Correct Estimation Research Articles

Population Dynamics, Energy Consumption and Economic Growth in Nigeria

This paper assessed the nexus between population dynamics, energy consumption and economic growth in Nigeria spanning from 1989 to 2020. The empirical paper employed a Granger causality examination test and vector error correction estimation technique. The Granger causality tests found that there is unidirectional interconnection arising from gross domestic product (GDP), energy consumption (ENEG), mortality rate (MORT), and fertility rate (FERT), which turns optimistically to economic growth in Nigeria influences. Also, the VECM technique exposed that the independent variables have undesirable but no substantial influence on GDP within the period of study. Further more, the empirical study recommends that the Nigerian authority be notified to make straight efforts to control Nigeria's disturbing fertility rate.

Predicting downstream transport distance of fish eDNA in loticenvironments.

Environmental DNA (eDNA) is an effective tool for describing fish biodiversity in lotic environments, but the downstream transport of eDNA released by organisms makes it difficult to interpret species detection at the local scale. In addition to biophysical degradation and exchanges at the water-sediment interface, hydrological conditions control the transport distance. A new eDNA transport model described in this paper considers downstream retention and degradation processes in combination with hydraulic conditions and assumes that the sedimentation rate of very fine particles is a correct estimate of the eDNA deposition rate. Based on meta-analyses of available studies, the particle size distribution of fish eDNA (PSD), the relationship between the sedimentation rate and the size of very fine particles in suspension, and the influence of temperature on the degradation rate of fish eDNA were successively modelled. After combining the results in a mechanistic-based model, the eDNA uptake distances (distance required to retain 63.21% of the eDNA particles in the riverbed) observed in a compilation of previous experimental studies were correctly simulated. eDNA degradation is negligible at low flow and temperature but has a comparable influence to background transfer when hydraulic conditions allow a long uptake distance. The wide prediction intervals associated with the simulations reflect the complexity of the processes acting on eDNA after shedding. This model can be useful for estimating eDNA detection distance downstream from a source point and discussing the possibility of false positive detection in eDNA samples, as shown in an example.

Study of the fingerprints of a Spanish sample for the determination of the hand and finger

The individuality and permanence of fingerprints make of them a very useful feature in the identification of individuals. There are now automated computer programmes that allow a quick comparison between a fingermark and a database. However, in order to assess the strength of evidence in fingerprint identification, complementary information on the frequencies of the different morphological features of the dermopapillary ridges is required. This idea is used in this work as a starting point to evaluate the frequencies of the parameters used in the determination of the hand and finger in a large sample of 2600 fingerprints taken from 134 male and 127 female Spanish population. Based on these fingerprints, the frequencies of different categories of the following parameters were obtained: type of pattern, slope of the apex ridge, subtype of two-delta pattern, ridge tracing, major angle, major ridge count, bisector, rotation of the central ridge, assimilation to loops and slant. Moreover, the results have shown that these characters are useful for the determination of the hand in whorls (two-delta pattern) and loops (one-delta pattern), but not for the determination of the finger. The most useful and classificatory parameter when determining the hand of origin of a two-delta fingerprint is the slope of the apex ridge, and for the one-delta pattern, knowing the location of the delta allows the correct estimation of the hand of a fingerprint in more than 93% of the cases. The data presented in this paper are novel and can be used by latent print examiners to improve the statistical basis of their decisions in reaching conclusions.

Four-Wheeled Vehicle Sideslip Angle Estimation: A Machine Learning-Based Technique for Real-Time Virtual Sensor Development

In the last few decades, the role of vehicle dynamics control systems has become crucial. In this complex scenario, the correct real-time estimation of the vehicle’s sideslip angle is decisive. Indeed, this quantity is deeply linked to several aspects, such as traction and stability optimization, and its correct understanding leads to the possibility of reaching greater road safety, increased efficiency, and a better driving experience for both autonomous and human-controlled vehicles. This paper aims to estimate accurately the sideslip angle of the vehicle using different neural network configurations. Then, the proposed approach involves using two separate neural networks in a dual-network architecture. The first network is dedicated to estimating the longitudinal velocity, while the second network predicts the sideslip angle and takes the longitudinal velocity estimate from the first network as input. This enables the creation of a virtual sensor to replace the real one. To obtain a reliable training dataset, several test sessions were conducted on different tracks with various layouts and characteristics, using the same reference instrumented vehicle. Starting from the acquired channels, such as lateral and longitudinal acceleration, steering angle, yaw rate, and angular wheel speeds, it has been possible to estimate the sideslip angle through different neural network architectures and training strategies. The goodness of the approach was assessed by comparing the estimations with the measurements obtained from an optical sensor able to provide accurate values of the target variable. The obtained results show a robust alignment with the reference values in a great number of tested conditions. This confirms that the adoption of artificial neural networks represents a reliable strategy to develop real-time virtual sensors for onboard solutions, expanding the information available for controls.

The Influence of Terrain Smoothing on Simulated Convective Boundary-Layer Depths in Mountainous Terrain

Many applications rely on a correct estimation of the convective boundary layer (CBL) depth over mountainous terrain, but often these applications use numerical model simulations. Although models inevitably smooth terrain, the amount of smoothing depends on grid spacing. We investigate the behavior of the CBL in coarse- and fine-grid models applied to mountainous terrain by using output from an operational mesoscale modeling system and by performing quasi-idealized simulations. We investigate different areas in different climate zones using different CBL top derivation methods, grid spacing ratios, planetary boundary layer (PBL) schemes, and terrain smoothing. We find that when compared to fine-grid simulations, CBL depths are systematically larger in coarse domains over mountaintops, and to a lesser extent in valleys. On average, differences between coarse- and fine-domains over mountaintops could reach around 10%. In certain locations, differences could be as high as 25%. We attribute the result to terrain smoothing. Similarly, when using a coarse-grid CBL height (relative to mean sea level) interpolated using fine-grid terrain information, there is good agreement with fine-grid CBL depths over mountaintops and less agreement in valleys. Our results have implications for applications that use output from coarse model grids in mountainous terrain. These include inverse modeling studies (e.g., greenhouse gas budget estimations or integrated water vapor transport), PBL evaluation studies, climate research, air quality applications, planning and executing prescribed burns, and studies associated with precipitation over mountainous terrain.

Methodology Advances in Vertebrate Age Estimation.

Age is a core metric in vertebrate management, and the correct estimation of the age of an individual plays a principal role in comprehending animal behavior, identifying genealogical information, and assessing the potential reproductive capacity of populations. Vertebrates have a vertebral column and a distinct head containing a developed brain; they have played an important role in the study of biological evolution. However, biological age estimations constantly exhibit large deviations due to the diversity of vertebrate taxon species, sample types, and determination methods. To systematically and comprehensively understand age estimation methods in different situations, we classify the degree of damage to vertebrates during sample collection, present the sample types and their applications, list commonly applied methods, present methodological recommendations based on the combination of accuracy and implementability, and, finally, predict future methods for vertebrate age assessments, taking into account the current level of research and requirements. Through comprehensive data gathering and compilation, this work serves as an introduction and summary for those who are eager to catch up on related fields and facilitates the rapid and accurate selection of an evaluation method for researchers engaged in related research. This is essential to promote animal conservation and guide the smooth implementation of conservation management plans.

Estimation of phase center corrections for BDS satellites aligned to the IGS20 frame

Estimation of phase center corrections for BDS satellites aligned to the IGS20 frame

SPATIAL VARIABILITY OF METHANE EMISSIONS FROM SOILS OF WET FORESTS: A BRIEF REVIEW

SPATIAL VARIABILITY OF METHANE EMISSIONS FROM SOILS OF WET FORESTS: A BRIEF REVIEW

Adaptive STCPF for UCA pose estimation with improved GA-BPNN and multiple fading factors

In response to the challenge of pose estimation accuracy for unmanned carrier-based aircraft in complex maritime landing environments, we propose an adaptive strong tracking cubature particle filtering method based on an improved Genetic Algorithm-Based Backpropagation Neural Network (GA-BPNN) and multiple fading factors. Despite the effectiveness of strong tracking cubature Kalman filtering (STCKF) in adjusting the covariance of prediction estimation errors using fading factors, it still has limitations in terms of model correction and parameter estimation. Addressing this issue, our research delves deeper into the role of fading factors in adjusting process noise covariance and introduces two methods to maintain covariance matrix symmetry: direct matrix solving and trace matrix solving. This novel approach directly implements real-time dynamic feedback correction of system model parameters within the filtering framework, effectively resolving filtering divergence issues while maintaining a high level of conceptual clarity and interpretability. To further enhance the algorithm’s performance, we introduce an improved neural network algorithm to compensate for linear approximation errors in fading factor calculations, thereby improving filtering accuracy. Moreover, considering the limitations of cubature Kalman filtering when dealing with non-Gaussian systems, we integrate the adaptive STCKF based on the improved GA-BPNN into particle filtering as an importance function, aiming to enhance non-Gaussian processing performance and alleviate particle degeneracy issues. In conclusion, both simulation and real-world data tests confirm the superiority of our research approach.

A dynamic linkage between greenhouse gas (GHG) emissions and agricultural productivity: evidence from Ethiopia

The consequences of greenhouse gas emissions are a global problem and are felt most clearly in poor countries. Every sector in Ethiopia is affected by greenhouse gas emissions, but the productivity of the agricultural sector is particularly at risk. Although climate change is a long-term phenomenon, no in-depth macro-level studies have been conducted to guide discussion in this area. Therefore, the study fills this gap and carefully examines these impacts over time from 2019 to 2022 using the Vector Auto Regressive Model. Our results show that a 1% increase in fertilizer consumption, agricultural land, nitrous oxide (N2O) emissions, rural population, and area devoted to grain production results in a 0.28, 2.09, 15.92, 5.33, and 1.31 percent increase in agricultural yield in the long-run, respectively. A negative relationship was found between agricultural employment, agricultural methane emissions (CH4), carbon dioxide emissions (CO2), and agricultural productivity with a significance level of 5%. This means that under a black box condition, a one percent increase in agricultural employment, CH4, and CO2emissions in the country in the long run will lead to a decrease in agricultural productivity by 5.82, 17.11, and 2.75 percent respectively, as we also found that all regressors except technology adoption had an elastic relationship with agricultural productivity. The short-term error correction estimates show that the coefficient of the “speed of adjustment” term for the expected productivity equation is both statistically significant and negative. The value of the coefficient term of −0.744 shows that an adjustment of 74.4% is made each year to converge the long-run equilibrium level. Therefore, Ethiopia needs to take measures that keep the economy away from sectors that produce a lot of carbon. These must be coordinated at a global level to achieve social change towards a fair and environmentally sustainable future and to increase agricultural productivity.

Emergency physician cost awareness of common orders in emergency department and awareness impact on medical decisions in Saudi Arabia

Background: With growing emergency department visits, there has been a growth in national health expenditure. This situation is exacerbated by physicians ordering ineffective investigations and procedures. This study aimed to assess the emergency physician's awareness of cost of diagnostic tests and interventions, and how awareness of costs may impact their medical decision. Methods: This cross-sectional study was conducted among emergency physicians of the Saudi Arabia. The data were collected via self-administered questionnaire disseminated through emails to all physicians. Results: A total of 233 physicians participated, majority of whom were males (73%) and aged 24-34 years-old (70.8%). Of the included physicians, 41.2% were junior residents, and three-quarters (75.5%) of the respondents had never worked in private hospitals. Two-thirds of respondents agreed/strongly agreed that knowing financial cost would affect their clinical practice. Physicians tended to overestimate cost of drugs while underestimate the cost of diagnostics and laboratory tests. The percentage of correct estimates was higher for diagnostics and lab (

Using a wearable micro-camera for dynamic glare evaluation: Corrections and verification assessment

Micro-cameras such as the ‘vision-in-package’ (VIP) can be used as a wearable for user-centric glare estimations. Yet, there is a lack of methodologies to use such head-mounted device and their outputs to obtain reliable glare evaluations. This paper focuses proposes a methodology for dynamic glare evaluation based on wearable micro-cameras. The methodology involves a novel image rotation algorithm and pixel overflow correction based the solar disk luminance measured using an HDR camera. We tested the methodology during an experiment where 21 participants were exposed to direct sunlight in an office-like set-up in Lausanne, Switzerland. For our dataset, the pixel overflow correction avoids an underestimation of the DGP metric in 94.8% of the cases. These corrections appear crucial for the correct estimation of glare using wearable VIP cameras.

Traffic model in heterogeneous networks based on experimental data

Almost all modern networks provide data transfer between different subscriber devices types using subnets operating in different standards. Therefore, they are heterogeneous. To obtain correct estimates of the load and throughput of heterogeneous networks, it is necessary to be able to simulate such networks traffic close to real. Therefore, the traffic model development in heterogeneous networks based on experimental data is relevant. To do this, the article conducted an experimental study of real traffic in the operator “New Technologies of the XXI Century” heterogeneous network, analyzed the traffic generation main models in wireless networks, mathematical models IPP, 4IPP, IDP and 2IRP were selected for generating traffic in heterogeneous networks and determined the necessary for them application settings. Subscribers are classified depending on the subscriber devices types, the traffic types they use, and the data transferred volume. Based on the experimental data obtained, subscribers are classified depending on the subscriber devices types, the traffic types they use, as well as the data volume they transmit. A list of the subscribers’ main classes has been generated, indicating the models used to generate their traffic, and their parameters have been determined. A traffic mathematical model in heterogeneous networks has been developed, the transmission simulation of real and generated according to the developed model traffic over a heterogeneous network has been carried out, on the basis of which the correctness of the developed model has been shown. The resulting model allows us to obtain correct estimates of heterogeneous networks main characteristics using the mathematical modeling method.

Ковалентное допирование нитрида углерода карбазолом и бензохалькодиазолами: моделирование электронных свойств в альтернативных приближениях

The results of theoretical consideration of the electronic properties are presented for compounds based on carbon nitride fragments consisting of three heptazine rings (melon) covalently bonded with heterocyclic substitutes (electron acceptors like 2,1,3-benzochalcodiazoles and electron do-nors like carbazole). The simulation has been performed at two alternative levels: a molecular gas-phase model and a model of one-dimensional polymer with periodic boundary conditions. These levels have allowed comparing the energy change of frontier orbitals and the band gap of a chain polymer for the same compounds. It has been found that, on the one hand, the selenium-containing heterocycles reduce the energy change of frontier orbitals significantly more than other covalently bonded dopants of the considered series; on the other hand, the doping of melon with mere carba-zole minimizes the band gap. At efficient doping, which minimizes the energy change of frontier orbitals the highest occupied orbital is localized on the electron-donor fragment of the molecule, and the lowest unoccupied orbital is localized on the electron-acceptor fragment of the molecule. It has also been shown that the doped melons form complexes with benzyl alcohol due to appear-ance of non-covalent bonds between them. Wherein, the lower strength of such bonds in complex-es with melon, substituted with the electron-acceptor selenium-containing fragments, suggests more efficient oxidation of an alcohol in such systems. The simulation results appropriately corre-spond to the available experimental data of the band gap of melon doped with electron acceptor molecules. The fast gas-phase calculations are suitable for qualitative estimation of dependences of the energy change of frontier orbitals upon their type and the amount of covalently bonded do-pant, whereas the more expensive calculations of polymer structures allow a correct estimation of the band gap values of carbon nitride polymers with various dopants.

Evidence of Age Estimation Procedures in Forensic Dentistry: Results from an Umbrella Review.

Background and objective: Age estimation is an important tool when dealing with human remains or undocumented minors. Although the skull, the skeleton or the hand-wrist are used in age estimation as maturity indicators, they often present a lack of good conditions for a correct identification or estimation. Few systematic reviews (SRs) have been recently published; therefore, this umbrella review critically assesses their level of evidence and provides a general, comprehensive view. Materials and methods: Considering the review question "What is the current evidence on age determination approaches in Forensic Dentistry?" an electronic database search was conducted in four databases (PubMed, Cochrane, WoS, LILACS) up to December 2022, focusing on SRs of age estimation through forensic dentistry procedures. The methodological quality was analyzed using the measurement tool to assess SRs criteria (AMSTAR2). Results: Eighteen SRs were included: five of critically low quality, six of low quality, three of moderate quality and four of high quality. The SRs posited that Willems' method is more accurate and less prone to overestimation; most methods seem to be geographically sensitive; and 3D-imaging and artificial intelligence tools demonstrate high potential. Conclusions: The quality of evidence on age estimation using dental approaches was rated as low to moderate. Well-designed clinical trials and high-standard systematic reviews are essential to corroborate the accuracy of the different procedures for age estimation in forensic dentistry.

АВТОМАТИЧНИЙ АНАЛІЗ ЗАВАНТАЖЕНОСТІ РАДІОЧАСТОТНОГО СПЕКТРА ТА СЕЛЕКЦІЯ СИГНАЛІВ

Constant increasing of radio-electronic devices number leads to an increase of spectrum occupancy and complication of radio-electronic environment. In such conditions, it is necessary to use automatic algorithms for prompt detection and selection of radio signals in radio monitoring systems. The purpose of the article is to develop algorithms and recommendations for their practical implementation to automate the process of radio frequency spectrum occupancy analysis and signal selection in a wide dynamic range with high occupancy of analyzed frequency band in conditions of a priori uncertainty of noise level. To achieve the stated goal were developed generalized algorithm for radio frequency spectrum analysis, algorithm of frequency domain processing and algorithm for frequency channels analysis. Examples of spectrum bands analysis with high occupancy and recommendations for developed algorithms implementation were represented. Depending on variability of noise power in analyzed frequency band an adaptive change of estimation time interval is provided, which will reduce the computational complexity. In conditions of no uniform noise level or a wide dynamic range of signals, it is proposed to use special test statistics to determine fullness of analyzed frequency channels. In the course of experimental studies, the analysis of recordings in frequency bands of 900 MHz, 1800 MHz and 2100 MHz was carried out. It was established that the developed algorithms provide a correct estimation of radio frequency spectrum occupancy and selection of signals with a dynamic range of at least 35 dB and occupancy of up to 92%. Developed algorithms do not require calibration of the equipment in case of fluctuations in characteristics of separate system elements and changes in radio-electronic environment and can be implemented in existing and perspective automatic radio monitoring systems. Keywords: radio frequency spectrum; spectrum occupancy; signal selection; test statistics; frequency channel.

THE RESULTS OF THEORETICAL STUDIES FOR DETERMINING OPERATIONAL LOADS ON GRAVITY-TYPE BERTHING STRUCTURES

The current waterfront of Ukrainian ports includes structures that have been developed in the past and have over 50 years of experience. Open-piled quay walls and sheet-pile quay walls are the most common types of quay walls used for berths in Ukrainian ports. However, there are gravity-type quay walls. The share in the total the waterfront is not large. They were built in the past and require modernization and reconstruction. Most of these berthing structures have defects in concrete and reinforcement, which reduce the durability and bearing capacity. Furthermore, the development of freight and passenger maritime traffic and the construction of modern ships led to the need to increase the depths at existing berths and define the operational loads meeting modern requirements. Thus, the issue of reconstruction of gravity-type quay walls is relevant for many ports of Ukraine. The choice of the reconstruction method depends on the correct estimation of the actual technical condition of an existing structure. Gravity-type structures are those that rely primarily on their weight and grip on the foundations to resist any possible adverse load combinations. The requirements for such structures lead to the solution of one of the main tasks ‒ the determination of the reactive capacity of the soil base. The reactive capacity calculation of the soil bases for considered structures is essential. The purpose of the calculation is to provide both strength and stability of soil bases. An improved method for determining the reactive capacity of the soil base of gravity-type quay walls has been developed. This method allows determining the reactive capacity of the soil base in conditions of the mixed stress state (limit and sublimit stress state zones in the soil base are considered). This paper reviews some results of applying the proposed method for the reactive capacity estimation of the soil base of gravity-type quay walls. The obtained results have been used to analyze the preliminary reconstruction options for the mentioned structures and determine operational loads.

Near-infrared digital hemispherical photography enables correction of plant area index for woody material during leaf-on conditions

Indirect optical measurement techniques enable efficient and non-destructive estimation of plant area index (PAI). However, because they cannot distinguish between foliage and other canopy elements, corrections are needed to determine leaf area index (LAI), which is typically the property of interest. In this study, we investigate near-infrared digital hemispherical photography (DHP) as a means of estimating and correcting for woody material. Using data collected at a deciduous broadleaf forest site, we show that near-infrared DHP could successfully estimate effective wood area index (WAIe) and wood area index (WAI) during leaf-on conditions, providing similar mean values (WAIe = 0.88, WAI = 1.53) to those determined from visible DHP during leaf-off conditions (WAIe = 0.87, WAI = 1.38). This information was used to correct estimates of effective PAI (PAIe) and PAI, enabling effective LAI (LAIe) and LAI to be derived with low RMSD (0.33 for LAIe and 0.76 for LAI), NRMSD (12% for LAIe and 19% for LAI), and bias (−0.01 for LAIe and −0.16 for LAI). Not correcting for woody material led to overestimation of LAIe by 31% on average and 46% in the worst observed case, and the degree of overestimation was further enlarged for LAI (42% on average and 61% in the worst observed case). In agreement with previous studies, the effects of clumping and woody area were found to be partly compensatory. On average, PAIe provided a reasonable approximation of LAI without correction, though overestimation of 52% and underestimation of 20% occurred at the lowest and highest LAI values, respectively. Compared to WAIe and WAI measurement using leaf-off visible DHP, near-infrared DHP offers two crucial advantages: i) data collection can be conducted at the same time as leaf-on PAIe and PAI measurements, and ii) it is likely that the approach could provide an indirect WAIe and WAI measurement option for evergreen species.

Optimized procedure for conventional TEM sample preparation using birefringence

Specimens for quality transmission electron microscopy (TEM) analyses must fulfil a range of requirements, which demand high precision during the prior preparation process. In this work, an optimized procedure for conventional TEM specimen preparation is presented that exploits the thickness-dependence of interference colors occurring in birefringent materials. It facilitates the correct estimation of specimen thickness to avoid damage or breaking during mechanical thinning and reduces ion-milling times below 30minutes. The benefits of the approach are shown on sapphire and silicon carbide cross-section samples. The presented method is equally suitable for assessing specimen thickness during dimpling and wedge-polishing, and is particularly useful at thicknesses below 20 μm, where the accuracy of mechanical techniques is insufficient. It is precise enough to be employed for a visual thickness estimation during the thinning process, but can be additionally optimized by analyzing the RGB spectrum of the occurring interference colors.

Deep learning reduces data requirements and allows real-time measurements in imaging FCS

Imaging fluorescence correlation spectroscopy (FCS) is a powerful tool to extract information on molecular mobilities, actions, and interactions in live cells, tissues, and organisms. Nevertheless, several limitations restrict its applicability. First, FCS is data hungry, requiring 50,000 frames at 1-ms time resolution to obtain accurate parameter estimates. Second, the data size makes evaluation slow. Third, as FCS evaluation is model dependent, data evaluation is significantly slowed unless analytic models are available. Here, we introduce two convolutional neural networks—FCSNet and ImFCSNet—for correlation and intensity trace analysis, respectively. FCSNet robustly predicts parameters in 2D and 3D live samples. ImFCSNet reduces the amount of data required for accurate parameter retrieval by at least one order of magnitude and makes correct estimates even in moderately defocused samples. Both convolutional neural networks are trained on simulated data, are model agnostic, and allow autonomous, real-time evaluation of imaging FCS measurements.