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This paper presents the results of a study that developed improved inverse models to accurately predict the annual daylighting performance (sDA and lighting energy use) of various window configurations. This inverse model is an improvement over previous inverse models because it can be applied to variable room geometries at different weather locations in the US. The room geometries can be varied from 3 m × 3 m × 2.5 m to 15 m × 15 m × 10 m (length × width × height). The other variables used in the model include orientation (N, E, S, W), window-to-floor ratio, window location in the exterior wall, glazing visible transmittance, ceiling visible reflectance, wall visible reflectance, shade type (overhangs, fins), shade visible reflectance, lighting power density (LPD) (W/m2), and lighting dimming setpoint (lux). Such models can quickly advise architects during the preliminary design phase about which daylighting design options provide useful daylighting, while minimizing the annual auxiliary lighting energy use. The inverse models tested and developed were multi-linear regression (MLR) models, which were trained and tested against Radiance-based annual daylighting simulation results. In the analysis, 482 cases with different model conditions were simulated, to develop and validate the inverse models. This study used 75% of the data to train the model and 25% of the data to validate the model. The results showed that the new inverse models had a high accuracy in the annual daylighting performance predictions, with an R2 of 0.99 and an CV(RMSE) of 15.19% (RMSE of 58.91) for the lighting energy (LE) prediction, and an R2 of 0.95 and an CV(RMSE) of 14.38% (RMSE of 8.02) for the sDA prediction. In addition, the validation results showed that the LE MLR model and sDA MLR model had an R2 of 0.96 and 0.85, and RASE of 121.89 and 8.54, respectively, which indicate that the inverse models could accurately predict daylighting results for sDA and lighting energy use.

According to Bureau of Transportation Statistics, the U.S. transportation system handled 14,329 million ton-miles of freight per day in 2020. Understanding the generation of these freight shipments is crucial for transportation researchers, planners, and policymakers to design and plan for a more efficient and connected freight transportation system. Traditionally, the freight generation modeling has been based on Ordinary Least Square (OLS) regression, although more advanced Machine Learning (ML) algorithms have been evaluated and proven to have excellent performance in various transportation applications in recent years. Furthermore, one modeling approach applied for one industry might not always be applicable for another as their freight generation logics can be quite different. The objective of this study is to apply and evaluate alternative ML algorithms in the estimation of freight generation for each of 45 industry types. Seven alternative ML algorithms, along with the base OLS regression, were evaluated and compared. In addition, the study considered different combinations of variables in both the original and logarithmic form as well as hyperparameters of those ML algorithms in the model selection for each industry type. The results showed statistically significant improvements in the root mean square error reduction by the alternative ML algorithms over the OLS for over 80% of cases. The study suggests utilizing the alternative ML algorithms can reduce the root mean square error by about 30%, depending on industry types.

In the United States, today’s ranches are engaging in small-scale nature-based endeavors to diversify their income base. But the geographic boundary of the land they own creates a relatively small area within which to operate, and fragmented ownership diminishes the ability of any single landowner to produce nature-based income. Collective action among nearby landowners can produce a set of resources from which all members of the group can profit. Such action can enhance the economic, social, and environmental sustainability of grasslands and the populations that use them. This article shows that common-interest communities can be used to provide and allocate wildlife and other resources on ranchlands, enabling individual landowners to generate more income from selling nature-based experiences to customers. Common-interest communities are familiar in urban settings but they have not yet been used in this setting. Thus, the article proposes a new approach to ranchland management based upon a familiar set of largely private legal arrangements. More broadly, the article illustrates the relevance of private law and private property to sustainable development by explaining how property owners can use private law to engage in environmentally beneficial and economically profitable enterprises on the vast privately owned landscape of the U.S. Great Plains.

We introduce a field survey method to assess the conservation condition of landscapes. Using a popular rapid assessment format, this study defines observable “stressed states” identified through the use of general metrics to gauge landscape degradation. Fifteen metrics within six thematic categories were selected through a literature review and extensive field trials. Field tests on the Greek island of Samothraki show a strong correlation between a single expert’s scores and five assessor’s scores at 35 landscape sites. Only three of the metrics did not maintain a high consistency among assessors; however, this is explained by the difficulty of interpreting certain anthropogenic stressors (such as livestock grazing) in Mediterranean semi-natural landscapes with culturally-modified vegetation patterns. The protocol and proposed index, with five conservation condition classes, identified areas of excellent and good quality, and reliably distinguished the most degraded landscape conditions on the island. Uncertainties and difficulties of the index are investigated, and further research and validation are proposed. The protocol effectively goes beyond a traditional visual aesthetic assessment; it can be used both by experts and non-scientists as a conservation-relevant multi-disciplinary procedure to support a holistic landscape diagnosis. The combination of an on-site experiential survey and its simple integrative format may be useful as a screening-level index, and for promoting local participation, landscape literacy and educational initiatives.

Community sociologists have examined community attachment through an almost exclusive focus on people’s social relations. Recent research efforts have noted the neglect of the physical place in traditional community sociological studies. Doing this has brought the physical environment into their discussions of community attachment. Despite this progress, we remain limited in our understanding of the physical environment’s contribution to peoples’ attachment to their communities and whether its effect on community attachment is applicable in the context of urban settings. In an effort to expand our knowledge of this topic, this study explored the contributions of the urban physical environment on community attachment. By selecting the Discovery Green Park as a typical form of physical environment in Houston, Texas, this study sought to investigate how people’s levels of community attachment could be predicted by: (1) peoples’ interactions with an urban park; (2) people’s emotional connections with such a park; and (3) peoples’ social interactions with others within the park. After conducting a series of block model regression analyses, we found that community attachment was not completely defined by social factors, but also depended upon peoples’ emotional connections with the local physical environment and the social interactions happening in those settings.

Petroleum oil pollution is a worldwide problem that results from the continuous exploration, production, and consumption of oil and its products. Petroleum hydrocarbons are produced as a result of natural or anthropogenic practices, and their common source is anthropogenic activities, which impose adverse effects on the ecosystem’s nonliving and living components including humans. Phytoremediation of petroleum hydrocarbon-polluted soils is an evolving, low-cost, and effective alternative technology to most traditional remediation methods. The objective of this study is to evaluate the phytoremediation potentiality of Vinca rosea for crude oil-contaminated soil by understanding its properties and involvement in the enhanced degradation of crude oil. The remediation potentiality was determined by evaluating the total petroleum hydrocarbon degradation percentage (TPH%) and changes in the molecular type composition of saturated and aromatic hydrocarbon fractions. TPH% was estimated gravimetrically, and changes in the molecular type composition of saturated and aromatic fractions were measured using gas chromatography and high-performance liquid chromatography, respectively. Sulfur concentration was measured using X-ray fluorescence. Cadmium and lead quantification was measured using Inductively Coupled Plasma-Atomic Emission Spectrometry (ICP-AES). The results revealed that V. rosea enhanced total petroleum hydrocarbon (TPH) degradation and altered the molecular composition of the crude oil. The saturated hydrocarbons increased and the aromatic hydrocarbons decreased. The saturated hydrocarbon fraction in the crude oil showed a wider spectrum of n-paraffin peaks than the oil extracted from unplanted and V. rosea-planted soils. Polyaromatic hydrocarbon degradation was enhanced in the presence of V. rosea, which was reflected in the increase of monoaromatic and diaromatic constituents. This was parallel to the increased sulfur levels in planted soil. The determination of sulfur and heavy metal content in plant organs indicated that V. rosea can extract and accumulate high amounts from polluted soils. The ability of V. rosea to degrade TPH and alter the composition of crude petroleum oil by decreasing the toxicity of polyaromatic hydrocarbons in soil, as well as its capability to absorb and accumulate sulfur and heavy metals, supports the use of plant species for the phytoremediation of crude oil-polluted sites.

The purpose of this research is to examine the precursors of a social media user’s purchase intention and intention to watch mukbang content. This study collected empirical data from 399 social media users who had viewed mukbang content and used smart PLS to analyze the data. This analysis found that vicarious satisfaction and attractiveness of content performers had an effect on attitudes toward mukbang. Moreover, this study found that perceived ease of use and attitude toward mukbang content had an impact on the intention to watch mukbang content. Furthermore, the intention to watch mukbang was a motivator of purchase intention regarding food items in mukbang content. This study contributes to the literature by investigating the influence of mukbang watching on purchasing intention.