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Phishing attacks are evolving with more sophisticated techniques, posing significant threats. Considering the potential of machine-learning-based approaches, our research presents a similar modern approach for web phishing detection by applying powerful machine learning algorithms. An efficient layered classification model is proposed to detect websites based on their URL structure, text, and image features. Previously, similar studies have used machine learning techniques for URL features with a limited dataset. In our research, we have used a large dataset of 20,000 website URLs, and 22 salient features from each URL are extracted to prepare a comprehensive dataset. Along with this, another dataset containing website text is also prepared for NLP-based text evaluation. It is seen that many phishing websites contain text as images, and to handle this, the text from images is extracted to classify it as spam or legitimate. The experimental evaluation demonstrated efficient and accurate phishing detection. Our layered classification model uses support vector machine (SVM), XGBoost, random forest, multilayer perceptron, linear regression, decision tree, naïve Bayes, and SVC algorithms. The performance evaluation revealed that the XGBoost algorithm outperformed other applied models with maximum accuracy and precision of 94% in the training phase and 91% in the testing phase. Multilayer perceptron also worked well with an accuracy of 91% in the testing phase. The accuracy results for random forest and decision tree were 91% and 90%, respectively. Logistic regression and SVM algorithms were used in the text-based classification, and the accuracy was found to be 87% and 88%, respectively. With these precision values, the models classified phishing and legitimate websites very well, based on URL, text, and image features. This research contributes to early detection of sophisticated phishing attacks, enhancing internet user security.

According to WHO 2019, Hepatocellular carcinoma (HCC) is the fourth highest cause of cancer death worldwide. More precise diagnostic models are needed to enhance early HCC and cirrhosis quick diagnosis, treatment, and survival. Breath biomarkers known as volatile organic compounds (VOCs) in exhaled air can be used to make rapid, precise, and painless diagnoses. Gas chromatography and mass spectrometry (GCMS) are utilized to diagnose HCC and cirrhosis VOCs. In this investigation, metabolically generated VOCs in breath samples (n = 35) of HCC, (n = 35) cirrhotic, and (n = 30) controls were detected via GCMS and SPME. Moreover, this study also aims to identify diagnostic VOCs for distinction among HCC and cirrhosis liver conditions, which are most closely related, and cause misleading during diagnosis. However, using gas chromatography-mass spectrometry (GC-MS) to quantify volatile organic compounds (VOCs) is time-consuming and error-prone since it requires an expert. To verify GC-MS data analysis, we present an in-house R-based array of machine learning models that applies deep learning pattern recognition to automatically discover VOCs from raw data, without human intervention. All-machine learning diagnostic model offers 80% sensitivity, 90% specificity, and 95% accuracy, with an AUC of 0.9586. Our results demonstrated the validity and utility of GCMS-SMPE in combination with innovative ML models for early detection of HCC and cirrhosis-specific VOCs considered as potential diagnostic breath biomarkers and showed differentiation among HCC and cirrhosis. With these useful insights, we can build handheld e-nose sensors to detect HCC and cirrhosis through breath analysis and this unique approach can help in diagnosis by reducing integration time and costs without compromising accuracy or consistency.

Corporate tax avoidance is planning strategies to use tax law and accounting standards to reduce the amount of tax payable to government and that is expected to improve firm performance. Nevertheless, agency theory explains the opportunistic behavior of manager who can reduce the tax obligation by using various complex transactions to fulfill their personal interest. Therefore, the present study explores the relationship between corporate tax avoidance and firm performance. It also identifies the moderating role of ownership concentration and board independence in this relationship. Until now there exists no empirical evidences in the context of South Asian Association for Regional Cooperation (SAARC) economies. To address this gap, the current study explores the empirical relationship by using the sample from the 91 banking firms of four SAARC countries namely Pakistan, India, Bangladesh and Sri Lanka during the period from 2010 to 2021. Moreover, this is a first study that provides interesting comparison among these countries by using 264 observations from Pakistan, 312 observations from India, 384 observations from Bangladesh and 132 observations from Sri Lanka by using pooled OLS, fixed and random effect regression techniques. Empirical findings confirm that, for Pakistan, Bangladesh, and Sri Lanka, corporate tax avoidance doesn't play significant role in improving firm performance, however, India confirms the positive relationship. The results indicate that ownership concentration and board independence do not moderate the relationship for banking firms in Pakistan, India, and Sri Lanka, whereas in Indian banking firms, ownership concentration positively moderates the relationship, while board independence has a negative moderating effect. The government officials and policymakers can comprehend corporate tax avoidance behaviors of businesses and assist them in the formulation of suitable and efficient taxation policies.

This study investigated the morphometric characteristics of adenohypophyseal thyrotrophs and circulating thyroid hormone profiles in dromedary camels (Camelus dromedarius) in relation to age and lactation status. Clinically healthy Brela breed camels were divided into lactating female, and non-lactating female groups across two age categories (5–10 years and ≥11 years), with fifty animals per group. Blood samples were collected before slaughter and pituitary glands were collected post-slaughter and processed for immunohistochemical detection of thyroid-stimulating hormone (TSH) using anti-porcine TSHβ antibody, while morphometric measurements of thyrotrophs were conducted through image analysis. Plasma concentrations of TSH, triiodothyronine (T3), and thyroxine (T4) were quantified using validated ELISA and enzyme immunoassay kits. Group differences were analyzed using one-way ANOVA followed by post hoc comparisons, with statistical significance set at p < 0.05. Morphometric analysis revealed that lactating female camels exhibited significantly higher thyrotroph counts compared with non-lactating counterparts, whereas non-lactating females displayed larger cell and nuclear dimensions. Age influenced these patterns, with older camels showing hypertrophied thyrotrophs but reduced functional plasticity compared to younger animals. Plasma hormone assays demonstrated that non-lactating camels had higher TSH and T4 concentrations, while lactating camels maintained elevated T3 levels, suggesting enhanced peripheral conversion of T4 to T3 during milk production. Additionally, younger camels exhibited higher T3 concentrations than older animals, indicating age-related decline in thyroidal activity. These findings highlight the dynamic regulation of the hypothalamic–pituitary–thyroid axis in camels, demonstrating how lactation and age shape thyroidal morphology and function to meet diverse physiological demands. These findings not only broaden the comparative endocrinology of underexplored species but also provide physiopathological insights relevant to farm animal management, lactation efficiency, and adaptive metabolism in harsh environments.

Ginger is an herbaceous and flowering plant renowned for its rhizome, which is widely employed as both a spice and an herb. Since ancient times, ginger has been consumed in folk medicine and traditional cuisines for its favorable health effects. Different in vitro and in vivo studies have disclosed the advantageous physiological aspects of ginger, primarily due to its antioxidant, anti-inflammatory, antimicrobial, and anti-carcinogenic properties. These health-promoting features are linked to the variety of bioactive compounds that are present in ginger. Following the advancement in consumer awareness and the industrial demand for organic antioxidants and functional ingredients, the application of ginger and its derivatives has been broadly investigated in a wide range of food products. The prominent features transmitted by ginger into different food areas are antioxidant and nutraceutical values (bakery); flavor, acceptability, and techno-functional characteristics (dairy); hedonic and antimicrobial properties (beverages); oxidative stability, tenderization, and sensorial attributes (meat); and shelf life and sensorial properties (film, coating, and packaging). This review is focused on providing a comprehensive overview of the tendencies in the application of ginger and its derivatives in the food industry and concurrently briefly discusses the beneficial aspects and processing of ginger.

The following study first examined how consumers are influenced by social media advertising and what role vloggers play in influencer marketing. Secondly, the factors considered by consumers while making a final decision for purchasing and traveling are explored, affected by the destination image for the country of origin. The aim of the research was fulfilled by reviewing relevant literature and defining and exploring the relationship among variables: vlog marketing, YouTube, Facebook, Instagram, Snapchat, E-WOMs, consumer travel intent, consumer purchase intent, destination image, and ease of travel. A self-administered survey questionnaire with a 5-point Likert scale was utilized and shared online among social media users for collecting data via two resources: personal connections and relevant social media platforms. A total of 428 valid responses were collected. The PLS-SEM software was used to analyze and interpret the data. The measurement model, structural model, regression analysis, and t-tests were used for testing and concluding results for hypotheses. The results concluded that vlog marketing, with all its higher-order constructs, except Snapchat, has a significant positive effect on consumer travel intent and purchase intent. Moreover, destination image and ease of travel enhance the relationship of vlog marketing and consumer travel intention but not that of vlog marketing and consumer purchase intention. The study contributes to the literature on influencer and content marketing on social media networks by emphasizing the role of vloggers in shaping consumer behavior and placing a distinct image of a destination place, and how ease of travel affects the decision to travel and buy travel goods and services advertised on vlogs.

The chance to start your own business is appealing when the labor market experiences a slump in the current crisis scenario. The factors that affect sustainable entrepreneurship were examined in this study. The factors contributing to an entrepreneur’s success, including successful intelligence, social skills, and entrepreneurial personality, were examined. In the causal relationship study, we used a questionnaire designed specifically for small and medium-sized business owners. We used multiple regression methods to analyze the data on a five-point Likert scale we collected. The findings demonstrate a strong positive relationship between entrepreneurial success, successful intelligence, and entrepreneurial personality. However, no connections between social skills and building a sustainable business have been made. The causes are rooted in culture. From the viewpoints of academics, practitioners, and policymakers, it has practical ramifications. Aspiring businesspeople can use the outcomes to guide their decisions. To better understand how personality factors affect the viability of recently created firms in emerging nations, this study is unique.

PurposeThis research explains the critical role of technological readiness and green dynamic capabilities in enhancing the sustainability performance of manufacturing firms, which is pivotal for achieving the United Nations’ Sustainable Development Goals. The theoretical framework is grounded in the dynamic capability theory, positing that technological readiness enhances a firm’s green dynamic capabilities, and employee green behavior moderates the effect on the sustainability performance of manufacturing firms.Design/methodology/approachQuantitative data from 1,660 managerial employees of a diverse sample of manufacturing firms was aggregated at the firm level using interclass correlation and interrater agreement, ensuring robustness using at least two responses per firm. With the final dataset of 418 firms, structural equation modeling was conducted using AMOS26.FindingsThe findings reveal that technological readiness positively affects sustainability performance and enhances it through green dynamic capabilities. Furthermore, the study highlights the positive moderating role of employees’ green behavior, amplifying the impact of green dynamic capabilities on sustainability performance.Originality/valueThis research makes a novel contribution to the body of knowledge by integrating dynamic capability theory with empirical evidence on sustainability performance. It represents a significant step toward promoting a more sustainable and responsible future for organizations and society and provides comprehensive insights into the complex interplay of these variables. These insights are crucial for academia, industry practitioners and policymakers striving to foster sustainable practices within the manufacturing sector.

Drawing motivation from the principles of Resource-Based View (RBV) and Stakeholder theory, this endeavor assessed the impact of the Project Manager’s (PM) competencies on Project Success (PS). The study further seeks to understand the mediating role of Functional Manager involvement (FM) and Stakeholder Engagement (SE) in the correlation between PM competencies and PS. Data were collected from 288 project team professionals operating in the healthcare sector in Pakistan. Partial Least Square – Structural Equation Modelling (PLS-SEM) was employed to validate both direct and mediating effects. The utilization of PLS-SEM for data analysis enhances the robustness of the study’s outcomes. The findings revealed a significant influence of PM competencies on PS. Furthermore, the results affirmed that functional manager involvement serves as a mediator in the relationship between PM competencies and PS; however, the mediating effect of stakeholder engagement remains insignificant. This research significantly contributes to the field of healthcare project management. The identification of PM competencies as a substantial predictor of PS underlines the importance of skilled project management practices to achieve project outcomes successfully. Moreover, the exposure of functional manager involvement as a mediating variable between PM competencies and PS offers valuable insights into the organizational dynamics influencing PS. While the mediating effect of stakeholder engagement remains insignificant, this finding opens the avenues for further exploration in this domain. Overall, the study contributes to both theoretical advancements in project management literature and practical implications for project management specialists in the healthcare sector in Pakistan.

The presence of bifascicular block on electrocardiography suggests that otherwise-unexplained syncope may be due to complete heart block. European Society of Cardiology (ESC) recommends investigating it with electrophysiology study (EPS). PPM is indicated if high-degree atrioventricular block is inducible. Long term rhythm monitoring with implantable loop recorder (ILR) is recommended if EPS is negative. We evaluated adherence to these guidelines. This is a single-center retrospective audit of adult patients with bifascicular block hospitalized for unexplained syncope between January 2018 and August 2019 under general medicine service. Patients with an alternative explanation for syncope were excluded. Guideline adherence was assessed by formal cardiology consult and whether EPS followed by ILR and/or PPM were offered. 65 out of 580 adult patients (11.2%) admitted to general medicine service for syncope had a bifascicular block; 29 (5%) were identified to have bifascicular block and unexplained syncope. Median age was 77 ±10 years; 9 (31%) were female, and 6 (20.7%) patients had at least one prior hospital visit for syncope at our academic medical center. Cardiology was consulted on 17 (58.6%) patients. Two patients were evaluated by EPS (1 refused) followed by ILR. Overall, 3 out of 29 patients (10.3%) received guideline-directed evaluation during the hospitalization based on ESC guidelines. None of the patients received empiric PPM during the index hospitalization. Among patients admitted to the general medicine service with unexplained syncope and bifascicular block, a minority (10.3%) underwent guideline-directed evaluation per ESC recommendations. Cardiology was consulted in 58.6% of cases.