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Precipitation in any form—such as rain, snow, and hail—can affect day-to-day outdoor activities. Rainfall prediction is one of the challenging tasks in weather forecasting process. Accurate rainfall prediction is now more difficult than before due to the extreme climate variations. Machine learning techniques can predict rainfall by extracting hidden patterns from historical weather data. Selection of an appropriate classification technique for prediction is a difficult job. This research proposes a novel real-time rainfall prediction system for smart cities using a machine learning fusion technique. The proposed framework uses four widely used supervised machine learning techniques, i.e., decision tree, Naïve Bayes, K-nearest neighbors, and support vector machines. For effective prediction of rainfall, the technique of fuzzy logic is incorporated in the framework to integrate the predictive accuracies of the machine learning techniques, also known as fusion. For prediction, 12 years of historical weather data (2005 to 2017) for the city of Lahore is considered. Pre-processing tasks such as cleaning and normalization were performed on the dataset before the classification process. The results reflect that the proposed machine learning fusion-based framework outperforms other models.

Energy affects the economic growth and development of a country. Renewable energy has become an important part of the world’s energy consumption. The use of fossil fuel energy contributes to global warming and carbon dioxide emissions, and has a detrimental effect on the environment. The long-run and short-run causality relationships between electric power consumption, renewable electricity output, renewable energy consumption, fossil fuel energy consumption, energy use, carbon dioxide emissions, and gross domestic product per capita for Pakistan over the period of 1990–2017 were investigated in this paper using the autoregressive distributed lag bounds testing approach to cointegration. The augmented Dickey–Fuller unit root test and the Phillips–Perron unit root test were used to check the stationarity of the variables, while the Johansen cointegration test was applied to check the robustness of the long-run relationships. The Granger causality test under the vector error correction model extracted during the short-run estimation showed a unidirectional relationship among all variables except for the relationship between gross domestic product per capita and carbon dioxide emission, which was bidirectional (feedback hypothesis). The evidence showed that in the long run, carbon dioxide emissions, electric power consumption, and renewable electricity output had a positive and significant relationship with the gross domestic product per capita, while the relationship of renewable energy consumption, energy use, and fossil fuel energy consumption with the gross domestic product per capita had a negative effect. Overall, the long-run effects of the variables were found to have a stronger effect on the gross domestic product per capita than the short-run dynamics, which indicated that the findings were heterogeneous. The evidence suggests that the government of Pakistan should take steps to enhance the use of renewable energy resources to resolve the energy crisis in the country and introduce new policies to reduce carbon dioxide emissions.

Since the turn of twenty first century, economic policy uncertainty (EPU) and geopolitical risk (GPR) have escalated across the globe. These two factors have both economic and environmental impacts. However, there exists dearth of literature that expounds the impact of EPU and GPR on environmental degradation. This study, therefore, probes the impact of EPU and GPR on ecological footprint (proxy for environmental degradation) in selected emerging economies. Cross-sectional dependence test, slope heterogeneity test, Westerlund co-integration test, fully modified least ordinary least square estimator, dynamic OLS estimator, and augmented mean group estimator are employed to conduct the robust analyses. The findings reveal that EPU and non-renewable energy consumption escalate ecological footprint, whereas GPR and renewable energy plunge ecological footprint. In addition, findings from the causality test reveal both uni-directional and bi-directional causality between a few variables. Based on the findings, we deduce several policy implications to accomplish the sustainable development goals in emerging economies.

Energy generation is heavily dependent on fossil fuels in Pakistan. Due to the huge population and current progress in industrialization, these sources are not fulfilling the existing energy needs of the country. Meanwhile, they have adverse environmental impacts and are economically unsuitable to electrify remote areas. Consequently, there is a need to look for alternate energy sources. The aim of this paper is to find out the best renewable energy option for Pakistan. For this purpose, we have collected data for solar radiation and wind speed for a period of one year in four major cities of Pakistan. Results indicate that solar energy is the best renewable energy option for Pakistan in terms of price, life span, operation and maintenance cost. Key barriers have been identified over the whole solar energy spectrum through semi-structured interviews with industry professionals. And finally, important policy recommendations have been proposed for institutions and government to overcome these barriers and utilize maximum solar energy in the country.

Diabetic retinopathy stands as a leading cause of blindness among people. Manual examination of DR images is labor-intensive and prone to error. Existing methods to detect this disease often rely on handcrafted features which limit the adaptability and classification accuracy. Thus, the aim of this research is to develop an automated and efficient system for early detection and accurate grading of diabetic retinopathy severity with less time consumption. In our research, we have developed a deep neural network named RSG-Net (Retinopathy Severity Grading) to classify DR into 4 stages (multi-class classification) and 2 stages (binary classification). The dataset utilized in this study is Messidor-1. In preprocessing, we have used Histogram Equalization to improve image contrast and denoising techniques to remove noise and artifacts which enhanced the clarity of the fundus images. We applied data augmentation techniques to our preprocessed images in order to tackle class imbalance issues. Augmentation techniques involve flipping, rotation, zooming and adjustment of color, contrast and brightness. The proposed RSG-Net model contains convolutional layers to perform automatic feature extraction from the input images and batch normalization layers to improve training speed and performance. The model also contains max pooling, drop out and fully connected layers. Our proposed RSG-Net model achieved a testing accuracy of 99.36%, specificity of 99.79% and a sensitivity of 99.41% in classifying diabetic retinopathy into 4 grades and it achieved 99.37% accuracy, 100% sensitivity and 98.62% specificity in classifying DR into 2 grades. The performance of RSG-Net is also compared with other state-of-the-art methodologies where it outperformed these methods.

Schizophrenia (SCZ) is a psychotic syndrome with well-defined signs and symptoms but indecisive causes and effective treatment. Unknown underpinning reasons and no cure of the disease profoundly elevate the risk of illness. Gut microbial dysbiosis related metabolic dysfunction is providing a new angle to look at the potential causes and treatment options for schizophrenia. Because of the number of side effects, including gut dysbiosis, of traditional antipsychotic drugs, new alternative therapeutic options are under consideration. We propose that non-pharmacotherapy using biotherapeutic products could be a potent treatment to improve cognitive impairment and other symptoms of schizophrenia. Use of live microorganisms (probiotics), fibers (prebiotics), and polyphenols alone or in a mixture can maintain gut microbial diversity and improve the two-way relationship of the gut microbiota and the central nervous system. Fiber and polyphenol induced management of gut microbiota may positively influence the gut–brain axis by increasing the level of brain-derived neurotrophic factors involved in schizophrenia. Furthermore, we endorse the need for comprehensive clinical assessment and follow-up of psychobiotic (pro and prebiotics) treatment in mental illness to estimate the level of target recovery and disability reduction in schizophrenia.

Long-duration droughts are usually tied to persistent local or remote forcings; for example, persistent droughts over California are frequently observed along with the 'ridiculously resilient ridge' over the West Coast. It is now evident that some oceanic forcings (e.g. El Niño-Southern Oscillation) have global reaches and affect multiple regions concurrently during their progression. Here, we show robust significant temporal concordancy of persistent droughts in many regions, revealing multiple teleconnections (distant regions experiencing droughts concurrently), such as the 'Western North America-Mediterranean (WNA-MED)' and the 'Southeast Asia-Southern Africa (SEA-SAF)' teleconnections. Composite pressure and sea surface temperature anomalies during concurrent droughts in WNA and the MED reveal a persistent weather regime that resembles the positive phase of Arctic Oscillation and negative phase of Pacific Decadal Oscillation. During concordant droughts of SEA and SAF, composite pressure anomalies remarkably resemble the El Niño pattern, which we infer as the leading cause of the teleconnection. The insights gained here offer a new dimension to understanding droughts and improving their long-term predictability.

Smart city is a collective term for technologies and concepts that are directed toward making cities efficient, technologically more advanced, greener and more socially inclusive. These concepts include technical, economic and social innovations. This term has been tossed around by various actors in politics, business, administration and urban planning since the 2000s to establish tech-based changes and innovations in urban areas. The idea of the smart city is used in conjunction with the utilization of digital technologies and at the same time represents a reaction to the economic, social and political challenges that post-industrial societies are confronted with at the start of the new millennium. The key focus is on dealing with challenges faced by urban society, such as environmental pollution, demographic change, population growth, healthcare, the financial crisis or scarcity of resources. In a broader sense, the term also includes non-technical innovations that make urban life more sustainable. So far, the idea of using IoT-based sensor networks for healthcare applications is a promising one with the potential of minimizing inefficiencies in the existing infrastructure. A machine learning approach is key to successful implementation of the IoT-powered wireless sensor networks for this purpose since there is large amount of data to be handled intelligently. Throughout this paper, it will be discussed in detail how AI-powered IoT and WSNs are applied in the healthcare sector. This research will be a baseline study for understanding the role of the IoT in smart cities, in particular in the healthcare sector, for future research works.

Face masks are considered an effective intervention in controlling the spread of airborne viruses, as evidenced by the 2009′s H1N1 swine flu and 2003′s severe acute respiratory syndrome (SARS) outbreaks. However, research aiming to examine public willingness to wear (WTW) face masks in Pakistan are scarce. The current research aims to overcome this research void and contributes by expanding the theoretical mechanism of theory of planned behavior (TPB) to include three novel dimensions (risk perceptions of the pandemic, perceived benefits of face masks, and unavailability of face masks) to comprehensively analyze the factors that motivate people to, or inhibit people from, wearing face masks. The study is based on an inclusive questionnaire survey of a sample of 738 respondents in the provincial capitals of Pakistan, namely, Lahore, Peshawar, Karachi, Gilgit, and Quetta. Structural equation modeling (SEM) is used to analyze the proposed hypotheses. The results show that attitude, social norms, risk perceptions of the pandemic, and perceived benefits of face masks are the major influencing factors that positively affect public WTW face masks, whereas the cost of face masks and unavailability of face masks tend to have opposite effects. The results emphasize the need to enhance risk perceptions by publicizing the deadly effects of COVID-19 on the environment and society, ensure the availability of face masks at an affordable price, and make integrated and coherent efforts to highlight the benefits that face masks offer.

Date palm trees, being an important source of nutrition, are grown at a large scale in Saudi Arabia. The biomass waste of date palm, discarded of in a non-environmentally-friendly manner at present, can be used for biofuel generation through the fast pyrolysis technique. This technique is considered viable for thermochemical conversion of solid biomass into biofuels in terms of the initial investment, production cost, and operational cost, as well as power consumption and thermal application cost. In this study, a techno-economic analysis has been performed to assess the feasibility of converting date palm waste into bio-oil, char, and burnable gases by defining the optimum reactor design and thermal profile. Previous studies concluded that at an optimum temperature of 525 °C, the maximum bio-oil, char and gases obtained from pyrolysis of date palm waste contributed 38.8, 37.2 and 24% of the used feed stock material (on weight basis), respectively, while fluidized bed reactor exhibited high suitability for fast pyrolysis. Based on the pyrolysis product percentage, the economic analysis estimated the net saving of USD 556.8 per ton of the date palm waste processed in the pyrolysis unit. It was further estimated that Saudi Arabia could earn USD 44.77 million per annum, approximately, if 50% of the total date palm waste were processed through fast pyrolysis, with a payback time of 2.57 years. Besides that, this intervention will reduce 2029 tons of greenhouse gas emissions annually, contributing towards a lower carbon footprint.