Lower Bound Value Research Articles

Cooperative Mission Planning Using Rollout Policy Optimization

This paper proposes a novel cooperative mission planning for uncrewed aerial systems (UAS), using roll-out policy optimization and a blend of straight and double-Archimedean-spiral (DAS) paths. Typical mission tasks using UAS, possibly multiple, include visiting distinct locations/waypoints and covering specific areas of interest (AOIs). For the UAS, to guarantee the coverage of the AOI, the algorithm generates a DAS path that even accounts for the sensor’s field-of-view constraints. A scalar spiral density parameter in the algorithm effectively provides customized adjustments to the length of the DAS over an AOI while developing mission plans. A lower and upper bound value for the spiral density parameter and the dependency of the parameter on the coverage of an AOI are derived. This scalar parameter gives the users potential flexibility in the design of mission plans. Roll-out policy optimization minimizes the overall path length for the cooperative UAS mission and, as a direct consequence, minimizes time and fuel. This method leads to finding a near-optimal path in that the computation time does not grow exponentially with the number of UAS. Results from the simulation studies with several cooperative mission scenarios, including multiple UASs visiting several waypoints and AOIs starting from identical and distinct initial locations, demonstrate the efficacy of the proposed concept. The results from a data-based complex test case using the LiDAR data set further demonstrate the adaptability of the proposed mission planning approach in a real-world setting.

Weakly-supervised auto-encoder via energy regularization and soft multi-label learning on k labeled samples

Image classification is a hot topic in computer vision tasks. As a simple unsupervised network model, auto-encoder can learn and apply features to classification. However, due to the lack of prior knowledge of auto-encoder, there are significant limitations in their performance in practical applications. On the contrary, although precise labels can guide supervised learning to learn accurately, it is difficult to obtain strong supervised information on many tasks because of the high cost of data annotation. In this paper, we propose a weakly-supervised auto-encoder via energy regularization and soft multi-label learning on k labeled samples (WSAE), where weakly-supervised learning not only reduces the workload of manual labeling but also introduces supervised information, which improves the performance of auto-encoder to a large extent. Specifically, we first explore a lower bound value of training data that needs to be labeled in weakly supervised learning. Then, we discriminate the visual consistency of k labeled samples compared to unlabeled samples with soft multi-label to efficiently increase the difference of features between classes and reduce the variation of features within classes for more discriminative feature representations and higher classification accuracy. In addition, an energy regularization is introduced to the model to fit the probability distribution of the data so that the smaller the energy, the more concentrated the probability distribution and the more ordered the system. The model with energy regularization can represent the data better, thereby improving the feature extraction ability and increasing the classification accuracy of the network. Extensive experiments are conducted on some publicly available datasets and demonstrate that our method outperforms the state-of-the-art methods.

Gaussian random field-based characterization and reconstruction of cancellous bone microstructure considering the constraint of correlation structure

The macro scale physical properties of cancellous bone materials are governed by the microstructural features, which is of great significance for the multi-scale research of cancellous bone and the inverse design of bone-mimicking materials. Therefore, it is essential to characterize the natural cancellous bone samples, and reconstruct the microstructures with the biomimetic osteointegration and mechanical properties. In this research, a novel approach for the characterization and reconstruction of cancellous bone was proposed, based on the medical image analysis and anisotropic three-dimensional Gaussian random field (GRF). The geometric similarity, i.e. the interface curvature distribution (ISD), was meticulously studied, which is important to the osteointegration ability. And the mechanical properties were validated by the stress-strain curves under the large compressive strain simulated by the smoothed particle hydrodynamic (SPH) method. In addition, the effects of the generation parameters of GRF-based biomimetic microstructures on the apparent properties were analyzed. The ISD results demonstrated that both GRF and micro-CT groups had the similar columnar morphological properties, while the latter had more hyperbolic features. And it was found that the GRF-based biomimetic microstructures and the natural bone samples based on micro-CT (MCT) had the similar failure mode. The concordance correlation coefficient between MCT and GRF pairs was 0.8685, with a Pearson ρ value of 0.8804, and significance level p<0.0001. The Bland-Altman LoA was 0.1647 MPa with 95 % (1.96SD) lower and upper bound value between −0.2892 and 0.6185 MPa. The two groups had almost the same elastic modulus with the mean absolute percentage error (MAPE) of 7.84 %. While the yield stress and total conversion energy of the GRF-based samples were lower than those of the natural bone samples, and the MAPE were 16.99 % and 16.27 %, respectively. Although it meant the lower structural efficiency, the huge design space of this approach and advanced 3D printing technology can provide great potential for the design of orthopedic implants.

Tam Bulanık Lineer Denklem Sistemleri için Bulanık Sayısal Simülasyon Tabanlı Sezgisel Bir Yöntem

In this paper, a new method is proposed to find the approximate solutions to fully fuzzy systems of linear equations (FFSLEs). The technique integrates a bisection method with Fuzzy Numerical Simulation (FNS). The procedure starts with generating single values of fuzzy parameters and solving the resulting crisp problems repeatedly to determine the lower and upper bounds of the solutions. After computing the mean lower and upper bound values, the obtained supremum and infimum values are considered to be the lower and upper bounds of the solutions, respectively. It is attempted to improve solutions by considering an error function related to the sum of the absolute differences between the corresponding lower and upper bounds of the left and right sides of the equalities. When very large intervals are obtained for the solutions, the bisection algorithm is applied to reduce the error value. The method intends to solve square systems of large dimensions for arbitrary fuzzy numbers (FNs) by removing non-negativity confinements of the variables and/or coefficients to be more realistic. After the computational method is presented thoroughly, some benchmark examples are finally provided.

Cultural Determinants of Community in Giving Breast Milk Complementary Food to Babies in Kotamobagu City

In Indonesia, achieving the proportion of timely provision of MP-ASI still needs to improve. Providing nutritious food for children and families is inseparable from the culture of the local community, especially the provision of complementary foods for breastfeeding, but cultural principles often contradict the principles of health. This study aims to ascertain the community's cultural norms regarding administering MP-ASI to infants—the cross-sectional design of this study's quantitative descriptive methodology. The population is 231 people. A sample of 148 people was determined based on the Slovin formula. The questionnaire measurement scale uses a Likert scale approach (1-5). Odds ratio analysis is used to analyze data. The study's findings revealed that most mothers with good knowledge in responding to their culture also had good complementary feeding (98.5%), where the odds ratio obtained was 19.950 with a P value. 0.002, with a Lower Bound value of 2.981 and an Upper Bound of 133.533. A good mother's attitude in responding to its relation to her culture mostly shows the proper complementary feeding (94%), with an odds ratio of 4.762 and a significance level of 0.038, a lower bound of 10.90 and an upper bound of 20,813. The cultural determinants of providing complementary feeding (MP-ASI) are in the mother's knowledge and attitude. The knowledge aspect contributes more than the mother's attitude towards proper complementary feeding practices in the local culture. Keywords: Behavior, complementary feeding, culture, knowledge

Learning from Imbalanced Datasets: The Bike-Sharing Inventory Problem Using Sparse Information

In bike-sharing systems, the inventory level is defined as the daily number of bicycles required to optimally meet the demand. Estimating these values is a major challenge for bike-sharing operators, as biased inventory levels lead to a reduced quality of service at best and a loss of customers and system failure at worst. This paper focuses on using machine learning (ML) classifiers, most notably random forest and gradient tree boosting, for estimating the inventory level from available features including historical data. However, while similar approaches adopted in the context of bike sharing assume the data to be well-balanced, this assumption is not met in the case of the inventory problem. Indeed, as the demand for bike sharing is sparse, datasets become biased toward low demand values, and systematic errors emerge. Thus, we propose to include a new iterative resampling procedure in the classification problem to deal with imbalanced datasets. The proposed model, tested on the real-world data of the Citi Bike operator in New York, allows to (i) provide upper-bound and lower-bound values for the bike-sharing inventory problem, accurately predicting both predominant and rare demand values; (ii) capture the main features that characterize the different demand classes; and (iii) work in a day-to-day framework. Finally, successful bike-sharing systems grow rapidly, opening new stations every year. In addition to changes in the mobility demand, an additional problem is that we cannot use historical information to predict inventory levels for new stations. Therefore, we test the capability of our model to predict inventory levels when historical data is not available, with a specific focus on stations that were not available for training.

Role of Copper on Repassivation of Stainless Steel Pits

The effect of copper on the repassivation of pits in stainless steels was examined through potentiodynamic polarization of one-dimensional (1D) pits at two different downward scan rates for 17–4 PH stainless steel. Post-mortem characterization of tested 1D pits revealed that copper enriches on the pit surface, which most likely occurs through reduction of CuCl3 2− inside the pit during the downward potential scan. 1D diffusion analysis revealed significant amounts of copper replating can occur before the attainment of critical pit chemistry for repassivation when scanning at a high rate, which complicates repassivation potential (E rp) measurements. Copper replating may lead to higher value of measured E rp by, (i) reducing the measured net anodic current density, (ii) blocking the dissolution of stainless steel underneath the copper deposits, (iii) enhancing local hydrogen evolution kinetics to raise the pH of the pit bottom. The relative strength of the copper replating effect is controlled by scan rate or more generally, pit growth conditions and local pit chemistry. Implications of the copper replating effect on measuring a lower-bound value of repassivation potentials are discussed in light of existing repassivation potential measurement techniques. The pit growth conditions that could lead to copper replating in real pits are discussed.

Application of the practical salinity scale to the waters of San Francisco estuary

The Practical Salinity Scale 1978 (PSS-78)—developed using samples of standard seawater—is widely used as a conductivity-based measure of salinity in oceans and estuaries. Here we evaluate the application of the scale across San Francisco Estuary where salinity reflects a mixture of seawater, riverine inflows, and agricultural return flows from islands within the estuary. We employ an extensive salinity data set that includes measurements of specific electrical conductance (EC) and major ion concentrations to demonstrate the scale's applicability in this estuary. We find the scale to be valid in waters dominated by seawater intrusion as well as in waters dominated by the largest riverine input to the estuary, i.e. the Sacramento River. In these waters, we further find that the scale to be valid well below its recommended lower-bound value of 2.0. However, the scale under-estimates salinity in waters dominated by the San Joaquin River, the second largest riverine input to the estuary. Similarly, the scale under-estimates salinity in agricultural return flows to the estuary. This work shows that a singular PSS-78 relationship cannot be used to accurately compute salinity across the entire estuary from EC measurements—a finding of considerable importance for salinity monitoring and modeling applications. Using data from the estuary, we propose appropriate corrections to the scale for these drainage-influenced waters. We recommend further research into how these modified PSS-78 formulations can be used to more accurately estimate salinity and ionic constituent concentrations in this and other estuaries.

Nexus between Exchange Rate Volatility and Oil Price Fluctuations: Evidence from India

The price of crude oil has fluctuated in India over the past few decades which has drawn significant attention because of it impact on all economic sectors. The present study aims to identify how oil price volatility affects the real exchange rate in India from 1st July 2009 to 2nd January 2020. For short-run and long-run analysis, various econometric methods have been applied, including Granger Causality, ARDL Bound test, FEVD, and IRF. The study divided the entire sample into sub-samples based on Breakpoint analysis and then performed the ARDL Bound testing procedure in each sub-sample. Causality results revealed that most samples exhibited strong unidirectional causality from oil prices to exchange rates. However, the long-run and short-run results from the ARDL model failed to detect any cointegration among the underlying variables for the entire sample. The calculated F-statistics is 4.35, which is less than the lower and upper critical bound values provided by Pesaran, Shin, and Smith (2001). The GIRF has been used to calculate the dynamic marginal effect of a one-standard-deviation shock in oil prices on the current and future values of the Rupee-Dollar exchange rate. The exchange rate fell in the first three samples due to one standard deviation shock in oil prices. However, the contribution of oil prices to the exchange rate is positive in the fourth sample period.

Analysis of the possibility of eliminating excess inequality through income redistribution

The main idea of the research paper is the redistribution of personal incomes. The subject is the effects of eliminating excess inequality. The core of the research paper is creating a model of erasing personal incomes under the subsistence minimum using a redistribution model. One possible way to get rid of personal incomes below the subsistence minimum is a partially progressive tax, which separates income into different sectors and settles a tax rate for each sector. Eventually, the redistribution can be held by certain society groups using a tax for wealthy people and government subsidies for poor segment of the population or evenly proportional income redistribution. It is useful to consider and compare two indicators: subsistence minimum and minimum consumer budget. A minimum consumer budget is a set cost of food and non-food products and services that meet basic physiological and sociocultural needs. In fact, it is the cost of the minimum consumer basket. The minimum consumer budget determines the lower bound value of life in society, followed by poverty. In Russia, this indicator is not popular, but it has a potential to be useful. In fact, one of the most important function of a tax system is keeping balance in society. The methodology is a redistribution optimization model. The optimal level of income inequality implies that there is a level of income inequality that maximizes economic growth. The optimization model consists of key parameters that determine inequality level, such as tax rate, education, and fertility. In some ways, the tax scale is not fair for definite groups of people. People have a social order to reconsider a tax scale. The result is a block of recommendations for shifting personal income inequality rate to a balanced point. The paper is aimed to provide a strategy for applying the fairer redistribution model with simple computations and prove that it can be applied with relatively low effort.

Uji Kinerja Metode Deep Convolutional Neural Networks Untuk Identifikasi Gangguan Daya Listrik

The identification model development of the power disturbance signals with the deep convolutional neural networks (CNNs) method involves a large amount of data. However, the real signal data is limited. Therefore, researchers employ synthetic signal data. These signals can be generated by the formula IEEE standardized. In these formulas, two categories have a similar formula i.e interruption and sag. The difference is only in the intensity parameter (α). This paper analyzed the model performance of identifying those disturbances where the intensity values are set differently for training and testing datasets based on the upper bound value α of sag and the lower bound value α of interruption. Several noise levels are included in the signals. So, there are several datasets with noises in this simulation. Furthermore, those datasets are trained using the model based on deep CNN. The test results show that the true positive (TP) of the model's performance in identifying the interruption signal is 93.54% and the sag signal is 78.78%. In addition, the performance of the model using a dataset without noise obtained a high percentage in accuracy, precision, and f1-score parameters with 92.4%, 97.4%, and 92,76%, respectively.

Sensitivity analysis of shear modulus on Lateral – Torsional Buckling of PFRP Beam

An elastic constant of Pultruded Fiber Reinforced Polymer (PFRP) has a wide range of value, especially for the shear modulus which is generally ranging from 3 to 5 GPa. Lateral – torsional buckling (LTB) is a type of global buckling occurs when the beam is under bending about its major axis. The instability happens when the beam twists and move laterally. This research analyses and evaluates the sensitivity of changing the shear modulus on the critical LTB load of PFRP beam using a numerical approach by ABAQUS. It is found that the critical LTB load increases 17% for short beam and 27% for long slender beam when shear modulus increases from 3 to 5 GPa. The influence of changing of shear modulus is reduced when load position moves from top to bottom flange. It is suggested that the determination of proper shear modulus (upper and lower bound of values) is of crucial in design and in validating the compatibility between experimental work and numerical or theoretical analysis.

Analysis of RC Beams under Combined Torsion and Shear Using Optimization Techniques Evaluation of NBR 6118 and AASHTO LRFD Standards

In this article, a novel calculation procedure using optimization techniques is proposed to compute the torsion–shear interaction curves for reinforced concrete (RC) beams. The calculation procedure is applied to NBR 6118 and AASHTO LRFD standards in order to evaluate their reliability. For this, some experimental results found in the literature and related to RC beams tested under combined torsion and shear, as well as results from the combined-action softened truss Model (CA-STM), are used for comparison. From the obtained results, AASHTO LRFD provisions are found to –be satisfactorily accurate. The NBR 6118 provisions are found to be consistent with the experimental results when the angle of the concrete struts is assumed to be variable or equal to the lower bound value of 30°, according to model II of the standard. For an angle assumed equal to 45°, according to model I of the NBR 6118 standard, the predicted strengths are found to be excessively conservative. The results demonstrate that formulating the analysis of RC beams under combined torsion and shear as an optimization problem, as proposed in this article, constitutes an alternative and efficient option. In addition, the generality of the proposed calculation procedure allows it to be applied to any design standard to compute the torsion–shear interaction curves for RC beams.

Assessing the neurotoxicity of airborne nano-scale particulate matter in human iPSC-derived neurons using a transcriptomics benchmark dose model

Airborne nano-scale particulate matter (nPM) exposure is a risk factor for neurological diseases. However, to date, there has been no comprehensive evaluation of ambient nPM's neurotoxicity. We examined the toxic effects of nPM on human neurons derived from induced pluripotent stem cells (iPSCs) at doses ranging from 0 to 200 μg/mL, and employed whole-genome RNA-sequencing in different dose groups to gain further insight into the neurotoxicity of ambient nPM. Our findings showed that nPM was absorbed by neurons, and induced a variety of toxic effects. The apical benchmark dose lower confidence bound (aBMDL) values of each effect endpoint were ranked as follows, in ascending order: mitochondrial membrane potential, neurite length, early apoptosis, cell viability. BMD analysis based on transcriptomic data revealed that the point of departure (PoD) of the 20 pathways with the lowest p-values (0.75 μg/mL), the top 20 upstream regulators (0.79 μg/mL) and the neurological diseases (0.77 μg/mL) could be appropriate for nPM neurotoxicity evaluation. The transcriptomic PoDs (tPoDs) were similar to apical PoDs (aPoDs) since their absolute fold differences were within 10-fold. Further analysis of the transcriptomic data revealed that nPM exposure could disturb the pathways related to ferroptosis, neurotransmitters, xenobiotic metabolism, etc., which might be critical in regulating nPM neurotoxicity. We also found that low-dose nPM induced cytokine signaling pathways, while high doses of nPM activated cell-cycle regulation and DNA repair pathways. Our results indicate that BMD modeling based on transcriptomic data could be useful in illustrating the neurotoxic mechanism, and also could be a promising method for evaluating the potential health risks of nPM.

A method to directly estimate the dynamic failure peak ground acceleration of a single-layer reticulated dome

The present paper gives a method to directly estimate the dynamic failure peak ground acceleration of single-layer reticulated domes according to the Chinese earthquake response spectrum. A method given by the Japanese code for seismic design of buildings is presented first, which can be used to estimate the dynamic failure peak ground acceleration of a single-layer dome. However, the existing method cannot be used to calculate the dynamic failure load for a structure in China because of the different codes and methods used for the seismic design of single-layer reticulated domes. Meanwhile, the existing method needs to repeat the finite element method (FEM) elasto-plastic static analysis and FEM nonlinear time–history response analysis to calculate the FEM plasticity adjustment factor and the dynamic ductility index of different domes. Secondly, on this basis, 28 Kiewitt-8 (K8) single-layer reticulated domes with different key structural design parameters are designed using the relevant Chinese specifications, they will be used for the parameter revision of the peak ground acceleration and the dome’s design spectral acceleration, the fitting of the formula to calculate the FEM plasticity adjustment factor, and the statistical analysis of the dynamic ductility index respectively. Thirdly, the peak ground acceleration and the dome’s design spectral acceleration in the existing method are revised according to the Chinese code for the seismic design of buildings. Fourthly, the FEM elasto-plastic static analysis is carried out to get a fitting formula to directly calculate the FEM plasticity adjustment factor with the static safety factor K of the dome. The calculation results of this formula are smaller than that of FEM analysis, which can ensure the safety of the dome. Fifthly, the failure criteria with clear physical meaning are selected from the existing dynamic failure criteria to predict the dynamic failure load of the domes. Then, a FEM nonlinear time–history response analysis is carried out using the FE package ANSYS for the 28 typical domes subjected to 30 different ground motions, and the dynamic failure loads are calculated by the selected failure criteria. It is found that the dynamic failure loads correspond to two different failure modes, one is the local failure caused by the sudden increase of the displacement of the dome’s local node, the other is the global failure caused by the sudden increase of the overall energy of the single-layer reticulated dome. After this, the lower bound failure accelerations with a certain guaranteed rate of those dynamic failure loads are determined using a lognormal distribution. The dynamic ductility index is then revised by the lower bound failure accelerations and the statistical analysis of the dynamic ductility index of the 28 typical domes is conducted to get a lower bound value for it. Finally, a method directly estimating the dynamic failure load of a dome is constructed and verified. The research will provide a new method under a certain degree of reliability to directly estimate the dynamic failure loads of single-layer reticulated shells in the earthquake area of China and contribute to the structural design with more overall confidence.

Thermodynamics of the Reissner-Nordström black hole with quintessence matter on the EGUP framework

In this paper, we investigate the quantum correction on thermodynamics of the Reissner-Nordström black hole in the presence of the quintessence matter associated with dark energy. To this end, the modified Hawking temperature, heat capacity, and entropy functions of the black hole are derived. Investigation reveals that the modified uncertainty principles and normalization factor α restrict the lower bound value of horizon radius to affect the Hawking temperature and the parameter η0 has a non-negligible effect on the heat capacity threshold. Furthermore, the correction of entropy does not depend on the quintessence matter. It is also examined the equation of state associated with the pressure and the volume in this framework. In addition, we employ graphical methods to compare and discuss the findings within the generalized uncertainty principle (GUP) and extended uncertainty principle (EUP).

Flood characterization based on forensic analysis of bridge collapse using UAV reconnaissance and CFD simulations

Flash floods are common manifestations of extreme weather events and one of the most severe natural hazards. In Europe, they have been responsible for 359 fatalities and an economic loss totalling 67 million USD in the past decade (EM-DAT), while their increasing severity is linked to climate change. Nevertheless, flash floods remain a poorly documented natural phenomenon due to the lack of flow intensity data in many of the affected watersheds. Based on a thorough field investigation, including UAV-based 3D mapping and material characterization with on-site testing, we carry out a numerical study of a notable flood that caused the collapse of bridges and buildings in Central Greece, following a recent Mediterranean hurricane. Focusing on a carefully selected case study, we combine 3D modelling of flow–structure interaction with detailed mechanical modelling of the nonlinear structural response to reproduce the flood-induced fracture of a bridge abutment. Back-analysis of this failure responds to the fundamental problem of estimating the undocumented magnitude of this extreme event. The paper estimates a lower bound value of the flow velocity at the studied location. This can be valuable input for the interpretation of the extensive damage that took place downstream and for the re-assessment of flood risk in a region where similar events are expected to become more frequent because of climate change. The approach, where disaster forensics and engineering analysis are used to fill the gap of missing real-time measurements, can be implemented for the a posteriori estimation of flood intensity in similar events. The well-documented case study of a bridge failure due to extreme flooding can also be used for validation of future numerical and experimental methods and motivate investigations of the mechanisms governing flow–soil–structure interaction in river crossings.

Technical-scientific considerations regarding the reduction of the explosion effects generated by the explosive materials on persons and industrial and civil objectives

The attenuation is a function of a structural strength and mass of the PES structure. Relatively light or weak structures (or open PES facilities) are assumed to not attenuate any of the pressure or impulse load. These not-attenuating facilities include: Pre-engineered metal building, Hollow clay tile building, Trailer (drop or stand-alone), Tractor-trailer and Bulk/tank truck/Van truck. If there is a barricade present between the PES and the ES and this barricade meets certain criteria, the user can direct the model to reduce the pressure and impulse arriving at the ES because of the presence of the barricade. The fractional damage of the PES structure remaining intact after an explosive event is a function of the equivalent NEW (Net Explosive Weight) and the PES building type. The fractional damage (a value between 0 and 1) of each PES component (roof, front wall, side walls, and rear wall) is determined by comparing the NEW to lower-bound and upper-bound damage limits for the PES types. So, if the NEW is below the lower-bound damage limit value, then the PES structure is assumed to remain totally intact; if the NEW is greater than upper-bound damage limit value, then the PES structure is assumed to be completely destroyed; if the NEW value is between the lower-bound damage limit and upper-bound damage limit, the PES structure is partially or fractionally damaged. If the equivalent NEW is between the two values, an algorithm is used to determine how fast the PES structure transitions from zero damage to full damage as the NEW increases between the lower-bound damage limit and upper-bound damage limit values. This algorithm and all associated parameters are described in the following.

Estimating the Size of Construction Industry Expenditure for Economic Development and Sustainability in Nigeria: Autoregressive Distributed Lag (ARDL) Approach

Abstract The expansion of annual capital budget over the years without a corresponding increase in the volume and quality of infrastructural development in Nigeria has been attributed to those factors assumed to have great impact on the economic performance of the country. This study examined the effect of selected economic factors on the size of construction sector expenditure in Nigeria using economic data from 1981-2020. It employed econometrics statistics. The result revealed that there was a long-run co-integration among the variables with ARDL bound estimate values of F-stat. (7.40) and t-stat. (-6.56) respectively. These are higher than both the lower and upper bound critical values at 1%, 2.5%, 5% and 10% respectively. The result further revealed that exchange rate, oil prices, population, trade openness, foreign direct investment, unemployment rate, public debt and real GDP were important determinants of the size of construction sector expenditure in Nigeria. It also revealed that construction output, inflation rate, government revenue and taxation had trivial determinants due to issues relating to policy, management and execution of capital budget. The study suggested that government should make and implement apposite policies, and be diligent in allocation and management of public fund to ensure a sustainable economy through infrastructural development.

Generalized quantum electrodynamics: One-loop correction

In this paper, we give an update on divergent problems concerning the radiative corrections of quantum electrodynamics in [Formula: see text] dimensions. In doing so, we introduce a geometric adaptation for the covariant photon propagator by including a higher derivative field. This derivation, so-called generalized quantum electrodynamics, is motivated by the stability and unitarity features. This theory provides a natural and self-consistent extension of the quantum electrodynamics by enlarging the space parameter of spinor-gauge interactions. In particular, Haag’s theorem undermines the perturbative characterization of the interaction picture due to its inconsistency on quantum field theory foundations. To circumvent this problem, we develop our perturbative approach in the Heisenberg picture and use it to investigate the behavior of the operator current at one-loop. We find the two- and three-point correlation functions are ultraviolet finite, electron self-energy and vertex corrections, respectively. On the other hand, we also explain how the vacuum polarization remains ultraviolet divergent only at [Formula: see text] order. Finally, we evaluate the anomalous magnetic moment, which allows us to specify a lower bound value for the Podolsky parameter.