanding these restrictions is a very important task for systems biology modeling. In this article, we examine the impact of such simplifications, taking the case of a single-gene autoinhibitory circuit; however, our conclusions are not limited solely to this instance. We demonstrate that models grounded in the same biological assumptions but described at varying levels of detail can lead to different outcomes, that is, different and contradictory phenotypes or behaviors. Indeed, incorporating specific molecular processes like translation and elongation into the model can introduce instabilities and oscillations not seen when these processes are assumed to be instantaneous. Furthermore, incorporating a detailed description of promoter dynamics, usually described by a phenomenological regulatory function, can lead to instability, depending on the cooperative binding mechanism that is acting. Consequently, although the use of a regulating function facilitates model analysis, it may mask relevant aspects of the system’s behavior. In particular, we observe that the two cooperative binding mechanisms, both compatible with the same sigmoidal function, can lead to different phenotypes, such as transcriptional oscillations with different oscillation frequencies.

Research on Sliding Mode Variable Structure Model Reference Adaptive System Speed Identification of Bearingless Induction Motor

To improve the speed observation accuracy of the bearingless induction motor (BL-IM) and achieve its high-performance speed sensorless control, an improved sliding mode variable structure model reference adaptive system speed identification method based on sigmoid function (sigmoid-VS-MRAS) is proposed. Firstly, to overcome the problem of initial values and cumulative errors in the pure integration link of the reference flux-linkage voltage model, the rotor flux-linkage reference voltage model has been improved by using an equivalent integrator instead of the pure integration link. Then, in order to improve the rapidity and robustness of speed identification, the sliding mode variable structure adaptive law is adopted instead of the PI adaptive law. In addition, in order to optimize the sliding mode variable structure adaptive law and overcome the sliding mode chattering problem, a sigmoid function with smooth continuity characteristics is used instead of the sign function. Finally, on the basis of the inverse system decoupling control of a BL-IM, simulation experiments were conducted to verify the sigmoid-VS-MRAS speed identification method. The research results indicate that when the proposed speed identification method is adopted, not only higher identification accuracy and rapidity can be achieved than traditional PI-MRAS methods, but it can also eliminate the problem of high-frequency vibration (with an amplitude of about 3.0 r/min) when using the sign-VS-MRAS method; meanwhile, the steady-state tracking speed with zero deviation can still be maintained after loading.

Domain wall magnetic tunnel junction-based artificial synapses and neurons for all-spin neuromorphic hardware

We report a breakthrough in the hardware implementation of energy-efficient all-spin synapse and neuron devices for highly scalable integrated neuromorphic circuits. Our work demonstrates the successful execution of all-spin synapse and activation function generator using domain wall-magnetic tunnel junctions. By harnessing the synergistic effects of spin-orbit torque and interfacial Dzyaloshinskii-Moriya interaction in selectively etched spin-orbit coupling layers, we achieve a programmable multi-state synaptic device with high reliability. Our first-principles calculations confirm that the reduced atomic distance between 5d and 3d atoms enhances Dzyaloshinskii-Moriya interaction, leading to stable domain wall pinning. Our experimental results, supported by visualizing energy landscapes and theoretical simulations, validate the proposed mechanism. Furthermore, we demonstrate a spin-neuron with a sigmoidal activation function, enabling high operation frequency up to 20 MHz and low energy consumption of 508 fJ/operation. A neuron circuit design with a compact sigmoidal cell area and low power consumption is also presented, along with corroborated experimental implementation. Our findings highlight the great potential of domain wall-magnetic tunnel junctions in the development of all-spin neuromorphic computing hardware, offering exciting possibilities for energy-efficient and scalable neural network architectures.

The [formula omitted]-logistic growth model. Qualitative and quantitative dynamics

The κ-exponential function, representing a generalization of the exponential function, has been firstly introduced in physics, and, then, it has been considered in a noteworthy number of fields because of its ability to take rare events into account. Among the possible applications of this function, one of particular interest is in economics in which rare events may consist in natural disasters, such as earthquakes that reduce the supply of capital, or epidemics or other external shocks influencing the supply of intermediate inputs, human or physical capital. Starting from the κ-exponential function, the κ-logistic function, which is a generalization of the sigmoidal function, can be obtained and used to describe production functions in a unique setting to take into account (1) several shapes usually considered in economics (i.e. concave and non-concave production functions), (2) economies at different development levels, and, (3) the possible occurrence of rare events. In this paper, we investigate the economic growth model as proposed by Böhm and Kaas (2000), wherein the production function utilizes the κ-logistic function. We provide theoretical results confirmed by extensive computational experiments and in line with economic literature showing that a poverty trap may emerge together with fluctuations, multistability and complex dynamics.

Prediction of asphalt concrete energy release rate from Texas Overlay Test using machine learning

This study aimed to apply machine learning (ML) models to predict the energy release rate (ERR) in the Texas Overlay Test (TXOT) for the reflective cracking of asphalt concrete (AC) overlay. The Generalised Finite Element Method model was developed with TXOT experimental results. Subsequently, a TXOT Finite Element database was constructed. Four different cases were formulated, each utilising distinct input variables and the same output variable (i.e. ERR). Five ML models were trained and evaluated for ERR prediction. The model performances were compared across four cases to determine the optimal set of input variables. Shapley Additive exPlanations methods were employed to interpret ML models. The results demonstrated that ML models performed differently across different cases. Artificial Neural Network achieved the highest accuracy in the case which included crack length, crack opening, AC modulus, and sigmoidal function parameters. Therefore, ML models prove to be accurate and reliable for predicting ERR.

Terrestrial solute fingering flow behind subsurface physical barriers during seawater intrusion

Blocking seawater intrusion in coastal aquifers through impervious subsurface physical barriers is a commonly used method, which creates a relatively closed hydrodynamic environment behind the barrier for the transport of land-sourced contaminants. This study investigated the phenomenon of terrestrial solute fingering flow behind subsurface physical barriers for the first time under field-scale hydrological and hydrogeological conditions. A prediction model for the onset of flow instability and the size of subsequent solute fingers was proposed based on the identified effects of these conditions. Once a modified Rayleigh number exceeded 4000, solute fingering flow behind subsurface physical barriers would occur, and the size of individual fingers followed a sigmoidal function growth with increasing Rayleigh number. Solute fingering flow resulted in a series of morphological changes in individual fingers from finger-like to foot-like with decreased salinity. The movement of solute fingers altered the trajectory and residence time of tracer particles, which led to an early particle arrival for those initially located behind. Compared to a no-finger situation, solute fingering flow accelerated the movement of tracer particles out of the suspended barriers with a reduced residence time by 45%. Shortened residence times and changed transport patterns as a result of solute fingering flow may have significant implications for the fate of terrestrial contaminants. Thus, caution should be exercised in the interpretation of field measurements on groundwater contamination in coastal aquifers if solute fingering flow behind subsurface physical barriers is expected, e.g., from the present prediction model.

Privacy-Preserving Breast Cancer Prediction Based on Logistic Regression

Abstract With the increasing strain on today’s healthcare resources, there is a growing demand for pre-diagnosis testing. In response, researchers have suggested diverse machine learning models for disease prediction, among which logistic regression stands out as one of the most effective models. Its objective is to enhance the accuracy and efficiency of pre-diagnosis testing, thereby alleviating the burden on healthcare resources. However, when multiple medical institutions collaborate to train models, the untrusted cloud server may pose a risk of private data leakage, enabling participants to steal data from one another. Existing privacy-preserving methods often suffer from drawbacks such as high communication costs, long training times and lack of security proofs. Therefore, it is imperative to jointly train an excellent model collaboratively and uphold data privacy. In this paper, we develop a highly optimized two-party logistic regression algorithm based on CKKS scheme. The algorithm optimizes ciphertext operations by employing ciphertext segmentation and minimizing the multiplication depth, resulting in time savings. Furthermore, it utilizes least squares to approximate sigmoid functions within specific intervals that cannot be handled by homomorphic encryption. Finally, the proposed algorithm is evaluated on a breast cancer dataset, and simulation experiments demonstrate that the model’s prediction accuracy, after machine learning training, exceeds 96% for two-sided encrypted data.

Vibration characteristics of a cylindrical sandwich shell with an FG auxetic honeycomb core and nanocomposite face layers subjected to supersonic fluid flow

In this paper, the flutter analysis is studied for a cylindrical sandwich shell subjected to external supersonic fluid flow. The sandwich shell consists of a re-entrant auxetic honeycomb (AH) core made of functionally graded materials (FGMs) which is covered with two polymeric face layers enriched with either carbon nanotubes (CNTs), graphene nanoplatelets (GNPs), or graphene oxide powders (GOPs). The volume fraction (percentage) of the ceramic phase in the functionally graded auxetic honeycomb (FGAH) core varies from zero at the inner surface to one at the outer one based on either power-law function (P-FGM), exponential function (E-FGM), or sigmoid function (S-FGM). It is assumed that the nanofillers are distributed uniformly inside the face layers. The first-order shear deformation theory (FSDT) and the piston theory are employed to provide the mathematical models of the shell and the aerodynamic pressure, respectively. The governing equations and boundary conditions are derived via Hamilton’s principle. An exact solution is performed in the circumferential direction via harmonic trigonometric functions (sine and cosine) and an approximate solution is performed in the axial direction utilizing the differential quadrature method (DQM). The natural frequencies and corresponding damping ratios are attained through the presented semi-analytical solution and the impacts of several factors on the natural frequencies and the critical aerodynamic pressure of the shell are examined. These factors the type of the FGM, the material index, and inclined angle of the cells in the FGAH core, the thickness of the FGAH core, the type and mass fraction of the nanofillers in the nanocomposite face layers, and boundary conditions.

Transient Temperature at Tool-Chip Interface during Initial Period of Chip Formation in Orthogonal Cutting of Inconel 718.

Machining nickel-based super alloys such as Inconel 718 generates a high thermal load induced via friction and plastic deformation, causing these alloys to be among most difficult-to-cut materials. Localized heat generation occurring in machining induces high temperature gradients. Experimental techniques for determining cutting tool temperature are challenging due to the small dimensions of the heat source and the chips produced, making it difficult to observe the tool-chip interface. Therefore, theoretical analysis of cutting temperatures is crucial for understanding heat generation and temperature distribution during cutting operations. Periodic heating and cooling occurring during cutting and interruption, respectively, are modeled using a hybrid analytical and finite element (FE) transient thermal model. In addition to identifying a transition distance associated with initial period of chip formation (IPCF) from apparent coefficient of friction results using a sigmoid function, the transition temperature is also identified using the thermal model. The model is validated experimentally by measuring the tool-chip interface temperature using a two-color pyrometer at a specific cutting distance. Due to the cyclic behavior in interrupted cutting, where a steady-state condition may or may not be achieved, transient thermal modeling is required in this case. Input parameters required to identify the heat flux for the transient thermal model are obtained experimentally and the definitions of heat-flux-reducing factors along the cutting path are associated with interruptions and the repeating IPCF. The thermal model consists of two main parts: one is related to identifying the heat flux, and the other part involves the determination of the temperature field within the tool using a partial differential equation (PDE) solved numerically via a 2D finite element method.

Research on Sustainability of Property Insurance Based on LSTM Model

Losses from extreme weather events are increasing in recent years, and climate change will drive premiums higher. At present, the adjustment of insurance policies by insurance companies, as well as the protection of real estate siting and ancient buildings, are the problems that we need to solve now. To explore the circumstances under which insurance companies undergo insurance, this paper identified two severely affected areas and collected monthly insurance claims data from 2010 to 2023. Based on the collected data, an LSTM is established to derive the amount of compensation, and then determine whether to underwrite. The research centers on "how and where to choose a site and whether to build in a certain place", we determined 5 aspects of research in natural conditions, transportation conditions, infrastructure, surrounding environment and economic factors, and selected 17 secondary indicators; firstly, the paper quantified the secondary indicators into 3 parts of proximity, remoteness and Neutral factor 3 parts. Firstly, using EDA and feature engineering &amp; insurance factor quantitative measurement to find out the required insurance factor F. Then using Kmeans cluster analysis to classify the 19 regions, and using Random Forest to find out the weights of the 17 indicators. Secondly, using TOPSIS comprehensive evaluation model to find out the scores of the 19 regions, and finally determining the highest-scoring the "leader" with the highest score is finally determined as the optimal region, For the research of developing building protection model, this paper identifies four aspects of research from basic information, economic contribution, historical contribution and architectural information. Finally, the Word to vector word embedding vector model is used to number the words after the text is transformed; then the LSTM is used to do the text binary classification to get the emotional percentage of each area, and the emotional percentage is used as a new column of indicators; the PCA is used to extract the variance contribution rate of each indicator; then the MinMax variance contribution rate is used to carry out the normalization and the sigmoid function for weighting; finally, use AHP to find out the score, and from the results, it can be seen that the regional conservation model of Yellowstone National Park is the most reasonable.

RELATIONS BETWEEN THE MASS OF THE FRUITS, THE NUMBER OF FRUITS PER PLANT AND THE AGRICULTURAL YIELD IN PEPPER, WITH INCREASING SILICON DOSES

&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Statistical relations between the components of agricultural yield and the yield itself after Si treatment as possible non-destructive methods are important for predicting and monitoring the harvest. &lt;strong&gt;Objective:&lt;/strong&gt; To measure the relationships between the mass of the fruit, the number of fruits per plant and the agricultural yield in pepper, with increasing silicon doses. &lt;strong&gt;Methodology:&lt;/strong&gt; The research was carried out at Quevedo State Technical University, Ecuador in a completely randomized design and three repetitions per treatment. The treatments used were 0, 20, 25 and 30 g potassium silicate plant&lt;sup&gt;-1&lt;/sup&gt;. The variables evaluated were fruit weight, number of fruits per plant and agricultural yield at the time of harvest. Path analysis, multiple and single linear regressions, and nonlinear regressions were performed. &lt;strong&gt;Results:&lt;/strong&gt; The path analysis showed a greater direct effect of the number of fruits per plant with and without the presence of silicon. Fruit weight, number of fruits per plant, and agricultural yield increased linearly with increasing silicon dose. &lt;strong&gt;Implications:&lt;/strong&gt; The best adjustment of fruit weight both in the presence and absence of silicon when it is related to the yield corresponded to a non-linear model, specifically to a Hill sigmoidal function. &lt;strong&gt;Conclusion: &lt;/strong&gt;Through multiple linear regression, it was shown that both yield component variables, i.e. fruit weight and number of fruits per plant, have a greater influence on agricultural yield when fertilized with silicon. &lt;/p&gt;

3D-printed portable device for illicit drug identification based on smartphone-imaging and artificial neural networks

In this manuscript, a 3D-printed analytical device has been successfully developed to classify illicit drugs using smartphone-based colorimetry. Representative compounds of different families, including cocaine, 3,4-methylenedioxy-methamphetamine (MDMA), amphetamine and cathinone derivatives, pyrrolidine cathinones, and 3,4-methylenedioxy cathinones, have been analyzed and classified after appropriate reaction with Marquis, gallic acid, sulfuric acid, Simon and Scott reagents. A picture of the colored products was acquired using a smartphone, and the corrected RGB values were used as input data in the chemometric treatment. ANN using two active layers of nodes (6 nodes in layer 1 and 2 nodes in layer 2) with a sigmoidal transfer function and a minimum strict threshold of 0.50 identified illicit drug samples with a sensitivity higher than 83.4 % and a specificity of 100 % with limits of detection in the microgram range. The 3D printed device can operate connected to a rechargeable lithium-ion cell portable battery, is inexpensive, and requires minimal training. The analytical device has been able to discriminate the analyzed psychoactive substances from cutting and mixing agents, being a useful tool for law enforcement agents to use as a screening method.

Memristor Equivalent Model of an Inductive Wireless Power Transfer System Based on the Sigmoid Function

Memristor Equivalent Model of an Inductive Wireless Power Transfer System Based on the Sigmoid Function

Modeling and Analysis of Central and Peripheral Autonomic Contributions to Decreased Cardiac Vagal Tone Following Myocardial Infarction

Loss of cardiac function following myocardial infarction is accompanied by neural adaptation in baroreflex systems that are compensatory in the short term but then become associated with long-term disease progression. A hallmark of these baroreflex changes is decreased vagal tone. The closed-loop feedback structure of the baroreflexes confounds the source of decreased vagal tone following myocardial infarction (MI). We recognize that in a feedback network causality is distributed in the network interactions, and so explore the effects of MI on the potential contribution to vagal withdrawal of each neural component of our previously published closed-loop baroreflex computational model. The model is composed of differential equations representing the blood flow through various body compartments and sigmoidal functions to represent the saturating firing behavior of neurons. Neuronal adaptations leading to decreased vagal activity could occur in various neuronal groups located centrally in the brainstem. Adaptations could also occur peripherally at the baroreceptors or the heart’s “little brain”, the intrinsic cardiac nervous system. We developed five alternative models to reflect these hypotheses. Of the alternative models, only the model representing adaptation in the baroreceptors predicted both physiological baroreflex function and a decrease in vagus nerve activity. These findings suggest that decreased vagal activity after myocardial ischemia may be due, at least in part, to a decrease in baroreceptor sensitivity. We also link these model predictions with experimental evidence from the literature. NIH U01-HL133360, NIH OT2-OD030534, NSF 1940700, and NSF OAC-1919839. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Treatment Planning Methods for Dose Painting by Numbers Treatment in Gamma Knife Radiosurgery

PURPOSEDose painting radiotherapy delivers a non-uniform dose to tumours to account for heterogeneous radiosensitivity. With recent and ongoing development of Gamma Knife (GK) machines making large-volume brain tumour treatments more practical, it is increasingly feasible to deliver dose painting treatments. The increased prescription complexity means automated treatment planning is greatly beneficial, and the impact of dose painting on SRS plan quality has not yet been studied. This research investigates the plan quality achievable for GK SRS dose painting treatments when using optimisation techniques and automated isocentre placement in treatment planning. METHODS & MATERIALSDose painting prescription functions with varying parameters are applied to convert voxel image intensities to prescriptions for ten sample cases. To study achievable plan quality and optimisation, clinically-placed isocentres are used with each dose painting prescription, and optimised using a semi-infinite linear programming formulation. To study automated isocentre placement, a grassfire sphere-packing algorithm and a clinically available Leksell Gamma Plan isocentre fill algorithm are used. Plan quality for each optimised treatment plan is measured with dose painting SRS metrics. RESULTSOptimisation can be used to find high quality dose painting plans, and plan quality is affected by the dose painting prescription method. Polynomial function prescriptions show better achievable plan quality than sigmoid function prescriptions even with high mean dose boost. Automated isocentre placement is shown as a feasible method for dose painting SRS treatment, and increasing the number of isocentres improves plan quality. The computational solve time for optimisation is within 5 minutes in most cases, which is suitable for clinical planning. CONCLUSIONSThe impact of dose painting prescription method on achievable plan quality is quantified in this study. Optimisation and automated isocentre placement are shown as possible treatment planning methods to obtain high quality plans.