Comparison of Soft Tissue Chin Thickness in Adult Patients With Various Mandibular Divergence Patterns.

Objective This study aimed to compare the soft tissue chin (STC) thickness at different levels in patients presenting for orthodontic treatment with different vertical facial types. Materials and methods This comparative cross-sectional study was conducted at Sharif Medical & Dental College, Lahore, Pakistan, on 195 subjects. Patients presenting for orthodontic treatment, both genders, aged from 18 to 32 years, and Pakistani nationals were included. Patients with any craniofacial deformity, syndrome, cleft lip and palate, previous orthodontic or orthognathic treatment, and multiple missing teeth and prostheses in edentulous areas were excluded. Vertical facial patterns and STC thickness were recorded from pre-treatment lateral cephalograms. One-way analysis of variance (ANOVA) was applied to compare STC among various vertical facial patterns. Post-hoc analysis was done using the Tukey test. Results There were 126 females (64.62%) and 69 males (35.38%). The mean age was 21.66 ± 3.44 years. All three soft tissue chin thickness distances significantly differed among vertical facial patterns (p<0.001). Multiple comparisons show that the distance between soft and hard tissue pogonion was insignificant between low and normal angle facial heights (p=0.5). Similarly, no significant difference was observed for the distance between soft and hard tissue menton in low and normal angle subjects (p=0.4). The rest of the multiple comparisons were statistically significant (p<0.05). Conclusion The STC thickness is significantly associated with vertical facial divergence. While planning orthognathic surgery or genioplasty of the mandible, due consideration should be given to vertical divergence of the face to avoid unwanted facial changes.

Inquiring the impact of rural-urban migration, construction sector, and agriculture irrigated land on environmental degradation: insights from urbanized Asian countries.

The present study investigates the influence of rural-urban migration, construction sector share, and agriculture-irrigated land on environmental quality in urbanized Asian nations. For analysis, panel data from seven highly urbanized economies from 1996 to 2020 is utilized. The study employed an augmented mean group (AMG) estimator to find short and long-run results. The empirical discoveries depict that rural-urban migration increases energy demand in urbanized areas and significantly contributes to deteriorating environmental quality. The findings also reveal that the expansion of construction sectors is a significant source of high cement production, which also increases carbon emissions and environmental pollution by increasing the concentration of particulate matter in the atmosphere. The findings also exposed the role of agriculture-irrigated land, contributing to carbon emissions in urbanized Asian economies. The study also investigated the impact of the square term of irrigated agricultural land on environmental deterioration, revealing that adding agricultural land will further intensify environmental degradation by increasing carbon emissions in the atmosphere. A policy framework to reduce environmental damage in Asian economies caused by rural-to-urban migration, the building industry, and irrigated agricultural land was recommended based on the study's findings.

What causes environmental degradation in Pakistan? Embossing the role of fossil fuel energy consumption in the view of ecological footprint.

For decades, environmental degradation has become a universal challenge, and for sustainable environment quality, a true and broader proxy is vital. Pakistan is an ecological deficient country in the world, being the sixth-largest economy (population-wise). This study investigates the prime sources of environmental degradation through ecological footprint in Pakistan. The yearly time-series data spanning 1972 to 2020 is utilized for a set of regressors as fossil fuel energy consumption, trade openness, arable land, industrial share to GDP, economic growth, and population growth. We use various econometric techniques, the bounds test, ARDL (short and long run) model, FMOLS, and Granger causality test. Bounds test confirms the existence of cointegration among variables included in our model. The ARDL estimates suggest that fossil fuel energy consumption, trade openness, and population growth are the leading factors affecting the environment. Fossil fuel consumption and population growth significantly damage the environment in the short and long run. Contrasting to that, trade openness is substantial to the environment quality. The FMOLS approves the robustness of the cointegrating findings. Moreover, a unidirectional causal relationship from economic growth to the ecological footprint (GDP → EFP). And also, the ecological footprint of arable land (EFP → AL) is witnessed. At the same time, bidirectional causality is found between growth rate and fossil energy consumption (GDP ↔ FEC). Lastly, we recommend some policy options to improve environmental quality in Pakistan.

Exploring environment sensitivity to fiscal and monetary policies in China: using ecological footprints as a contemporary proxy.

This paper is a pioneer attempt using ecological footprints, the latest environment sensitivity proxy to be regressed, contributing to the scarce literature concerning one of the most burning global dilemmas of the era. For econometric analysis, fiscal and monetary tools, green energy consumption, and economic growth have been chosen as a set of regressors data spanning 1990-2020 in China facing the highest total ecological footpaths. And giving priority to the relevancy, reliability, and robustness autoregressive distributed lag (ARDL), fully modified ordinary least squares (FMOLS) and dynamic ordinary least squares (DOLS) have been applied for instant and eternal sensitivities, followed by the widely used stationarity tests (augmented Dicky-Fuller and Phillips-Perron tests) and bounds test. Granger's ordeal has also noticed causal inferences. Cointegrating findings are robust across all techniques, and ARDL results remain consistent regardless of short and prolonged duration. We witness positive and statistically significant (at 10%) responsiveness of ecological footprints (EFP) to China's rapid gross domestic output (GDP) growth per capita fueled by fossil fuels (primarily coal). Contrarily, negative/inverse sensitivity to expansionary fiscal (higher government expenditures, GEx), contractionary monetary policies (higher policy rate, DR), and green energy use (REnC). Besides, EFP demonstrates statistically significant reciprocal interconnection with GDP and REnC but a unidirectional connection with DR (DR → EFP). GDP has effective collaboration with REnC and GEx whereas single-sided relationship DR as (GDP → DR). Finally, some policy choices are endorsed.