Pakistan has the highest rate of urbanization in South Asia. The climate change effects felt all over the world have become a priority for regulation agencies and governments at global and regional scales with respect assessing and mitigating the rising temperatures in urban areas. This study investigated the temporal variability in urban microclimate in terms of land surface temperature (LST) and its correlation with land use-land cover (LULC) change in Lahore city for prediction of future impact patterns of LST and LULC. The LST variability was determined using the Landsat Thermal Infrared Sensor (TIRS) and the land surface emissivity factor. The influence of LULC, using the normalized difference vegetation index (NDVI), the normalized difference building index (NDBI), and the normalized difference bareness index (NDBaI) on the variability LST was investigated applying Landsat Satellite data from 1992 to 2020. The pixel-level multivariate linear regression analysis was employed to compute urban LST and influence of LULC classes. Results revealed that an overall increase of 41.8% in built-up areas at the expense of 24%, 17.4%, and 0.4% decreases in vegetation, bare land, and water from 1992–2020, respectively. Comparison of LST obtained from the meteorological station and satellite images showed a significant coherence. An increase of 4.3 °C in temperature of built-up areas from 1992–2020 was observed. Based on LULC and LST trends, the same were predicted for 2025 and 2030, which revealed that LST may further increase up to 1.3 °C by 2030. These changes in LULC and LST in turn have detrimental effects on local as well as global climate, emphasizing the need to address the issue especially in developing countries like Pakistan.

At present online reviews are becoming an important source for Kansei engineering of the services provided by ridesharing platforms. Kansei engineering deals with incorporating customer feedback and demands into product and service design. Thus, it is used as a tool for organizations to uplift their businesses by considering customer reviews and feedback. Customer reviews available on social media are in unstructured form; therefore, sentiment analysis is employed to extract customer's opinions in a systematic manner. In India-Pakistan, these reviews are mostly in Roman Urdu/Hindi and English, which are of great value for ridesharing platforms as a part of their Kansei engineering strategy. However, sentiment analysis cannot be performed directly on these reviews as they are mostly in Roman Urdu/Hindi. Therefore, the objective of this paper is to conduct aspect based sentiment analysis on these reviews after translating them into English for Kansei engineering of the service. Consequently, sentiment analysis is carried out to extract the most frequent features along with nouns and adjectives used by the customers to express their sentiments. We extracted prominent aspects of the service (i.e., `Driver', `Company', `Service', and `Ride') based on their highest frequencies using aspect based sentiment analysis. The customer sentiments are then clustered into these main aspects using unsupervised machine learning technique. Each aspect is further analyzed based on their polarity, which serves as an input for Kansei engineering of the service. As a result, it can facilitate ridesharing companies to enhance their businesses by improving services in accordance with customer demands.

Sensor fault classification and reconstruction frameworks are crucial for the stable, safe, and reliable operations of Structural Health Monitoring (SHM) systems. Existing literature addressing reliability and efficiency is confronted with several challenges; especially, lacking a combined framework addressing both issues of classification and reconstruction at the same time. To tackle these issues, this paper proposes a fault-tolerant mechanism that uses various combinations of Deep Learning (DL) techniques to ensure the effectiveness and reliability of SHM systems in a resource-efficient way. The proposed mechanism is an integrated framework consisting of two modules: the sensor faults classification module and the faulty signal reconstruction module. We develop integrated architectures of CNN and RNN to classify faulty signals and employ various architectures of LSTM models for faulty signal reconstruction. Both modules are tested on the benchmark Canton Tower dataset. We augment the dataset with faulty signals created through simulations for an accurate analysis. The sensor faults classification module is evaluated by utilizing precision, recall, F1-score, and accuracy; it achieves a maximum accuracy of 94%. Additionally, the root mean square error (RMSE) value for the faulty signals’ reconstruction stands at zero. The experimental results show that our proposed mechanism outperforms existing state-of-the-art techniques regarding sensor fault classification accuracy and the quality of reconstructed faulty signals.

Cotton is a global cash crop with a significant contribution in the world economy. Optimum nutrient and water supply are most important for sustainable cotton production under warmer and dry environments. Field experiments were carried out to evaluate the cumulative impacts of various nitrogen doses and mulches on sustainable cotton production under semi-arid conditions during 2018 and 2019. Four nitrogen doses; 0, 70, 140, and 210 kg ha⁻¹ and three types of mulch: control (without mulch), natural mulch (5 tons/ha wheat straw), and chemical mulch (methanol (30%). Nitrogen 210 kg ha⁻¹ with natural mulching increased 40.5% gunning out turn, 30.0% fiber length, 31.7% fiber strength, 32.6% fiber fineness, 20.8% fiber uniformity, and 34.0% fiber elongation. Shoot nitrogen, phosphorous, potassium, calcium, and magnesium contents were maximum where 210 kg ha⁻¹ nitrogen and mulch was applied. Natural mulch reduced the soil temperature as compared to chemical and no mulch conditions. The soil temperature was 0.5 to 1.8 ℃ lower in mulching treatments as compared to the control. Maximum economic yield was around 90% higher in natural mulch with the 210 kg ha⁻¹ nitrogen application. It is concluded that optimum nitrogen application with natural mulch not only enhanced plant growth and development but also induced sustainability in quality cotton production under semi-arid conditions.

Malware presents a significant threat to computer networks and devices that lack robust defense mechanisms, despite the widespread use of anti-malware solutions. The rapid growth of the Internet has led to an increase in malicious code attacks, making them one of the most critical challenges in network security. Accurate identification and classification of malware variants are crucial for preventing data theft, security breaches, and other cyber risks. However, existing malware detection methods are often inefficient or inaccurate. Prior research has explored converting malicious code into grayscale images, but these approaches are often computationally intensive, especially in binary form. To address these challenges, we propose the Malware Variants Detection System (MVDS), a novel technique that transforms malicious code into color images, enhancing malware detection capabilities compared to traditional methods. Our approach leverages the richer information in color images to achieve higher classification accuracy than grayscale-based methods. We further improve the detection process by employing transfer learning to automatically identify and classify malware images based on their distinctive features. Empirical results demonstrate that MVDS achieves 97.98% accuracy with high detection speed, highlighting its potential for practical implementation in strengthening network security.