Distributed Solar Generation: Current Knowledge And Future Trends

Distributed solar generation (DSG) has been growing over the previous years because of its numerous advantages of being sustainable, flexible, reliable, and increasingly affordable. DSG is a broad and multidisciplinary research field because it relates to various fields in engineering, social sciences, economics, public policy, and others. Developing a holistic understanding of the state of research related to DSG can be difficult. Motivated to provide that understanding, the goal of this paper is to explore current and emerging multidisciplinary research trends associated with DSG. To achieve that, (1) a large data set of approximately 66,000 publications was collected; (2) the papers were labeled using keywords for topics including Batteries and Storage, Solar, Complex Modeling, Machine Learning (ML) and Artificial Intelligence (AI), Resilience, Vulnerability, and Disasters, Policies and Incentives, Social Aspects, Economics, Smart Grid, Finance, Social Equity, Microgrid, and Virtual Power Plant ; and (3) the data set was analyzed using scientometric and social network analysis (SNA) in respect to publication counts, citation counts, and interconnectivity between the topics. Notable findings were analyzed to describe current and emerging trends. It was found that social equity has high citation counts contrasted by few publications, indicating a possible strong need for research. There is also rapidly growing research in ML and AI in the context of DSG during recent years. Other research topics, such as smart grids, have been attracting fewer publications. The results also highlight the need for multidisciplinary research connecting the topics. To conclude, future research is suggested to explore research needs in the areas of social aspects, social justice and equity, public policy and incentives, and ML/AI. The findings should benefit researchers and stakeholders with a holistic understanding of multidisciplinary DSG-related research and provide insight for planning new research and funding opportunities

Skilled Worker Shortage Across Key Labor-Intensive Construction Trades In Union Versus Nonunion Environments

Skilled labor plays a crucial role in ensuring that construction projects are completed on time, within budget, and to the required standards of quality and safety. However, the construction industry has been facing a labor shortage in recent years, which poses a significant challenge to the industry\u27s growth and sustainability. Therefore, it is important to examine the characteristics of the construction skilled labor market to understand the factors that contribute to the shortage of skilled workers and develop strategies to address the issue. This paper fills this knowledge gap. To this end, the authors (1) collected and processed project documentation in relation to 67 construction projects to identify key construction labor-intensive trades, (2) conducted an expert-based survey to collect data in relation to union participation rates and degrees of skilled labor shortages across the identified trades, (3) performed clustering analysis to examine the observed levels of labor shortage across the identified trades, (4) applied a binomial test to analyze the levels of union participation for each of the labor trades, and (5) used a chi-square test of independence to investigate the correlations between workforce location and union participation on the one hand and union participation and labor shortage on the other. As such, the authors identified 10 key labor-intensive trades. It was found that plumbing and electrical trades have the highest degrees of skilled labor shortage, whereas finishing work trades (i.e., plastering and painting, flooring, and waterproofing) had the lowest. Results also showed a significant correlation between high union membership rates and the availability of skilled workers in 3 of the 10 identified trades (i.e., ironworking, flooring, and waterproofing) and that union reach in urban locations is less than that in rural areas where workers are employed. Ultimately, this paper adds to the body of knowledge by offering a closer look into the construction skilled labor market. Such knowledge can be used to mitigate the current labor shortages

Threshold Displacement Energies And Primary Radiation Damage In AlN From Molecular Dynamics Simulations

Aluminum nitride (AlN) is an attractive material for sensing application in radiation environments owing to its radiation resistance, optical wide-bandgap, and piezoelectric properties. Yet, the variations of its physical properties under exposure to energetic particle needs to be better understood. Here, we report the results of the molecular dynamics simulations of the structural changes in AlN under irradiation via the knock-on atom technique. By creating and evolving irradiation cascades due to energetic particle interactions with the atoms of the crystalline lattice, we determine the rate of defect production as a function of the deposited energy. Further, we determine the threshold displacement energy, a key characteristic that describes how efficient the defect production in the given material is. We find that displacement threshold is slightly greater than isostructural gallium nitride and is lower than metal oxides used in radiation environments

Hanford Low-Activity Waste Vitrification: A Review

This Paper Summarizes the Vast Body of Literature (Over 200 Documents) Related to Vitrification of the Low-Activity Waste (LAW) Fraction of the Hanford Tank Wastes. Details Are Provided on the Origins of the Hanford Tank Wastes that Resulted from Nuclear Operations Conducted between 1944 and 1989 to Support Nuclear Weapons Production. Waste Treatment Processes Are Described, Including the Baseline Process to Separate the Tank Waste into LAW and High-Level Waste Fractions, and the LAW Vitrification Facility Being Started at Hanford. Significant Focus is Placed on the Glass Composition Development and the Property-Composition Relationships for Hanford LAW Glasses. Glass Disposal Plans and Criteria for Minimizing Long-Term Environmental Impacts Are Discussed Along with Research Perspectives

A High-Order Multi-Resolution Wavelet Method for Nonlinear Systems of Differential Equations

In This Article, the Applications of the New Haar Wavelet Collocation Methods Called as Haar Wavelet Collocation Method (HWCM) and Higher-Order Haar Wavelet Collocation Method (H-HWCM) Are Developed for the Solution of Linear and Nonlinear Systems of Ordinary Differential Equations. the Proposed H-HWCM is Compared with a Variety of Other Methods Including the Well-Known HWCM. the Quasi-Linearization Technique is Introduced in the Nonlinear Cases. the Stability and Convergence of Both Techniques is Studied in Detail, Which Are the Important Parts to Analyze the Proposed Methods. the Efficiency of the Methods is Illustrated with Certain Numerical Examples, But the H-HWCM is More Accurate with Faster Convergence Than the HWCM and Other Methods Reported in the Literature

BLIND: A Privacy Preserving Truth Discovery System For Mobile Crowdsensing

Nowadays, an increasing number of applications exploit users who act as intelligent sensors and can quickly provide high-level information. These users generate valuable data that, if mishandled, could potentially reveal sensitive information. Protecting user privacy is thus of paramount importance for crowdsensing systems. In this paper, we propose BLIND, an innovative open-source truth discovery system designed to improve the quality of information (QoI) through the use of privacy-preserving computation techniques in mobile crowdsensing scenarios. The uniqueness of BLIND lies in its ability to preserve user privacy by ensuring that none of the parties involved are able to identify the source of the information provided. The system uses homomorphic encryption to implement a novel privacy-preserving version of the well-known K-Means clustering algorithm, which directly groups encrypted user data. Outliers are then removed privately without revealing any useful information to the parties involved. We extensively evaluate the proposed system for both server-side and client-side scalability, as well as truth discovery accuracy, using a real-world dataset and a synthetic one, to test the system under challenging conditions. Comparisons with four state-of-the-art approaches show that BLIND optimizes QoI by effectively mitigating the impact of four different security attacks, with higher accuracy and lower communication overhead than its competitors. With the optimizations proposed in this paper, BLIND is up to three times faster than the baseline system, and the obtained Root Mean Squared Error (RMSE) values are up to 42% lower than other state-of-the-art approaches

Modeling And Understanding Dispute Causation In The US Public-Private Partnership Projects

The partnership between the public and private sectors has led to a new and innovative way of delivering infrastructure projects that is referred to as public-private partnership (PPP). There are various benefits associated with PPP delivery methods including risk sharing, access to private funding, innovation, and flexibility, among others. Despite the proved benefits, contract conflicts and disputes are very common in PPP projects. While previous research studies examined the risks and the potential causes of conflicts in PPP projects, little-to-no research efforts were directed to study and model the interconnectivities between the different causes of conflicts in PPP agreements. To this end, the aim of this paper is to fill the gap in knowledge by providing a deeper understanding of the causalities or relationships between the different factors that cause disputes in PPP projects in the United States. The authors used a comprehensive analytical approach that involved three primary steps. First, 37 PPP case studies of infrastructure and construction projects were collected and analyzed using manual content analysis. Second, social network analysis was conducted to study the interdependencies between the different causal factors leading to disputes in PPP in general and in relation to Execution, Investment and Operation, and Third-Party Claims, in particular. Third, association rule analysis was conducted to identify key associations between the different causal factors that may trigger the three different types of PPP disputes. The findings showed that the key causes of disputes in PPP projects are related to (1) legal and regulatory, (2) payment and financial, and (3) poor management. While Execution-related disputes were found to be caused by complex interactions of causal factors, dispute causation of Investment and Operation-related and Third-Party Claims-related disputes seemed to be less simplistic. As such, the outcomes of this paper highlighted the important aspects required to avoid dispute occurrence in PPP projects. Ultimately, this paper contributes to the body of knowledge by providing directions for scholars and practitioners toward the aspects and interdependencies that require optimization and/or thorough consideration to avoid dispute occurrence and subsequently ensure successful implementation of PPPs

Advancing Occupational Health In Mining: Investigating Low-cost Sensors Suitability For Improved Coal Dust Exposure Monitoring

Exposure to coal dust in underground coal mines poses significant health risks to workers, including the development of diseases such as coal workers\u27 pneumoconiosis and silicosis. Current available methods for monitoring coal dust exposure are expensive and time-consuming, necessitating the exploration of alternative approaches. Low-cost light scattering particulate matter sensors offer a promising solution, and its development in recent years has demonstrated some success in air quality monitoring However, its application in sensing coal particles is limited partially due to that the operating condition in a mine is different than the atmosphere. Thus, the objective of this paper is to evaluate the impact of common factors encountered in a mining environment on these sensors. The findings revealed that the Air trek and Gaslab sensors were unsuitable, showing poor correlation with reference monitors. SPS30 was promising for low concentrations (0-1.0 mg m−3), while PMS5003 effectively monitored up to 3.0 mg m−3. Changing sensor orientation reduced accuracy. Higher wind speeds (3 m s−1) improved results. Low-cost sensors performed well with coal dust but poorly with Arizona road dust. This study underscores the imperative for enhancing these sensors, thereby facilitating their potential application to enhance the occupational health of miners

Modified Thermographic Signal-to-Noise Ratio For Active Microwave Thermography

Active microwave thermography (AMT) is an active thermographic nondestructive testing and evaluation (NDT&E) technique that utilizes an active electromagnetic-based excitation. This excitation is achieved through a radiating antenna and is spatially nonuniform in nature. As such, the electromagnetically-induced heat is also spatially nonuniform, as it is directly related to the radiated power density incident on the specimen under test (SUT). After excitation, infrared measurements on the surface of the SUT are completed using an infrared camera. Common post processing techniques including thermal contrast (TC) and signal-to-noise ratio (SNR) are often applied to these measured results. As these post processing techniques were developed for inspections with a spatially uniform thermal excitation, challenges arise when they are applied to inspections that use a nonuniform thermal excitation. To this end, this work considers two fundamental heating scenarios common in AMT: defect heating and structure heating. Defect heating occurs when the defect is the primary electromagnetic absorber in a SUT, resulting in an induced heat source at the defect location. Structure heating takes place when the surrounding structure of the SUT is the primary electromagnetic absorber (e.g., heat source), and a defect present will affect the thermal diffusion through the SUT. For each scenario, TC and SNR are calculated. The results indicate that a reformulation of SNR is required for structure heating as SNR exceeds 0 dB for cases when a defect is and is not present (and hence creates a false positive detection). As such, a new formula is proposed and implemented (SNRr). The new formula provides a clear indication of the presence of a defect through the calculation of variance over the cooling period (resulting in a difference of SNRr variance of 9 dB2 between defect and defect-free specimens). Additionally, this new definition is also successfully applied to defect heating (difference of SNRr variance of 22 dB2 between cases of with and without a defect)