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The main aim of this analysis is to determine the contribution of exports of communication technology, goods and services, food, and manufacturing exports and foreign investments to economic growth in Pakistan by utilizing time series data from 1976 to 2019. NARDL (non-linear autoregressive distributed lag) technique is utilized to encounter the linkages of variables via positive and negative shocks both in the long run and in the short run. The examined result of the short run indicates that the export of communication technology has a productive and adversative linkage to economic progress during positive and negative shocks. On the other side, exports of goods and services and food exports have a negative relationship with economic growth. Similarly, foreign investment exposed the constructive and negative linkage to economic growth during positive and negative shocks. Furthermore, manufacturing exports exposed an adversative and productive association to economic development in Pakistan. Moving toward the long-run analysis's results which show communication technology has constructive and adversative linkages to economic growth. Exports of goods and services and food exports also uncovered the adversative association to economic development in Pakistan. The foreign investment positive and negative shocks show adversative and productive linkages to economic growth. Furthermore, the variable manufacturing exports exposed a negative relation with economic growth in Pakistan in both positive and negative shocks. Based on the examined results, it is recommended that government policy experts must adopt new steps to boost exports to support the country's economic growth. [ABSTRACT FROM AUTHOR]

Climate change represents a grave challenge to the global economy, environment, and societal well-being, jeopardizing their long-term sustainability. In response to this urgent issue, the study emphasizes the significance of environmental policy and energy transitions as fundamental factors in addressing the climate change crisis. The research draws upon data from OECD countries spanning the period between 1990 and 2020, utilizing robust econometric techniques to assess data properties. The study utilizes a comprehensive CS-ARDL model, incorporating multiple control variables like non-renewable energy GDP, foreign direct investment (FDI), and research and development (R&D). The results show that environmental policy and energy transitions are effective in reducing climate change impacts in the form of CO2 emissions. The non-environmental factors like GDP and FDI are positively associated and thereby accelerate climate change processes, whereas R&D promotes environmental protection by reducing CO2 emissions. Based on these findings, the study advocates for the implementation of rigorous policy measures by OECD economies to strengthen and enforce environmental policies to ensure compliance and foster sustainable practices across sectors. The study also suggests that OECD must promote energy transitions by investing in renewable energy sources at the mass level (micro and macro) and phasing out reliance on non-renewable energy. [ABSTRACT FROM AUTHOR]

Climate change issues present substantial obstacles to the global community's stability and humanity's overall welfare. Reducing carbon emissions is crucial in attaining environmental sustainability and addressing the consequences of SDG 13 (climate actions). The G7 nations, representing some of the largest economies globally and significantly contributing to global carbon emissions, have achieved certain advancements in mitigating their carbon footprint. Nevertheless, the attainment of carbon neutrality continues to pose a substantial obstacle. This study examines the mechanisms leading to environmental sustainability in G7 economies, explicitly emphasizing the contribution of research and development (R&D) toward attaining carbon neutrality. The present study utilizes G7 data from 1990 to 2020 to conduct an empirical analysis employing a cross-sectional autoregressive distributed lag (CSARDL) panel model. The primary objective of this investigation is to examine the influence of R&D expenditure (R&DE) on carbon emissions metric ton (CO2Mt). Furthermore, this study investigates the current state of the EKC in the economies of the G7 nations, as well as the influence of renewable energy (RE) and non-renewable energy (NRE) on CO2Mt. The results suggest that R&DE is critical in mitigating CO2Mt and attaining carbon neutrality. The study also validates the EKC implies a negative and non-linear relationship between growth and CO2Mt. Moreover, renewable and non-renewable energy validate their respective negative and positive effects on CO2Mt. The findings of our study offer valuable insights for policymakers in the G7 nations, aiding them in developing effective regulatory measures for achieving carbon neutrality goals. [ABSTRACT FROM AUTHOR]

Public demand for environmental protection and environmental governance has a profound impact on enterprises' innovation behavior. Based on the panel data of 30 provinces in China from 2001 to 2018, this study uses a unit root to co-integration approach and the Panel Data Vector Autoregression model to empirically test the two-way dynamic relationship among green technology innovation, public environmental demands, and government environmental governance. The results show that both public environmental demands and environmental governance stimulate enterprises' green technology innovation; the estimated coefficient of government environmental governance and public environmental demands is 0.6681 and 0.0337, respectively. The incentive effect of environmental governance was more significant than that of public demands. Public environmental demands and green technology innovation have a bidirectional link, while environmental governance and green technology innovation share a two-way causality; meanwhile, environmental governance and public environmental demands share a unidirectional causal link. This study uses Impulse Response Function and Variance Decomposition to validate the outcomes, which shows that environmental governance's positive effect is more direct, while the effect of public demand has a time-lag impact, and the relationship among the three main variables is stable after the tenth period. The largest contribution to green technology innovation is environmental governance, with an explanation degree of 22.01%, followed by institutional quality (13.30%), and then public environmental demands (4.8%). These conclusions are not only significant for China to deal with the relationship among the public, government, and enterprises in the process of environmental governance, but also a valuable reference for other countries. [ABSTRACT FROM AUTHOR]

This study investigates the effects of military expenditure, final consumption expenditure, gross national expenditure, net income, broad money, and total reserves on Pakistan's economic growth. The nonlinear autoregressive distributed lag (NARDL) and robust regressions methods are used to examine the relationship among variables. The outcomes revealed that total reserves positively impacted the economic growth during positive and negative short- and long-run shocks. Final consumption expenditure and gross national expenditure negatively influenced the economic growth. Similarly, military expenditure and net income positively and negatively impacted the economic growth, while broad money has adverse and positive relation to economic growth. The results of the robust least squares and stepwise least squares approaches showed that all variables except military expenditure and reserves have positive impact on economic development. This study suggests that government should carefully monitor defence-related investment to maintain economic development. [ABSTRACT FROM AUTHOR]

This study employed fixed effects (FE) models, difference-in-differences (DID) methods, and mediating effect (ME) models to explore the total effect, structural effect, heterogeneous characteristics, and impact mechanism of digital inclusive finance (DIF) on green technology innovation (GTI) from 2011 to 2020. We derived the following results. First, DIF can significantly improve the level of GTI, and the positive role of internet digital inclusive finance is greater than that of traditional banks, but the three dimensions of the DIF index have different impacts on such innovation. Second, the impact of DIF on GTI has a "siphon effect," which is significantly promoted in regions with stronger economic power and inhibited in those with weaker economic power. Finally, there is an influence mechanism of "digital inclusive finance → financing constraints → green technology innovation." Our findings provide evidence to establish a lasting effect mechanism for DIF to promote GTI, and they have important reference value for other countries to develop DIF. [ABSTRACT FROM AUTHOR]

Promoting the green development of resource-based cities is an essential way to achieve sustainable regional economic development. Based on 2009–2019 panel data of the Yellow River Basin cities, this study adopts the super-directional distance function model to measure the green development efficiency of these selected cities. Furthermore, based on the Malmquist-Luenberger index, this paper focuses on the dynamic change trend of green development efficiency and internal driving factors. Furthermore, the Tobit model is used to specifically explore the influencing factors affecting the green development of cities. The findings suggested that the green development efficiency of selected cities falls in the middle to high range and that the efficiency varies among all cities in the Yellow River Basin. Likewise, technical efficiency improvements and technological progress drive development efficiency, and the former contributes more to green development. However, financial development, energy structure adjustments, and environmental regulation can strongly contribute to the green development of cities, and each influencing factor has obvious temporal and regional differences. This paper proposes appropriate policy suggestions to promote the coordinated development of the economic development and environmental protection of the Yellow River Basin. [ABSTRACT FROM AUTHOR]

China seems ambitious to achieve a "carbon emissions peak" before 2030 and "carbon neutrality" before 2060. To deal with this emissions mitigation plan, technology innovations are regarded as a crucial factor. However, considering its rebound effect (CO2 emissions driving effect) through economic growth, technology innovations might not prove a promising contributor to CO2 reduction. Therefore, there is a need to investigate further the nexus between technology innovations and CO2 emissions for conclusive debate. Based on the data of 215 cities in China, this paper uses mediating effects model to investigate the direct and indirect impacts (through economic growth and industrial structure transformation) of technology innovations on CO2 emissions from a microeconomic perspective. The main results suggest that technology innovations generally increase CO2 emissions in China both directly and indirectly. The impact of technology innovations and economic growth on CO2 emissions indicated the EKC characteristics. Furthermore, the contributions of technology innovations to CO2 emissions are distinguished in different regions. Thus, there is an urgent need for China to promote innovations in "clean technology" and to transform industrial structure to the tertiary industry to achieve the targets of carbon neutrality and emissions peaking. [ABSTRACT FROM AUTHOR]

Among all other leading challenges of this century, climate change affects people's livelihoods, particularly those residing in rural areas. This study is the first attempt to assess the Long-run (LR) and Short-run (SR) impacts of climatic factors, namely CO2 emissions, temperature, and precipitation on maize production in Nepal from 1983 to 2016, with the incorporation of cultivated area, fertilizers use, and credit supply as important input factors of maize production. To analyze the time-series dataset, we applied the Autoregressive distributed lag (ARDL), Vector error correction model (VECM), Impulse response function (IRF), and Variance decomposition (VD) methodologies. The results reveal that CO2 emissions and temperature decreased maize production in the short- and long-run while precipitation improved maize production in both cases. Also, cultivated area and fertilizers use significantly contributed to maize production in both periods. The results of the VECM causality indicate that rainfall and cultivated area have two-way causal associations with maize production. The CO2 emissions and credit represent unidirectional causality with maize production in the short-run. Besides, all variables share a significant long-run connection. Finally, the IRF and VD outcomes suggested that the effects of climatic and non-climatic factors are consistent on maize production. The policies recommend that better environmental quality is crucial for sustainable maize production in Nepal. [ABSTRACT FROM AUTHOR]

This paper examines the impact of climate variables and financial development on agricultural value-added and cereal production in selected Southeast Asian economies from 1970 to 2016. The current research applies second-generation advanced techniques to accomplish robust and reliable outcomes. The findings from the FM-OLS estimation disclose that climatic factors, for instance, CO2e and average temperature, impact both agricultural value-added and cereal production negatively, and financial development has an inverted U-shaped influence on both agricultural value-added and cereal production. Meanwhile, other important factors, including cropped area, income level, and rural labor force, significantly improve agricultural value-added and cereal production. Furthermore, the FM-OLS estimator's D-H panel causality test outcomes are reliable. The findings of our study reveal that both the short- and long-run risks of climatic changes to the agricultural sector pose a large-scale threat to food security in Southeast Asian economies. As a result, a robust and stable financial development in terms of governance of climate change finance in the agriculture sector must be achieved to enhance farmers' ability to adapt to current and future climate change adverse impacts. [ABSTRACT FROM AUTHOR]

This study is the first attempt to examine the effects of changing climate and financial development on rice cultivation in the context of agrarian economy like Thailand from 1969 to 2016. The current study also uses other important variables, such as cultivated area, organic fertilizers usage, and rural labor to determine the long-term connection amid variables. In this study, we applied several econometric techniques, for instance the autoregressive distributive lag–error correction model (ARDL–ECM), vector error correction model (VECM), impulse response functions (IMFs), and variance decomposition (VARD) method to estimate the most reliable and robust outcomes. The empirical results showed that in the long- and short-run, there is a reduction in rice cultivation as temperature increase. The carbon dioxide (CO2) positively affects rice cultivation in the long-run, while this association is negative in the short-run. The findings further revealed that in the long- and short-run, domestic credit provided by the financial sector significantly positive improved rice cultivation, while domestic credit to private sector by banks negatively affects rice cultivation. The important input factors, including cultivated area, organic fertilizers usage, and rural labor significantly positive contributed toward rice cultivation in the long- and short-run. The calculated long-run causal connection of all the studied variables with rice cultivation is validated. The estimated short-run causal relationship is unidirectional among temperature, CO2 emissions, financial development, rural labor, and rice cultivation. In addition, our outcomes are robust and also verified by IMFs and VARD method. The study offers some important policy suggestions to increase rice production with the help of sound and well-developed financial systems and climate controls. [ABSTRACT FROM AUTHOR]

Isatin-core-containing ligands were synthesized by reacting an equimolar ratio of isatin with ethanolamine and anthranilic acid, respectively. The ligands were characterized by using FT‐IR, UV–Vis, 1H-NMR, ESIMS and elemental analysis. The synthesized ligands were further complexed with transition metal ions in a 1:2 molar ratio. Metal complexes were also characterized by physical, analytical and spectral methods in addition to molar conductance and magnetic moment measurements. The DFT calculations at B3LYP/6–311 + + G(d,p) level were accomplished for comparative study of isatin-based compounds. A good agreement among experimental UV–Vis, FT-IR and corresponding DFT results was found, which confirmed the purity of the studied compounds. The antimicrobial profile of all the compounds was evaluated by testing their antibacterial activity against two species of Gram-positive bacteria (Staphylococcus aureus, Streptococcus pyogenes) and two species of Gram-negative bacteria (Pseudomonas syringae, Escherichia coli) and antifungal activity against three species of fungi (Aspergillus niger, Rhizopus stolonifer and Trichoderma spirale) using the disk diffusion method. Moreover, the compounds were also investigated for antioxidant activity using phosphomolybdate and DPPH assays. The ligands revealed moderate activity against targets, which increased further upon complexation with different metal ions due to the chelation process. In addition, ANOVA and Tukey test for the experimental results of biological activities for all the prepared compounds supported the significant difference in all preclinical treatments' performance. The results showed that the isatin metal-based compounds are potent for being active as antimicrobial and antioxidant prototypes. [ABSTRACT FROM AUTHOR]

Agriculture plays an integral part in facilitating socioeconomic development in Nepal. However, it is also associated with environmental concerns which need to be controlled for the sake of ensuring environmental and agricultural sustainability in tandem. Against this backdrop, this current study aims to check whether shocks to the levels of agricultural productivity, land utilization for crop production, and fertilizer consumption influence the carbon dioxide emission figures of Nepal over the 1965-2018 period. The long-run associations between these variables are confirmed from the cointegration analysis. Besides, the outcomes from the asymmetric non-linear autoregressive distributed lag regression analysis show that crop productivity does not influence the emission levels in Nepal. However, a decline in the land area used for crop production purposes is evidenced to trigger higher emissions of carbon dioxide both in the short- and long run. On the other hand, higher fertilizer consumption is found to boost the short- and long-run carbon dioxide emission levels in Nepal. Accordingly, considering the objective of reducing agriculture-based emissions, this study recommends the Nepalese government to adopt policies that can enhance the productivity of low energy-intensive crop production, stimulate green agriculture and non-agriculture activities, and minimize the use of chemical fertilizers in arable lands. [ABSTRACT FROM AUTHOR]

Since the open economy macroeconomic indicators such as the balance of trade and exchange rate interact with the economic and environmental indicators, it is worthy of delving into their interactive linkages. This study investigates the long-run and short-run dynamic interactive links among the balance of trade, aggregate economic output, real exchange rate, and carbon dioxide (CO2) emissions in Pakistan. Bayer and Hanck's combined cointegration and the auto-regressive distributed lag method are applied on annual time-series data from 1970 through 2018. The key findings are: (1) Balance of trade and real exchange rate imparted the CO2 emissions mitigation influence in both the long run and the short run. In contrast, the aggregate economic output exhibited the CO2 emissions driving influence in the long run and short run. (2) Balance of trade and real exchange rate induced enhancing and impeding influence on aggregate economic output, respectively, in the short run. However, they exposed the aggregate economic output strengthening influence in the long run. Besides, CO2 emissions produced a neutral influence on the aggregate economic output in the short run, whereas it put forward the aggregate economic output hampering influence in the long run. (3) Aggregate economic output revealed a balance of trade improvement influence for both the long run and short run. Nevertheless, the real exchange rate showed the balance of trade deterioration (improvement) influence in the short run (long run), confirming the J-curve hypothesis in Pakistan. Furthermore, (a) a bidirectional causality existed between CO2 emissions and aggregate economic output, and balance of trade and aggregate economic output. (b) A unidirectional causality existed from real exchange rate to balance of trade and aggregate economic output, and from the balance of trade and real exchange rate to CO2 emissions. The diversification of exports and energy mix is recommended to improve the balance of trade, economic aggregates, and environmental sustainability. [ABSTRACT FROM AUTHOR]

The underpinned study examines the effects of climatic and non-climatic factors on Indian agriculture, cereal production, and yield using the country-level time series data of 1965–2015. With the autoregressive distributed lag (ARDL) bounds testing approach, the long-term equilibrium association among the variables has been explored. The results reveal that climatic factors like CO2 emissions and temperature adversely affect agricultural output, while rainfall positively affects it. Likewise, non-climatic factors, including energy used, financial development, and labor force, affect agricultural production positively in the long run. The estimated long-run results further demonstrate that CO2 emissions and rainfall positively affect both cereal production and yield, while temperature adversely affects them. The results exhibit that the cereal cropped area, energy used, financial development, and labor force significantly and positively impact the long-run cereal production and yield. Finally, pairwise Granger causality test confirmed that both climatic and non-climatic factors are significantly influencing agriculture and cereal production in India. Based on these results, policymakers and governmental institutions should formulate coherent adaptation measures and mitigation policies to tackle the adverse climate change effects on agriculture and its production of cereals. [ABSTRACT FROM AUTHOR]

Since the development of the service sector and renewable energy reduce fossil-based energy consumption which mitigates CO2 emissions and this nexus provides a better understanding of the environmental sustainability. Considering the substantially increasing contribution of service sector and tremendous potential for renewable energy in ASEAN5 countries, leaning forward from ASEAN's energy and growth nexus, this study examines the impact of service sector contribution and renewable energy on the environmental quality of ASEAN5 using annual data from 1990 to 2018. The results of the fully modified ordinary least squared, dynamic ordinary least squared, and canonical co-integrating regressions depicted that the service sectors of Thailand, the Philippines, and Singapore augment CO2 emissions; however, the service sectors of Malaysia and Indonesia could reduce CO2 emissions. The increasing share of renewable energy can enhance environmental quality, but its magnitude varies in ASEAN5 economies; non-renewable energy, population, and economic development deteriorate the environment. Our results confirm the existence of environmental Kuznets curve in all the ASEAN5; the Gregory-Hansen test confirmed that results are robust. Finally, the Granger causality designated that economic development and non-renewable energy have a significant causal relationship with CO2 emission of ASEAN5 countries. These findings suggest that the ASEAN5 economies need to optimize their economic structure for promoting sustainable development in the long run. Graphical abstract [ABSTRACT FROM AUTHOR]

This work analyzed the long-run (LR) and short-run (SR) effects of renewable and non-renewable energy (RE and NRE) usage, economic development (ED), agricultural value-added (AVA), and forestry area (FA) on the environmental quality (EQ) in China spanning from 1990 to 2015. The autoregressive distributed lags (ARDL) bounds testing method and the Johansen cointegration approach are applied to produce empirical estimates. The empirical results of the ARDL and the fully modified ordinary least square (FMOLS) estimators established that renewable energy usage and forest area reduce CO2 emissions and improve the environmental quality, while non-renewable energy consumption, economic development, and agricultural output increase the level of CO2 emissions in China. The robustness of outcomes is checked through the Granger causality test, impulse response function (IRF), and variance decomposition method (VDM) suggesting that fossil fuel usage in the agriculture production process is mainly accountable for China's CO2 emissions. These findings have inherent policy implications for the central and local Chinese government, which are exhibited in the "Conclusions" section. [ABSTRACT FROM AUTHOR]

This study assessed the long-run (LR) and short-run (SR) impacts of climatic and non-climatic factors, i.e., CO2 emissions (CO2e), average level of temperature (ALT), average level of precipitation (ALP), area harvested of wheat and rice crops (AHW and (AHR), domestic credit (DCR), and agricultural labor (ALB) on wheat and rice production (WP and RP) in Turkey by using annual time series data ranging from 1980 to 2016 and by employing several econometric techniques. The autoregressive distributed lag-bounds (ARDL) approach and the Johansen and Juselius cointegration (JJC) test confirmed a valid long-term connection among underlying variables. The estimation results from the ARDL model reveal that climatic factors such as CO2 emissions and temperature adversely affected wheat production in the long run as well as in the short run, whereas precipitation positively improved wheat production in both periods. Further results indicate that non-climatic factors like area harvested of wheat and domestic credit positively and significantly enhanced wheat production in the long run and short run. Similarly, CO2 emissions also adversely affected rice production in both periods, while temperature and precipitation positively contributed towards rice production in both cases. In addition, area harvested of rice positively and significantly boosted rice production in the long run as well as in the short run, while domestic credit negatively influenced rice production in the long run but in the short run positively improved rice production. Additionally, the outcomes of the VECM Granger Causality for both rice and wheat production confirm that both climatic and non-climatic variables have a strong influence on the production of both crops. This study found that climate change has a deleterious influence on both wheat and rice production; therefore, the study suggests that temperature-resistant varieties of both crops should be developed and introduced by agricultural research institutions. In addition to this, up-to-date information is more needed related to climate change, and in the farming communities, it should be provided by agricultural extension workers. [ABSTRACT FROM AUTHOR]

As an important production factor, land resources significantly impact green technology innovation. However, the misallocation of land resources caused by the government's "second-hand" land supply strategy has become increasingly prominent, which will adversely affect green technology innovation by affecting the allocation of innovative elements. Based on the research data of 252 cities in China from 2008 to 2017, this paper uses panel space measurement estimation and panel threshold estimation empirical methods to test the theoretical hypothesis of the impact of misallocation of land resources on green technology innovation. The study finds that local or neighboring land resources' misallocation has a hindering effect on local green technology innovation. Furthermore, the misallocation of land resources has a threshold effect on the impact of green technology innovation. The relatively high level of local economic development and environmental regulation reduces the restraining effect of the misallocation of land resources on green technology innovation, and vice versa. Therefore, local governments should optimize the allocation of innovative elements, accelerate the construction of an efficient and market-oriented green technology innovation system, reduce the excessive intervention in land resources, and enhance the vitality of innovation entities to improve the level of green technology innovation. [ABSTRACT FROM AUTHOR]

Sedentary life styles coupled with high-calorie diets and unhealthy social habits such as smoking, have put an ever-increasing number of people at risk of cardiovascular disorders (CVD), worldwide. A concomitant increase in the prevalence of type 2-diabetes (hyperglycemia), a risk factor for CVD, has further contributed towards escalating CVD-related mortalities. The increase in number of cases of type 2-diabetes underscores the importance of early diagnosis of cardiovascular disease in those with diabetes. In this work, we have evaluated the sensitivity and specificity of dyslipidemia and proinflammatory cytokines to be used as biomarkers for predicting the risk of CVD in those with diabetes. We hypothesize that interplay between dyslipidemia and diabetes-induced low-grade inflammation in those with type 2-diabetes increases the risk of CVD. A total of 215 participants were randomly recruited from the Cameron County Hispanic Cohort (CCHC). Of these, 99% were Mexican Americans living on Texas-Mexico border. Levels of cytokines, adipokines and lipid profile were measured. Cardiovascular disease (CVD) for this study was defined as prior diagnosis of heart attack, angina and stroke, while diabetes was defined by fasting blood glucose (FBG) of > 100 mg/dL and HbA1c of > 6.5, in accordance with American Diabetes Association (ADA) guidelines. Depending on type and distribution of data, various statistical tests were performed. Our results demonstrated higher rates of heart attack (14% vs 11.8%) and stroke (19.8% vs 10%) in those with diabetes as compared to non-diabetes. The odds of having a heart attack were eight times higher in the presence of elevated triglycerides and pro-inflammatory markers (TNFα and IL6) as compared to presence of pro-inflammatory markers only. The odds for heart attack among those with diabetes, increased by 20 fold in presence of high levels of triglycerides, TNFα, and IL6 when coupled with low levels of high-density lipid cholesterol (HDL-C). Lastly, our analysis showed that poorly controlled diabetes, characterized by HbA1c values of > 6.5 increases the odds of stroke by more than three fold. The study quantifies the role of lipid profile and pro-inflammatory markers in combination with standard risk factors towards predicting the risk of CVD in those with type 2-diabetes. The findings from the study can be directly translated for use in early diagnosis of heart disease and guiding interventions leading to a reduction in CVD-associated mortality in those with type 2-diabetes. [ABSTRACT FROM AUTHOR]

This study aims to explore the dynamic association among crop production, livestock production, power consumption in agriculture, forest area, and carbon dioxide (CO2) emissions. Based on the annual data of China, spanning the period 1990 to 2016, the study applied the auto-regressive distributed lag (ARDL) bounds testing approach. In addition, the fully modified ordinary least squares (FMOLS) canonical cointegration regression (CCR) and the Granger causality tests are employed to check the robustness of the ARDL estimations. The ARDL-bounds testing approach indicated that all variables share a long-run connection. The long- and short-run ARDL estimations confirmed that crop production, as well as livestock production, has a significant positive effect on CO2 emissions in both cases. However, power consumption in agriculture and forest area has a negative effect on it, indicating that both variables reduce CO2 emissions in the long and short run. These results stood robust under various regression estimators and confirmed the findings of the ARDL method. Additionally, the results of the causality approach specified that a unidirectional causality is running from crop production, power consumption in agriculture, and forest area to CO2 emissions. The causality between livestock production and CO2 emissions is bidirectional. Therefore, the directions of this connection also validate the outcomes under various techniques used for robustness. These findings suggest that the government must reconsider its policies related to agricultural and livestock production and adopt environment-friendly practices in the agriculture sector that may reduce the carbon footprints in the long run. [ABSTRACT FROM AUTHOR]

In this study, we attempt to investigate the relationship between electricity consumption, foreign direct investment (FDI) and economic progress in Pakistan during the period from 1997 to 2017. We applied the unit root tests to cointegration approach with the vector error correction model (VECM) approach. We also authenticated the robustness of the outcomes with the help of the regression methods. The main findings indicated that variables are cointegrated in the long-run and the VECM approach verified long-run causal links among the variables. The short-run causality is running from electricity consumption and economic development to FDI. The results remained uniform under the fully modified ordinary least squares (FMOLS), dynamic ordinary least squares (DOLS), and the autoregressive distributed lag (ARDL) estimations. Correspondingly, these three methods ratified that there is a significant long-run linkage between electricity consumption and the economic development of Pakistan. Additionally, the diagnostic assessments fixed that results are free from correlations and models are stable. Thus, based on these results, electricity generation and consumption, as well as FDI, is crucial for the economic progress of Pakistan. The priority should be given to promote the optimum use of available resources to generate energy, and FDI in the energy sector should be attracted through various incentives to support the economic advancement of Pakistan. Renewable energy resources are recyclable springs that can cut the intensity of carbon emissions and encourage to the promising magnitude of FDI and also certify sustainable and economic progression of Pakistan. [ABSTRACT FROM AUTHOR]

This research has examined the dynamic linkages among climate change factors, such as CO2 emissions, temperature, rainfall, and cereal yield in Turkey from 1968 to 2014. At first step, we tested stationary properties of the climatic factors and crop yield by using both traditional and breakpoint unit root tests. After the confirmation of given properties, we used the autoregressive distributed lag (ARDL) model to capture the dynamic relationship among the variables in the given span of time. The empirical results show that there is a long-run equilibrium relationship that exists between climate change factors and cereal yield. CO2 emissions and average temperature have a diverse effect on the cereal yield, whereas average rainfall has a positive effect on the cereal yield in both the long run and short run. To check the causality, we use the Granger causality test that reveals a significant effect of climate change variables on the cereal yield. The unidirectional causal link is significant among temperature and rainfall factors. The results show that the cereal yield is affected by more climate factors like rain fall and temperature due to CO2 emissions as compared to land and labor use. Based on the findings of the study, few suggestions have been made to address the climate change factors. Devise agriculture-specific adaptation policy for the farmers to build their capacity and resilience to tackle climate changes, for example, farm practices. Agriculture research and development should work on cereal crop varieties that can tolerate the high temperature and precipitation. These policies could help the agriculture sector to sustain production and allocation efficiency in the long run. [ABSTRACT FROM AUTHOR]

The steady flow of a third-grade fluid due to pressure gradient is considered between parallel plane walls which are kept at different temperatures. The space between the plane walls is assumed to be a porous medium of constant permeability. The viscosity of the fluid is taken as constant as well as a function of temperature. It is further assumed that the fluid may slip at the wall surfaces. The consequence of this assumption results in non-linear boundary conditions at the plane walls. The temperature field is also supposed to satisfy thermal slip condition at the walls. The governing equations are modelled under these assumptions and the approximate solution is obtained using the perturbation theory. The skin friction coefficient is a decreasing function of slip parameters in the case of temperature-dependent viscosity models while no variation is noted for the case of constant viscosity via boundary slip parameter. The heat transfer rate increases with the boundary slip parameter and decreases with the thermal slip parameter. The validity of the approximated solution is checked by calculating the numerical solution as well. The absolute error is calculated and listed in tabular form in the case of constant and temperature-dependent viscosity via boundary and thermal slip parameters. The influence of various emerging parameters on flow velocity and temperature profile is discussed through graphs. [ABSTRACT FROM AUTHOR]

The present study investigate the numerical solution of nonlinear singular system represented with sixth Painlev́e equation by the strength of artificial intelligence using feed-forward artificial neural networks (ANNs) optimized with genetic algorithms (GAs), interior point technique (IPT), sequential quadratic programming (SQP), and their hybrids. The ANN provided a compatible method for finding nature-inspired mathematical model based on unsupervised error for sixth Painlev́e equation and adaptation of weights of these networks is carried out globally by the competency of GA aided with IPT or SQP algorithms. Moreover, a hybrid approach has been adopted for better proposed numerical results. An extensive statistical analysis has been performed through several independent runs of algorithms to validate the accuracy, convergence, and exactness of the proposed scheme. [ABSTRACT FROM AUTHOR]

Bogoya, Böttcher, Grudsky, and Maximenko have recently obtained the precise asymptotic expansion for the eigenvalues of a sequence of Toeplitz matrices {Tn(f)}, under suitable assumptions on the associated generating function f. In this paper, we provide numerical evidence that some of these assumptions can be relaxed and extended to the case of a sequence of preconditioned Toeplitz matrices {Tn−1(g)Tn(f)}, for f trigonometric polynomial, g nonnegative, not identically zero trigonometric polynomial, r = f/g, and where the ratio r plays the same role as f in the nonpreconditioned case. Moreover, based on the eigenvalue asymptotics, we devise an extrapolation algorithm for computing the eigenvalues of preconditioned banded symmetric Toeplitz matrices with a high level of accuracy, with a relatively low computational cost, and with potential application to the computation of the spectrum of differential operators. [ABSTRACT FROM AUTHOR]

Nitrogen efficiency of lactating buffalo can be increased by providing dietary crude protein (CP) precisely to the requirement. Twelve lactating Nili-Ravi buffaloes (6 primiparous and 6 multiparous) at 76 ± 37.5 days in milk (DIM) were used in this study. The treatments were diets providing three levels of CP (% DM basis): (1) low-protein = 11%; (2) medium-protein = 13.1%; (3) high-protein = 14.2% according to a 3 × 3 Latin square design. The period length of each treatment was 21 days and the total duration of experiment was 63 days. The diets were designed to provide similar energy. The nitrogen intake of buffalo increased linearly by increasing CP levels. Dry matter intake showed a tendency toward decrease in quadratic fashion, whereas milk yield decreased linearly in high-protein diet. No effect was observed on milk protein yield and content. Increasing the dietary CP levels increased plasma urea nitrogen, whereas glucose and triacylglycerol levels remain unaffected. Efficiency of dietary nitrogen utilization to milk averaged 21% and showed both linear and quadratic decreases by increasing the protein supply levels. In conclusion, low CP level showed higher milk production with low plasma urea nitrogen and high nitrogen efficiency in this experiment. [ABSTRACT FROM AUTHOR]

In this study, biologically inspired intelligent computing approached based on artificial neural networks (ANN) models optimized with efficient local search methods like sequential quadratic programming (SQP), interior point technique (IPT) and active set technique (AST) is designed to solve the higher order nonlinear boundary value problems arise in studies of induction motor. The mathematical modeling of the problem is formulated in an unsupervised manner with ANNs by using transfer function based on log-sigmoid, and the learning of parameters of ANNs is carried out with SQP, IPT and ASTs. The solutions obtained by proposed methods are compared with the reference state-of-the-art numerical results. Simulation studies show that the proposed methods are useful and effective for solving higher order stiff problem with boundary conditions. The strong motivation of this research work is to find the reliable approximate solution of fifth-order differential equation problems which are validated through strong statistical analysis. [ABSTRACT FROM AUTHOR]

Functional magnetic resonance imaging (fMRI) data analysis is developing rapidly in a fast emerging society, because of temporal and spatial resolution and the inoffensive feature of their acquisition in human brains. Spatial resolution governs how 'sharp' the image is in appearance, whereas temporal resolution denotes the precision of a measurement with respect to time. The goal of fMRI technique is to identify the activation pattern and functional connectivity in the brain regions. In our study, artificial neural network technique was used for the identification and classification of decision-making voxels of fMRI data based on Brodmann areas 10 and 47 from the prefrontal cortex of human brain. The total number of voxels of Brodmann areas was 159, and we determined that some particular voxels played dominant role for decision-making process while performing a visual task. We also analyzed true positive and false positive classification rates between two decisions in the context of a well-known receiver operating characteristic curve (ROC). [ABSTRACT FROM AUTHOR]

We address in this article, how to calculate the restoring characteristic and the excitation of a nonlinear forced oscillating system. Under the assumption that the forced nonlinear oscillator has a periodic solution with period $$T=2\pi / \omega$$ , we constructed a system of linear equations by introducing time-dependent multipliers. The periodicity assumption helps simplify the system of linear equations. The stability and uniqueness are also presented for the inverse problem. Numerical testing is conducted to show the effectiveness of our presented methodology. [ABSTRACT FROM AUTHOR]

We construct a novel multi-step iterative method for solving systems of nonlinear equations by introducing a parameter θ to generalize the multi-step Newton method while keeping its order of convergence and computational cost. By an appropriate selection of θ, the new method can both have faster convergence and have larger radius of convergence. The new iterative method only requires one Jacobian inversion per iteration, and therefore, can be efficiently implemented using Krylov subspace methods. The new method can be used to solve nonlinear systems of partial differential equations, such as complex generalized Zakharov systems of partial differential equations, by transforming them into systems of nonlinear equations by discretizing approaches in both spatial and temporal independent variables such as, for instance, the Chebyshev pseudo-spectral discretizing method. Quite extensive tests show that the new method can have significantly faster convergence and significantly larger radius of convergence than the multi-step Newton method. [ABSTRACT FROM AUTHOR]

Objective: The aim of this work was to radiolabel and bioevaluate the technetium-99m labeled dextran dicysteine mannose (DCCM) [(99m)Tc(CO)₃]-DCCM for sentinel lymph node detection.Methods: Dextran dicysteine mannose was radiolabeled using the carbonyl method. Various parameters were studied such as in vitro stability at room temperature up to 5 h, protein binding and partition coefficient. Bioevaluation was performed in a rabbit model by developing images under a gamma camera at various time intervals. Biodistribution was performed in Wistar rat models (n = 3) by dissection and measurement of percent injected dose in various body organs, at 60 and 180 min post-injection intervals. Biodistribution was performed in two different groups of animals: in the first group, the radiolabeled compound was injected at a concentration of 200 μg/ml, thus delivering 10 μg radiolabeled compound at the site of injection; in the second group, the radiolabeled compound was injected at a concentration of 50 μg/ml, delivering 2.5 μg radiolabeled compound at the site of injection.Results: Radiolabeling efficacy was 97.5 ± 1% which remained quite stable till 5 h. Protein binding data show that 71.1 ± 5% drug exhibited binding with blood proteins. Partition coefficient results show that our radiopharmaceutical is quite hydrophilic in nature. It can be inferred from the imaging data that sentinel node can be visualized within 30 min post-injection. Rat dissection data showed that when the radiolabeled compound was injected at a concentration of 50 μg/ml, at 60 min post-injection, ~2.85% of activity was retained in the sentinel node with a significantly less accumulation, e.g., ~0.12%, in the secondary node, which resulted in very high popliteal extraction (PE) value, e.g., ~98%. At 180 min post-injection, 2.46 ± 0.29 % was found to be retained in the sentinel node and PE (99.64 ± 0.23%), thus resulting in almost complete washout from the secondary node (0.05 ± 0.01%).Conclusion: The study demonstrates that radiolabeled DCCM might be a successful radiopharmaceutical for sentinel node detection. [ABSTRACT FROM AUTHOR]

Objective: The aim of this work was to radiolabel and bioevaluate the technetium-99m labeled dextran dicysteine mannose (DCCM) [Tc(CO)]-DCCM for sentinel lymph node detection. Methods: Dextran dicysteine mannose was radiolabeled using the carbonyl method. Various parameters were studied such as in vitro stability at room temperature up to 5 h, protein binding and partition coefficient. Bioevaluation was performed in a rabbit model by developing images under a gamma camera at various time intervals. Biodistribution was performed in Wistar rat models ( n = 3) by dissection and measurement of percent injected dose in various body organs, at 60 and 180 min post-injection intervals. Biodistribution was performed in two different groups of animals: in the first group, the radiolabeled compound was injected at a concentration of 200 μg/ml, thus delivering 10 μg radiolabeled compound at the site of injection; in the second group, the radiolabeled compound was injected at a concentration of 50 μg/ml, delivering 2.5 μg radiolabeled compound at the site of injection. Results: Radiolabeling efficacy was 97.5 ± 1 % which remained quite stable till 5 h. Protein binding data show that 71.1 ± 5 % drug exhibited binding with blood proteins. Partition coefficient results show that our radiopharmaceutical is quite hydrophilic in nature. It can be inferred from the imaging data that sentinel node can be visualized within 30 min post-injection. Rat dissection data showed that when the radiolabeled compound was injected at a concentration of 50 μg/ml, at 60 min post-injection, ~2.85 % of activity was retained in the sentinel node with a significantly less accumulation, e.g., ~0.12 %, in the secondary node, which resulted in very high popliteal extraction (PE) value, e.g., ~98 %. At 180 min post-injection, 2.46 ± 0.29 % was found to be retained in the sentinel node and PE (99.64 ± 0.23 %), thus resulting in almost complete washout from the secondary node (0.05 ± 0.01 %). Conclusion: The study demonstrates that radiolabeled DCCM might be a successful radiopharmaceutical for sentinel node detection. [ABSTRACT FROM AUTHOR]

The study investigated the performance response of different dietary protein and energy levels of Nili-Ravi buffalo calves. Sixty buffalo calves of 12-15 months of age and similar body weight (140 ± 14 kg) were divided in to 12 groups, five animals in each group, according to 4 × 3 factorial arrangements. Twelve rations were formulated containing four levels of crude protein (CP; 10.5%, 12.20%, 13.80%, and 15.55%) each with three levels (1.72, 2.11, and 2.5 Mcal/Kg) of metabolizable energy (ME). Experiment lasted for 100 days; first 10 days were given for dietary adaptation. Daily feed consumption in calves fed all experimental diets were statistically significant ( p < 0.05) across all treatments. Higher feed intake was observed in buffalo calves fed diets containing 12.20% and 13.85% CP with 2.11 Mcal/kg ME. Results of dry matter (DM) digestion were significantly different ( p < 0.05) across all treatments. There was a quadratic ( p < 0.05) response of DM digestibility for levels of dietary protein while, curvilinear ( p < 0.05) trend with respect to dietary energy levels. Daily weight gain of buffalo calves did not show any treatment effect. The outcome of the present study indicate that 12-15-month-old buffalo calves perform adequately well when fed on diets containing 12.2% CP and 2.10 ME Mcal/Kg. [ABSTRACT FROM AUTHOR]

Top-Down Proteomics (TDP) is an emerging proteomics protocol that involves identification, characterization, and quantitation of intact proteins using high-resolution mass spectrometry. TDP has an edge over other proteomics protocols in that it allows for: (i) accurate measurement of intact protein mass, (ii) high sequence coverage, and (iii) enhanced identification of post-translational modifications (PTMs). However, the complexity of TDP spectra poses a significant impediment to protein search and PTM characterization. Furthermore, limited software support is currently available in the form of search algorithms and pipelines. To address this need, we propose 'SPECTRUM', an open-architecture and open-source toolbox for TDP data analysis. Its salient features include: (i) MS2-based intact protein mass tuning, (ii) de novo peptide sequence tag analysis, (iii) propensity-driven PTM characterization, (iv) blind PTM search, (v) spectral comparison, (vi) identification of truncated proteins, (vii) multifactorial coefficient-weighted scoring, and (viii) intuitive graphical user interfaces to access the aforementioned functionalities and visualization of results. We have validated SPECTRUM using published datasets and benchmarked it against salient TDP tools. SPECTRUM provides significantly enhanced protein identification rates (91% to 177%) over its contemporaries. SPECTRUM has been implemented in MATLAB, and is freely available along with its source code and documentation at https://github.com/BIRL/SPECTRUM/. [ABSTRACT FROM AUTHOR]

A correction to the article "Effects of Feeding Different Dietary Protein and Energy Levels of the Performance of 12-15-month-old Buffalo Calves" that was published in a previous issue is presented.