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The advent of the World Wide Web and the rapid adoption of social media platforms (such as Facebook and Twitter) paved the way for information dissemination that has never been witnessed in the human history before. With the current usage of social media platforms, consumers are creating and sharing more information than ever before, some of which are misleading with no relevance to reality. Automated classification of a text article as misinformation or disinformation is a challenging task. Even an expert in a particular domain has to explore multiple aspects before giving a verdict on the truthfulness of an article. In this work, we propose to use machine learning ensemble approach for automated classification of news articles. Our study explores different textual properties that can be used to distinguish fake contents from real. By using those properties, we train a combination of different machine learning algorithms using various ensemble methods and evaluate their performance on 4 real world datasets. Experimental evaluation confirms the superior performance of our proposed ensemble learner approach in comparison to individual learners.

In recent years, prognostic and health management (PHM) has played an important role in industrial engineering. Efficient remaining useful life (RUL) prediction can ensure the development of maintenance strategies and reduce industrial losses. Recently, data-driven based deep learning RUL prediction methods have attracted more attention. The convolution neural network (CNN) is a kind of deep neural network widely used in RUL prediction. It shows great potential for application in RUL prediction. A CNN is used to extract the features of time-series data according to the spatial feature method. This way of processing features without considering the time dimension will affect the prediction accuracy of the model. On the contrary, the commonly used long short-term memory (LSTM) network considers the timing of the data. However, compared with CNN, it lacks spatial data extraction capabilities. This paper proposes a double-channel hybrid prediction model based on the CNN and a bidirectional LSTM network to avoid those drawbacks. The sliding time window is used for data preprocessing, and an improved piece-wise linear function is used for model validating. The prediction model is evaluated using the C-MAPSS dataset provided by NASA. The predicted results show the proposed prediction model to have a better prediction performance compared with other state-of-the-art models.

The study’s main purpose is to investigate the impacts of human capital efficiency (HCE) on the firm performance of non-certified and quality-certified firms from the European Foundation for Quality Management (EFQM) Excellence Model. The study also examines the relationship between firm performance and quality-certificates from the European Foundation.By using a sample of 282 non-certified and 22 quality-certified firms from 2017 to 2021, the current study employed a two-step system generalized method of moments (GMM) estimation to analyse the empirical data. The dummy variable is used to examine the relationship between quality-certificates and firm performance. The dummy interaction term is employed to quantify the impacts of HCE on the firm’s performance for quality-certified firms.The results revealed that quality-certificates and firm performance have a positive relationship. Quality-certified firms perform better and earn more profits than non-certified firms. HCE has a positive impact on firm performance for both types of firms. Moreover, the quality-certified firms utilize HCE in an efficient way to earn more profits compared to the non-certified firms.This is the first study to use a comprehensive analysis to emphasize the HCE for non-certified and quality-certified firms separately. The effects of quality-certificates on firm performance in the context of HCE are also being highlighted for the first time in this research.The current study’s findings are fruitful for academics, managers, researchers, policymakers, and other firm management. The findings will encourage the management of the firms to implement the total quality management (TQM) approach within their firms.

China is the world’s largest renewable energy installer with a capacity of 1020 gigawatts in 2021. This study aims to analyze the public discourse around China’s green energy and green technology and the paths to sustainable development by comparing public policy. The public discourse analysis approach and Grey Prediction Model are applied to analyze the motives for the distinct inferences being reached over the influences of renewable energy initiatives (REIs). The findings show that the modeling and assumptions are found different in theoretical perspectives, especially in the case of economic and environmental sustainability. The results are close to the other jurisdictions following REIs, including feed-in-tariff, standards and renewable liabilities. Based on statistics during 2012–2021 Five-year plan period, three major renewables are forecasted under base, reference and aggressive scenarios with interesting results. The wind would rise by 109 terawatt hours in an aggressive scenario while solar will rise from 83–99% with a rise of four times in the next decade. Finally, China’s current energy policy has been proven to be a series of effective public policies by making the discourse analysis, which can energetically widen the subsidy funds’ sources, discover miscellaneous financing techniques, standardized the subsidy process, supervise in applying the renewable energy technologies, and enhance the feed-in-tariff attraction of consumers and private investors.

Studying and analyzing energy consumption and structural changes in Pakistan’s major economic sectors is crucial for developing targeted strategies to improve energy efficiency, support sustainable economic growth, and enhance energy security. The logarithmic mean Divisia index (LMDI) method is applied to find the factors’ effects that change sector-wise energy consumption from 1990 to 2019. The results show that: (1) the change in mixed energy and sectorial income shows a negative influence, while energy intensity (EI) and population have an increasing trend over the study period. (2) The EI effects of the industrial, agriculture and transport sectors are continuously rising, which is lowering the income potential of each sector. (3) The cumulative values for the industrial, agricultural, and transport sectors increased by 57.3, 5.3, and 79.7 during 2019. Finally, predicted outcomes show that until 2035, the industrial, agriculture, and transport incomes would change by -0.97%, 13%, and 65% if the energy situation remained the same. Moreover, this sector effect is the most crucial contributor to increasing or decreasing energy consumption, and the EI effect plays the dominant role in boosting economic output. Renewable energy technologies and indigenous energy sources can be used to conserve energy and sectorial productivity.

Currently, Pakistan is in a stage of urbanization and industrialization, raising its energy demand and supply and carbon dioxide emissions (CO2Es) due to the excessive use of fossil fuels. In meeting future demand and supply predictions, much emphasis should be given to both energy consumption and the level of inter-factor and inter-fuel substitution possibilities. Specifically, future outcomes for energy demand are more valid when production models contemplate substitution elasticity occurring during the period. To analyze the potential for little reliance on fossil fuels and diminish CO2Es, the present research has examined the potential for the substitution of energy and non-energy factors (i.e., natural gas, electricity, petroleum, labor, and capital) by using translog productions function over the period between 1986–2019. The ridge regression method is applied to evade the multicollinearity issue in the data. The model analyzes the output elasticity, substitution elasticity, technical progress, and carbon emission scenarios. The results show that the output elasticities are growing, presenting that the contribution of all factors adds to economic growth. The inputs between capital-petroleum, capital-electricity, labor-electricity, capital-natural gas, and natural gas-electricity are extreme substitutes. These substitutes are increasing capital growth and production sizes. The relative difference in technical progress shows a small positive change between 3–7% with convergence evident. Lastly, the investment scenarios under 5% and 10% investment in petroleum reduction are evidence that the CO2Es would reduce by 7.5 Mt and 10.43 Mt under scenario 1 and 7.0 Mt and 10.9 Mt under scenario 2. The results have broader suggestions for energy-conserving policies, particularly under the China–Pakistan Economic Corridor.

This study aims to examine the impact of public-private partnerships (PPP) investment in energy, technological innovations (TI), economic growth (EG), exports, and foreign direct investment (FDI) on CO 2 emissions in Brazil over the period from 1984 to 2018. In doing so, we employ the Ng-Perron unit root test to examine the stationarity and autoregressive lag distributed (ARDL) model for cointegration between CO 2 emissions and its determinants. The outcomes are as follows: first, in the long run, the PPP investment in energy deteriorates the environmental quality by increasing CO 2 emissions, while TI has a significant negative effect on CO 2 emissions. It is also found that the exports and FDI degrade the environmental quality and the relationship between EG and CO 2 emissions is inverted U-Shaped, presence of the EKC hypothesis. Second, in the short run, PPP investment in the energy sector is negatively influencing, while TI has a positive association with carbon emissions. The empirical findings provide new insights for policymakers to regulate PPP investment in the energy sector for the improvement of environmental quality in Brazil. Graphical abstract

Salinity stress severely hampers wheat productivity by impairing growth, photosynthesis, and metabolic balance. Potassium nutrition, however, can mitigate these effects by supporting physiological and biochemical stability. This study assessed the impact of foliar potassium application (0, 200 and 400 ppm) on two wheat cultivars, Galaxy-13 and Uqab-2000, exposed to normal (0 mM NaCl) and saline conditions (100 and 150 mM NaCl, respectively). Salinity significantly reduced root and shoot growth, biomass, chlorophyll content, photosynthetic rate, and stomatal conductance. Potassium supplementation, particularly at 400 ppm, alleviated these reductions, with Galaxy-13 showing a 32.01% increase in shoot length and a 45.11% increase in shoot dry weight compared to Uqab-2000. Biochemical analyses revealed that Galaxy-13 sustained higher nitrate and nitrite reductase activities (6.23 and 3.63 μmol NO 2 g -1 FW h -1 , respectively) and total soluble proteins (10.1 mg g -1 FW), whereas Uqab-2000 accumulated more soluble sugars and free amino acids under stress (9.8 and 19.8 mg g -1 FW, respectively). Oxidative stress indicators (malondialdehyde and hydrogen peroxide) rose under salinity, but potassium reduced their levels, with Galaxy-13 exhibiting stronger antioxidant regulation. Nutrient profiling further demonstrated that Galaxy-13 maintained higher N, P, and K contents and minimized Na uptake, unlike Uqab-2000, which showed severe ionic imbalance. Multivariate analyses (PCA, heatmap, and correlation) highlighted strong positive associations of potassium, especially K400, with biomass accumulation, photosynthetic efficiency, and nutrient homeostasis. The findings establish that Galaxy-13 possesses superior salinity tolerance and responds more favorably to potassium nutrition. This study provides novel evidence that cultivar-specific potassium management can enhance wheat resilience in saline environments, offering a practical strategy for sustaining yield under stress.

As an intelligent search optimization technique, genetic algorithm (GA) is an important approach for non-deterministic polynomial (NP-hard) and complex nature optimization problems. GA has some internal weakness such as premature convergence and low computation efficiency, etc. Improving the performance of GA is a vital topic for complex nature optimization problems. The selection operator is a crucial strategy in GA, because it has a vital role in exploring the new areas of the search space and converges the algorithm, as well. The fitness proportional selection scheme has essence exploitation and the linear rank selection is influenced by exploration. In this article, we proposed a new selection scheme which is the optimal combination of exploration and exploitation. This eliminates the fitness scaling issue and adjusts the selection pressure throughout the selection phase. The χ 2 goodness-of-fit test is used to measure the average accuracy, i.e., mean difference between the actual and expected number of offspring. A comparison of the performance of the proposed scheme along with some conventional selection procedures was made using TSPLIB instances. The application of this new operator gives much more effective results regarding the average and standard deviation values. In addition, a two-tailed t test is established and its values showed the significantly improved performance by the proposed scheme. Thus, the new operator is suitable and comparable to established selection for the problems related to traveling salesman problem using GA.

The lack of effective treatment for neurological disorders has encouraged the search for novel therapeutic strategies. Remarkably, neuroinflammation provoked by the activated microglia is emerging as an important therapeutic target for neurological dysfunction in the central nervous system. In the pathological context, the hyperactivation of microglia leads to neuroinflammation through the release of neurotoxic molecules, such as reactive oxygen species, proteinases, proinflammatory cytokines and chemokines. Cannabidiol (CBD) is a major pharmacologically active phytocannabinoids derived from Cannabis sativa L. CBD has promising therapeutic effects based on mounting clinical and preclinical studies of neurological disorders, such as epilepsy, multiple sclerosis, ischemic brain injuries, neuropathic pain, schizophrenia and Alzheimer’s disease. A number of preclinical studies suggested that CBD exhibited potent inhibitory effects of neurotoxic molecules and inflammatory modulators, highlighting its remarkable therapeutic potential for the treatment of numerous neurological disorders. However, the molecular mechanisms of action underpinning CBD’s effects on neuroinflammation appear to be complex and are poorly understood. This review summarises the anti-neuroinflammatory activities of CBD against various neurological disorders with a particular focus on their main molecular mechanisms of action, which were related to the downregulation of NADPH oxidase-mediated ROS, TLR4-NFκB and IFN-β-JAK-STAT pathways. We also illustrate the pharmacological action of CBD’s derivatives focusing on their anti-neuroinflammatory and neuroprotective effects for neurological disorders. We included the studies that demonstrated synergistic enhanced anti-neuroinflammatory activity using CBD and other biomolecules. The studies that are summarised in the review shed light on the development of CBD, including its derivatives and combination preparations as novel therapeutic options for the prevention and/or treatment of neurological disorders where neuroinflammation plays an important role in the pathological components.