Gap Fill Research Articles

Mucorales fungi suppress nitric oxide production by macrophages.

In October 2022, Mucorales fungi were listed in the "High Priority Group" on the first-ever list of fungal priority pathogens by the World Health Organization. As the causative agent of mucormycosis, Mucorales have become of great clinical and public health importance with growing mucormycosis numbers, notably with the exponential rise of COVID-19-associated mucormycosis cases. Despite the dire need, there are limited therapeutic options to treat mucormycosis. Our research fills in critical gaps of knowledge about how Mucorales fungi evade the host immune system. Specifically, we offer evidence that Mucorales block nitric oxide production, which is a key mediator and signaling molecule of the mammalian innate immune response to microbial pathogens. Our work offers new insight into immune evasion mechanisms by Mucorales fungi.

A global daily gap-filled chlorophyll-a dataset in open oceans during 2001–2021 from multisource information using convolutional neural networks

Abstract. Ocean color data are essential for developing our understanding of biological and ecological phenomena and processes and also of important sources of input for physical and biogeochemical ocean models. Chlorophyll-a (Chl-a) is a critical variable of ocean color in the marine environment. Quantitative retrieval from satellite remote sensing is a main way to obtain large-scale oceanic Chl-a. However, missing data are a major limitation in satellite remote-sensing-based Chl-a products due mostly to the influence of cloud, sun glint contamination, and high satellite viewing angles. The common methods to reconstruct (gap fill) missing data often consider spatiotemporal information of initial images alone, such as Data Interpolating Empirical Orthogonal Functions, optimal interpolation, Kriging interpolation, and the extended Kalman filter. However, these methods do not perform well in the presence of large-scale missing values in the image and overlook the valuable information available from other datasets for data reconstruction. Here, we developed a convolutional neural network (CNN) named Ocean Chlorophyll-a concentration reconstruction by convolutional neural NETwork (OCNET) for Chl-a concentration data reconstruction in open-ocean areas, considering environmental variables that are associated with ocean phytoplankton growth and distribution. Sea surface temperature (SST), salinity (SAL), photosynthetically active radiation (PAR), and sea surface pressure (SSP) from reanalysis data and satellite observations were selected as the input of OCNET to correlate with the environment and phytoplankton biomass. The developed OCNET model achieves good performance in the reconstruction of global open ocean Chl-a concentration data and captures spatiotemporal variations of these features. The reconstructed Chl-a data are available online at https://doi.org/10.5281/zenodo.10011908 (Hong et al., 2023). This study also shows the potential of machine learning in large-scale ocean color data reconstruction and offers the possibility of predicting Chl-a concentration trends in a changing environment.

The beauty in imperfection: how naturalness cues drive consumer preferences for ugly produce and reduce food waste

PurposeAn important reason for food waste is the rejection of ugly produce by consumers. Most previous research has examined the absolute negative impacts of ugly produce on consumers’ preferences, no research has examined the conditions in which consumers prefer ugly (vs. typical) produce instead.This research investigates the circumstances under which these aesthetic imperfections become advantageous.MethodsWe conducted two between-subject design randomized experiments featuring two produce categories to examine when and why consumers prefer ugly produce.ResultsWe found that naturalness cues boost and even reverse consumers’ preferences for ugly produce when combining ugly appearance with naturalness cues. The subtyping effect mediates the interaction of appearance (typical vs. ugly) of produce and naturalness cues (present vs. absent) on produce’s evaluations.DiscussionOur findings provide more cost-effective strategies for retailers to reduce food waste. This paper fills in the research gaps on taping into the novel condition in which consumers prefer ugly (vs. typical) produce and the psychological mechanism behind this process. Based on schema incongruity theory, we argue that naturalness cues, as an enabler corresponding to the incongruous features of ugly produce, facilitate consumers to resolve the schema incongruity triggered by the ugly appearance and, in turn, boost consumers’ preferences for ugly produce.

Detection of large-scale Spartina alterniflora removal in coastal wetlands based on Sentinel-2 and Landsat 8 imagery on Google Earth Engine

The invasion of Spartina alterniflora has posed significant threats to the ecosystem health and biodiversity in coastal wetlands in China. In recent years, China has enacted large-scale S. alterniflora removal projects. Accurate monitoring of the S. alterniflora removal dynamics is crucial for evaluating the effectiveness of the removal projects and coastal wetland management. In this study, we presented a novel method for automatic and rapid detection of S. alterniflora removal events and removal timing at large scale based on time series Sentinel-2 and Landsat 8 imagery on Google Earth Engine. The method first detected and reconstructed the tidal-inundation-related Normalized Difference Vegetation Index (NDVI) using a newly proposed Tide Gap Filling algorithm, aiming to alleviate the impact of tidal inundation on S. alterniflora removal detection; then, the potential removal period were extracted based on the reconstructed NDVI time series; finally, the removal event and the corresponding removal timing were identified and mapped within the potential removal period by considering the phenological characteristics of the undisturbed S. alterniflora. We took the Shandong Province in northern coastal China and Fujian Province in southern coastal China as our study areas, where large-scale S. alterniflora removal projects were carried out in 2021 and 2022, respectively. Accuracy assessment based on field surveys and high-spatial-resolution PlanetScope imagery showed that the method achieved good accuracies for removal detection, with overall accuracies of 88.89 % for Shandong and 81.33 % for Fujian, respectively. The mean absolute error day (MAED) between the detected removal timing and the reference removal timing was 6.77 days in Shandong and 13.75 days in Fujian. Both provinces have successfully implemented the S. alterniflora removal projects. In Shandong, 82.32 % (7,243.94 ha) of S. alterniflora were removed during July to December in 2021; in Fujian, 88.81 % (6,649.21 ha) of S. alterniflora were removed during July to December in 2022. We expect that the method has great potential to be applied at national scale to provide baseline data for coastal wetland restoration and management.

Gap infilling of daily streamflow data using a machine learning algorithm (MissForest) for impact assessment of human activities

Machine learning algorithm has been increasingly used to fill missing daily streamflow data from neighboring gauges in data-scarce regions. However, how human activities, especially reservoir construction, may affect the performance of these gap-filling algorithms has not been explicitly assessed. This study applied the MissForest algorithm (MF) to infill daily streamflow gaps at 58 selected stations in the north area of Tianshan Mountains, an arid region in northwest China. The stations without reservoir impacts (i.e., with natural flow regimes) and with reservoir impacts (i.e., with high impacts of human activities) have been separately used to implement the infilling such that the impact of human activities on the performance of MF can be analyzed. Results show that MF using the station without reservoir impacts performed very well in filling daily streamflow gaps. Its performance had been insignificantly influenced by an increasing amount of missing data, different numbers of stations, and different lengths of observed data at each station. Without human activities, MF performance obviously improved with an increasing of 2.2%-3.2% in mean of R2 and a reducing of 21%-40% in mean of SMAPE. However, the performance of MF exhibited a noticeable degradation as a result of human activities that mean of R2 reduced by 1.1%-3.4% and mean of SMAPE increased by −19.5%-30.6%. This could be attributed to the fact that the MF algorithm is unable to fully capture the relevant information regarding human activities. The reconstructed daily streamflow series showed little flow regime change at the stations without reservoir impacts, but an advanced peak flow from July to June and a decrease of peak flow by 13% at the stations with reservoir impacts during the period of 2006–2011 compared with the baseline period of 1964–1989. It is recommended to first fill in the data gaps at stations without human activity, and then fill in the data gaps at stations influenced by human activities. We recommend exercising caution when using the MF method in rivers where human activities are significant or where low flow and intermittent flow events occur frequently.

STEPSBI: Quick spatiotemporal fusion with coarse- and fine-resolution scale transformation errors and pixel-based synthesis base image pair

Spatiotemporal fusion (STF) is crucial for reconciling the conflict between temporal and spatial resolutions of remote sensing observations. However, fusing images in heterogeneous areas remains challenging under continuous missing values. Moreover, most current STF methods only consider temporal errors and disregard the spatial scale error in time variation. Therefore, we proposed a Quick Spatiotemporal Fusion with Coarse- and Fine-Resolution Scale Transformation Errors and Pixel-Based Synthesis Base Image Pair (STEPSBI). First, the optimal pixel-based image synthesis strategy was designed using all available fine- and coarse-resolution images. Then, the scale transformation error (STE) of coarse-resolution downscaling to fine-resolution in temporal variation was quantified. And a residual term was introduced to reduce the prediction error from the temporal variation. Finally, the spatial scale and temporal errors were corrected using the results of super-pixel segmentation as spatial weights. This model has two strengths: (1) Pixel-based image synthesis alleviates the absence of base images under continuous missing values; and (2) STE correction restores spatial details of heterogeneous areas in rapid land cover change. In scenarios of continuous missing images, abrupt land cover changes, and high-resolution heterogeneity, we evaluated STEPSBI, the Gap Filling and Savitzky–Golay filtering method (GF-SG), the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), the Flexible Spatiotemporal DAta Fusion (FSDAF), the cross-attention-based adaptive weighting fusion network (CAFE), and the Multi-scene Spatiotemporal Fusion Network (MUSTFN). The results indicate that STEPSBI yields better overall performance than other models in cropland, woodland, grassland, and other land cover types. Furthermore, ablation experiments demonstrated that each component improved the model’s performance. In addition, STEPSBI had higher fusion efficiency because it was developed on the Google Earth Engine cloud computing platform. Therefore, STEPSBI was feasible for advancing fine monitoring of spatiotemporal image fusion under continuous missing values and heterogeneous surfaces. The program of STEPBSI is freely available at: https://code.earthengine.google.com/684844aa42f64fa6b4eebe3bc0dd6483.

Phenethyl isothiocyanate Regulates the Cancer Stem Cell Phenotype of SNU449 Hepatocellular Carcinoma Cells via STAT3-CD44 Axis

Cancer stem cells play an important role in resistance to therapy, invasion, metastasis, and recurrence. CD44 is one of the well-known surface markers for hepatocellular carcinoma and its expression level is related to poor survival and a high recurrence rate. The effect of phenethyl isothiocyanate (PEITC) on SNU449 hepatocellular carcinoma cell line cancer stem cells is not known. The goal of the present study was to investigate whether PEITC regulates the cancer stem cell phenotype of SNU449 cells. Here, cell viability, colony formation, and wound healing assays were performed to determine proliferative and migratory characteristics. Additionally, Caspase 3, CD44, Akt/mTOR, and p38/STAT3 protein expression levels were measured by western blotting. We found that compared to control confluence, gap fill, and migration rate were increased while half gap time was decreased in PEITC-treated cells. Compared to control-treated cells CD44 (3.2 fold) and p-STAT3 (2.44 fold) protein expressions were upregulated in PEITC-treated cells. Results of this study suggest that STAT3-mediated upregulation of CD44 leads to the gain of cancer stem cell phenotype of PEITC-treated SNU449 cells.

The Impact Of Globalization On Local Economic Activities In Moro, Kwara State, Nigeria, 2000 – 2020

Globalization is a process of interaction and integration among people, companies, and governments worldwide. With the advances in communication, transportation, and technology, this process has increased in intensity and has become increasingly more prevalent in the 21st century. Globalization has affected local, national, and worldwide economic activities since the turn of the millennium, with local areas receiving enormous impact. Therefore, the purpose of this article is to investigate the degree to which economic activities have been impacted by globalization, especially during the first two decades of the twenty-first century. It adopts historical research method in data collection and analytical approach in presenting them. Because writers often ignore the region's economic past, a large amount of the material comes from oral interviews, which fills in the information gaps.

Why the Moral Equality Account of Hypocrisy Does Not Fail After All

The Moral Equality Account of Hypocrisy (ME) is a prominent theory of why hypocrites lack moral standing to blame. Hypocrites make exceptions for themselves and thereby implicitly deny moral equality, which is an essential premise of moral standing to blame. ME has recently faced challenges from philosophers who deny that it is the hypocrite’s rejection of moral equality that causes her to lose moral standing to blame. I have distinguished three main challenges which I discuss and rebut in this article: “The Internal Blame and Blame of Fictional Characters Challenge”, which I attribute to Todd (2019), and “The Hypercrite Challenge” and “The Inegalitarian Norm Challenge”, which are due to Lippert-Rasmussen (2021). The article begins by offering an account of ME. It fills in a few gaps in the theory, by explaining why it is necessary for a blamer to invoke a special right to blame and by determining the type of right that standing to blame is. It also distinguishes between the question of why hypocrisy is wrong or bad, and the question of why it undermines moral standing to blame. I hold that several theories provide plausible answers to the first question, but only ME has so far given a good answer to the second question. When distinguishing properly between these two questions, we see that ME can respond satisfactorily to the three challenges.

An interprofessional geriatrics faculty development program: exploration of the barriers and facilitators of capstone projects

ABSTRACT Faculty development programs (FDPs) are an effective, evidence-based method of promoting knowledge, skills and self-efficacy of faculty. However, implementation and sustainability of curricular capstone projects developed by faculty as part of these programs are rarely reported. Challenges to sustaining programmatic implementation of interprofessional FDP curricular content into academic and clinical settings over time were not found in peer-reviewed literature. To better understand the sustained impact of our geriatrics-focused FDP, we explored barriers and facilitators to implementation and sustainability of capstone projects designed by faculty Scholars in our FDP. Thematic analysis of virtual interviews with 17 Scholars revealed several key factors that impacted the implementation and Dynamic sustainability of curricular projects. Three major themes and sub-themes were identified: Project Implementation (Supportive Factors, Hindering Factors and Filling in Gaps in the Field); Pedagogical Development (Enhancement of Skills and Culture Change); and Sustainability Impact (Project Sustainability, Career Development and Passing the Torch). Results suggest it is important to ensure logistical support, dedicated time, and organizational or institutional support. Implementation of geriatrics-focused FDPs provides an evidence-based approach to sustainability. Further study of the ongoing barriers and facilitators to sustainability is encouraged.

Book Review Advanced Materials for Wastewater Treatment and Desalination: Fundamentals to Applications

Book Review Advanced Materials for Wastewater Treatment and Desalination: Fundamentals to Applications

EatSense: Human centric, action recognition and localization dataset for understanding eating behaviors and quality of motion assessment

Current datasets for computer vision-based action recognition and localization cover a wide range of classes and challenging scenarios. However, these datasets don't cater to healthcare applications that involve long-term monitoring, tracking minor changes in movements over time for healthcare purposes, or completely modeling a specific human behavior that includes multiple sub-actions. Specifically, there are no existing datasets for research on either health monitoring on atomic-action-based eating behavior or for a full range of eating sub-actions that fully segment the main action. Addressing these gaps is valuable for extending research on the health monitoring of elderly people and is needed for creating richer and more complete descriptions of actions. This paper introduces a new benchmark dataset named EatSense that targets both the computer vision and healthcare communities and fills in the aforementioned gaps. EatSense is recorded while a person eats in an uncontrolled dining setting. The key features of EatSense are the introduction of challenging atomic actions for action recognition, the significantly diverse durations of actions that make it difficult for current temporal action localization frameworks to localize, the capability to model comprehensive eating behavior in terms of a sequence of action-based behaviors, and the simulation of minor variations in motion or performance. We conduct extensive experiments on EatSense with baseline deep learning-based approaches for benchmarking and hand-crafted feature-based approaches for explainable applications. We believe this dataset will benefit future researchers in building robust temporal action localization networks, behavior recognition, and performance assessment models for eating.

Using water structuring by ions to address pattern loading in wet HF-based oxide recess etch

In the FinFET fabrication flow, a gap fill oxide is used to fill the space between the Si fins that needs to be recessed to define the height of the fin trench. However, with the used flowable chemical vapor deposition (FCVD) oxide, the eth rate depends on fin spacing, a phenomenon called pattern loading. In this work, we used water structuring by dissolved ions to decrease the etch rate between fins and control pattern loading. We first showed, using ATR-FTIR (Attenuated Total Reflection – Fourier Transform Infra-Red), that structure making and breaking ions proposed by Marcus (2009) for bulk solutions, could be used to modulate water structuring in nanotrenches. Then, the Tetra-propyl Ammonium cation (TPA+) and the triply ionized Phosphoric acid anion (PO43−) were selected as structure making additives in HF 0.5%. Optimization of the etchant composition allowed to suppress pattern loading for a gap of 15 nm width using TPAH:HF mixtures, but not using HF:H3PO4 mixtures. The etch rate obtained with TPAH:HF at unity pattern loading and at room temperature was about 4.7 nm/min. It could be improved to about 7 nm/min by increasing the temperature. The inefficacy of the Phosphoric acid was ascribed to a pH effect, as the mono-ionized Phosphoric acid species formed in HF was classified as a borderline ion with little structuring capability by Marcus.

Grassland mowing event detection using combined optical, SAR, and weather time series

The European Union’s Common Agricultural Policy (CAP) and the Habitats Directive aim to improve biodiversity in agricultural landscapes. Both policies require enormous monitoring, which can be facilitated by remote sensing. Use intensity, measured by mowing frequency is an important indicator of biodiversity in permanent grasslands. The frequency and timing of mowing can be determined using satellite remote sensing because photosynthetically active biomass changes rapidly in response to mowing. However, the rapid regrowth of grasses requires very dense satellite time series for reliable detection. Radar time series can complement optical time series and fill in cloud-related gaps to overcome this problem. Additional weather data can support the detection of grassland mowing events, as mowing events are associated with specific meteorological conditions. However, previous studies have not fully exploited both potentials or different machine learning approaches for mowing event detection.This study presents a new transferable two-step approach to detect grassland mowing events using combined optical and SAR data and additional weather data. First, we filled cloud-related gaps in optical time series using a supervised machine learning regression with optical and SAR data. We then classified time series sequences of optical, SAR and weather data into mown and unmown using four different machine learning algorithms. We used time series of NDVI and EVI (combined Sentinel-2 and Landsat 8), SAR backscatter, six-day interferometric coherence, backscatter radar vegetation index, backscatter cross-ratio (Sentinel-1), and temperature and precipitation sums. Our test sites are distributed across Germany and cover the entire gradient of grassland use intensities.Mowing events could be detected with F1 values of up to 89%, first cut with up to 94%. Our results show no structural advantage of infilling time series with machine learning over linearly interpolated time series. The combined Sentinel-2 and Landsat-8 time series provided dense time series with mostly median gaps less than 20 days, which proved sufficient to reliably detect mowing events. SAR data were not essential for mowing event detection in our study, but weather data improved classification results for models trained on all areas and years. However, when the model was transferred to unknown years or areas that were not used for training, SAR data improved detection accuracy, whereas weather data degrade it. Models trained on all years but not all study sites detected mowing events with an accuracy of up to F1 = 76%. Models trained with all regions but not all years detected mowing events in untrained years with F1 up to 80%.

Cloud-based data mapper (CDM): application for monitoring dry deposition of reactive nitrogen

Spatiotemporal monitoring of reactive nitrogen atmospheric deposition is essential for understanding its impact on sensitive ecosystems and quantifying cumulative effects. However, the sparsity of direct surface flux measurements combined with barriers in dissemination are major limiting factors in providing this information to decision makers and non-experts in a timely manner. This work addresses both aspects of this information gap by, 1) utilizing satellite-derived reactive nitrogen dry deposition data products that can be used by decision-makers to supplement the sparse direct surface flux measurements and 2) fill in measurement gaps. Therefore, we have developed a Reactive Nitrogen Flux Mapper (RNFM) component of the interactive Cloud-based Data Mapper (CDM) for providing easy access of satellite-derived reactive nitrogen (defined here as nitrogen dioxide (NO2) and ammonia (NH3)) dry deposition flux spatial maps/data to decision-makers/stakeholders over North America. The RNFM component of CDM has a Graphical User Interface (GUI) that allows users to specify the geographical regions and time periods for computing the average fluxes on the fly using an integrated cloud-based computing platform. The CDM architecture is flexible and can be upgraded in the future to take advantage of upstream satellite data directly on cloud platforms to provide results in near real-time.

Students Perception on Vocabulary Mastery through Song Lyric Gap Fill

Mastering a lot of vocabulary will make it easier for someone to read, write, listen and speak. Learners need to adjust their learning style, strategy as well as the use of media that could help students develop their vocabulary mastery early. This study aims to get information on the effect of using song lyric gap fill to improve student vocabulary mastery and student perception of mastering vocabulary using song lyric gap-fill method. This study design is descriptive quantitative whose data collecting method used test and questionnaire as instrument. The test would be conducted by having students answer pre-test and post-test. The questionnaire was being done when the student had received treatment using song lyric gap fill. The result obtained shows that song lyric gap-fill method that was used in this research would increase the average student’s score by 40.8%. The statistical test results show that there is an effect of using the Song Lyric Gap Fill method in increasing vocabularies in 8-grade junior high school students. And the results of student perception of using song lyric gap fill on the vocabulary learning showed that the highest achievement (80%) that students increased new vocabulary with help of song lyric gapfill. While 67% students agreed if they had difficulties in learning parts of speech.

Overview of Committed Quantities in Commodity Demand Analysis with a Focus on Energy

An overview of the literature considering committed quantities in demand estimation for various commodities with an emphasis on energy commodities is presented. This overview provides a definition and the history of committed quantities, along with different theoretical modeling methodologies. Committed quantities are quantities that are consumed in the short run with little regard for price. Previous studies suggest that committed quantities for various commodities range from 15 to 98% of consumption. The inclusion of committed quantities appears to improve estimates generally, but it is not clear-cut. Problems arise when estimated committed quantities are negative or larger than the consumption amount. This review concludes with a recommendation that further research is necessary to resolve such issues, provide an improved understanding of the committed quantities in estimation, and fill in knowledge gaps concerning committed quantities ranging from theoretical to practical issues.

Eddy Covariance CO2 Flux Gap Filling for Long Data Gaps: A Novel Framework Based on Machine Learning and Time Series Decomposition

Continuous long-term eddy covariance (EC) measurements of CO2 fluxes (NEE) in a variety of terrestrial ecosystems are critical for investigating the impacts of climate change on ecosystem carbon cycling. However, due to a number of issues, approximately 30–60% of annual flux data obtained at EC flux sites around the world are reported as gaps. Given that the annual total NEE is mostly determined by variations in the NEE data with time scales longer than one day, we propose a novel framework to perform gap filling in NEE data based on machine learning (ML) and time series decomposition (TSD). The novel framework combines the advantages of ML models in predicting NEE with meteorological and environmental inputs and TSD methods in extracting the dominant varying trends in NEE time series. Using the NEE data from 25 AmeriFlux sites, the performance of the proposed framework is evaluated under four different artificial scenarios with gap lengths ranging in length from one hour to two months. The combined approach incorporating random forest and moving average (MA-RF) is observed to exhibit better performance than other approaches at filling NEE gaps in scenarios with different gap lengths. For the scenario with a gap length of seven days, the MA-RF improves the R2 by 34% and reduces the root mean square error (RMSE) by 55%, respectively, compared to a traditional RF-based model. The improved performance of MA-RF is most likely due to the reduction in data variability and complexity of the variations in the extracted low-frequency NEE data. Our results indicate that the proposed MA-RF framework can provide improved gap filling for NEE time series. Such improved continuous NEE data can enhance the accuracy of estimations regarding the ecosystem carbon budget.

From guidelines to decision aids and adherence supports: Insights from the process of evidence translation

ObjectiveTo explore the evidence-translator’s experience of the expert-recommended process of translating guidelines into tools for decision making, action, and adherence with the goal of improvement. MethodsA single reviewer dual reviewed the content, quality, certainty, and applicability of primary atherosclerotic cardiovascular prevention guidelines from the U.S. Preventive Services Task Force at the time of this work and used targeted searches of Medline to define the ideal structure and outcomes of tools; fill in gaps in guidelines; identify end-user needs; and choose and optimize existing tools in preparation for testing. ResultsGuidelines addressed screening, treatments, and/or supports, but never the combination of all three. None provided all of the information needed for evidence translation. Searches in Medline filled in some evidence gaps and provided key insights into end-user needs and effective tools. However, evidence translators are left with complicated decisions about how to use and align evidence. ConclusionGuidelines provide some, but not all, of the evidence needed for evidence translation, requiring additional intensive work. Evidence gaps result in complicated decisions about how to use and align evidence and balance feasibility and rigor. Practice implicationsGuidelines, standards groups, and researchers should work to better support the process of evidence translation.

Determining the Factors Affecting Filipinos’ Acceptance of the Use of Renewable Energies: A Pro-Environmental Planned Behavior Model

Renewable energies (RE) are naturally replenishable and sustainable energy sources. Solar, wind, hydro, geothermal, biomass, and ocean energy are among these sources. This study sought to determine the factors influencing the acceptance of Filipinos’ renewable energy utilization using the Pro-Environmental Planned Behavior (PEPB) model. A questionnaire survey was disseminated to obtain the required information and ascertain the variables affecting the behavioral intention of Filipinos in accepting RE. In examining the results, Structural Equation Modeling (SEM) was utilized with Partial Least Squares SEM (PLS-SEM) to identify other existing relationships. Findings revealed that Perceived Authority Support (PAS) is the most significant variable affecting Perceived Environmental Concern (PEC), inducing an indirect effect on the Behavioral Intention (BI) of Filipinos. The factor with the highest significance affecting BI is the Subjective Norm (SN). Accordingly, such results suggest that the government should implement incentive-based strategies, while considering the impact of SN, by gaining the public’s favor on the transition from traditional energy sources to green alternatives. In addition, educational programs and campaigns may be administered to spread awareness and fill in information gaps among Filipino citizens.