Search Results (41)

This research investigates the multiplier effect of ferry transportation development on the medium-term economy of Samosir Island from 2016 to 2022. The study will provide insights into the implications of ferry transportation for the economies of rural or remote regions like Samosir Island. The analysis was conducted by comparing the multiplier effects before and after the development, utilizing the input–output approach, which encompassed the output multiplier, the multiplier on gross value added (GVA), and the household income multiplier. The findings from the input–output analysis indicate that the average output multiplier for 37 industries on Samosir Island has declined by 0.84% annually, with the average output multiplier recorded at 1.80 in 2016, decreasing to 1.71 by 2022. This suggests that, overall, the advancement of ferry transportation in the medium term is comparatively ineffective in promoting economic growth in rural or remote regions such as Samosir Island. Conversely, the average GVA multiplier rose by 1.04% annually. Similarly, the household income multiplier index experienced an increase of 1.91% each year. This indicates that ferry transportation seems to exert a notable influence on both GVA and the household income multiplier, albeit the effect is comparatively modest. Full article

The lack of an in-depth understanding of electrical conduction behaviour in anisotropic carbon fibre-reinforced laminates was reflected by the fact that there was no measurement standard. Various ad hoc experimental techniques were used, involving a range of extrinsic parameters with little or no rigorous control. Not only were widely varying values of electrical conductivity, if not incorrect values, generated, but also the effects of extrinsic parameters were attributed erroneously to those of intrinsic parameters. This predicament was compounded by different techniques used in measurements of volume and surface electrical conduction. This paper formulated the most effective experimental method, using two well-argued solid electrodes, to evaluate electrical conduction with rigorous control of all extrinsic parameters. Its main objectives were to investigate anisotropic volume and surface electrical conduction with a focus on the effects of electrode–specimen contact resistance, clamping pressure, conductive paint, contact face preparations, lay-ups, and specimen dimensions. Unique results and data trends provided the step-changing understanding of electrical conduction, such that the contributions of extrinsic factors were clearly established. The specifical findings showed that (1) the two-probe method was the only viable technique to measure both volume and surface conductivities, (2) all conductivity values were dependent on clamping torques and contact face machining, (3) the conductive paint enhancement effect was an artefact, and (4) obtaining surface conductivities by multiplying volume conductivities with laminate thickness was incorrect. Full article

Revolute and prismatic pair clearances are common in various mechanisms, and their motion state seriously affects the accuracy of the mechanism. Adding lubricant to a kinematic pair can effectively counteract the adverse influence of a collision force. Thus, this work introduces an advanced modeling method that considers the combined effects of a lubricated revolute and prismatic clearance, as well as component flexibility, and studies the influence of their coupling effect on the dynamic response and nonlinear characteristic of mechanisms. The specific content of this paper is as follows: Firstly, revolute lubrication clearance and prismatic pair clearance models are established. Secondly, rigid components and flexible components are described based on the reference point coordinate method and absolute nodal coordinate formulation. Then, based on the Lagrange multiplier method, a rigid–flexible coupling dynamics model with revolute lubrication clearance and prismatic clearance is established. Finally, the dynamic responses of the mechanism are analyzed, including the displacement, velocity, and acceleration of the slider, the driving torque of the crank, and the center trajectories of the revolute clearance and prismatic clearance. Qualitative research is conducted on the nonlinear characteristics of the system through a phase diagram and Poincaré map. This quantitative study is conducted on the nonlinear characteristics of a system using the maximum Lyapunov exponent. The influences of different parameters on the dynamic response and nonlinear characteristic of the mechanism are analyzed. The results indicate that lubrication effectively reduces the influence of the clearance on the dynamic response and nonlinear characteristic of the mechanism, resulting in a decrease in the peak dynamic response and a weakening of the chaotic phenomenon. Further, as the driving speed increases, the dynamic viscosity decreases the clearance value increases, and the stability of the mechanism decreases. Full article

Anomaly detection (AD) is an important topic in remote sensing, aiming to identify unusual or abnormal features within the data. However, most existing low-rank representation methods usually use the nuclear norm for background estimation, and do not consider the different contributions of different singular values. Besides, they overlook the spatial relationships of abnormal regions, particularly failing to fully leverage the 3D structured information of the data. Moreover, noise in practical scenarios can disrupt the low-rank structure of the background, making it challenging to separate anomaly from the background and ultimately reducing detection accuracy. To address these challenges, this paper proposes a weighted multidirectional sparsity regularized low-rank tensor representation method (WMS-LRTR) for AD. WMS-LRTR uses the weighted tensor nuclear norm for background estimation to characterize the low-rank property of the background. Considering the correlation between abnormal pixels across different dimensions, the proposed method introduces a novel weighted multidirectional sparsity (WMS) by unfolding anomaly into multimodal to better exploit the sparsity of the anomaly. In order to improve the robustness of AD, we further embed a user-friendly plug-and-play (PnP) denoising prior to optimize the background modeling under low-rank structure and facilitate the separation of sparse anomalous regions. Furthermore, an effective iterative algorithm using alternate direction method of multipliers (ADMM) is introduced, whose subproblems can be solved quickly by fast solvers or have closed-form solutions. Numerical experiments on various datasets show that WMS-LRTR outperforms state-of-the-art AD methods, demonstrating its better detection ability. Full article

Tensor restoration finds applications in various fields, including data science, image processing, and machine learning, where the global low-rank property is a crucial prior. As the convex relaxation to the tensor rank function, the traditional tensor nuclear norm is used by directly adding all the singular values of a tensor. Considering the variations among singular values, nonconvex regularizations have been proposed to approximate the tensor rank function more effectively, leading to improved recovery performance. In addition, the local characteristics of the tensor could further improve detail recovery. Currently, the gradient tensor is explored to effectively capture the smoothness property across tensor dimensions. However, previous studies considered the gradient tensor only within the context of the nuclear norm. In order to better simultaneously represent the global low-rank property and local smoothness of tensors, we propose a novel regularization, the Tensor-Correlated Total Variation (TCTV), based on the nonconvex Geman norm and total variation. Specifically, the proposed method minimizes the nonconvex Geman norm on singular values of the gradient tensor. It enhances the recovery performance of a low-rank tensor by simultaneously reducing estimation bias, improving approximation accuracy, preserving fine-grained structural details and maintaining good computational efficiency compared to traditional convex regularizations. Based on the proposed TCTV regularization, we develop TC-TCTV and TRPCA-TCTV models to solve completion and denoising problems, respectively. Subsequently, the proposed models are solved by the Alternating Direction Method of Multipliers (ADMM), and the complexity and convergence of the algorithm are analyzed. Extensive numerical results on multiple datasets validate the superior recovery performance of our method, even in extreme conditions with high missing rates. Full article

The sweet potato [Ipomoea batatas (L.) Lam] is considered one of the most important crops in the world as food, fodder, and raw material for starch and alcohol production. Sweet potato consumption and demand for its value-added products have increased significantly over the past two decades, leading to new cultivars, expansion in acreage, and increased demand in the United States and its export markets. Due to its health benefits, sweet potato production has multiplied over the past decade in Brazil, promoting food security and economic development in rural areas. Their adaptability and nutritional value make them a food of great importance for Brazil. As pest attacks and disease infection are the main limiting aspects that often cause yield loss and quality degradation in sweet potatoes, there is a great demand to develop effective defense strategies to maintain productivity. There is a critical need for research into non-pesticide control approaches that can provide safe, cost-effective, sustainable, and environmentally friendly pest and disease management techniques. Pests which feed on roots have trade implications worldwide. For example, sweet potato tuber shipments infested with the sweet potato weevil are generally not allowed for trade in North and South America. Full article

Morphological scoring is a common evaluation method for domestic animals. The National Association of Maremmano Breeders (ANAM) has provided a dataset containing the records of 600 horses, four metric measurements (cm) and 24 traits with a continuous evaluation scale, each one with 15 classes. Moreover, a body condition score (BCS) with five classes is included. In this study, factor analysis was conducted to create a small number of informative factors (3) obtained from these traits, and a new BLUP-AM-MT index was established. The New Estimated Breeding Value (NEBV1) of each horse was computed by adding the genetic indexes of the three factors, with each one multiplied using a coefficient indicated by ANAM. The practical feasibility of the NEBV1 was evaluated through Spearman correlations between the rankings of the NEBV1 and the rankings of the BLUP-AM-MT, estimated through the four biometric measures and the morphological score (MS) assigned to each horse by the ANAM judges. The factorial analysis was used to estimate three factors: the “Trunk Dimension”, “Legs” and “Length”. As the explained variance was only 32%, the model was rotated, and the heritability of the three factors were 0.51, 0.05 and 0.41, respectively. After rotation, the estimated correlations between the new NEBV1 and the biometric measures were improved. These results should encourage breeders to adopt a breeding value index that takes into consideration the factors derived from all the variables observed in the morphological evaluation of the Maremmano. In this way, breeders can use it to select the best animals for breeding. Full article

This study delves into the intricacies of variable load combination factors (ψ) within structural codes under fundamental design scenarios, with Eurocodes serving as the primary reference. Currently, variable loads are combined by adding one load, the leading load with its full value, and the other load, the accompanying load, with a reduced value multiplied by a combination factor ψ. This approach employs an independent load combination methodology, utilizing hypothetical reference materials. In contrast, this paper advocates for a shift towards dependent load combination, anchored in the use of actual reference materials. Specifically, it is proposed that imposed loads be combined without the combination factor, i.e., ψ = 1. Given that combination factors are in approximate unity or pertain to infrequent load cases, this research recommends the elimination of ψ from codes altogether. This recommendation stems from the recognition that the current combination factor calculation excels in cases with approximately equal loads with a significant reliability gain, while more frequent unequal loads introduce a minor reliability gain and harmful unsafe errors. Despite the overall minor safety advantage of about 2–3% being negligible considering unavoidable safe errors of about 7% in codes, this simplification significantly reduces code complexity, enhances user-friendliness, and substantially decreases the workload associated with design processes. Full article

The development of irrigation systems is strategically used to improve food security and achieve the Sustainable Development Goals (SDGs 2) of ending hunger and poverty. The objective of this research was to evaluate the effect of irrigation agriculture on the economy of the Limpopo Province, South Africa. This study used the 2017 national social accounting matrix (SAM) as a database with detailed information on irrigation and rainfed agricultural activities and land accounts to compute the effect of exogenous shock on output, income, land, and value added using SAM multiplier analysis. The findings showed that output multiplier effects were more significant for rainfed agriculture compared to irrigation agriculture. However, irrigation agriculture had the highest institutional income, land return, and value-added multiplier compared to rainfed agriculture. The type of crop did not influence the findings, with irrigation consuming more input per unit of output. We conclude that investing in irrigation agriculture and increasing the efficiency and sustainability of existing irrigation agriculture in Limpopo is significant and profitable because dry land production is hazardous when there is insufficient rainfall or recurrent drought. Full article

Audit committees are appointed by the board of directors of corporations to oversee the financial reporting process, monitor financial control processes, hire and assess independent auditors, and communicate findings with management and auditors. We propose two new measures of audit oversight quality. The first measure is purely subjective, in that it scores audit committees on a scale based on their ability to fulfill one or more of their responsibilities, as mentioned in annual reports, Form 10-K and DEF 13A. The second measure concerns audit committee activity, as it measures the number of times the term ‘audit committee’ is mentioned in these documents. Both measures were obtained for U.S. pharmaceutical companies and energy companies from 2010 to 2022. The audit oversight quality measures were regressed in regard to profitability (measured by return on assets and return on equity), debt capacity (measured by equity multiplier), and firm value (measured by Tobin’s q and economic value added). Audit oversight quality, using both measures, reduces the return on equity. Audit oversight quality, using both measures, had a disciplining effect on debt. Increases in the oversight of increasing debt discourage the propensity to increase borrowing using collateral (debt capacity), and reduce investor returns through investment in debt-financed projects (return on equity). Audit oversight quality, using both measures, exhibited a size effect on the firm’s value, in that an increase in the firm size with high audit oversight quality increases the firm’s value. However, it is possible that only the first measure of audit oversight quality significantly increased the firm’s value, as only the first measure exhibited robustness to the endogeneity effect of size. Full article

This study examines the convergence between green technology and building construction in Korea using both input-output and network analysis from 1990 to 2015. The industry type of the input-output tables used in the Bank of Korea is reclassified into 20 categories. The analytical results are summarized as follows: First, the construction industry is expanding its production area by adopting green technologies (KRW 2245 billion → KRW 7842 billion). Second, the impact of green technologies on the growth rate of the construction industry is greater than that of traditional construction technologies (technical coefficient 0.5410 → 0.5831). Third, the results of the analysis show that smart green technology enhances efficiency in the construction industry (multiplier coefficient 2.3673 → 2.4972). Our input-output model reveals that the smart green technology coefficient input to construction is relatively small, but the output is bigger in effects. Also, the results of the input-output analysis show that both hardware and software smart technologies continuously increase energy demand. Finally, the network analysis demonstrates the rapid convergence of smart technologies in the construction industry (pathway 13 → 22). These results demonstrate that smart green technology leads to a high value-added output in the construction industry. Full article

Three-dimensional hydrodynamic modeling for small, stratified tropical lakes in the Philippines and in Southeast Asia in general is not deeply explored. This study pioneers investigating the hydrodynamics of a small crater lake in the Philippines with a focus on temperature simulation using a Fantom Refined 3D model that has been tested mostly for temperate and sub-tropical lakes. The lake’s monthly temperature during the dry season served as a reference for the model’s initial condition and validation. For the simulation to proceed, input data such as weather, inflow, and bathymetry were prepared. In the absence of hourly meteorological data from local weather stations, this paper adopted the satellite weather data from Solcast. Simple correlation analysis of daily weather data between local stations and Solcast showed valid and acceptable results. Inflow values were estimated using the rational method while the stream temperature was estimated from a regression equation using air temperatures as input. The validated satellite-derived data and runoff model can therefore be employed for 3D modeling. The simulations resulted in extremely higher temperatures compared with those observed when using previous default model settings. Direct modifications were then applied to weather parameters, compromising their integrity but resulting in reasonable profiles. By adding scaling factors to heat flux equations and multiplying their components by 0.75 (shortwave), 1.35 (longwave), 0.935 (air temperature), and 0.80 (wind), better results were achieved. This study identifies several challenges in performing 3D hydrodynamic modeling, such as paucity in input hydro-meteorologic and limnologic data and the need for heat flux model improvement. Overall, this study was successful in employing 3D hydrodynamic modeling in a tropical lake, which can pave directions and serve as an excellent reference for future modeling in the same region. Full article

Microgrids are decentralized power production systems, where the energy production and consumption are very close to each other. Microgrids generally exploit renewable energy sources, encountering a problem of storage, as the power production from solar and wind is intermittent. This research presents a new integrated methodology and discusses a comparison of batteries and pumped storage hydropower (PSH) as energy storage systems with the integration of wind and solar PV energy sources, which are the major upcoming technologies in the renewable energy sector. We implemented the simulator and optimizer model (HOMER), which develops energy availability usage to obtain optimized renewable energy integration in the microgrid, showing its economic added value. Two scenarios are run with this model—one considers batteries as an energy storage technology and the other considers PSH—in order to obtain the best economic and technical results for the analyzed microgrid. The economic analysis showed a lower net present cost (NPC) and levelized cost of energy (LCOE) for the microgrid with PSH. The results showed that the microgrid with the storage of PSH was economical, with an NPC of 45.8 M€ and an LCOE of 0.379 €/kWh, in comparison with the scenario with batteries, which had an NPC of 95.2 M€ and an LCOE of 0.786 €/kWh. The role of storage was understood by differentiating the data into different seasons, using a Python model. Furthermore, a sensitivity analysis was conducted by varying the capital cost multiplier of solar PV and wind turbines to obtain the best optimal economic solutions. Full article

In recent years, the tensor completion algorithm has played a vital part in the reconstruction of missing elements within high-dimensional remote sensing image data. Due to the difficulty of tensor rank computation, scholars have proposed many substitutions of tensor rank. By introducing the smooth rank function (SRF), this paper proposes a new tensor rank nonconvex substitution function that performs adaptive weighting on different singular values to avoid the performance deficiency caused by the equal treatment of all singular values. On this basis, a novel tensor completion model that minimizes the SRF as the objective function is proposed. The proposed model is efficiently solved by adding the hot start method to the alternating direction multiplier method (ADMM) framework. Extensive experiments are carried out in this paper to demonstrate the resilience of the proposed model to missing data. The results illustrate that the proposed model is superior to other advanced models in tensor completeness. Full article

Flooding due to climate change is recurrent and has intensified in the lower course of the Coyuca River. This paper implements a multivariate analysis, including conditioning and triggering factors to develop flood susceptibility mapping in an information-deprived region to help prevent/mitigate flooding. Flood-susceptible areas were identified using the multi-criteria decision analysis (MCDA) methodology, specifically, with the hierarchy analysis process (AHP). Four conditioning and one triggering influence factors were analyzed. The influence weights of each variable were determined using Saaty’s methodology (AHP). Thematic maps for each variable were created and multiplied by their influence value using the raster calculator and added to their variable group to obtain the flood susceptibility map. The findings showed that the susceptibility to flooding was very high in 41.82%, high in 35.95%, medium in 21.25% and low in 0.98% of the study areas. It was revealed that 44.44% of the localities occupy areas of very high susceptibility to flooding. Susceptibility increases in the localities closest to the river. Full article