| 1. |
- Modini, R. L., et al.
(author)
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Primary marine aerosol-cloud interactions off the coast of California
- 2015
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In: Journal of Geophysical Research - Atmospheres. - 2169-897X .- 2169-8996. ; 120:9, s. 4282-4303
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Journal article (peer-reviewed)abstract
- Primary marine aerosol (PMA)-cloud interactions off the coast of California were investigated using observations of marine aerosol, cloud condensation nuclei (CCN), and stratocumulus clouds during the Eastern Pacific Emitted Aerosol Cloud Experiment (E-PEACE) and the Stratocumulus Observations of Los-Angeles Emissions Derived Aerosol-Droplets (SOLEDAD) studies. Based on recently reported measurements of PMA size distributions, a constrained lognormal-mode-fitting procedure was devised to isolate PMA number size distributions from total aerosol size distributions and applied to E-PEACE measurements. During the 12 day E-PEACE cruise on the R/V Point Sur, PMA typically contributed less than 15% of total particle concentrations. PMA number concentrations averaged 12 cm(-3) during a relatively calmer period (average wind speed 12m/s(1)) lasting 8 days, and 71cm(-3) during a period of higher wind speeds (average 16m/s(1)) lasting 5 days. On average, PMA contributed less than 10% of total CCN at supersaturations up to 0.9% during the calmer period; however, during the higher wind speed period, PMA comprised 5-63% of CCN (average 16-28%) at supersaturations less than 0.3%. Sea salt was measured directly in the dried residuals of cloud droplets during the SOLEDAD study. The mass fractions of sea salt in the residuals averaged 12 to 24% during three cloud events. Comparing the marine stratocumulus clouds sampled in the two campaigns, measured peak supersaturations were 0.20.04% during E-PEACE and 0.05-0.1% during SOLEDAD. The available measurements show that cloud droplet number concentrations increased with >100 nm particles in E-PEACE but decreased in the three SOLEDAD cloud events.
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| 2. |
- Sanchez, K. J., et al.
(author)
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Meteorological and aerosol effects on marine cloud microphysical properties
- 2016
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In: Journal of Geophysical Research - Atmospheres. - 2169-897X .- 2169-8996. ; 121:8, s. 4142-4161
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Journal article (peer-reviewed)abstract
- Meteorology and microphysics affect cloud formation, cloud droplet distributions, and shortwave reflectance. The Eastern Pacific Emitted Aerosol Cloud Experiment and the Stratocumulus Observations of Los-Angeles Emissions Derived Aerosol-Droplets studies provided measurements in six case studies of cloud thermodynamic properties, initial particle number distribution and composition, and cloud drop distribution. In this study, we use simulations from a chemical and microphysical aerosol-cloud parcel (ACP) model with explicit kinetic drop activation to reproduce observed cloud droplet distributions of the case studies. Four cases had subadiabatic lapse rates, resulting in fewer activated droplets, lower liquid water content, and higher cloud base height than an adiabatic lapse rate. A weighted ensemble of simulations that reflect measured variation in updraft velocity and cloud base height was used to reproduce observed droplet distributions. Simulations show that organic hygroscopicity in internally mixed cases causes small effects on cloud reflectivity (CR) (<0.01), except for cargo ship and smoke plumes, which increased CR by 0.02 and 0.07, respectively, owing to their high organic mass fraction. Organic hygroscopicity had larger effects on droplet concentrations for cases with higher aerosol concentrations near the critical diameter (namely, polluted cases with a modal peak near 0.1 mu m). Differences in simulated droplet spectral widths (k) caused larger differences in CR than organic hygroscopicity in cases with organic mass fractions of 60% or less for the cases shown. Finally, simulations from a numerical parameterization of cloud droplet activation suitable for general circulation models compared well with the ACP model, except under high organic mass fraction.
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| 3. |
- Partridge, Daniel G., 1984-, et al.
(author)
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Inverse modeling of cloud-aerosol interactions : Part 1: Detailed response surface analysis
- 2011
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In: Atmospheric Chemistry And Physics. - : Copernicus GmbH. - 1680-7316 .- 1680-7324. ; 11:14, s. 7269-7287
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Journal article (peer-reviewed)abstract
- New methodologies are required to probe the sensitivity of parameters describing cloud droplet activation. This paper presents an inverse modeling-based method for exploring cloud-aerosol interactions via response surfaces. The objective function, containing the difference between the measured and model predicted cloud droplet size distribution is studied in a two-dimensional framework, and presented for pseudo-adiabatic cloud parcel model parameters that are pair-wise selected. From this response surface analysis it is shown that the susceptibility of cloud droplet size distribution to variations in different aerosol physiochemical parameters is highly dependent on the aerosol environment and meteorological conditions. In general the cloud droplet size distribution is most susceptible to changes in the updraft velocity. A shift towards an increase in the importance of chemistry for the cloud nucleating ability of particles is shown to exist somewhere between marine average and rural continental aerosol regimes. We also use these response surfaces to explore the feasibility of inverse modeling to determine cloud-aerosol interactions. It is shown that the "cloud-aerosol" inverse problem is particularly difficult to solve due to significant parameter interaction, presence of multiple regions of attraction, numerous local optima, and considerable parameter insensitivity. The identifiability of the model parameters will be dependent on the choice of the objective function. Sensitivity analysis is performed to investigate the location of the information content within the calibration data to confirm that our choice of objective function maximizes information retrieval from the cloud droplet size distribution. Cloud parcel models that employ a moving-centre based calculation of the cloud droplet size distribution pose additional difficulties when applying automatic search algorithms for studying cloud-aerosol interactions. To aid future studies, an increased resolution of the region of the size spectrum associated with droplet activation within cloud parcel models, or further development of fixed-sectional cloud models would be beneficial. Despite these improvements, it is demonstrated that powerful search algorithms remain necessary to efficiently explore the parameter space and successfully solve the cloud-aerosol inverse problem.
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| 4. |
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| 5. |
- Partridge, Daniel G., 1984-, et al.
(author)
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Inverse modeling of cloud-aerosol interactions : Part 2: Sensitivity tests on liquid phase clouds using a Markov Chain Monte carlo based simulation approach
- 2012
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In: Atmospheric Chemistry And Physics. - : Copernicus GmbH. - 1680-7316 .- 1680-7324. ; 12:6, s. 2823-2847
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Journal article (peer-reviewed)abstract
- This paper presents a novel approach to investigate cloud-aerosol interactions by coupling a Markov Chain Monte Carlo (MCMC) algorithm to a pseudo-adiabatic cloud parcel model. Despite the number of numerical cloud-aerosol sensitivity studies previously conducted few have used statistical analysis tools to investigate the sensitivity of a cloud model to input aerosol physiochemical parameters. Using synthetic data as observed values of cloud droplet number concentration (CDNC) distribution, this inverse modelling framework is shown to successfully converge to the correct calibration parameters. The employed analysis method provides a new, integrative framework to evaluate the sensitivity of the derived CDNC distribution to the input parameters describing the lognormal properties of the accumulation mode and the particle chemistry. To a large extent, results from prior studies are confirmed, but the present study also provides some additional insightful findings. There is a clear transition from very clean marine Arctic conditions where the aerosol parameters representing the mean radius and geometric standard deviation of the accumulation mode are found to be most important for determining the CDNC distribution to very polluted continental environments (aerosol concentration in the accumulation mode >1000 cm−3) where particle chemistry is more important than both number concentration and size of the accumulation mode. The competition and compensation between the cloud model input parameters illustrate that if the soluble mass fraction is reduced, both the number of particles and geometric standard deviation must increase and the mean radius of the accumulation mode must increase in order to achieve the same CDNC distribution. For more polluted aerosol conditions, with a reduction in soluble mass fraction the parameter correlation becomes weaker and more non-linear over the range of possible solutions (indicative of the sensitivity). This indicates that for the cloud parcel model used herein, the relative importance of the soluble mass fraction appears to decrease if the number or geometric standard deviation of the accumulation mode is increased. This study demonstrates that inverse modelling provides a flexible, transparent and integrative method for efficiently exploring cloud-aerosol interactions efficiently with respect to parameter sensitivity and correlation.
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| 6. |
- Durojaye Moshood, Taofeeq, et al.
(author)
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Green and low carbon matters: A systematic review of the past, today, and future on sustainability supply chain management practices among manufacturing industry
- 2021
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In: Cleaner Engineering and Technology. - : Elsevier BV. - 2666-7908. ; 4
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Journal article (peer-reviewed)abstract
- Climate change has arisen as one of human beings' most significant threats. A higher proportion of carbon dioxide emissions are produced from developing countries as manufacturing globalization requires more and more emerging nations. The predicament between carbon reduction and fast industrial development makes firms in developing countries reluctant to take thoughtful commitment and actions in carbon dioxide emission reduction in their global manufacturing practices. Sustainability, green, and low-carbon supply chain networks research is still also in its infant stage from a theoretical perspective and requires further research and development. Therefore this research aims to address the research question: “Why and how organizations are adopting sustainable strategies in developing countries to increase sustainable supply chain management practices in the manufacturing?“. In order to thoroughly analyse the literature, this paper used ATLAS.ti 9 software tools to save, identify, and assess the data for this study. We reviewed, compiled, and sorted papers from 326 publications for this article and then recognized 141 as critical to the work scope. The results reveal that the organization's current concentration is on improving internal environmental efficiency related to its mid-stream SCM activities. The introduction of environmental practices at the external level (downstream and upstream) is relatively limited, and few organizations are collaborating effectively with supply chain partners to improve their SSCM performance. The results also show that organizational performance, reputation/risk management, customer pressure, and top management support are primary motivators for organizations to embrace SSCM practices. The typology suggests that companies need to consider and recognize their key sustainability risk in the past, current, and future to have a simple organizational design to innovative management methods to handle their sustainable supply chain practices.
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| 7. |
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| 8. |
- Moshood, Taofeeq D., et al.
(author)
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“I Want to Serve, But the Public Does Not Understand”—An Approach to Employees’ Intention to Stay In the Malaysian Construction Companies
- 2021
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In: Applied System Innovation. - : MDPI AG. - 2571-5577. ; 4:1
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Journal article (peer-reviewed)abstract
- © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This paper explicitly clarifies an employee’s goal to voluntarily stay in his/her current employment. A large volume of research has concentrated on corporate environments on the causes of workforce turnover. Nevertheless, little was done to investigate workers’ desire to remain, which was the essential parameter in determining their stay in the construction sector. Therefore, this research was undertaken to explore the relationship between job embeddedness (off-the-job and on-the-job and the intent of staying in Malaysian construction companies with the mediating impact of continuance commitment. For the analysis, a simple random under probability sampling technique was used. Of the overall 280 samples surveyed, 243 responded and used it in the report, 86.8% of the response rate. A structural equation modeling approach was used to analyze the direct and indirect relationships as drawn by the hypotheses. This research showed that the component of the off-the-job, on-the-job embeddedness and intention to stay were substantially linked. At the same time, continuance commitment plays a full mediation between the convergence of off-the-job, on-the-job and the intention to stay. These findings suggest that construction companies in Malaysia need to consider organizational and community embeddedness relationships along with continuance commitment in the invention of programs designated to influence workers’ intention to stay on their current jobs.
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| 9. |
- Nikmanesh, M., et al.
(author)
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Employee Productivity Assessment Using Fuzzy Inference System
- 2023
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In: Information. - 2078-2489. ; 14:7
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Journal article (peer-reviewed)abstract
- The success of an organization hinges upon the effective utilization of its human resources, which serves as a crucial developmental factor and competitive advantage, and sets the organization apart from others. Evaluating staff productivity involves considering various dimensions, notably structural, behavioral, and circumferential factors. These factors collectively form a three-pronged model that comprehensively encompasses the facets of an organization. However, assessing the productivity of employees poses challenges, due to the inherent complexity of the humanities domain. Fuzzy logic offers a sound approach to address this issue, employing its rationale and leveraging a fuzzy inference system (FIS) as a sophisticated toolbox for measuring productivity. Fuzzy inference systems enhance the flexibility, speed, and adaptability in soft computation. Likewise, their applications, integration, hybridization, and adaptation are also introduced. They also provide an alternative solution to deal with imprecise data. In this study, we endeavored to identify and measure the productivity of human resources within a case study, by developing an alternative framework known as an FIS. Our findings provided evidence to support the validity of the alternative approach. Thus, the utilized approach for assessing employee productivity may provide managers and businesses with a more realistic asset.
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| 10. |
- Sorooshian, Shahryar, 1980, et al.
(author)
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Influence of readiness measures on planning tourism digital shift
- 2021
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In: Academy of Strategic Management Journal. - 1544-1458 .- 1939-6104. ; 20:2
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Journal article (peer-reviewed)abstract
- Industrial revolution 4.0 is pushing industries toward digitalization including tourism. Although as almost all modes of tourism are currently declining as a result of the new COVID-19 pandemic, information and technology is expected to play a role not just during, but also after, the crisis. As a result, the aim of this letter is to formulate a strategic framework for readiness measures for this digitalization transition and see the dependency of the defined measures in tourism industry. Hence, a PROMETEE-based program was used to demonstrate that the top ready clusters for digital tours are not sensitive to the weights of specified readiness measures. This analysis can be used to direct transition planning for top ready tourism clusters
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| 11. |
- Sorooshian, Shahryar, 1980, et al.
(author)
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Toward a Modern Last-Mile Delivery: Consequences and Obstacles of Intelligent Technology
- 2022
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In: Applied System Innovation. - : MDPI AG. - 2571-5577. ; 5:4
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Journal article (peer-reviewed)abstract
- Last-mile delivery (LMD) is essential in supply chains, and providers of logistics services are aware that they must adapt to changing customer and society expectations, competition challenges, and modern technologies. In light of the importance of artificial intelligence (AI) in modern technologies, this article employed a narrative literature review to recognize impacts of AI-powered technologies that aid in optimizing the LMD component. For this study, two possible classes of technologies were considered: tangible technologies, such as robots, drones, and autonomous vehicles, and intangible technologies, such as decision support tools and operating systems. Thus, this article frames potential developments in modern LMD, taking into account the fact that technological advancement brings both opportunities and challenges. According to this literature review, the modern version of LMD is capable of providing services that are both more productive and more sustainable, thus satisfying demands for better services. Last but not least, another goal of this article was to look into the challenges that limit the benefits of modern LMDs. Finally, suggestions are provided as a resource for practitioners and policymakers working in the supply chain to improve performance.
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| 12. |
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