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In general, groundwater flow and transport models are being applied to investigate a wide variety of hydrogeological conditions besides to calculate the rate and direction of movement of groundwater through aquifers and confining units in the subsurface. Transport models estimate the concentration of a chemical in groundwater which requires the development of a calibrated groundwater flow model or, at a minimum, an accurate determination of the velocity and direction of groundwater flow that is based on field data. All the available hydrogeological, geophysical and water quality data in Musi basin, Hyderabad, India, were fed as input to the model to obtain the groundwater flow velocities and the interaction of surface water and groundwater and thereby seepage loss was estimated. This in turn paved the way to calculate the capacity of the storage treatment plants (STP) to be established at the inlets of six major lakes of the basin. The total dissolved solid was given as the pollutant load in the mass transport model, and through model simulation, its migration at present and futuristic scenarios was brought out by groundwater flow and mass transport modeling. The average groundwater velocity estimated through the flow model was 0.26 m/day. The capacities of STP of various lakes in the study area were estimated based on the lake seepage and evaporation loss. Based on the groundwater velocity and TDS as pollutant load in the lakes, the likely contamination from lakes at present and for the next 20 years was predicted. [ABSTRACT FROM AUTHOR]

Shear-wave velocity-depth information plays a significant role in assessing earthquake hazard of any region. Further, it is required for most investigations of ground motion response to earthquakes in areas where significant soil cover exists. The thickness and velocity of geologic formations are the vital parameters above the bedrock that affect the frequency band of the ground motion that may be strongly amplified by the local site conditions. In view of this, shear-wave velocity assumes great importance in earthquake hazard assessment studies. The characteristics of shear-wave velocity profile were determined by multichannel analysis of surface waves (MASW) technique in as many as 99 sites over different geological units in parts of Muscat city in order to evaluate probable hazard if any. The variations of shear wave velocity down to depths of 5 m, 10 m, 15 m and 30 m, respectively are presented. However, the average shear wave velocities calculated down to a depth of 30 m (VS30) vary between 345 m/s and 1197 m/s. The high range of shear-wave velocity almost rules out the liquefaction potential due to earthquakes of moderate to high magnitude. [ABSTRACT FROM AUTHOR]

The subsurface fault structure near Al Khoud area of Muscat city, Sultante of Oman, was investigated using very low frequency electromagnetic (VLF-EM) method at two different transmitter frequencies namely 15.1 and 16.0 kHz. The measured in-phase and out-of-phase components were processed and interpreted in order to determine the depth to the top and width of the fault by Kharous-Hjelt filtering and the amplitude of analytical signal approach. In addition, the principal profiles from the contour maps of in-phase and out-of-phase components when processed and interpreted yield similar results for depth and width of the fault structure. The over all results suggest that the nature of fault structure is dipping as it lies at a wide range of depth from different profiles. The results are presented highlighting the merits of the methods. [ABSTRACT FROM AUTHOR]

In this paper, we present two fast learning complex-valued, single hidden layer neural network classifiers namely, ‘bilinear branch-cut complex-valued extreme learning machine (BB-CELM)’ and ‘phase encoded complex-valued extreme learning machine (PE-CELM)’ to solve real-valued classification problems. BB-CELM and PE-CELM use the bilinear transformation with a branch-cut at 2π and the phase encoded transformation, respectively, at the input layer to transform the feature space from the real domain to complex domain ( $${\Re \rightarrow \mathbb{C}}$$ ). A complex-valued activation function of the type of hyperbolic secant employed at the hidden layer maps the complex-valued feature space to a hyper dimensional complex space ( $${\mathbb{C}^m \rightarrow \mathbb{C}^K\quad K > m}$$ ). BB-CELM and PE-CELM are trained by choosing the hidden layer parameters randomly and computing the output weights analytically. Therefore, these classifiers require minimal computational effort during the training process. The performances of these classifiers are evaluated on a set of benchmark classification problems from the UCI machine learning repository and a practical acoustic emission signal classification problem. The results of the performance study highlight the superior classification ability of BB-CELM and PE-CELM classifiers. [ABSTRACT FROM AUTHOR]

This paper presents the performance evaluation of the recently developed Sequential Adaptive Fuzzy Inference System (SAFIS) algorithm for classification problems. In SAFIS the number of fuzzy rules can be automatically determined according to learning process and the parameters in fuzzy rules can be updated simultaneously. Earlier SAFIS has been evaluated only for function approximation problems. Improvements to SAFIS for enhancing its performance in both accuracy and speed are described in the paper and the resulting algorithm is referred to as Extended SAFIS (ESAFIS). In ESAFIS, the concept of the modified influence of a fuzzy rule is introduced for adding or removing the fuzzy rules. If the input data does not warrant adding of fuzzy rules, the parameters of the fuzzy rules are updated using a Recursive Least Square Error (RLSE) scheme. Empirical study of ESAFIS is executed based on several commonly used classification benchmark problems. The results indicate that the proposed ESAFIS produces higher classification accuracy with reduced computational complexity compared with original SAFIS and other algorithms, such as eTS (Angelov and Filev 2004), Simpl_eTS (Angelov and Filev 2005) and k-NN (Huang et al. 2006). [ABSTRACT FROM AUTHOR]

Shear wave velocity ( V) estimation is of paramount importance in earthquake hazard assessment and other geotechnical/geo engineering studies. In our study, the shear wave velocity was estimated from ground roll using multichannel analysis of surface wave (MASW) technique making use of dispersive characteristics of Rayleigh type surface waves followed by imaging the shallow subsurface basaltic layers in an earthquake-prone region near Jabalpur, India. The reliability of MASW depends on the accurate determination of phase velocities for horizontally traveling fundamental mode Rayleigh waves. Inversion of data from surface waves resulted in a shear wave velocity ( V) in the range of 200-1,200 m/s covering the top soil to weathering and up to bedrock corresponding to a depth of 10-30 m. The P-wave velocity ( V) obtained from refraction seismic studies at these locations found to be comparable with V at an assumed specific Poisson's ratio. A pair of selected set of V profiles over basalt which did not result in a hazardous situation in an earthquake of moderate magnitude are presented here as a case study; in other words, the shear wave velocity range of more than 200 m/s indicate that the area is highly unlikely prone to liquefaction during a moderate or strong earthquake. The estimated depth to basalt is found to be 10-12 m in both the cases which is also supported by refraction studies. [ABSTRACT FROM AUTHOR]

Site characterization was carried out for Muscat region using the ambient noise measurements applying the horizontal-to-vertical spectral ratio (HVSR) technique and using active seismic survey utilizing the multichannel analysis of surface waves (MASW) of survey data. Microtremors measurements were carried out at 459 sites using short-period sensors. This extensive survey allowed the fundamental resonance frequency of the soft soil to be mapped and areas prone to site amplification to be identified. The results indicate a progressive decrease in the fundamental resonance frequencies from the southern and eastern parts, where the bedrock outcrops, toward the northern coast where a thickness of sedimentary cover is present. Shear wave velocity (Vs) was evaluated using the 2-D MASW at carefully selected 99 representative sites in Muscat. These 99 sites were investigated with survey lines of 52 m length. 1-D and interpolated 2-D profiles were generated up to a depth range 20–40 m. The vertical Vs soundings were used in the SHAKE91 software in combination with suitable seismic input strong motion records to obtain the soil effect. Most of the study area has amplification values less than 2.0 for all the considered spectral periods. The estimated fundamental frequencies obtained using the H/V spectral ratio method and using SHAKE91 are found to be in a relatively good agreement. Maps of spectral amplification, earthquake characteristics on the ground surface for peak ground and spectral accelerations at 0.1, 0.2, 0.3, 1.0, and 2.0 s, for 475 years return period are produced. The surface ground motion maps show that the hazard level is moderate with expected PGA in the range 0.059–0.145 g for 475 years return period. [ABSTRACT FROM AUTHOR]

We present an inversion technique based on the Marquardt algorithm to estimate the depth of a 2.5-D sedimentary basin in addition to the regional gravity anomaly that is associated with the residual gravity anomaly, wherein the density contrast varies parabolically with depth. Forward modeling is carried out through a derived analytical gravity expression of a 2.5-D vertical prism. Inversion of a theoretical gravity anomaly with and without a regional gravity anomaly illustrates the procedure that it is found to be insensitive to the regional gravity effect. Furthermore, the algorithm is exemplified with the gravity anomalies of the derived density-depth model of the Godavari subbasin, India with a parabolic density profile resulting in a more consistent geological model rather than a constant density profile. The main advantage of this method is that it works well even when the profile of interpretation does not bisect the strike length of the sedimentary basin. [ABSTRACT FROM AUTHOR]

This paper presents a congestion control scheme for ATM traffic using a minimal radial basis function neural network referred to as Minimal Resource Allocation Network (MRAN). Earlier studies have shown that MRAN is well suited for online adaptive control of nonlinear time varying systems as it can adjust its size by adding and pruning the hidden neurons based on the input data. Since ATM traffic is nonlinear and time varying performance of MRAN as a congestion controller is investigated here. These studies are carried out using OPNET to model the ATM traffic. The ATM traffic model consists of bursty, Variable Bit-Rate (VBR) and custom traffic in a multiplexed form so as to generate a heavily congested traffic situation. For this scenario, the controller has to minimize the congestion episodes and maintain the Quality of Service (QoS) requirements. This paper compares the performance of the MRAN congestion controller with that of a modified Explicit Rate Indication with Congestion Avoidance (ERICA) scheme and a Back-Propagation (BP) neural controller. Simulation results indicate that MRAN controller performs better than the modified ERICA and BP controller in reducing the congestion episodes and maintaining the desirable QoS. [ABSTRACT FROM AUTHOR]

This paper addresses a class of algorithms for solving bound-constrained black-box global optimization problems. These algorithms partition the objective function domain over multiple scales in search for the global optimum. For such algorithms, we provide a generic procedure and refer to as multi-scale optimization ( MSO). Furthermore, we propose a theoretical methodology to study the convergence of MSO algorithms based on three basic assumptions: (a) local Hölder continuity of the objective function f, (b) partitions boundedness, and (c) partitions sphericity. Moreover, the worst-case finite-time performance and convergence rate of several leading MSO algorithms, namely, Lipschitzian optimization methods, multi-level coordinate search, dividing rectangles, and optimistic optimization methods have been presented. [ABSTRACT FROM AUTHOR]

The application of artificial neural network committee machine (ANNCM) for the inversion of self-potential anomalies caused by a long 2D horizontal circular cylinder is presented. ANNCM inversion extracts the parameters of the source including depth to the center of the cylinder( z), the angle between the horizontal axis and the axis of polarization( α), and the constant term( A) involving the current polarization (I) and resistivity of the earth( ρ). The inversion is demonstrated on theoretical models with and without random noise in order to study the effect of noise on the method and then extended to real field data. The ANNCM analysis of self-potential data of the Sulleymonkey anomaly in the Ergani Copper district, Turkey, has shown satisfactory results in comparison with other inversion techniques that are in vogue. [ABSTRACT FROM AUTHOR]

This paper presents a stable, on-line identification scheme for multivariable nonlinear dynamic system. Growing Gaussian Radial Basis Function (GRBF) network with all its parameters being adaptable is used to approximate an unknown nonlinear system. Based on a continuous-time framework, the parameter adjustment law is derived using Lyapunov synthesis approach, which guarantees the stability of the overall system. In addition, to ensure the convergence of the approximation error, a growing strategy for the network is selected and a dead zone is incorporated in the tuning law. Simulation studies on identifying a time-varying nonlinear missile dynamics illustrate the superior performance of the proposed scheme. The studies also indicate that stability and reduction in approximation error can be extended to a network with pruning strategy, thereby resulting in a Growing and Pruning (GAP) RBF network, which can implement a more compact network structure. [ABSTRACT FROM AUTHOR]

This paper presents a modification to the Minimal Resource Allocation Network (MRAN) of Yingwei et al. by introducing direct links from inputs to output and investigates its performance for noise cancellation problems. MRAN has the same structure as a Radial Basis Function network but uses a sequential learning algorithm that adds and prunes hidden neurons as input data is received sequentially so as to produce a parsimonious network. Earlier work by Sun Yonghong et al. has demonstrated the capability of MRAN to produce a compact network with excellent noise reduction properties. In this paper the capability of the direct link Minimal Resource Allocation Network (DMRAN) is evaluated by comparing it with MRAN on several nonlinear adaptive noise cancellation problems. The direct link MRAN uses the same learning algorithm as MRAN but with the introduction of direct links we are able to realise even smaller networks than MRAN with better noise reduction properties. [ABSTRACT FROM AUTHOR]

Geochemical analysis of ground water samples in the vicinity of lakes and drainage network were carried out in and around a selected set of polluted lakes in the city of Hyderabad, India for possible restoration of the polluted lakes from the state of eutrophication. The results reveal, in general the concentration of TDS and major ions higher in the down stream areas due to shallow water table as well as seepage from lakes and nalas. Further, the concentration of trace elements in ground water was found to be within the permissible limits. The Piper and Wilcox diagrams indicate that the ground water in the study area has low sodium and high salinity hazard. [ABSTRACT FROM AUTHOR]

The quality analysis of surface water from a selected set of six lakes has been carried out for major ionic as well as trace elements for three different periods. The surface water quality is within the permissible limits. Furthermore, biochemical oxygen demand (BOD) and chemical oxygen demand (COD) tests signify a relative oxygen-depletion effect and are known as a measure of pollution effect. Bimonthly variations of BOD and COD indicate high concentration in water samples and are given as bar charts. The concentration of total nitrogen (TN) and total phosphate (TP) expressed in terms of trophic status index (TSI) is a measure of water column productivity to assess water lake quality. High concentrations of BOD, COD, TN, and TP may be attributed to the urban sewage. The TSI based on TN and TP concentration of lake water and sediment samples indicate that all the six studied lakes are under hyper eutrophic condition. Classification of surface water by Piper and Wilcox diagrams indicates a high sodium and salinity hazard. [ABSTRACT FROM AUTHOR]

A detailed hydrogeological investigations comprising well inventory, water level monitoring, lake bathymetry, infiltration and pump tests were carried out in a few selected observation wells in order to estimate transmissivity (T) and storativity (S) in all major lakes which are highly polluted by the discharge of sewage and other chemical effluents in greater Hyderabad. The salient features of results are presented lake wise which would be of immense use in restoration of all lakes. Also, the outcome of this study along with infiltration rates may be used for modeling in future to simulate the surface water-ground water interaction. [ABSTRACT FROM AUTHOR]

The dispersive characteristics of Rayleigh type surface waves were utilized to estimate shear wave velocity (Vs) profile followed by imaging the shallow subsurface granitic layers in the heart of Hyderabad. The reliability of Multichannel Analysis of Surface Waves (MASW) depends on the accurate determination of phase velocities for horizontally traveling fundamental mode Rayleigh waves. Multichannel recording leads to effective identification and isolation of various factors of noise. Calculating the 1-D shear wave velocity (Vs) field from surface waves ensures high degree of accuracy irrespective of cultural noise. The main advantage of mapping the bed rock surface with shear wave velocity (Vs) is the insensitivity of MASW to velocity inversion besides being free from many constraints such as contrast in physical properties etc. Modeling of surface waves data results a shear wave velocity (Vs) of 250-750 m/s covering the top soil to weathering and up to bedrock corresponding to a depth range of 10-30 m. Further, the computed N values (which is an indicator of site characteristic) based on the harmonic shear wave velocity up to a depth of 5 m is found to be quite high (> 25-30) well above 5 indicating the site to be safe and strong enough and not prone to liquefaction. A pair of selected set of results over granites are presented here as a case study highlighting the salient features of MASW. [ABSTRACT FROM AUTHOR]

A combined geophysical investigation consisting of vertical electrical sounding (VES) and multielectrode system was carried out to map the subsurface resistivity in all major lakes which are highly polluted by the discharge of sewage and other chemical effluents in greater Hyderabad, India. The structural features identified in the study area play a major role in groundwater flow and storage. The interpretation of geophysical data and lithologs indicates that a silt/clay zone (predominantly silt) has a thickness of 5-10 m all along the drainage from Patelcheruvu to the Musi River. The silt/clay zone inferred close to the lakes is a mixture of clay, silt and sand with more silt content as indicated from the lithologs during drilling. The low resistivity values obtained can be attributed to the pollutant accumulated in the silt which can reduce the resistivity values. Further, the TDS of the water samples in these wells are more than 1,000 mg/l which further confirms the above scenario. The pollution spread is less in the upstream areas whereas it is more in the downstream which can be attributed to the shallow water table conditions and also due to the interaction of surface water and groundwater. [ABSTRACT FROM AUTHOR]

The shear wave velocity (V) profile based on the dispersive characteristics of fundamental mode of Rayleigh type surface waves indicate underground stiffness change with depth as well as near surface stiffness. The most important utility of shear wave velocity (V) is to estimate the liquefaction hazard potential of an area particularly in seismically active region. Rayleigh type surface waves were utilized to estimate the velocity (V) of shallow subsurface covering a depth range of 30-50 m employing multichannel analysis of surface waves. The liquefaction hazard map predicts an approximate percentage of an area that will have surface manifestation of liquefaction during an earth quake. The surface wave data acquired in an earth quake prone region of Jabalpur (Seismic zone III), India, yields a velocity (V) range of 200-750 m/s corresponding to the subsurface depth of 30-35 m. The results were analyzed for possible liquefaction hazard in the study area and presented here besides the N values. [ABSTRACT FROM AUTHOR]

The very low frequency electromagnetic (VLF-EM) method is a rapid and reliable geophysical method was used in the investigation for uranium bearing structures in the Proterozoic basement of the Dhofar region that may be a potential source and trap for uranium and associated minerals in Sultanate of Oman. Accordingly, the in-phase and out of phase components of VLF-EM field along 11 traverses were recorded at two different transmitter frequencies viz. 16 kHz and 16.4 kHz at an approximate line interval of 20 m with a measurement interval of 10 m. These measured components were subjected to Fraser and Karous-Hjelt filtering in order to decipher the nature and depth to the sub-surface conductor. The Fraser filtered in-phase VLF-EM components yield a strong amplitude almost over the top of the conductor. On the other hand, the Hjelt filtered responses yield the depth to the source in the range 10-80 m. Spectral analysis of selected a few traverses result the depth to source in the range 31-39 m. Analytical signal approach of total magnetic field of line-I substantiates this depth. The peaks of Fraser filtered stacked profiles delineate approximately the trend of the sub-surface fault which mimic the trend inferred from the combined interpretation of VLF-EM and magnetic anomalies. The results of Fraser and Karous-Hjelt filter output suggest a mineralized conductive sub-surface fault aligned along NW-SE. It may be noted that the random sampling of gamma ray spectrometer has recorded a maximum uranium concentration of up to 30-40 ppm prior to VLF-EM survey. [ABSTRACT FROM AUTHOR]

In this paper, a novel stacked encoded cascade error feedback deep extreme learning machine (SEC-E-DELM) network is proposed to predict order completion time (OCT) considering the historical production planning and control data. Usually, the actual OCT significantly deviates from the planned because of recessive disturbances. The disturbances do not shut down production but slow down the production that accumulates over time, causing significant deviation of actual time from planned. The generation of weight parameters in neural networks using a randomization approach has a significant effect on generalization performance. To predict the OCT, firstly, the stacked autoencoder is used to generate input connection weights for the network by learning a deep representation of the real data. Secondly, the learned distribution of the encoder is connected to the network output through output connection weights incrementally learned by the Moore–Penrose inverse. Thirdly, the new hidden unit is added one by one to the network, which receives input connections from the input units and the last layer of the encoder to avoid overfitting and improve model generalization. The input connection weights for the newly added hidden unit are analytically calculated by the error feedback function to enhance the convergence rate by further learning deep features. Lastly, the hidden unit keeps on adding one by one by receiving connections from input units and some of the existing hidden units to make a deep cascade architecture. An extensive comparative study demonstrates that calculating connection weights by the proposed method helps to significantly improve the generalization performance and robustness of the network. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicle (UAV) path planning is a constrained multi-objective optimization problem. With the increasing scale of UAV applications, finding an efficient and safe path in complex real-world environments is crucial. However, existing particle swarm optimization (PSO) algorithms struggle with these problems as they fail to consider UAV dynamics, resulting in many infeasible solutions and poor convergence to optimal solutions. To address these challenges, we propose a spherical vector-based adaptive evolutionary particle swarm optimization (SAEPSO) algorithm. This algorithm, based on spherical vectors, directly incorporates UAV dynamic constraints and introduces improved tent map and reverse learning to enhance the diversity and distribution of initial solutions. Additionally, dynamic nonlinear and adaptive factors are integrated to balance exploration and exploitation capabilities. To avoid local optima in highly complex environments, we propose an adaptive acceleration strategy for poor particles, and an evolutionary programming strategy is incorporated to further improve the optimization capability. Finally, we conducted comparative studies and in six benchmark scenarios with varying threat levels, and the results demonstrated that the proposed algorithm outperforms others in the initial solution effectiveness, the final solution accuracy, convergence stability, and scalability. [ABSTRACT FROM AUTHOR]

Incremental broad learning system (IBLS) is an effective and efficient incremental learning method based on broad learning paradigm. Owing to its streamlined network architecture and flexible dynamic update scheme, IBLS can achieve rapid incremental reconstruction on the basis of the previous model without the entire retraining from scratch, which enables it adept at handling streaming data. However, two prominent deficiencies still persist in IBLS and constrain its further promotion in large-scale data stream scenarios. Firstly, IBLS needs to retain all historical data and perform associated calculations in the incremental learning process, which causes its computational overhead and storage burden to increase over time and as such puts the efficacy of the algorithm at risk for massive or unlimited data streams. Additionally, due to the random generation rule of hidden nodes, IBLS generally necessitates a large network size to guarantee approximation accuracy, and the resulting high-dimensional matrix calculation poses a greater challenge to the updating efficiency of the model. To address these issues, we propose a novel bidimensionally partitioned online sequential broad learning system (BPOSBLS) in this paper. The core idea of BPOSBLS is to partition the high-dimensional broad feature matrix bidimensionally from the aspects of instance dimension and feature dimension, and consequently decompose a large least squares problem into multiple smaller ones, which can then be solved individually. By doing so, the scale and computational complexity of the original high-order model are substantially diminished, thus significantly improving its learning efficiency and usability for large-scale complex learning tasks. Meanwhile, an ingenious recursive computation method called partitioned recursive least squares is devised to solve the BPOSBLS. This method exclusively utilizes the current online samples for iterative updating, while disregarding the previous historical samples, thereby rendering BPOSBLS a lightweight online sequential learning algorithm with consistently low computational costs and storage requirements. Theoretical analyses and simulation experiments demonstrate the effectiveness and superiority of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Air Traffic Flow Management (ATFM) is the backbone of modern aviation and ensures that aircraft move safely and efficiently through increasingly congested skies. As global air travel grows, managing air traffic has become more pressing than ever. This review assesses ten years of the ATFM literature, the period between 2014 and 2024, and discusses 162 studies published in peer-reviewed journals. Employing VOSViewer and Biblioshiny, this review analyzes the history of ATFM research. It explores the trends and gaps in research, which suggest there is room for improvement for more sound approaches. While optimization techniques have significantly improved efficiency and eased bottlenecks, the future lies in real-time solutions that can handle unpredictable events, from weather disruptions to technical failures. The review identified key areas for optimizing ATFM, categorized by primary focus: delay minimization, airspace congestion, and scheduling. It suggests ways in which more dynamic ATFM systems are possible in the growing global aviation network. By synthesizing the current research landscape, this review addresses the progress made. It offers a roadmap for future innovations that will enhance the safety, efficiency, and sustainability of air traffic management.Article Highlights: Identifies key trends and challenges in optimizing air traffic flow management through a comprehensive literature review. Highlights three critical areas: delay minimization, airspace congestion, and scheduling for improved aviation operations. Provides future directions for sustainable and efficient air traffic management using innovative optimization techniques. [ABSTRACT FROM AUTHOR]

We report six experiments that examine how two essential components of a category-learning paradigm, training and feedback, can be manipulated to maximize learning and transfer of real-world, complex concepts. Some subjects learned through classification and were asked to classify hypothetical experiment scenarios as either true or non-true experiments; others learned through observation, wherein these same scenarios were presented along with the corresponding category label. Additionally, some subjects were presented correct-answer feedback (the category label), whereas others were presented explanation feedback (the correct answer and a detailed explanation). For classification training, this feedback was presented after each classification judgment; for observation training this feedback was presented simultaneously with the hypothetical experiment. After the learning phase, subjects completed a posttest that included one task that involved classifying novel hypothetical scenarios and another task comprising multiple-choice questions about novel scenarios, in which subjects had to specify the issue with the scenario or indicate how it could be fixed. The posttest either occurred immediately after the learning phase (Experiments 1–2), 10 min later (Experiments 3–4), two days later (Experiment 5), or one week later (Experiment 6). Explanation feedback generally led to better learning and transfer than correct-answer feedback. However, although subjects showed clear evidence of learning and transfer, posttest performance did not differ between classification and observation training. Critically, various learning theories and principles (e.g., retrieval practice, generation, active learning) predict a classification advantage. Our results thus call into question the extent to which such theories and principles extend to the transfer of learning. [ABSTRACT FROM AUTHOR]

An emerging challenge in swarm shepherding research is to design effective and efficient artificial intelligence algorithms that maintain simplicity in their decision models, whilst increasing the swarm's abilities to operate in diverse contexts. We propose a methodology to design a context-aware swarm control intelligent agent (shepherd). We first use swarm metrics to recognise the type of swarm that the shepherd interacts with, then select a suitable parameterisation from its behavioural library for that particular swarm type. The design principle of our methodology is to increase the situation awareness (i.e. contents) of the control agent without sacrificing the low computational cost necessary for efficient swarm control. We demonstrate successful shepherding in both homogeneous and heterogeneous swarms. [ABSTRACT FROM AUTHOR]

This study investigates the relationship between the moduli of continuity of a function and its Hartley transform. We explore this connection by deriving significant results such as the Riemann–Lebesgue lemma, Parseval's theorem, and the Hausdorff–Young inequality for the Hartley transform in both the Euclidean space and torus. Using a translation operator, we obtain an analog of Titchmarsh's theorem for the Hartley transform. In addition, we extend our analysis to the Hartley series on the torus. [ABSTRACT FROM AUTHOR]

The retention of foundational knowledge is crucial in learning and teaching mathematics. However, a significant part of university students do not achieve long-term knowledge and problem-solving skills. A possible tool to increase further retention is testing, the strategic use of retrieval to enhance memory. In this study, the effect of a special kind of testing versus worked examples was investigated in an authentic educational setting, in an algebra course for pre-service mathematics teachers. The potential benefits of using tests versus showing students worked examples at the end of each practice session during a semester were examined. According to the results, there was no difference between the effectiveness of the two methods in the medium term—on the midterm that students took on the 6th week and the final that students took on the 13th week of the semester, the testing group performed the same as the worked example group. However, testing was more beneficial regarding long-term retention in studying and solving problems in abstract mathematics. Analyzing the results of the post-test that students took five months after their final test, the authors found that the improvement of those students who learned the material with testing was significantly larger than that of the worked example group. These findings suggest that testing can have a meaningful effect on abstract algebra knowledge and a long-lasting impact on solving complex, abstract mathematical problems. [ABSTRACT FROM AUTHOR]

We present here the first experimental science (consensus)-based mineral prospectivity mapping (MPM) method and its validation results in the form of national prospectivity maps and datasets for PGE–Ni–Cu–Cr and Witwatersrand-type Au deposits in South Africa. The research objectives were: (1) to develop the method toward applicative uses; (2) to the extent possible, validate the effectiveness of the method; and (3) to provide national MPM products. The MPM method was validated by targeting mega-deposits within the world's largest and best exploited geological systems and mining districts—the Bushveld Complex and the Witwatersrand Basin. Their incomparable knowledge and mega-deposit status make them the most useful for validating MPM methods, serving as "certified reference targets". Our MPM method is built using scientific consensus via deep ensemble construction, using workflow experimentation that propagates uncertainty of subjective workflow choices by mimicking the outcome of an ensemble of data scientists. The consensus models are a data-driven equivalent to expert aggregation, increasing confidence in our MPM products. By capturing workflow-induced uncertainty, the study produced MPM products that not only highlight potential exploration targets but also offer a spatial consensus level for each, de-risking downstream exploration. Our MPM results agree qualitatively with exploration and geological knowledge. In particular, our method identified areas of high prospectivity in known exploration regions and geologically and geospatially corresponding to the known extents of both mineral systems. The convergence rate of the ensemble demonstrated a high level of statistical durability of our MPM products, suggesting that they can guide exploration at a national scale until significant new data emerge. Potential new exploration targets for PGE–Ni–Cu–Cr are located northwest of the Bushveld Complex; for Au, promising areas are west of the Witwatersrand Basin. The broader implications of this work for the mineral industry are profound. As exploration becomes more data-driven, the question of trust in MPM products must be addressed; it can be done using the proposed scientific method. [ABSTRACT FROM AUTHOR]

Over the past decades, researchers have investigated the effects of multimedia design principles to enhance learning. These evidence-based principles are known to enable students to learn from multimedia resources and support cognitive processing. However, it is unclear if and which of these multimedia design principles are implemented in instructional videos selected by teachers. The purpose of this study is to investigate whether the videos screened by secondary school teachers in their classroom are consistent with the multimedia design principles based on the cognitive theory of multimedia learning. For this study a design review of 78 videos was conducted. The findings indicate variations in the application of multimedia principles. While most of the multimedia design principles were present, some were transgressed or absent. Moreover, the presence of the three different groups of principles, based on their impact on the cognitive load (i.e., reducing extraneous processing, managing essential processing, and fostering generative processing) were rarely present in the same video. Finally, this study also revealed that the teachers were actively implementing the multimedia design principles when they screened the videos in the classroom. Indeed, they frequently applied the pre-training principle (e.g., by introducing new words to the students before the screening), the segmentation principle (e.g., by pausing the video during the screening), and the generative activity principle (e.g., by asking students to complete a task during or right after the screening). Thus, the present study not only provides an insight on the design of authentic instructional videos but also highlights the addition of multimedia principles by teachers during the screening. [ABSTRACT FROM AUTHOR]

The increasing number of medical devices in the Internet of Medical Things (IoMT) environment has raised significant cybersecurity concerns. These devices often have weak security features, poor design, and insufficient authentication protocols, making them vulnerable to cyberattacks and intrusions. To mitigate these threats, robust security measures are essential. This includes implementing strong security protocols, ensuring continuous security monitoring, enforcing regular updates, and maintaining a constant response plan. Additionally, designing an effective Intrusion Detection System (IDS) is crucial to safeguard patient data and devices. This paper systematically studies the current state of the literature and the essential methods for intrusion detection in the IoMT. Employing a selection process, the paper identifies 28 critical studies published between 2018 and April 2024. The intrusion detection mechanisms in the IoMT are divided into five categories: IDS based on artificial intelligence models, datasets used in IoMT for IDS, fundamental security requirements, intrusion detection processes, and evaluation metrics. This paper dissects the various mechanisms within each category in a meticulous and comprehensive analysis. Finally, the paper examines the challenges and open issues in developing IDSs in IoMT. By offering a roadmap for researchers to enhance IDSs in the IoMT, this paper has the potential to significantly impact the fields of computer engineering, cybersecurity, and healthcare, thereby contributing to the advancement of these crucial fields. [ABSTRACT FROM AUTHOR]

Reservoir petrophysical assessments are essential for determining hydrocarbon reserves, production, and characterizing reservoir layers. Advanced logging technology identifies crucial petrophysical parameters, including porosity type, rock pore size and type, and static/dynamic properties. The aim of this study is to present a petrophysical evaluation of the studied reservoir and to identify the reservoir layers by calculating and determining petrophysical indicators using well logging data. Additionally, various machine learning methods, including Adaptive Neuro-Fuzzy Inference System, Extreme Learning Machine, Multi Gene Genetic Programming, Decision Tree, and Adaptive Boosting, were compared to model the water saturation data according to different logs. The investigated depth ranged from 4050.6 to 4560 m, with each image containing over 3000 data at the desired depth. The main lithology of the formation was limestone with some shale. By conducting a petrophysical evaluation and applying parameter cutoffs, productive zones within the reservoir were identified. Layer 3 had the highest average net porosity (18%) and net water saturation (17%), with secondary porosity observed in most layers. Among the machine learning models tested the AdaBoost model demonstrated the lowest error value for estimating water saturation, with an RMSE of 0.0152 and an AARE% of 3.1610, establishing it as the most effective model in this study. Furthermore, the GP model provided a correlation between the input parameters and predicted water saturation, demonstrating good accuracy with an RMSE of 0.0231 and an AARE of 4.3597. [ABSTRACT FROM AUTHOR]

The utilization of motor imagery-based brain-computer interfaces (MI-BCI) has been shown to assist stroke patients activate motor regions in the brain. In particular, the brain regions activated by unilateral upper limb multi-task are more extensive, which is more beneficial for rehabilitation, but it also increases the difficulty of decoding. In this paper, self-attention convolutional neural network based partial prior transfer learning (SACNN-PPTL) is proposed to improve the classification performance of patients' MI multi-task. The backbone network of the algorithm is SACNN, which accords with the inherent features of electroencephalogram (EEG) and contains the temporal feature module, the spatial feature module and the feature generalization module. In addition, PPTL is introduced to transfer part of the target domain while preserving the generalization of the base model while improving the specificity of the target domain. In the experiment, five backbone networks and three training modes are selected as comparison algorithms. The experimental results show that SACNN-PPTL had a classification accuracy of 55.4%±0.17 in four types of MI tasks for 22 patients, which is significantly higher than comparison algorithms (P < 0.05). SACNN-PPTL effectively improves the decoding performance of MI tasks and promotes the development of BCI-based rehabilitation for unilateral upper limb. [ABSTRACT FROM AUTHOR]

Ships and other maritime objects are often unable to endure the harsh and dynamic sea environment. Collecting real-time data and detecting these objects using various sensors such as RADARs, Synthetic Aperture RADARs, and mounted RADARs present significant challenges due to numerous influencing factors. To address this issue, our research aims to develop an Internet of Things (IoT)-based multi-scale and multi-scene ship identification system. This system leverages a multi-scale neural network integrated with a high-response convolutional neural network (CNN)-based Kalman filter architecture. To construct this model, we selected various ship categories and initially employed a base CNN model to develop a new model with different convolutional layers. Our approach utilizes mixed methods for tracking and detecting objects, with a focus on small ships. The dataset is processed through multiple neural network layers, and we implemented the Kalman filter to estimate and predict the ships' positions. Additionally, using the YOLOv3 model, we achieved improved accuracy and reduced error rates through mathematical optimization. Our method utilizes a dataset of 5,604 samples and incorporates a hybrid approach with YOLOv3. Our model demonstrates significant improvements for both medium-sized and small ships. The proposed work provides both qualitative and quantitative advancements. Our model exceeded the best results from parallel experiments by 3.9% and 1.2% in terms of Average Precision (AP). Furthermore, YOLOv3 achieved a performance score of 97.34% across various metric parameters, while our proposed approach attained the highest scores of 97.8% and 94.87%, respectively. [ABSTRACT FROM AUTHOR]

Benefits of self-testing for learning have been consistently shown for simple materials such as word lists learned by rote memorization. Considerably less evidence for such benefits exists for complex, more educationally relevant materials and its application to new situations. The present study explores the mechanisms underlying this transfer. To this end, a typical retrieval-practice-effect paradigm was applied to foster the learning of an artificial language. Participants either repeatedly studied grammatically correct exemplar sentences of the artificial language or engaged in a cloze test as the interim test after learning. To assess far transfer, participants in both groups of restudy and retrieval practice engaged in a grammaticality judgment test after a delay of 5 min and 1 week. In addition, participants in both groups completed a final memory test (i.e., a cloze test identical to the initial test) 1 week after learning. In addition to a long-term memory benefit of retrieval practice, results revealed also a retrieval-practice benefit in the far-transfer test after the 1-week delay. The findings further support the view that far transfer is supported by learning the underlying grammatical rules as opposed to memorizing the material. Thus, retrieval practice is also effective for fostering learning of complex materials and, even more importantly, for promoting transfer of learning—a crucial goal in modern educational practices. [ABSTRACT FROM AUTHOR]

Problem: Attention deficit hyperactivity disorder (ADHD) is the most commonly found neurodevelopmental condition among children with an estimated 2.5% to 9% global prevalence. While ADHD has been regarded as a lifelong condition, its early diagnosis elevates the probability of recovery and normal life for children. Despite clinical diagnosis being the primary one, substantial developments in ADHD diagnosis have been made during the past decade. Aim: Several imaging-based technologies and approaches have been presented in the existing literature including magnetic resonance imaging (MRI) and functional MRI (fMRI). In addition, the deployment of machine learning and deep learning models has paved the way for automated diagnosis of ADHD which increases both accuracy and robustness of ADHD detection. A comprehensive and systematic literature review (SLR) of imaging technologies and machine learning approaches is highly desired to comprehend the current status of such approaches concerning their potential and challenges to outline future directions. Although a substantial body of literature exists on imaging-based ADHD diagnosis, comprehensive SRL on such approaches is scarce. This SLR aims to provide a comprehensive overview of imaging-based ADHD diagnosis with emphasis on machine learning approaches, reveals their pros and cons, and provides potential future research directions thereby contributing to the scientific community to accelerate further research for ADHD diagnosis. Methods: This SRL focuses on analyzing recently published studies between 2010 and 2023. For this purpose, preferred reporting items for systematic review and meta-analyses (PRISMA) approach is performed in this study. Five eminent academic databases Web of Science, ACM, Springerlink, Elsevier, and PubMed are selected for article search. The SLR follows a systematic methodology comprising article search, selection based on inclusion and exclusion criteria, and rigorous assessment for categorization. Results: It is found that MRI and fMRI are the dominant approaches integrated with machine learning models for ADHD detection and function near-infrared spectroscopy is also adopted by a few studies. Predominantly, the ATHENA preprocessing approach is used to preprocess MRI data before model training. Due to the public availability of the ADHD-200 dataset, it is widely used in the existing literature while a few studies utilized their self-collected datasets. Machine learning models are the choice of the majority of the studies, particularly, the support vector machines model has been widely used for ADHD detection. Feature fusion is observed to be a better choice for obtaining more accurate results. Conclusion: Machine learning and deep learning models provide automated ADHD detection with better accuracy and robustness, however, such models are not generalizable and their performance varies concerning the locality of data used for experiments. In addition, the heterogeneity of data collection from various devices is a challenge, and the use of a standard device may provide better solutions. The lack of labeled data also adds difficulties to training models. Besides the use of MRI and fMRI, other novel technologies should be explored for better ADHD detection performance. [ABSTRACT FROM AUTHOR]

The organic evidence from neuroscience proves that precise spike times are used for information exchange between two biological neurons rather than the firing rates. One of the prominent reasons, along with energy and computational efficiency, is that spiking neural networks (SNNs) are getting more attention nowadays. The spiking neurons in SNN mimic the biological neuron more than its predecessors. Despite the few efficient supervised learning algorithms for SNN, only some investigated the biological properties such as axonal noise, random synaptic delays, spontaneous spike-firing, and random switching of the gamma-aminobutyric acid (GABA)-switch. The aforementioned properties are essential for making spiking neurons more biologically realistic, which is one of the major strengths of SNN. The GABA switch decides the most crucial activity, whether a neuron will be excitatory or inhibitory. This paper proposes a novel and efficient approach to handle non-linear patterns using a single leaky-integrate-and-fire (LIF) spiking neuron connected with many noisy synapses with random synaptic delays. In addition, the spontaneous firing of a neuron and random switching of signs in synaptic weights having equal probability akin to GABA-switch are efficiently implemented. Moreover, a hybrid kernel is proposed as the synapse model to cope with the noise properly, which makes the synapse model more efficient. The error-tuning is carried out using the elitist floating-point genetic algorithm. Four datasets were used for benchmarking, and experimentally, better results were obtained than state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Incremental extreme learning machines (I-ELMs) can automatically determine the structure of neural networks and achieve high learning speeds. However, during the process of adding hidden nodes, unnecessary hidden nodes that have little relevance to the target may be added. Several studies have proposed methods to overcome this problem by measuring the relevance between hidden nodes and outputs and adding or removing hidden nodes accordingly. Random hidden nodes have the advantage of creating diverse patterns, but they encounter a problem in which hidden nodes that generate patterns with little or no relevance to the target can be added, thereby increasing the number of hidden nodes. Unlike in existing I-ELMs, which use random hidden nodes, we propose a compact I-ELM algorithm that initially adds linear regression nodes and subsequently applies a method to ensure that the hidden nodes have patterns differing from the existing ones. Based on benchmark data, we confirmed that the proposed method constructs a compact neural network structure with fewer hidden nodes compared to the existing I-ELM systems. [ABSTRACT FROM AUTHOR]

The purpose of this study was to address the role of social comparison in determining individual development by exploring the relationship of four types of social comparison with academic persistence‒procrastination and subjective well-being among Chinese adolescents (n = 530, Mage = 14.59) from both variable- and person-centered perspectives. Results showed that the paths from social comparison to outcome were significant in most cases. Upward contrast (i.e., focusing on differences with superior targets) and downward identification (i.e., focusing on similarities with inferior targets) seemed detrimental, but upward identification (i.e., focusing on similarities with superior targets) and downward contrast (i.e., focusing on differences with inferior targets) seemed beneficial for adolescent development. Moreover, four social comparison profiles (moderate, 52.1%; high, 14.3%; upward contrast, 5.7%; and positive interpretation, 27.9%) emerged. The upward contrast profile was the least adaptive, and the positive interpretation profile (with high upward identification and downward contrast) was the most adaptive. This study provides new insights for understanding the role of social comparison in adolescent development and how to make more constructive use of it. [ABSTRACT FROM AUTHOR]

The aim of this study was to examine which topic- and learning-related knowledge and motivational beliefs predict the use of specific deep-level learning strategies during an independent learning task. Participants included 335 Estonian fourth- and sixth-grade students who were asked to read about light processes and seasonal changes. The study was completed electronically. Topic-related knowledge was assessed via an open question about seasonal changes, and learning-related knowledge was assessed via scenario-based tasks. Expectancies, interest, and utility values related to learning astronomy and using deep-level learning strategies were assessed via questions based on the Situated Expectancy-Value Theory. Deep-level learning strategies (using drawings in addition to reading and self-testing) were assessed while completing the reading task. Among topic-related variables, prior knowledge and utility value—but not interest or expectancy in learning astronomy—were related to using deep-level learning strategies. Among learning-related variables, interest and utility value of effective learning—but not metacognitive knowledge of learning strategies or expectancy in using deep-level learning strategies—were related to using deep-level learning strategies. This study confirms that it is not enough to examine students' knowledge and skills in using learning strategies with general or hypothetical questions, instead, it is of crucial importance to study students in real learning situations. [ABSTRACT FROM AUTHOR]

A generalized forward modelling equation to calculate the magnetic anomalies of a randomly magnetized finite-strike listric fault source is derived using Poisson's relation. This new equation combines both analytic and numeric approaches to realize forward modelling of the anomalous source in any component. Polynomial functions are adopted to simulate the geometry of the curved fault plane between the displaced hanging wall and the footwall of the fault morphology. The utility of the derived equation is epitomized with a theoretical model of a limited-strike listric fault morphology by computing the anomaly in the vertical, horizontal and total field components. It is demonstrated that the magnitude of the anomalous field (in any component) does not remain the same but changes with the profile offset, albeit the anomalous source remains the same. The effect of structure dimensionality (2D vs. 21/2D) on the magnitude of the anomalous field is also discussed. [ABSTRACT FROM AUTHOR]

Visual displays, such as illustrated web pages, animations, and simulations, can both aid and pose challenges for learners. The first generation of educational research on visuals focused on the basic processes underlying comprehension. A second generation of research tested various instructional supports for visual displays, and this research was summarized by Renkl and Scheiter (2017). The authors of the present article summarize those earlier findings and update the state of the literature to the present day. Trends in more recent findings partially support the recommendations from the previous review, but suggest that much nuance is needed to understand on what learning outcomes, for whom, with what learning content, which technology, and other factors, visual display supports are effective. [ABSTRACT FROM AUTHOR]

In a two-class classification task, if the number of examples of one class (majority) is much greater than that of another class (minority), then the classification is said to be class imbalanced. It can occur among many real-world applications, such as intrusion detection, medical diagnosis, etc. The class imbalance issue can make learning difficult since learning opts to bias towards the majority class. Outliers are cases with anomalous behaviors and are extreme cases of class imbalance. Despite late advances in extreme learning machines (ELMs), there are not many experimental investigations in the field of ELM with outlier detection. In this survey, we provide a comprehensive overview of existing ELMs to address the problem of outlier detection under a unified perspective. Firstly, we describe the background of our work, which includes a brief overview of previous surveys and a detailed description of the enhanced ELMs. Next, available studies regarding why ELMs are used to tackle the class imbalance problem are reviewed. Furthermore, cutting-edge algorithms are surveyed for improved ELMs to detect outliers. We classify these methods under three different machine learning perspectives (i.e., supervised, unsupervised, and semi-supervised approaches). In addition, we explore the developments of existing solutions based on three standardized quality metrics (i.e., accuracy, robustness, and speed) and other performance metrics (e.g., mean absolute percentage error and mean absolute error). After that, related datasets are detailed to facilitate future studies in this field. Last but the most important, this study concludes with discussions, challenges, and suggestions to guide future research. [ABSTRACT FROM AUTHOR]

A crucial determining factor in agricultural productivity is biotic stress. In addition, supply of quality food to the ever-increasing world's population has raised the food demand tremendously. Therefore, enhanced agricultural crop productivity is the only option to mitigate these concerns. It ultimately demanded the often and indiscriminate use of synthetic agrochemicals such as chemical fertilizers, pesticides, insecticides, herbicides, etc. for the management of various biotic stresses including a variety of plant pathogens. However, the food chain and biosphere are severely impacted due to the use of such harmful agrochemicals and their byproducts. Hence, it is need of hour to search for novel, effective and ecofriendly approaches for the management of biotic stresses in crop plants. Particularly, in plant disease management, efforts are being made to take advantage of newly emerged science i.e. nanotechnology for the creation of inorganic nanoparticles (NPs) such as metallic, oxide, sulphide, etc. through different routes and their application in plant disease management. Among these, green nanomaterials which are synthesized using environmentally friendly methods and materials reported to possess unique properties (such as high surface area, adjustable size and shape, and specific functionalities) making them ideal candidates for targeted disease control. Nanotechnology can stop crop losses by managing specific diseases from soil, plants, and hydroponic systems. This review mainly focuses on the application of biologically produced green NPs in the treatment of plant diseases caused due to bacteria, viruses, and fungi. The utilization of green synthesis of NPs in the creation of intelligent targeted pesticide and biomolecule control delivery systems, for disease management is considered environmentally friendly due to its pursuit of less hazardous, sustainable, and environmentally friendly methods. [ABSTRACT FROM AUTHOR]