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A multi responsive Schiff base sensor L with thiophenol moiety was synthesized by the single-step condensation method. The structural analysis of the sensor was proposed by 1H-NMR, UV- Visible absorption, HRMS and FT-IR spectroscopy. Sensor L exhibited an instant appearance of yellow color on the addition of copper ions. Since copper gave an intense color change, therefore detailed studies were performed for copper sensing w.r.t. designed sensor L. Selectivity and sensitivity of sensor concerning copper metal ions were established by performing various UV-Visible spectroscopic and colorimetric studies. The 1:1 binding framework of the L-Cu2+ complex is confirmed by Job’s Plot and further supported by DFT studies. With the help of Benesi-Hildebrand equation, the detection limit and the association constants were found to be $3.15 \times 10^{-6}$ M and $1.16 \times 10^{4}$ M−1, respectively. In addition, sensor L was transferred onto solid substrates including silica and paper strips for establishing its utility in the onsite detection of copper ions. [ABSTRACT FROM AUTHOR]

This editorial summarizes the papers published in issue 16(4) (2021). The first paper adopts a higher order spatial autoregressive model with endogenous spatial weight matrices. The second paper investigates the existence of the law of one price using regional observations over time. The third paper develops an economic-theoretical model that goes against the common belief that the most productive individuals and firms agglomerate at the core. The fourth paper provides empirical evidence that merger and acquisition deals are more likely to occur between firms in culturally than in geographically contiguous countries. The fifth paper develops a spatial econometric estimator based on the indirect inference principle. The sixth paper examines the investment behaviour of First Nation governments through joint ventures. The seventh paper employs a spatial econometric model with an endogenous spatial weight matrix to construct intraregional input-output models. [ABSTRACT FROM AUTHOR]

This editorial summarizes the papers published in issue 17(1) (2022). This issue begins with a second editorial calling on researchers to publish replication results from previous studies. The first paper applies a spatiotemporal Bayesian hierarchical model for understanding the dynamics of second home ownership in Corsica. The second paper determines the optimal time to invest in a new airport using real options analysis. The third paper employs unit root tests to provide empirical evidence that environmental policy changes have not been effective up to now. The fourth paper provides empirical evidence that regional spillover effects should play a crucial role in the policy discussion about climate change. The fifth paper forecasts the direct impact of climate change on crop yields in the agricultural sector and the indirect impacts on other sectors of the Brazilian economy up to 2100. The sixth paper investigates whether the percentage of women in national parliaments positively affects public expenditures on social needs both internally and in neighbouring countries. The seventh paper sets out a general framework for store sales evaluation and prediction. [ABSTRACT FROM AUTHOR]

This paper investigates the practicality of an autonomous and resilient positional navigation using corner cube reflectors on the lunar surface. The proposed approach stands as an efficient navigation solution when the current system, e.g. GPS, is unavailable due to being attacked, jammed or spoofed by a third party. The paper provides a complete setup of the mathematical models using a nonlinear least squares algorithm and covariance analysis. A study is done that determines how the blockage of the Earth affects the proposed solution’s ability to perform positional navigation. Numerical simulations are presented to show the accuracy, performance and practicality of the proposed solution. The results of the proposed solution are compared with the results of an Earth-based ground station system that uses radio frequency to track the given satellite. The results show that the lunar reflector-based system generates more accurate results than the Earth-based ground station system. [ABSTRACT FROM AUTHOR]

Estimating depth from RGB images is a long-standing ill-posed problem, which has been explored for decades by the computer vision, graphics, and machine learning communities. Among the existing techniques, stereo matching remains one of the most widely used in the literature due to its strong connection to the human binocular system. Traditionally, stereo-based depth estimation has been addressed through matching hand-crafted features across multiple images. Despite the extensive amount of research, these traditional techniques still suffer in the presence of highly textured areas, large uniform regions, and occlusions. Motivated by their growing success in solving various 2D and 3D vision problems, deep learning for stereo-based depth estimation has attracted a growing interest from the community, with more than 150 papers published in this area between 2014 and 2019. This new generation of methods has demonstrated a significant leap in performance, enabling applications such as autonomous driving and augmented reality. In this paper, we provide a comprehensive survey of this new and continuously growing field of research, summarize the most commonly used pipelines, and discuss their benefits and limitations. In retrospect of what has been achieved so far, we also conjecture what the future may hold for deep learning-based stereo for depth estimation research. [ABSTRACT FROM AUTHOR]

Currently, SLAM (simultaneous localization and mapping) systems based on monocular cameras cannot directly obtain depth information, and most of them have problems with scale uncertainty and need to be initialized. In some application scenarios that require navigation and obstacle avoidance, the inability to achieve dense mapping is also a defect of monocular SLAM. In response to the above problems, this paper proposes a method which learns depth estimation by DenseNet and CNN for a monocular SLAM system. We use an encoder-decoder architecture based on transfer learning and convolutional neural networks to estimate the depth information of monocular RGB images. At the same time, through the front-end ORB feature extraction and the back-end direct RGB-D Bundle Adjustment optimization method, it is possible to obtain accurate camera poses and achieve dense indoor mapping when using estimated depth information. The experimental results show that the monocular depth estimation model used in this paper can achieve good results, and it is also competitive in comparison with the current popular methods. On this basis, the error of camera pose estimation is also smaller than traditional monocular SLAM solutions and can complete the dense indoor reconstruction task. It is a complete SLAM system based on monocular camera. [ABSTRACT FROM AUTHOR]

Building and other construction assets constitute a significant part of a country's physical and economic infrastructure. According to several writers, the knowledge of reliable data of building and other construction assets of a specific country or region is a crucial element for the long-term management of these assets. Built capital stock statistics at the national or international levels have been available for most countries of the world, both developed and less developed ones, for some time, but construction infrastructure stock statistics at the disaggregated level are very scarce, even for most developed countries. Furthermore, the methodologies to produce the estimates of built capital stock, at the international level, do not consider countries' specificities. This paper discusses the methodologic issues for producing construction infrastructure stock statistics for Cape Verde, and makes estimates for the period 1980-2019. The paper outlines the Perpetual Inventory Method (PIM) used to produce capital estimation, data employed, and the assumptions made to estimate missing data. The paper analyses the level of the construction infrastructure stock estimates for Cape Verde, as well as their impact on the development pattern of the country's construction industry, and suggests how further studies can enhance our comprehension of the relationship between construction investment and economic growth and development. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of process faults in the Lorenz system that can affect any of the system parameters and cause the system to exhibit various behaviors. In this paper, the homoclinic orbits in the Lorenz system are described and then the occurrence of process faults in the system is investigated that can cause a homoclinic explosion, bifurcation, change of fixed point, or even instability in the system. In such systems, where a small change in one of the parameters causes large changes in the behavior of the system, to prevent disaster in industrial systems and also to stop the propagation of faults in the system, the faults must be identified as soon as possible. In this paper, the states in the system are estimated by using a reduced-order observer, and the faults are detected. The purpose of this article is to recognize the change in behavior of this system in the face of this type of fault and to express the importance of timely detection and identification of faults in the system so as not to lead to failure and disaster in industrial systems. Finally, the effect of process fault, disturbance, and sensor fault are investigated simultaneously and the states and faults in the system are estimated. [ABSTRACT FROM AUTHOR]

An application of moving-horizon strategy for nonlinear systems with possible outliers in measurements is addressed. With the increased success of movinghorizon strategy in the state estimation for linear systems with outliers acting on the measurement, investigating the nonlinear approach is highly required. In this paper we applied the nonlinear version which has been presented in the literature in term of discrete-time linear time-invariant systems, where the applied strategy considers minimizing a least-squares functions in which each measure possibly contaminated by outlier is left out in turn and the lowest cost is propagated. The moving horizon filter effectiveness as compared with the extended Kalman filter is shown by means of simulation example and estimation error comparison. The moving horizon filter shows the feature of resisting outliers with robust estimation. [ABSTRACT FROM AUTHOR]

This paper analyses the development of private actions for damages as a significant pillar of private enforcement of EU competition law and discusses the quantification and estimation of harm. Since the adoption of the EU Directive 2014/104/EU on Antitrust Damages Actions, private enforcement in Europe has undergone crucial clarifications in the case law of EU courts, yet the critical issue of quantifying damages in private actions has only recently been addressed by the Court of Justice of the European Union (CJEU). This paper takes a closer look at the recent ruling in the Tráficos Manuel Ferrer case (C-312/21) and assesses the implications of that case law for private enforcement proceedings. Concerning the allocation of procedural costs, this judgment has established that injured parties may be required to bear a cost risk, even if they are partially successful. This finding needs further reflection in the context of consumer claims for damages, as it is susceptible to add an additional cost burden on plaintiffs. The second intricate finding concerns the relation between the ability of national courts to estimate damages and the exhaustion of evidence disclosure. Taking the example of Spain, the paper advocates for the specific inclusion of the principle of proportionality when determining harm in private actions for damages and especially in cases involving consumers or smaller harmed parties. [ABSTRACT FROM AUTHOR]

A recent paper introduced the interpolating family (IF) of distributions, and they also derived various mathematical properties of the family. Some of the most important properties discussed were the integer order moments of the IF distributions. The moments were expressed as an integral (which were not evaluated) or as finite sums of the beta function. In this paper, more general expressions for moments of any integer order or any real order are derived. Apart from being more general, our expressions converge for a wider range of parameter values. The expressions for entropies are also derived, the maximum likelihood estimation is considered and the finite sample performance of maximum likelihood estimates is investigated. [ABSTRACT FROM AUTHOR]

In this paper, we study the evasion game of high speed evader involving two pursuers and a single evader with geometric constraints on the control parameters of the players in the plane. The game is described by linear equations. Evasion is said to be possible if the state of the evader doesn’t coincide with the state of any pursuer for all time. We construct an evasion strategy for the evader which ensure completion the evasion game for any initial positions of players. In addition, we introduce a new concept of approach times and demonstrate that the number of approach times does not exceed 3. [ABSTRACT FROM AUTHOR]

Due to the emergence of social networking platforms, a large number of users around the world are being part and partial of this platform. At a fraction of the time users on social media are communicating digital files in the form of text, video, images, voice and music which ultimately generates big data. The matter of interest is to estimate precisely the average file size at time duration (occasion). The time may hours or days or months. This paper presents a sample-based methodology to deal with mean size estimation of digital communication content spreading on a social media platform. An estimator is suggested using a random sample from big data and its properties are derived. A simulation method is suggested that computes the confidence interval (CI) for the prediction of précised range of digital file size. The proposed method produces an optimal confidence interval at the suitable choice of constant. These estimated confidence intervals can be used for developing α-control charts for constant monitoring of the growth in file size in social media storage at the data centre. If the growth of mean digital file size crosses the upper limit then additional storage infrastructure is needed at the administration level of the social media site. One can generate machine learning algorithms proposed method for monitoring the growth of average digital file size over time duration. [ABSTRACT FROM AUTHOR]

Causal inference involves determining whether a treatment (e.g., an education program) causes a change in outcomes (e.g., academic achievement). It is well-known that causal effects are more challenging to estimate than associations. Over the past 50 years, the potential outcomes framework has become one of the most widely used approaches for defining, identifying, and estimating causal effects. In this paper, we review the potential outcomes framework with a focus on potential outcomes notation to define individual and average causal effects. We then show how three canonical assumptions, Unconfoundedness, Positivity, and Consistency, may be used to identify average causal effects. The identification results motivate methods for estimating causal effects in practice, which include model-based estimators, such as regression, inverse probability weighting, and doubly robust estimation, and procedures that target covariate balance, such as matching and stratification. Examples and discussion are grounded in the context of a running example of a study aimed at assessing the causal effect of receipt of special education services on 5th grade mathematics achievement in school-aged children. Practical considerations for education research are discussed. [ABSTRACT FROM AUTHOR]

Ridge Estimation (RE) is a widespread method to overcome the problem of collinearity defining a class of estimators depending on the non-negative scalar parameter k. A great number of papers focus on the estimation of this biasing parameter. Traditionally, the mean squared error criterion is used to compare the performance of the different proposed estimators. However, the minimization of the mean squared error (MSE) does not always guarantee the mitigation of collinearity, meaning it is possible, for example, to obtain a variance inflation factor (VIF) higher than 10 for the k that minimizes the MSE. In this paper, we propose the VIF criteria to select the biased ridge parameter. A Monte Carlo simulation is presented with results that support this idea. Also, two real life empirical applications are used to illustrate the contribution of this paper. [ABSTRACT FROM AUTHOR]

This paper proposes a new probability model called as Maxwell-Boltzmann-Exponential (MBE) distribution. The MBE distribution arises as a mixture distribution of the Maxwell-Boltzmann and exponential distributions. The statistical properties of the distributions are studied and obtained in closed-form expressions. Three methodologies are assessed and compared for the estimation of parameters in the MBE distribution. The MBE regression model is defined, with the proposed regression model being an alternative to the gamma regression model for response variables that are extremely right-skewed and bimodal. Two real data sets are used to demonstrate the applicability of the proposed models against the existing models. [ABSTRACT FROM AUTHOR]

Emotion recognition is a critical component of affective computing. Training accurate machine learning models for emotion recognition typically requires a large amount of labeled data. Due to the subtleness and complexity of emotions, multiple evaluators are usually needed for each affective sample to obtain its ground-truth label, which is expensive. To save the labeling cost, this paper proposes an inconsistency-based active learning approach for cross-task transfer between emotion classification and estimation. Affective norms are utilized as prior knowledge to connect the label spaces of categorical and dimensional emotions. Then, the prediction inconsistency on the two tasks for the unlabeled samples is used to guide sample selection in active learning for the target task. Experiments on within-corpus and cross-corpus transfers demonstrated that cross-task inconsistency could be a very valuable metric in active learning. To our knowledge, this is the first work that utilizes prior knowledge on affective norms and data in a different task to facilitate active learning for a new task, even the two tasks are from different datasets. [ABSTRACT FROM AUTHOR]

When data exhibit a high frequency of small to medium values and a low frequency of large values, fitting a classical distribution might fail. This is why spliced models defined from different distributions on distinct intervals are proposed in the literature. In contrast to the intensive study of two-spliced distributions, the case with more than two components is scarcely approached. In this paper, we focus on three-spliced distributions and on their ability to improve the modeling of extreme data. For this purpose, we consider a popular insurance data set related to Danish fire losses, to which we fit several three-spliced distributions; moreover, the results are compared to the best-fitted two-spliced distributions from previous studies. [ABSTRACT FROM AUTHOR]

In this paper, an on-line closed-loop identification of mainly mass and inertia of an under-actuated aerial vehicle, namely a quadrotor acting as an aerial manipulator, is presented. Being treated as a rigid body, only one set of eighteen- or six tri-axial linear accelerometers is used to facilitate such estimation. Force and torque disturbances acting upon the vehicle during identification are also estimated which will refine the overall estimation quality of the unknown parameters. Namely, recursive linear and nonlinear least squares methods are used to obtain initial and refined estimations, respectively. Random disturbances are introduced to the closed-loop system to ensure enough excitation of the overall system. Issues related to identifiability, stability and performance are discussed. Since the proposed method depends on general kinematical and dynamical analyses of motion, it is claimed that the presented method will be also applicable to a wider range of, mainly, aerial vehicles. [ABSTRACT FROM AUTHOR]

This paper presents the overview and rationale behind the Decoder-Side Depth Estimation (DSDE) mode of the MPEG Immersive Video (MIV) standard, using the Geometry Absent profile, for efficient compression of immersive multiview video. A MIV bitstream generated by an encoder operating in the DSDE mode does not include depth maps. It only contains the information required to reconstruct them in the client or in the cloud: decoded views and metadata. The paper explains the technical details and techniques supported by this novel MIV DSDE mode. The description additionally includes the specification on Geometry Assistance Supplemental Enhancement Information which helps to reduce the complexity of depth estimation, when performed in the cloud or at the decoder side. The depth estimation in MIV is a non-normative part of the decoding process, therefore, any method can be used to compute the depth maps. This paper lists a set of requirements for depth estimation, induced by the specific characteristics of the DSDE. The depth estimation reference software, continuously and collaboratively developed with MIV to meet these requirements, is presented in this paper. Several original experimental results are presented. The efficiency of the DSDE is compared to two MIV profiles. The combined non-transmission of depth maps and efficient coding of textures enabled by the DSDE leads to efficient compression and rendering quality improvement compared to the usual encoder-side depth estimation. Moreover, results of the first evaluation of state-of-the-art multiview depth estimators in the DSDE context, including machine learning techniques, are presented. [ABSTRACT FROM AUTHOR]

Copyright of SDU Journal of Engineering Sciences & Design / Mühendislik Bilimleri ve Tasarım Dergisi is the property of Journal of Engineering Sciences & Design and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the count data set, the frequency of some points may occur more than expected under the standard data analysis models. Indeed, in many situations, the frequencies of zero and of some other points tend to be higher than those of the Poisson. Adapting existing models for analyzing inflated observations has been studied in the literature. A method for modeling the inflated data is the inflated distribution. In this paper, we extend this inflated distribution. Indeed, if inflations occur in three or more of the support point, then the previous models are not suitable. We propose a model based on zero, one, ... , and k inflated points with probabilities w 0 , w 1 , ... , and w k , respectively. By choosing the appropriate values for the weights w 0 , ... , w k , various inflated distributions, such as the zero-inflated, zero–one-inflated, and zero–k-inflated distributions, are derived as special cases of the proposed model in this paper. Various illustrative examples and real data sets are analyzed using the obtained results. [ABSTRACT FROM AUTHOR]

Estimation of the roughness coefficient is important for reliable hydraulic design in erodible channels. In this paper, the capability of multi-gene Genetic Programming (GP), a combined Genetic Algorithm and Fuzzy Inference System (GA-FIS) model, and Multi Regression (MR) methods are employed to estimate the roughness coefficient. These methods try to extract either an explicit or an implicit relationship between the roughness coefficient and input variables. In addition, traditional GP, widely used by researchers, and conventional empirical formulas are implemented to evaluate the models. Results show that the employed methods are more accurate than empirical methods. In addition, the effects of some other parameters, such as non-dimensional water depth and shear Reynolds number, are highlighted over the roughness coefficient while previously ignored in the empirical methods. Also, findings prove that the GA is a helpful tool to optimize a FIS compared with gradient-based models like ANFIS, while the scale of input variables is not in the same order. The R2 for multi-gene GP and GA-FIS are 0.8504 and 0.8842, respectively, while this value for the most accurate empirical method (Yalin, 1992) is 0.6286. [ABSTRACT FROM AUTHOR]

The estimation of agricultural output is an important issue in the digital development of modern agriculture. Using embedded technology, this paper designs a multimedia platform for digital estimation of agricultural output. The terminal design of the multimedia platform selects the Intel Xscale series PXA255 processor as the core processor, uses multiple sensors to collect agricultural output-related environmental parameters and selects the digital media processing chip TMS320DM642 for multimedia processing. The software used selects the grey relational Support Vector Machine (SVM) algorithm to estimate agricultural output. First, the collected agricultural sown area, labor force, mechanical power, irrigated area, agricultural production environment and other parameters are collected as input to grey relational SVM. Second, the estimated results are outputted. Finally, the results of agricultural output estimation are digitally displayed by a display interface on the embedded multimedia terminal. The experimental results show that the prediction error is less than 1%, and the prediction accuracy is higher than 99%, which validates that the method can accurately predict agricultural output with an abundance of applications. [ABSTRACT FROM AUTHOR]

In a global scenario, the exhaust emissions of automobiles need to be regulated. These emission regulations are met by maintaining the air-fuel ratio resulting in improved automobile performance like better engine torque and fuel economy. The air-fuel ratio of Spark Ignition (SI) engines needs to be maintained at a stoichiometric value of 14.7:1. To maintain this ratio, the estimation of the in-cylinder mass airflow rate is highly significant. One of the important parameters that influence the mass airflow rate is Manifold Absolute Pressure (MAP). The conventional methods that make use of MAP sensors have oscillations at engine firing frequency and poor dynamic response. This research paper uses an Unscented Kalman Filter (UKF) for the estimation of MAP in SI engines. The complete system consisting of the engine model and UKF algorithm has been simulated in Matlab/Simulink. With Root Mean Square Error (RMSE) as the performance metric, the accuracy in the estimation of MAP with UKF is higher than the Extended Kalman Filter (EKF). Hence, the UKF algorithm works out to be a better estimator for the efficient fuel injection in an engine management system. [ABSTRACT FROM AUTHOR]

Capturing the ‘mutual gaze’ of people is essential for understanding and interpreting the social interactions between them. To this end, this paper addresses the problem of detecting people Looking At Each Other (LAEO) in video sequences. For this purpose, we propose LAEO-Net++, a new deep CNN for determining LAEO in videos. In contrast to previous works, LAEO-Net++ takes spatio-temporal tracks as input and reasons about the whole track. It consists of three branches, one for each character's tracked head and one for their relative position. Moreover, we introduce two new LAEO datasets: UCO-LAEO and AVA-LAEO. A thorough experimental evaluation demonstrates the ability of LAEO-Net++ to successfully determine if two people are LAEO and the temporal window where it happens. Our model achieves state-of-the-art results on the existing TVHID-LAEO video dataset, significantly outperforming previous approaches. Finally, we apply LAEO-Net++ to a social network, where we automatically infer the social relationship between pairs of people based on the frequency and duration that they LAEO, and show that LAEO can be a useful tool for guided search of human interactions in videos. [ABSTRACT FROM AUTHOR]

This paper proposes a deep learning framework to design distributed compression strategies in which distributed agents need to compress high-dimensional observations of a source, then send the compressed bits via bandwidth limited links to a fusion center for source reconstruction. Further, we require the compression strategy to be progressive so that it can adapt to the varying link bandwidths between the agents and the fusion center. Moreover, to ensure scalability, we investigate strategies that depend only on the local channel state information (CSI) at each agent. Toward this end, we use a data-driven approach in which the progressive linear combination and uniform quantization strategy at each agent are trained as a function of its local CSI. To deal with the challenges of modeling the quantization operations (which always produce zero gradients in the training of neural networks), we propose a novel approach of exploiting the statistics of the batch training data to set the dynamic ranges of the uniform quantizers. Numerically, we show that the proposed distributed estimation strategy designed with only local CSI can significantly reduce the signaling overhead and can achieve a lower mean-squared error distortion for source reconstruction than state-of-the-art designs that require global CSI at comparable overall communication cost. [ABSTRACT FROM AUTHOR]