Decision Function Research Articles

Individual Alpha Frequency Contributes to the Precision of Human Visual Processing.

Brain oscillatory activity within the alpha band has been associated with a wide range of processes encompassing perception, memory, decision-making, and overall cognitive functioning. Individual alpha frequency (IAF) is a specific parameter accounting for the mean velocity of the alpha cycling activity, conventionally ranging between ∼7 and ∼13 Hz. One influential hypothesis has proposed a fundamental role of this cycling activity in the segmentation of sensory input and in the regulation of the speed of sensory processing, with faster alpha oscillations resulting in greater temporal resolution and more refined perceptual experience. However, although several recent theoretical and empirical studies would support this account, contradictory evidence suggests caution and more systematic approaches in the assessment and interpretation of this hypothesis. For example, it remains to be explored to what degree IAF shapes perceptual outcomes. In the present study, we investigated whether inter-individual differences in bias-free visual contrast detection threshold in a large sample of individuals in the general population (n = 122) could be explained by inter-individual differences in alpha pace. Our results show that the contrast needed to correctly identify target stimuli (individual perceptual threshold) is associated with alpha peak frequency (not amplitude). Specifically, individuals who require reduced contrast show higher IAF than individuals requiring higher contrasts. This suggests that inter-individual differences in alpha frequency contribute to performance variability in low-level perceptual tasks, supporting the hypothesis that IAF underlies a fundamental temporal sampling mechanism that shapes visual objective performance, with higher frequencies promoting enhanced sensory evidence per time unit.

Artificial neural networks to solve dynamic programming problems: A bias-corrected Monte Carlo operator

Artificial Neural Networks (ANNs) are powerful tools that can solve dynamic programming problems arising in economics. In this context, estimating ANN parameters involves minimizing a loss function based on the model's stochastic functional equations. In general, the expectations appearing in the loss function admit no closed-form solution, so numerical approximation techniques must be used. In this paper, I analyze a bias-corrected Monte Carlo operator (bc-MC) that approximates expectations by Monte Carlo. I show that the bc-MC operator is a generalization of the all-in-one expectation operator, already proposed in the literature. I demonstrate that, under some conditions on the primitives of the economic model, the bc-MC operator is the unbiased estimator of the loss function with the minimum variance. I propose a method to optimally set the hyperparameters defining the bc-MC operator, and illustrate the findings numerically with well-known economic models. I also demonstrate that the bc-MC operator can scale to high-dimensional models. With just approximately a minute of computing time, I find a global solution to an economic model with a kink in the decision function and more than 100 dimensions.

Dynamic Projection Method of Electronic Navigational Charts for Polar Navigation

Electronic navigational charts (ENCs) are geospatial databases compiled in strict accordance with the technical specifications of the International Hydrographic Organization (IHO). Electronic Chart Display and Information System (ECDIS) is a Geographic Information System (GIS) operated by ENCs for real-time navigation at sea, which is one of the key technologies for intelligent ships to realize autonomous navigation, intelligent decision-making, and other functions. Facing the urgent demand for high-precision and real-time nautical chart products for polar navigation under the new situation, the projection of ENCs for polar navigation is systematically analyzed in this paper. Based on the theory of complex functions, we derive direct transformations of Mercator projection, polar Gauss-Krüger projection, and polar stereographic projection. A rational set of dynamic projection options oriented towards polar navigation is proposed with reference to existing specifications for the compilation of the ENCs. From the perspective of nautical users, rather than the GIS expert or professional cartographer, an ENCs visualization idea based on multithread-double buffering is integrated into Polar Region Electronic Navigational Charts software, which effectively solves the problem of large projection distortion in polar navigation applications. Taking the CGCS2000 reference ellipsoid as an example, the numerical analysis shows that the length distortion of the Mercator projection is less than 10% in the region up to 74°, but it is more than 80% at very high latitudes. The maximum distortion of the polar Gauss-Krüger projection does not exceed 10%. The degree of distortion of the polar stereographic projection is less than 1% above 79°. In addition, the computational errors of the direct conversion formulas do not exceed 10−9 m throughout the Arctic range. From the point of view of the computational efficiency of the direct conversion model, it takes no more than 0.1 s to compute nearly 8 million points at 1′×1′ resolution, which fully meets the demand for real-time nautical chart products under information technology conditions.

Contrasting Approaches to Managing the Debate on Same-Sex Blessing and Same-Sex Marriage in New Zealand and Australia: Applying Insights from Jungian Psychological Type Theory

Abstract In October 2022 the Church of England commissioned an examination of the impact of the debate on same-sex blessing and same-sex marriage within other Anglican Churches. The examination involved a literature search, an original survey among key informers and a general internet search. This paper draws on the general internet search to contrast the impacts in New Zealand and Australia. Drawing on Jungian psychological type theory, this analysis employs the contrasting decision-making functions of feeling (concerned with subjective interpersonal values) and thinking (concerned with objective logical analysis). The data suggest that the feeling approach dominant in New Zealand, which prioritized offering space and time for those of differing opinions to meet together, reported more positive outcomes than the thinking approach dominant in Australia, which gave greater priority to adversarial debate.

Choice impulsivity after repeated social stress is associated with increased perineuronal nets in the medial prefrontal cortex

Repeated stress can predispose to substance abuse. However, behavioral and neurobiological adaptations that link stress to substance abuse remain unclear. This study investigates whether intermittent social defeat (ISD), a stress protocol that promotes drug-seeking behavior, alters intertemporal decision-making and cortical inhibitory function in the medial prefrontal cortex (mPFC). Male long evans rats were trained in a delay discounting task (DDT) where rats make a choice between a fast (1 s) small reward (1 sugar pellet) and a large reward (3 sugar pellets) that comes with a time delay (10 s or 20 s). A decreased preference for delayed rewards was used as an index of choice impulsivity. Rats were exposed to ISD and tested in the DDT 24 h after each stress episode, and one- and two-weeks after the last stress episode. Immunohistochemistry was performed in rat’s brains to evaluate perineuronal nets (PNNs) and parvalbumin GABA interneurons (PV) labeling as markers of inhibitory function in mPFC. ISD significantly decreased the preference for delayed large rewards in low impulsive, but not high impulsive, animals. ISD also increased the density of PNNs in the mPFC. These results suggest that increased choice impulsivity and cortical inhibition predispose animals to seek out rewards after stress.

A Fuzzy Logic-Based Automobile Fault Detection System Using Mamdani Algorithm

Due to advancement and complexity of modern automobiles, fault detection has gone beyond manual or trial by error methods. The fault detection technologies in automotive industry is used to identify any potential or already existing fault in automobiles. Faults in automobiles are usually mechanical or electrical faults that may include airbag control unit, radiator, gearbox, transmission control unit, tyre pressure, brakes, air conditioner, cylinder casket, alternator, hubs malfunctions etc. Each fault has a specific or related sign and symptoms. There are several methods of fault detections in automobiles like the binary logic technique, the fuzzy logic method technique and artificial intelligence technique with different algorithms. In this research work, we employed a fuzzy logic based technique that uses a Mamdani Algorithm which presented a better fault detection mechanism. Mamdani’s algorithm was proposed by Ebrahim Mamdani as a fuzzy inference method which has a rule-bases that are more intuitive and easier to analyse and implement. Mamdani’s algorithm produces fuzzy sets that originate from fuzzy inference system’s output membership function for decision making. This research work is a web-based technology that was implemented using JavaScript, JQuery and SQL server, ASP.Net, Bootstrap 3.5 and CSS. The output of the system showed a greater improvement from other existing methods of fault detections in automobiles.

Optimal regimes for algorithm-assisted human decision-making

Summary We consider optimal regimes for algorithm-assisted human decision-making. Such regimes are decision functions of measured pre-treatment variables and, by leveraging natural treatment values, enjoy a superoptimality property whereby they are guaranteed to outperform conventional optimal regimes. When there is unmeasured confounding, the benefit of using superoptimal regimes can be considerable. When there is no unmeasured confounding, superoptimal regimes are identical to conventional optimal regimes. Furthermore, identification of the expected outcome under superoptimal regimes in nonexperimental studies requires the same assumptions as identification of value functions under conventional optimal regimes when the treatment is binary. To illustrate the utility of superoptimal regimes, we derive identification and estimation results in a common instrumental variable setting. We use these derivations to analyse examples from the optimal regimes literature, including a case study of the effect of prompt intensive care treatment on survival.

Light electric vehicle sharing systems: Functional design of a comprehensive decision making solution

The functional design of a decision support system for a state-of-the-art vehicle sharing system (VSS) inevitably includes the selection or definition of methodologies for planning and managing the observed VSS. In this paper, our focus is on an innovative VSS that utilizes light electric vehicles. Our goal is to specify the functions of a comprehensive decision making solution for such a VSS through a qualitative and systematic literature and system analysis. To achieve this goal, we rely on the holistic VSS management framework developed in our previous research. As a result of our study, we identify possible solution methodologies or algorithms for implementing functions of the designed decision making solution, and propose the necessary adjustments for their application in the case of light electric vehicles, or note any research gap. With these findings, we provide justified guidance to practitioners in creating these solutions, with the aim of reducing development time.

Examining the relationships between cognition and auditory hallucinations: A systematic review.

Auditory hallucinations (hearing voices) have been associated with a range of altered cognitive functions, pertaining to signal detection, source-monitoring, memory, inhibition and language processes. Yet, empirical results are inconsistent. Despite this, several theoretical models of auditory hallucinations persist, alongside increasing emphasis on the utility of a multidimensional framework. Thus, clarification of current evidence across the broad scope of proposed mechanisms is warranted. A systematic search of the Web of Science, PubMed and Scopus databases was conducted. Records were screened to confirm the use of an objective behavioural cognitive task, and valid measurement of hallucinations specific to the auditory modality. Auditory hallucinations were primarily associated with difficulties in perceptual decision-making (i.e. reduced sensitivity/accuracy for signal-noise discrimination; liberal responding to ambiguity), source-monitoring (i.e. self-other and temporal context confusion), working memory and language function (i.e. reduced verbal fluency). Mixed or limited support was observed for perceptual feature discrimination, imagery vividness/illusion susceptibility, source-monitoring for stimulus form and spatial context, recognition and recall memory, executive functions (e.g. attention, inhibition), emotion processing and language comprehension/hemispheric organisation. Findings were considered within predictive coding and self-monitoring frameworks. Of concern was the portion of studies which - despite offering auditory-hallucination-specific aims and inferences - employed modality-general measures, and/or diagnostic-based contrasts with psychologically healthy individuals. This review highlights disparities within the literature between theoretical conceptualisations of auditory hallucinations and the body of rigorous empirical evidence supporting such inferences. Future cognitive investigations, beyond the schizophrenia-spectrum, which explicitly define and measure the timeframe and sensory modality of hallucinations, are recommended.

MultiTASC++: A continuously adaptive scheduler for edge-based multi-device cascade inference

Cascade systems, consisting of a lightweight model processing all samples and a heavier, high accuracy model refining challenging samples, have become a widely-adopted distributed inference approach to achieving high accuracy and maintaining a low computational burden for mobile and IoT devices. As intelligent indoor environments, like smart homes, continue to expand, a new scenario emerges, the multi-device cascade. In this setting, multiple diverse devices simultaneously utilize a shared heavy model hosted on a server, often situated within or close to the consumer environment. This work introduces MultiTASC++, a continuously adaptive multi-tenancy-aware scheduler that dynamically controls the forwarding decision functions of devices to optimize system throughput while maintaining high accuracy and low latency. Through extensive experimentation in diverse device environments and with varying server-side models, we demonstrate the scheduler's efficacy in consistently maintaining a targeted satisfaction rate while providing the highest available accuracy across different device tiers and workloads of up to 100 devices. This demonstrates its scalability and efficiency in addressing the unique challenges of collaborative DNN inference in dynamic and diverse IoT environments.

Forecasting peak electric load: Robust support vector regression with smooth nonconvex ϵ‐insensitive loss

AbstractPeak power load forecasting is a key part of the commercial operation of the energy industry. Although various load forecasting methods and technologies have been put forward and tested in practice, the growing subject of tolerance for abnormal accidents is to develop robust peak load forecasting models. In this paper, we propose a robust smooth non‐convex support vector regression method, which improves the robustness of the model by adjusting adaptive control loss values and adaptive robust parameters and by reducing the negative impact of outliers or noise on the decision function. A concave‐convex programming algorithm is used to solve the non‐convexity of the optimization problem. Good results are obtained in both linear regression model and nonlinear regression model and two real data sets. An experiment is carried out in a power company in Jiangxi Province, China, to evaluate the performance of the robust smooth non‐convex support vector regression model. The results show that the proposed method is superior to support vector regression and generalized quadratic non‐convex support vector regression in robustness and generalization ability.

Application of a Three-Stage Procedure for Extracting a Small-Sized Anchor Object on a Noisy Image

The purpose of the article is to develop a three-stage procedure for identifying an anchor object in a noisy current image. This goal is achieved by determining the sampling threshold levels at which the greatest similarity of the compared images is ensured; development of a procedure for refining the maximum of the decision function by quantizing the current image. The solution to the first problem is based on the formation of a correlation field of radio brightness temperatures and the choice of a sampling threshold. It is proposed to use the cross-correlation coefficient as a criterion for the degree of image matching. The effectiveness of the procedure for selecting a fragment of a reference image is assessed based on the criterion of the probability of selecting a fragment of a reference image. It is shown that image noise can lead to a decrease in the probability of selecting a fragment of a reference image, down to 0.4. It is proposed to refine the maximum of the decision function based on the iteration method. The most significant results are the obtained dependences of the probability of choosing a fragment of the reference image on the threshold value and the signal-to-noise ratio, as well as the analytical relation for the asymptote of this dependence. The novelty of the work lies in the fact that the procedure for forming the decisive function has been further developed. This will significantly improve the operating efficiency of unmanned aerial vehicles, especially in conditions of interference.

Ground collision avoidance system with multi-trajectory risk assessment and decision function

AbstractThe traditional ground collision avoidance system (GCAS) makes avoidance decisions based on predicted collision time, without considering the impact of terrain environment and dynamic changes in load factor on avoidance decisions. This increases the risk of ground collisions for the aircraft. To solve the problem, a GCAS with multi-trajectory risk assessment and decision function is proposed. Firstly, a variety of predicted flight avoidance trajectories are established within the final manoeuvering capability of the aircraft. Secondly, for each predicted trajectory, the uncertain length between adjacent prediction points is used to construct a rectangular distance bin, and the terrain data below the avoided trajectory is extracted. Finally, the regret theory is used to establish a multi-attribute avoidance decision model to evaluate and prioritise the risk of collision avoidance trajectories, to provide effective collision avoidance decision for pilots. The algorithm is tested and verified with real digital elevation model and simulated flight data, and compared with traditional GCAS. Simulation results show that the proposed algorithm can comprehensively consider manoeuvering performance and threatening terrain, and provide safe and effective avoidance decisions for pilots.

Automated Mapping of Land Cover Type within International Heterogenous Landscapes Using Sentinel-2 Imagery with Ancillary Geospatial Data.

A near-global framework for automated training data generation and land cover classification using shallow machine learning with low-density time series imagery does not exist. This study presents a methodology to map nine-class, six-class, and five-class land cover using two dates (winter and non-winter) of a Sentinel-2 granule across seven international sites. The approach uses a series of spectral, textural, and distance decision functions combined with modified ancillary layers (such as global impervious surface and global tree cover) to create binary masks from which to generate a balanced set of training data applied to a random forest classifier. For the land cover masks, stepwise threshold adjustments were applied to reflectance, spectral index values, and Euclidean distance layers, with 62 combinations evaluated. Global (all seven scenes) and regional (arid, tropics, and temperate) adaptive thresholds were computed. An annual 95th and 5th percentile NDVI composite was used to provide temporal corrections to the decision functions, and these corrections were compared against the original model. The accuracy assessment found that the regional adaptive thresholds for both the two-date land cover and the temporally corrected land cover could accurately map land cover type within nine-class (68.4% vs. 73.1%), six-class (79.8% vs. 82.8%), and five-class (80.1% vs. 85.1%) schemes. Lastly, the five-class and six-class models were compared with a manually labeled deep learning model (Esri), where they performed with similar accuracies (five classes: Esri 80.0 ± 3.4%, region corrected 85.1 ± 2.9%). The results highlight not only performance in line with an intensive deep learning approach, but also that reasonably accurate models can be created without a full annual time series of imagery.

Rural communities experience higher radon exposure versus urban areas, potentially due to drilled groundwater well annuli acting as unintended radon gas migration conduits

Repetitive, long-term inhalation of radioactive radon gas is one of the leading causes of lung cancer, with exposure differences being a function of geographic location, built environment, personal demographics, activity patterns, and decision-making. Here, we examine radon exposure disparities across the urban-to-rural landscape, based on 42,051 Canadian residential properties in 2034 distinct communities. People living in rural, lower population density communities experience as much as 31.2% greater average residential radon levels relative to urban equivalents, equating to an additional 26.7 Bq/m3 excess in geometric mean indoor air radon, and an additional 1 mSv/year in excess alpha radiation exposure dose rate to the lungs for occupants. Pairwise and multivariate analyses indicate that community-based radon exposure disparities are, in part, explained by increased prevalence of larger floorplan bungalows in rural areas, but that a majority of the effect is attributed to proximity to, but not water use from, drilled groundwater wells. We propose that unintended radon gas migration in the annulus of drilled groundwater wells provides radon migration pathways from the deeper subsurface into near-surface materials. Our findings highlight a previously under-appreciated determinant of radon-induced lung cancer risk, and support a need for targeted radon testing and reduction in rural communities.

Research on the Design of Integrated Energy Management and Optimization Control Systems for Novel Power Systems

This research paper is determining the impact of integrated energy management and optimization control for Novel power systems. Research objectives have been made based on integrating power system for reducing power consumption based on optimization parameters. Optimal control is mainly dealing with control law by finding out a given system and delivering certain criteria for achieving goals. There are mainly three parts for problem optimization purposes those are included decision, constraints and objective functions. Four different objectives has been made those are mainly discussed about the importance of the power systems regarding integrated management system. There are various factors that are affecting optimizations including the role of the multigrain recursion, global convergence, properties of the optimization model and local convergence. Along with this, the optimization control system has been played a crucial role for the power systems development purposes. Research has been based on the effective design of energy management for power systems. The primary application for the optimization techniques is based on the storage system, electromagnetic-based design and mapping design for the microwave structure. The rate of unsustainable energy management is increasing and sustainable energy management system is decreasing. The conclusion has been based on the significance of the optimization control for the power systems.

Слабо-контролируемая групповая классификация

В работе решается задача слабо-контролируемого обучения в постановке групповой бинарной классификации. Предполагается, что каждый объект выборки может включать набор подобъектов, относящихся к разным классам. Предлагаемый метод решения основан на выборе информативного признакового пространства и фильтрации обучающей выборки. Описывается применение разработанного метода для прогнозирования степени поражения участков головного мозга у пациентов с ишемическим инсультом по снимкам компьютерной томографии. Weakly supervised learning implies possible uncertainty or fuzziness of the labelling. Current study addresses this problem using the formulation of group binary classification. It is assumed that each sample object may include a set of sub-objects belonging to one of two classes. Objects are described by a set of features; the predicted feature determines the degree to which an object belongs to the “positive” class. It is required to construct a decision function from the training sample to predict the target feature for new objects. The proposed method is based on the selection of informative feature space and filtering the training sample. Both the selection of informative features and the removal of noise observations are carried out on the basis of analysis of the local environment of objects. The degree of similarity between the object and the class is determined by the k nearest neighbours of the object, taking into account their degree of belonging to the target class. For an experimental study of the developed method, the real problem of analyzing tomography images of the brain to predict the degree of damage to its areas in stroke is solved. The results are compared with a number of known methods. A method for constructing a decision function for predicting the degree of belonging of an object to the target class has been developed. The results of an experimental study and comparison with a number of well-known machine learning algorithms (random forest, support vector machine, kNN) confirmed the efficiency of the method for solving the problem of predicting the degree of damage to brain areas in stroke patients. Unlike other similar algorithms, the proposed method allows establishing a set of the most informative features in order to improve the interpretability of the solution and reduce the effect of overfitting.

Quadratic Neural Networks for Solving Inverse Problems

In this paper we investigate the solution of inverse problems with neural network ansatz functions with generalized decision functions. The relevant observation for this work is that such functions can approximate typical test cases, such as the Shepp-Logan phantom, better, than standard neural networks. Moreover, we show that the convergence analysis of numerical methods for solving inverse problems with shallow generalized neural network functions leads to more intuitive convergence conditions, than for deep affine linear neural networks.

Clinical potential of sensory neurites in the heart and their role in decision-making.

The process of decision-making is quite complex involving different aspects of logic, emotion, and intuition. The process of decision-making can be summarized as choosing the best alternative among a given plethora of options in order to achieve the desired outcome. This requires establishing numerous neural networks between various factors associated with the decision and creation of possible combinations and speculating their possible outcomes. In a nutshell, it is a highly coordinated process consuming the majority of the brain's energy. It has been found that the heart comprises an intrinsic neural system that contributes not only to the decision-making process but also the short-term and long-term memory. There are approximately 40,000 cells present in the heart known as sensory neurites which play a vital role in memory transfer. The heart is quite a mysterious organ, which functions as a blood-pumping machine and an endocrine gland, as well as possesses a nervous system. There are multiple factors that affect this heart ecosystem, and they directly affect our decision-making capabilities. These interlinked relationships hint toward the sensory neurites which modulate cognition and mood regulation. This review article aims to provide deeper insights into the various roles played by sensory neurites in decision-making and other cognitive functions. The article highlights the pivotal role of sensory neurites in the numerous brain functions, and it also meticulously discusses the mechanisms through which they modulate their effects.

Navigating the Evolution of Digital Twins Research through Keyword Co-Occurence Network Analysis.

Digital twin technology has become increasingly popular and has revolutionized data integration and system modeling across various industries, such as manufacturing, energy, and healthcare. This study aims to explore the evolving research landscape of digital twins using Keyword Co-occurrence Network (KCN) analysis. We analyze metadata from 9639 peer-reviewed articles published between 2000 and 2023. The results unfold in two parts. The first part examines trends and keyword interconnection over time, and the second part maps sensing technology keywords to six application areas. This study reveals that research on digital twins is rapidly diversifying, with focused themes such as predictive and decision-making functions. Additionally, there is an emphasis on real-time data and point cloud technologies. The advent of federated learning and edge computing also highlights a shift toward distributed computation, prioritizing data privacy. This study confirms that digital twins have evolved into complex systems that can conduct predictive operations through advanced sensing technologies. The discussion also identifies challenges in sensor selection and empirical knowledge integration.