Preprocessing Strategies for Sparse Infrared Spectroscopy: A Case Study on Cartilage Diagnostics.

The aim of the study was to optimize preprocessing of sparse infrared spectral data. The sparse data were obtained by reducing broadband Fourier transform infrared attenuated total reflectance spectra of bovine and human cartilage, as well as of simulated spectral data, comprising several thousand spectral variables into datasets comprising only seven spectral variables. Different preprocessing approaches were compared, including simple baseline correction and normalization procedures, and model-based preprocessing, such as multiplicative signal correction (MSC). The optimal preprocessing was selected based on the quality of classification models established by partial least squares discriminant analysis for discriminating healthy and damaged cartilage samples. The best results for the sparse data were obtained by preprocessing using a baseline offset correction at 1800 cm-1, followed by peak normalization at 850 cm-1 and preprocessing by MSC.

Preclassification of Broadband and Sparse Infrared Data by Multiplicative Signal Correction Approach.

Preclassification of raw infrared spectra has often been neglected in scientific literature. Separating spectra of low spectral quality, due to low signal-to-noise ratio, presence of artifacts, and low analyte presence, is crucial for accurate model development. Furthermore, it is very important for sparse data, where it becomes challenging to visually inspect spectra of different natures. Hence, a preclassification approach to separate infrared spectra for sparse data is needed. In this study, we propose a preclassification approach based on Multiplicative Signal Correction (MSC). The MSC approach was applied on human and the bovine knee cartilage broadband Fourier Transform Infrared (FTIR) spectra and on a sparse data subset comprising of only seven wavelengths. The goal of the preclassification was to separate spectra with analyte-rich signals (i.e., cartilage) from spectra with analyte-poor (and high-matrix) signals (i.e., water). The human datasets 1 and 2 contained 814 and 815 spectra, while the bovine dataset contained 396 spectra. A pure water spectrum was used as a reference spectrum in the MSC approach. A threshold for the root mean square error (RMSE) was used to separate cartilage from water spectra for broadband and the sparse spectral data. Additionally, standard noise-to-ratio and principle component analysis were applied on broadband spectra. The fully automated MSC preclassification approach, using water as reference spectrum, performed as well as the manual visual inspection. Moreover, it enabled not only separation of cartilage from water spectra in broadband spectral datasets, but also in sparse datasets where manual visual inspection cannot be applied.

Exploring the moderating effect of globalization, financial development and environmental degradation nexus: a roadmap to sustainable development.

Financial development is a multidimensional process that contributes to economic growth but sometimes it has a devastating effect on climate change. No country can achieve sustainable development goals without caring the environmental quality. The present study investigates the moderating role of globalization (KOF) in determining the financial development (FD) on environmental degradation in the SAARC countries from 1990 to 2020. The long-run coefficients are estimated using the panel quantile regression (PQR) approach at lower, middle and upper quantile groups. The study shows the U-shaped relationship across three quantile groups based on financial development and carbon emissions. The moderator globalization (KOF) brings up the change in the turning point and flattens before the maturity of the U-shaped curve at the middle quantile while flattens after the maturity of the U-shaped curve at the upper quantile. The study recommends that by using energy-efficient technologies, better financial sector interaction with globalization enhances the environmental quality in SAARC countries.

SARS-CoV-2: big seroprevalence data from Pakistan-is herd immunity at hand?

PURPOSE: Seroprevalence surveys from different countries have reported SARS CoV-2 antibodies below 20% even in the most adversely affected areas and herd immunity cannot be predicted till more than half of the population gets the disease. The purpose of this survey was to estimate the magnitude of community-based spread of the infection, associated immunity, and the future prospects and proximity to a 'herd community'. METHODS: The study was undertaken as a cluster randomized, cross-sectional countrywide survey. This largest community-based seroprevalence data of SARS-CoV-2 were collected between 15th and 31st July, 2020 from seven randomly selected cities belonging to the three most populous provinces of Pakistan. The FDA approved kit of ROCHE was used for detection of SARS-CoV-2 antibodies. RESULTS: Serum samples of 15,390 participants were tested for SARS CoV-2 antibodies with an overall seroprevalence of 42.4%. The seroprevalence ranged from 31.1% to 48.1% in different cities with the highest in Punjab province (44.5%). In univariable analysis, the odds of seropositivity was higher in men compared to women (OR: 1.10, 95% CI: 1.01-1.19, P < 0.05). In multivariable analysis, the risk of being seropositive was lower (OR 0.72, 95% CI: 0.60-0.87, P < 0.01) in younger group (≤ 20 years) than in those aged above 60 years. CONCLUSION: The study concluded that despite a reasonable seroprevalence, the country is yet to reach the base minimum of estimations for herd immunity. The durability of immunity though debated at the moment, has shown an evidenced informed shift towards longer side.

Shape-Memory Polymeric Artificial Muscles: Mechanisms, Applications and Challenges.

Shape-memory materials are smart materials that can remember an original shape and return to their unique state from a deformed secondary shape in the presence of an appropriate stimulus. This property allows these materials to be used as shape-memory artificial muscles, which form a subclass of artificial muscles. The shape-memory artificial muscles are fabricated from shape-memory polymers (SMPs) by twist insertion, shape fixation via Tm or Tg, or by liquid crystal elastomers (LCEs). The prepared SMP artificial muscles can be used in a wide range of applications, from biomimetic and soft robotics to actuators, because they can be operated without sophisticated linkage design and can achieve complex final shapes. Recently, significant achievements have been made in fabrication, modelling, and manipulation of SMP-based artificial muscles. This paper presents a review of the recent progress in shape-memory polymer-based artificial muscles. Here we focus on the mechanisms of SMPs, applications of SMPs as artificial muscles, and the challenges they face concerning actuation. While shape-memory behavior has been demonstrated in several stimulated environments, our focus is on thermal-, photo-, and electrical-actuated SMP artificial muscles.

A graphene oxide and functionalized carbon nanotube based semi-open cellular network for sound absorption.

Noise pollution has been recognized as one of the leading environmental problems worldwide and has a negative impact on the physiological and psychological health of humans. Various porous polymeric materials have been found to be inefficient for sound absorption, especially in low-frequency interval; in addition, these materials have other disadvantages such as hygroscopicity and flammability. Herein, an efficient and economical semi-open cellular structure consisting of tiny self-assembled graphene oxide (GO) sheets interrupted by functionalized carbon nanotubes (CNT) was demonstrated as an acoustic composite foam. This innovative composite foam exhibits an approximately 100% and 20% enhancement over a band gap between 250 and 1600 Hz as compared to the pure melamine foam and graphene oxide-incorporated melamine foam with a thickness of 20 mm, respectively. The semi-open cellular structure results in optimal air-flow resistance, tortuosity against sound wave propagation, smaller viscous characteristic lengths, internal reflection and interfacial damping; hence, the composite foam shows advanced energy exhaustion and improved acoustic absorption. Furthermore, the inclusion of GO and CNT provides other properties including moisture insulation and fire retardancy. This kind of innovative sound absorbing material is inexpensive, easy to synthesize and environmentally friendly for commercial and industrial applications.

Clean energy transition and climate vulnerabilities: A comparative analysis of European and non-European developed countries.

Currently, the world faces an existential threat of climate change, and every government across the globe is trying to come up with strategies to tackle the severity of climate change in every way possible. To this end, the use of clean energy rather than fossil fuel energy sources is critical, as it can reduce greenhouse gas emissions and pave the way for carbon neutrality. This study examines the impact of the energy cleanability gap on four different climate vulnerabilities, such as ecosystem, food, health, and housing vulnerabilities, considering 47 European and non-European high-income countries. The study considers samples from 2002 to 2019. This study precedes the empirical analysis in the context of a quadratic relationship between the energy cleanability gap and climate vulnerability. The study uses system-generalized methods of the moment as the main technique, while panel quantile regression is a robustness analysis. Fixed effect and random effect models have also been incorporated. The study finds that the energy cleanability gap and all four climate vulnerabilities demonstrate a U-shaped relationship in both European and non-European countries, implying that when the energy cleanability gap increases, climate vulnerability decreases, but after reaching a certain threshold, it starts to increase. Development expenditure is found to be negatively affecting food and health vulnerabilities in European nations, while it increases food vulnerability and decreases health vulnerability in non-European nations. Regarding industrialization's impact on climate vulnerabilities, the study finds opposite effects for the European and non-European economies. On the other hand, for both groups, trade openness decreases climate vulnerabilities. Based on these results, the study recommends speeding up the energy transition process from fossil fuel energy resources towards clean energy resources to obtain carbon neutrality in both European and non-European groups.

Hyperspectral identification of oil adulteration using machine learning techniques.

Food adulteration is a global concern, drawing attention from safety authorities due to its potential health risks. Detecting and categorizing oil adulteration is crucial for consumer safety and food industry integrity. This research explores hyperspectral imaging (HSI) analysis to identify substandard oil adulteration at different stages. Using the non-destructive HSI Specim Fx 10 system, a method for precise and easy imaging-based fraud detection and classification was proposed. The 670 oil samples, including pure (Almond, Mustard, Coconut, Olive) and adulterated (Sunflower, Castor, Liquid Paraffin), were analyzed. The Savitzky-Golay filter preprocessed the images to remove noise and smooth spectral signatures. The oils were identified using various machine learning approaches, including Support Vector Machines, Logistic Regression, Linear Discriminant Analysis, Random Forests, Decision Trees, K-Nearest Neighbors, and Naïve Bayes with Linear Discriminant Analysis excelling in identification. Performance parameters, including precision, recall, F1-score, and overall accuracy, were calculated. The proposed method achieved a validation accuracy of 100%, outperforming numerous state-of-the-art approaches. This study introduces a robust pipeline for effective oil adulteration detection, offering a significant advancement in food safety and quality control.

Assessing the environmental sustainability gap in G20 economies: The roles of economic growth, energy mix, foreign direct investment, and population.

There is serious debate among researchers regarding the sustainability implications of economic prosperity and energy dependence. Energy consumption has a critical linkage with economic growth, but it also degrades environmental quality. Therefore, it is important to investigate the relationship between economic growth, the energy mix, and environmental sustainability. However, empirical literature utilizes narrow variables to capture environmental sustainability. Because of this, this research introduces a new environmental sustainability variable using entropy weighting and combining deforestation, household carbon emissions, and life expectancy. This study examines the relationship between environmental sustainability, economic growth, and other selected variables using data from 2002 to 2019 for the G20 and its high-, upper-, and low-middle-income member countries. Since shocks in one G20 country can affect another, this study uses the Augmented Mean Group (AMG) technique for empirical analysis. The results of this study indicate that Gross Domestic Product (EG) and its square term did not support the Environmental Kuznets Curve (EKC) theory. The energy mix has a positive impact on the environmental sustainability gap across all the samples except for the upper-middle-income group. Foreign direct investment positively affects this gap, while population growth has no significant impact. These findings demonstrate that policymakers should support environmentally friendly and clean energy sources to foster long-term economic growth and sustainability.

Magnetic Material in Triboelectric Nanogenerators: A Review.

Nowadays, magnetic materials are also drawing considerable attention in the development of innovative energy converters such as triboelectric nanogenerators (TENGs), where the introduction of magnetic materials at the triboelectric interface not only significantly enhances the energy harvesting efficiency but also promotes TENG entry into the era of intelligence and multifunction. In this review, we begin from the basic operating principle of TENGs and then summarize the recent progress in applications of magnetic materials in the design of TENG magnetic materials by categorizing them into soft ferrites and amorphous and nanocrystalline alloys. While highlighting key role of magnetic materials in and future opportunities for improving their performance in energy conversion, we also discuss the most promising choices available today and describe emerging approaches to create even better magnetic TENGs and TENG-based sensors as far as intelligence and multifunctionality are concerned. In addition, the paper also discusses the integration of magnetic TENGs as a power source for third-party sensors and briefly explains the self-powered applications in a wide range of related fields. Finally, the paper discusses the challenges and prospects of magnetic TENGs.

Optimization of an ultrasound-assisted extraction method to obtain gallic acid-rich extracts from mango seed kernels.

Gallic acid is a widely recognized bioactive compound that falls under the category of secondary polyphenolic metabolites and is fairly found in mango fruit waste, specifically in mango seed kernel (MSK). This study aimed to adopt a green extraction approach to extract this valuable compound via ultrasound-assisted extraction (UAE) without using organic solvents but only water to obtain hazard-free extracts, and the cost of extraction can be minimal. pH (2-8), solvent ratio (20-60 mL/g), temperature (30-60°C) and time (30-60 min) of extraction were the independent variables used for extraction optimization. Single-factor experiments to obtain working ranges for selected extraction variables were carried out. A central composite design using response surface methodology was used to determine the optimum condition to obtain the maximum yield of gallic acid from MSK. The optimized extraction conditions were 3.9 pH, 36.25 mL/g solvent ratio, and 39.4°C of extraction temperature for 21.3 min. As a result, the optimized yield was 5.76 ± 0.41 mg/g, which was comparably equal to and/or better than the other solvent extraction systems. The results showed that gallic acid could efficiently be extracted via UAE under these optimal conditions. It is safer than extraction systems involving hazardous solvents that can be feasibly used for its nutraceutical and therapeutic applications.

Investigating the growth promotion potential of biochar on pea (Pisum sativum) plants under saline conditions.

Pea, member of the plant family Leguminosae, play a pivotal role in global food security as essential legumes. However, their production faces challenges stemming from the detrimental impacts of abiotic stressors, leading to a concerning decline in output. Salinity stress is one of the major factors that limiting the growth and productivity of pea. However, biochar amendment in soil has a potential role in alleviating the oxidative damage caused by salinity stress. The purpose of the study was to evaluate the potential role of biochar amendment in soil that may mitigate the adverse effect of salinity stress on pea. The treatments of this study were, (a) Pea varieties; (i) V1 = Meteor and V2 = Green Grass, Salinity Stress, (b) Control (0 mM) and (ii) Salinity (80 mM) (c) Biochar applications; (i) Control, (ii) 8 g/kg soil (56 g) and (iii) 16 g/kg soil (112 g). Salinity stress demonstrated a considerable reduction in morphological parameters as Shoot and root length decreased by (29% and 47%), fresh weight and dry weight of shoot and root by (85, 63%) and (49, 68%), as well as area of leaf reduced by (71%) among both varieties. Photosynthetic pigments (chlorophyll a, b, and carotenoid contents decreased under 80 mM salinity up to (41, 63, 55 and 76%) in both varieties as compared to control. Exposure of pea plants to salinity stress increased the oxidative damage by enhancing hydrogen peroxide and malondialdehyde content by (79 and 89%), while amendment of biochar reduced their activities as, (56% and 59%) in both varieties. The activities of catalase (CAT), superoxide dismutase (SOD), and peroxidase (POD) were increased by biochar applications under salinity stress as, (49, 59, and 86%) as well as non-enzymatic antioxidants as, anthocyanin and flavonoids improved by (112 and 67%). Organic osmolytes such as total soluble proteins, sugars, and glycine betaine were increased up to (57, 83, and 140%) by biochar amendment. Among uptake of mineral ions, shoot and root Na+ uptake was greater (144 and 73%) in saline-stressed plants as compared to control, while shoot and root Ca2+ and K+ were greater up to (175, 119%) and (77, 146%) in biochar-treated plants. Overall findings revealed that 16 g/kg soil (112 g) biochar was found to be effective in reducing salinity toxicity by causing reduction in reactive oxygen species and root and shoot Na+ ions uptake and improving growth, physiological and anti-oxidative activities in pea plants (Fig. 1). Figure 1 A schematic diagram represents two different mechanisms of pea under salinity stress (control and 80 mM NaCl) with Biochar (8 and 16 g/kg soil).

Analyzing the impact of access to electricity and biomass energy consumption on infant mortality rate: a global perspective.

Conserving the lives of newborns has been a long-standing issue around the world, where 2.4 million babies die in the first month of the life. The literature indicates that the important challenges of social development goals around the globe include affordable and easy access to electricity, promotion of sustainable economic development, and provision of better social services and creation of job opportunities which help in reducing infant mortality rate. This calls for the need to probe into this matter minutely and brings up the ways for reducing the infant mortality rate. The present study is an attempt to analyze the impact of rural and urban electrification and biomass energy consumption on infant mortality rate for the period 1990-2020 using the Panel Quantile Regression (PQR) approach. The results of the study show that in both developed and developing countries, biomass energy consumption has positive impact on infant mortality rate, while rural and urban electrification has proposed the inverted U-shaped relationship with infant mortality in different quantile groups. It is also concluded that few developing countries are failed to achieve the maturity of the inverted U-shaped curve while all developed countries have achieved at the maturity stage. This study recommended that for reducing the infant mortality rate, the world should discourage the use of biomass energy and promote the affordable and easy access to electricity on priority basis.

Stretchable, Strong, Recyclable Helicide Elastomer Based on Dynamic Covalent Interactions.

Current methods for making and disposing synthetic polymers have been widely pursued and are largely unsustainable. As a part of the solution, the reversible nature of dynamic covalent bonds emerges as an extraordinarily diverse and valuable feature in the development of exotic molecules and extended structures. With these bonds, it should be possible to construct recyclable and mechanically interlocked molecular structures using relatively simple precursors with preorganized geometries. A new helicide-based elastomer network is developed here with self-healing, recycling, and degradation features using a similar concept. The best self-healing performance (100%) was noted over 10-20 min, with various H2O, HCl, and NaOH solutions that delivered mechanical properties in the 1-1.4 MPa range. For hydrolytic degradation, the parameters are defined based on the type of binding, the pH of the solutions, and the copolymer network, which endowed a degradation time of approximately 4-11 h for each prepared sample. However, due to the reversible nature of the dynamic bonds, the material showed good recyclable mechanical properties compared to the pristine samples after five consecutive cycles, which meet the requirements of recyclable materials and recyclable packaging.

Does globalization and ecological footprint in OECD lead to national happiness.

This study examines the relationship between globalization, ecological footprint, innovation, and subjective wellbeing in the form of happiness, using a comprehensive assessment of OECD countries from 2008 to 2020. The study employs FGLS, Quantile, and Bootstrap Quantile regression estimation to investigate the quadratic effects of globalization, ecological footprint, and the moderating effect of innovation while controlling for renewable energy and population density. Happiness is a multidisciplinary subject, and this study focuses on the economic dimensions of happiness. The findings reveal a nonlinear relationship between ecological footprint and globalization, with negative effects on subjective wellbeing at high levels of ecological footprint and globalization. However, the moderating effect of innovation mitigates these adverse effects, indicating that innovation can help to offset the detrimental impacts of ecological footprint and globalization on subjective wellbeing. The study's implications are significant for policymakers promoting sustainable economic growth while enhancing subjective wellbeing. The findings highlight the importance of investing in innovation and sustainable development to promote subjective wellbeing in the face of increasing ecological footprint and globalization. Additionally, this research contributes to the multidisciplinary understanding of happiness and provides valuable insights for future research in this area.

Nanobiotechnology in crop stress management: an overview of novel applications.

Agricultural crops are subject to a variety of biotic and abiotic stresses that adversely affect growth and reduce the yield of crop plantss. Traditional crop stress management approaches are not capable of fulfilling the food demand of the human population which is projected to reach 10 billion by 2050. Nanobiotechnology is the application of nanotechnology in biological fields and has emerged as a sustainable approach to enhancing agricultural productivity by alleviating various plant stresses. This article reviews innovations in nanobiotechnology and its role in promoting plant growth and enhancing plant resistance/tolerance against biotic and abiotic stresses and the underlying mechanisms. Nanoparticles, synthesized through various approaches (physical, chemical and biological), induce plant resistance against these stresses by strengthening the physical barriers, improving plant photosynthesis and activating plant defense mechanisms. The nanoparticles can also upregulate the expression of stress-related genes by increasing anti-stress compounds and activating the expression of defense-related genes. The unique physico-chemical characteristics of nanoparticles enhance biochemical activity and effectiveness to cause diverse impacts on plants. Molecular mechanisms of nanobiotechnology-induced tolerance to abiotic and biotic stresses have also been highlighted. Further research is needed on efficient synthesis methods, optimization of nanoparticle dosages, application techniques and integration with other technologies, and a better understanding of their fate in agricultural systems.

High-throughput vibrational spectroscopy methods for determination of degree of acetylation for chitin and chitosan.

The rising demand for chitin and chitosan in chemical, agro-food, and healthcare industries is creating a need for rapid and high-throughput analysis. The physicochemical properties of these biopolymers are greatly dependent on the degree of acetylation (DA). Conventional methods for DA determination, such as LC-MS and 1H NMR, are time-consuming when performed on many samples, and therefore efficient methods are needed. Here, high-throughput microplate-based FTIR and FT-Raman methods were compared with their manual counterparts. Partial least squares regression models were based on 30 samples of chitin and chitosan with reference DA values obtained by LC-MS and 1H NMR, and the models were validated on an independent test set of 16 samples. The overall predictive accuracy of the high-throughput methods was at the same level as the manual methods and the well-established LC-MS and 1H NMR methods. Therefore, high-throughput FTIR and FT-Raman DA determination methods have great potential to serve as fast and economical substitutes for traditional methods.

Magnesium fertilization reduces high-temperature damages during anthesis in spring wheat (Triticum aestivum L.) by affecting pollen viability and seed weight.

Terminal heat during reproductive stages of wheat (Triticum aestivum L.) limits the productivity of the crop. Magnesium (Mg) is an essential macronutrient that is involved in many physiological and biochemical processes to affect photosynthesis and seed weight. The present study comparatively evaluated Mg applied to soil (80 kg MgSO4·7H2O ha-1) and to plant foliage (4% w/v) in improving wheat performance under terminal heat. Wheat crop was grown in two sets of treatments until the booting stage, and then one set of plants was shifted to a glasshouse (±5 °C) at the booting stage to grow until maturity in comparison to control plants kept under ambient warehouse condition. Heat stress reduced the pollen viability while foliar- and soil-applied Mg improved it by 3% and 6% under heat stress, respectively, compared to the control without Mg treatment. The 100-seed weight, spike length, and biological yield reduced by 39%, 19%, and 50% under heat stress; however, foliar and soil application increased 100-seed weight by 45% and 40%, spike length by 8% and 5%, and biological yield by 35% and 25% under heat stress, respectively. Soil Mg showed maximum SPAD chlorophyll values; however, response was statistically similar to that of foliar Mg as compared to the control without Mg supply. Membrane stability decreased (4%) due to heat stress while foliar and soil treatments improved membrane stability by 8% and 5% compared to that of the control, respectively. Thus, Mg application through soil or plant foliage can be an effective way to reduce negative impacts of terminal heat in wheat by improving pollen viability at anthesis and 100-seed weight that was attributed to increased chlorophyll contents during anthesis.

m6A-Finder: Detecting m6A methylation sites from RNA transcriptomes using physical and statistical properties based features.

N6-methyladenosine (m6A) is one of the abundant post-transcription modification in cellular RNA. It regulates different biological processes, such as, protein synthesis, X-chromosome inactivation, cell stability, cell-reprogramming and miRNA regulation etc. Most recently, various studies claimed that mutations in m6A sites are linked with various diseases, such as, brain-tumor, heart attack, obesity and cancer. The correct identification of m6A sites is essential to overcome these diseases. However, the state-of-the-art predictors face many challenges for precise detection of m6A sites. Even for model organisms, such as Saccharomyces cerevisiae, the detection of m6A sites is difficult due to complex patterns surrounding the m6A sites. These patterns are not widely understood and lead to non-discriminative features for detecting m6A sites. To overcome this problem, we propose a novel predictor called m6A-Finder that creates features based on global and local sequence order. The global sequence order is captured by physical properties based features, while the local sequence order is captured by the statistical features. The fusion of these features results in high dimensional vector which lead to over-fitting, to solve this problem, we use mRMR algorithm to remove redundant features. The proposed technique is evaluated on the most widely used Saccharomyces cerevisiae species dataset. Overall, the m6A-Finder achieved an accuracy of 82.02%, the sensitivity of 82.10%, specificity of 81.94% and a Matthew correlation coefficient value of +0.64.

Transportation moderation in agricultural sector sustainability - a robust global perspective.

The agriculture sector creates nearly a quarter of the total GHG emissions globally as production and transportation activities in the agriculture sector mostly use fossil fuels, creating carbon emissions. In this regard, it is highly important to study the environmental sustainability of agriculture sector growth by using the theory of environmental Kuznets curve (EKC). Furthermore, this research study is aimed to assess the moderation role of transportation competitiveness in determining the carbon emissions of transportation sector by using agriculture sector value addition. The study uses panel quantile regression technique for data analysis of 121 countries by covering time period from 2008 to 2018. The study results validated the agricultural EKC across four different quantile groups based on carbon emissions of transport sector. The moderation of transportation competitiveness is observed in changing the turning point and flattening of agricultural EKC indicating the early achievement of maturity. The quality of institutions and planned increase of population can help reduce carbon emissions of transportation sector. The moderation of transportation competitiveness implicates the importance of planning and efficiently operating the transportation sector to mitigate carbon emissions.