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Sustainable consumption is increasingly considered critical for the sustainable growth of developing countries. Consequently, these countries exert significant efforts to foster sustainable consumption behaviors among the consumer public to maximize their sustainability outcomes. This research, while making appeals to the sustainable consumption theory and the stakeholder theory, aims to determine the role of a few critical antecedents, namely the attitude of consumers towards sustainable consumption, their overall satisfaction with the consumption of sustainable products/services, consumers' environmental knowledge and sustainable consumption promotions together with social media influence in engendering sustainable consumption behaviors among consumer publics, along with the role of governments' sustainable consumption policies in enabling such effects. After performing PLS-based structural equation modeling on 500 data points, we found a significant positive role in the consumers' attitude towards sustainable consumption, their sustainable consumption promotions, the impact of social media, and the environmental knowledge possessed by the consumers in inducing sustainable consumption behaviors. In addition, the mediating effects of sustainable consumption promotions and the instrumental role of the government's sustainable consumption policy have also been empirically substantiated. Besides contributing to advancing the body of knowledge encompassing sustainable consumption and sustainable growth/development, the study puts forward several practical implications for the consumers, practitioners, and public policymakers to transmogrify sustainable consumption behaviors by influencing consumers' attitudes, enhancing their environmental knowledge and social media enabled discourse enkindled by an efficacious sustainable consumption policy put in place by the government.

Due to the influx of advancements in technology and the increased simplicity of communication through emails, there has been a severe threat to the global economy and security due to upsurge in volume of unsolicited During the training of models, high-dimensional and redundant datasets may reduce the classification results of the model due to high memory costs and high computation. An important data processing technique is feature selection which helps in selecting relevant features and subsets of information from the dataset. Therefore, choosing efficient feature selection techniques is very important for the best performance of classification of a model. Moreover, most of the research has been performed using traditional machine learning techniques, which are not enough to deal with the huge amount of data and its variations. Also, spammers are becoming smarter with technological advancement. Therefore, there is a need for hybrid techniques consisting of deep learning and conventional algorithms to cope with these problems. We have proposed a novel scheme in this paper for email spam detection, which will result in an improved feature selection approach from the original dataset and increase the accuracy of the classifier as well. The literature has been studied to explore the efficient machine learning models that have been applied by different researchers for email spam detection and feature selection to acquire the best results. Our method, GWO-BERT, has given remarkable results with deep learning techniques such as CNN, biLSTM and LSTM. We have compared our models with RF and LSTM and used dataset: “Lingspam,” which is a publicly available dataset. With different experiments, our technique, GWO-BERT, obtained 99.14% accuracy, which is almost equal to 100 percent.

Recommender Systems (RSs), which generate personalized content, have become a technological tool with diverse applications for users. While numerous RSs have been proposed and successfully implemented across various domains, traditional AI-based RSs still encounter certain challenges, such as data sparsity, cold start, and diversity. Generative Artificial Intelligence in recommender systems is a recent advancement used by platforms like Netflix, Spotify, and Amazon to recommend items, news, videos, audios, goods, and services to their customers/users or to personalize experiences for their customers/users. The main purpose of this review is to compare traditional AI-based recommender systems with generative AI-based recommender systems. A total of fifty-two (52) papers, published between 2019 and February 2024, were selected from six major online libraries. To get a more comprehensive understanding of the selected study, we reviewed the selected studies techniques, and the models, datasets, and metrics used. Our systematic review reveals that generative AI models, such as generative adversarial networks (GANs), variational autoencoder (VAEs) and autoencoders have been widely used in recommender systems and they perform better than traditional AI techniques. Among the 30 datasets analyzed, MovieLens was the most frequently used, accounting for 33%, while Amazon datasets accounted for 11%, Recall and RSME are the most commonly used metrics. Our literature review offers understandings into the Generative AI techniques used across different recommender systems and provides suggestions for the future research. Finally, we elaborated on open issues and discussed current and future trends in generative AI-based recommendation systems.

Software Defect Prediction (SDP) reduces the overall cost of software development by identifying the code at a higher risk of defects at the initial phase of software development. SDP helps the test engineers to optimize the allocation of testing resources more effectively. Traditional SDP models are built using handcrafted software metrics that ignore the structural, semantic, and contextual information of the code. Consequently, many researchers have employed deep learning models to capture contextual, semantic, and structural information from the code. In this article, we propose the DP-CCL (Defect Prediction using CodeBERT with Contrastive Learning) model to predict the defective code. The proposed model employs supervised contrastive learning using this CodeBERT Language model to capture semantic features from the source code. Contrastive learning extracts valuable information from the data by maximizing the similarity between similar data pairs (positive pair) and meanwhile minimizing the similarity between dissimilar data pairs (negative pair). Moreover, The model combines the semantic features with software metrics to obtain the benefits of both semantic and handcrafted features. The combined features are input to the logistic regression model for code classification as either buggy or clean. In this study, ten PROMISE projects were utilized to conduct the experiments. Results show that the DP-CCL model achieved significant improvement i.e., 4.9% to 14.9% increase in F-Score as compared to existing approaches.

Background: Metastasis is the main cause of cancer-related deaths, but efficient targeted therapies against metastasis are still missing. Major gaps exist in our understanding of the metastatic cascade, as existing methods cannot combine sensitivity, robustness, and practicality to dissect cancer progression. Addressing this issue requires improved strategies to distinguish early metastatic colonization from metastatic outgrowth. Methods: Luciferase-labelled MDA-MB-231, MCF7, and 4T1 breast cancer cells were spiked into samples from tumour-naïve mice to establish the limit of detection for disseminated tumour cells. Luciferase-labelled breast cancer cells (±unlabelled cancer-associated fibroblasts; CAFs) were orthotopically implanted in immunocompromised mice. An ex vivo luciferase assay was used to quantify tumour cell dissemination. Results: In vitro luciferase assay confirmed a linear and positive correlation between cancer cell numbers and the bioluminescence detected at single cell level in blood, brain, lung, liver, and mammary fat pad samples. Remarkably, single luciferase-labelled cancer cells were detectable in all of these sites, as the bioluminescence quantified in the analysed samples was substantially higher than background levels. Ex vivo, circulating tumour cells, metastasis, and tumour self-seeding were detected in all samples from animals implanted with highly metastatic luciferase-labelled MDA-MB-231 cells. In turn, detection of poorly metastatic luciferase-labelled MCF7 cells was scarce but significantly enhanced upon co-implantation with CAFs as early as 20 days after the experiment was initiated. Conclusions: These results demonstrate the feasibility of using an ultrasensitive luciferase-based method to dissect the mechanisms of early metastatic colonization to improving the development of antimetastatic therapies.

Objective: To assess the degree of impact the pandemic had on the Oncology Department regarding patient visits and elective procedures. Study Design: Comparative pre-post analysis study. Place and Duration of Study: Medical Oncology Department of Combined Military Hospital, Lahore Pakistan, from Oct to Dec 2019 before the pandemic, and then 2020 during the pandemic. Methodology: Data was collected from the Department regarding the number of outpatient visits, indoor admissions, new cases diagnosed, chemotherapies administered, blood transfusions, and various procedures like lumbar puncture, ascitic fluid paracentesis, pleural fluid drainage, etc. Results: Our study showed an overall decrease in all the study parameters. The average monthly Outpatient visits fell by 395 patients (55.6%). The new cases decreased by 25 patients (36.2%). The indoor admissions also came down by 17 patients (42.5%). Furthermore, the procedures performed in the Oncology department were also observed to have decreased. Conclusion: Oncology has suffered dramatically in the face of the COVID-19 pandemic, with fewer patients visiting the hospital for their consultations, follow-ups, as well as treatments. This has resulted in a delay in diagnosis and treatment and has had a severe adverse psychological impact on the patients. Keywords: COVID-19, Elective procedures, Pandemic, Oncology.

Online purchasing is increasing because customers are shifting to digital wallets and digital money, as these services are provided by different microfinance and other commercial banking sectors, and different online brands are working in Pakistan to support environmental sustainability. The objective of this study was to demonstrate to what extent low distribution charges and low transit time is contributing to impulsive buying when customers can use digital money in Pakistan. The study was conducted using survey research. Importantly, 650 questionnaires were distributed to the respondents with a received response rate of 40%. The study found that digital money (e-wallet) is positively associated with impulsive buying. Moreover, the moderating role of distribution charges and low transit time has been significant in impulsive buying has been significant. This study concludes that low transit time and load distribution charges must be considered by online businesses and brands working in Pakistan to ensure productivity and capture a larger market share of impulsive buying in Pakistan. Also, the current study contributes a theoretical framework to the knowledge and literature related to impulsive buying. The scope of this study is limited to the online businesses and brands that are working to provide products and services to the Pakistani people with the help of digital money and digital transactions. Significantly, this study provides significant future directions that are important to consider for upcoming studies to focus on and contribute to effectively.

Introduction: Since SARS-CoV2 is a novel virus, not much was previously known about the disease, however recent studies have shown that it is transmitted via droplet infection and mainly affects the respiratory tract, causing symptoms of fever, fatigue and shortness of breath. Comorbidities increase risk of severe disease. Aims & Objectives: Our study aims to determine the predominant manifestations and correlations of COVID-19 in Pakistan. Place and duration of study: The study was carried out at CMH Lahore over ten-day duration from 1st June 2020 to 10th June 2020. Material & Methods: Samples of 107 confirmed cases of COVID-19 was taken. Participants were administered a questionnaire by attending doctor which enquired regarding their symptoms, presence of complications, and comorbidities. Data was analyzed using SPSS 25.0. A p-value of

This study explores and documents the traditional medicinal uses of weed flora commonly found in field crops (rice, vegetables, wheat, and maize) in District Dir Upper, Khyber Pakhtunkhwa. Many medicinal plants grow wild or as weeds worldwide, including in Pakistan. The Dir Upper district boasts a diverse weed flora with significant medicinal value for daily use. Surveys using semi-structured open-ended questionnaires were carried out in distant villages of the Larjam subdivisions, specifically interviewing 120 local respondents (85 males, including herbalists and pansiris, and 35 women) regarding the naturally occurring medicinal weeds in the area. During the field survey, traditional knowledge and the ethnobotanical relevance of seasonal weeds were collected. A total of 65 species from 24 families were recorded across the four different seasons (spring, summer, autumn, and winter) in 2021--2023, including 54 angiosperms, 10 tracheophytes, and one pteridophyte. The dominant family was Poaceae (25%), followed by Asteraceae (15%) and Asclepiadaceae (2%). Given the essential roles that these weed species play in medicine, future research on the use and conservation of indigenous weed flora is highly recommended. [ABSTRACT FROM AUTHOR]