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In light of the growing role of social media marketing in the success of businesses and its low adoption rate among small and medium enterprises (SMEs), this study aims to identify determinants of SMEs’ social media marketing adoption by considering the competitive industry as a moderator. Data were collected from 214 SMEs in Malaysia. Unlike extant literature, this study proposed a dual-stage analysis involving partial least squares (PLS) technique and artificial intelligence named deep artificial neural network (ANN). The application of deep ANN architecture is used to predict 91% of accuracy for the proposed model. The results showed that perceived relative advantage, perceived cost, top management support, perceived competitor pressure, and perceived vendor pressure have a significant impact on social media marketing adoption. Furthermore, the competitive industry moderates the effects of competitive pressure and customer pressure on social media marketing adoption. The results of the study extend the literature on social media marketing by illustrating the influence of technological, organizational, and environmental (TOE) factors on social media marketing adoption among SMEs concerning the extent of industry competition. The results of the study enable policymakers and managers of SMEs to understand the factors that influence social media marketing adoption in both competitive and non-competitive industries and invest effectively in digital marketing.

In recent years, the growth of cryptocurrency has undergone an enormous increase in cryptocurrency markets all around the world. Sadly, only insignificant heed has been paid to the unveiling of determinants of cryptocurrency adoption globally, particularly in emerging markets like Malaysia. The purpose of the study is to examine whether the application of deep learning-based dual-stage Partial Least Square-Structural Equation Modelling (PLS-SEM) & Artificial Neural Network (ANN) analysis enable better in-depth research results as compared to single-step PLS-SEM approach and to excavate factors which can predict behavioural intention to adopt cryptocurrency. The Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model were extended with the inclusion of trust and personnel innovativeness. The model was further validated by introducing a new path model compared to the original UTAUT2 model and the moderating role of personal innovativeness between performance expectancy and price value, with a sample of 314 respondents. Contrary to previous technology adoption studies that used PLS-SEM & ANN as single-stage analysis, this study further enhanced the analysis by applying a deep learning-based dual-stage PLS-SEM and ANN method. The application of deep learning-based dual-stage PLS-SEM & ANN analysis is a novel methodological approach, detecting both linear and non-linear associations among constructs. At the same time, it is regarded as a superior statistical approach as compared to traditional hybrid shallow SEM & ANN single-stage analysis. Also, sensitivity analysis provides normalised importance using multi-layer perceptron with the feed-forward-back-propagation algorithm. Furthermore, the deep learning-based dual-stage PLS-SEM & ANN revealed that trust proved to be the strongest predictor in driving user intention. The introduction of this new methodology and the theoretical contribution opens the vistas of the extant body of knowledge in technology-adoption related literature. This study also provides theoretical, practical and methodological contributions.

Enhancing biological nitrogen (N) fixation in cereal crops has been a long-sought objective. Recently, Yan et al. identified plant compounds that induce biofilm production of diazotrophic bacteria and then performed genetic engineering in order to improve nitrogen fixation in rice plants. These findings hold promise for sustainable agriculture.

Antibiotic resistance (AR) is a global phenomenon that has severe epidemiological ramifications world-wide. It has been suggested that antibiotics that have been discharged into the natural aquatic environments after usage or manufacture can promote the occurrence of antibiotic resistance genes (ARG). These environmental ARGs could serve as a reservoir and be horizontally transferred to human-associated bacteria and thus contribute to AR proliferation. The aim of this study was to investigate the anthropogenic load of antibiotics in Northern Pakistan and study the occurrence of ARGs in selected samples from this region. 19 sampling sites were selected; including six rivers, one dam, one canal, one sewage drain and four drug formulation facilities. Our results show that five of the rivers have antibiotic levels comparable to surface water measurements in unpolluted sites in Europe and the US. However, high levels of antibiotics could be detected in the downstream river in close vicinity of the 10 million city Lahore, 1100, 1700 and 2700 ng L(-1) for oxytetracycline, trimethoprim, and sulfamethoxazole respectively. Highest detected levels were at one of the drug formulation facilities, with the measured levels of 1100, 4100, 6200, 7300, 8000, 27,000, 28,000 and 49,000 ng L(-1) of erythromycin, lincomycin, ciprofloxacin, ofloxacin, levofloxacin, oxytetracycline, trimethoprim and sulfamethoxazole respectively. ARGs were also detected at the sites and the highest levels of ARGs detected, sulI and dfrA1, were directly associated with the antibiotics detected at the highest concentrations, sulfamethoxazole and trimethoprim. Highest levels of both antibiotics and ARGs were seen at a drug formulation facility, within an industrial estate with a low number of local residents and no hospitals in the vicinity, which indicates that the levels of ARGs at this site were associated with the environmental levels of antibiotics.

PurposeThe purpose of this study is to unearth the factors that influence tourists’ revisit intention. The proposed model of the study is grounded on using the theory of planned behaviour (TPB) and extending it with additional variables, i.e. satisfaction, destination image, perceived risk, service quality and perceived value.Design/methodology/approachThis study adopted a cross-sectional approach to collect data. The data were collected by conducting a field survey questionnaire on 330 respondents and were analysed using partial least squares version 3.2.9.FindingsThe results show that perceived behavioural control, perceived value, destination image and satisfaction significantly affect visitors’ revisit intention. The influence of perceived value, perceived service quality and destination image on satisfaction is also confirmed. On the other hand, satisfaction is found to be a significant mediator between perceived service quality, destination image and perceived value.Originality/valueThe extended TPB model that includes perceived service quality, perceived value, perceived risk and satisfaction provided a model with a theoretical basis to explain tourist revisit intentions to a tourist destination.

It has been widely known that oxidative stress disrupts the balance between reactive oxygen species (ROS) and the antioxidant system in the body. During pregnancy, the physiological generation of ROS is involved in a variety of developmental processes ranging from oocyte maturation to luteolysis and embryo implantation. While abnormal overproduction of ROS disrupts these processes resulting in reproductive failure. In addition, excessive oxidative stress impairs maternal and placental functions and eventually results in fetal loss, IUGR, and gestational diabetes mellitus. Although some oxidative stress is inevitable during pregnancy, a balancing act between oxidant and antioxidant production is necessary at different stages of the pregnancy. The review aims to highlight the importance of maintaining oxidative and antioxidant balance throughout pregnancy. Furthermore, we highlight the role of oxidative stress in pregnancy-related diseases.

The pathogenicity in Candida spp was attributed by several virulence factors such as production of tissue damaging extracellular enzymes, germ tube formation, hyphal morphogenesis and establishment of drug resistant biofilm. The objective of present study was to investigate the effects of silver nanoparticles (AgNPs) on growth, cell morphology and key virulence attributes of Candida species. AgNPs were synthesized by the using seed extract of (Sc), and were characterized by UV-Vis spectrophotometer, Fourier-transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), energy-dispersive X-ray (EDX), and transmission electron microscopy (TEM). ScAgNPs were used to evaluate their antifungal and antibacterial activity as well as their potent inhibitory effects on germ tube and biofilm formation and extracellular enzymes viz. phospholipases, proteinases, lipases and hemolysin secreted by spp. The MICs values of ScAgNPs were ranged from 0.125-0.250 mg/ml, whereas the MBCs and MFCs were 0.250 and 0.500 mg/ml, respectively. ScAgNPs significantly inhibit the production of phospholipases by 82.2, 75.7, 78.7, 62.5, and 65.8%; proteinases by 82.0, 72.0, 77.5, 67.0, and 83.7%; lipase by 69.4, 58.8, 60.0, 42.9, and 65.0%; and hemolysin by 62.8, 69.7, 67.2, 73.1, and 70.2% in , , , and , respectively, at 500 μg/ml. ScAgNPs inhibit germ tube formation in C. albicans up to 97.1% at 0.25 mg/ml. LIVE/DEAD staining results showed that ScAgNPs almost completely inhibit biofilm formation in C. albicans. TEM analysis shows that ScAgNPs not only anchored onto the cell surface but also penetrated and accumulated in the cytoplasm that causes severe damage to the cell wall and cytoplasmic membrane. To summarize, the biosynthesized ScAgNPs strongly suppressed the multiplication, germ tube and biofilm formation and most importantly secretion of hydrolytic enzymes (viz. phospholipases, proteinases, lipases and hemolysin) by Candia spp. The present research work open several avenues of further study, such as to explore the molecular mechanism of inhibition of germ tubes and biofilm formation and suppression of production of various hydrolytic enzymes by Candida spp.

The prevalence of metabolic-associated fatty liver disease (MAFLD) is increasing globally due to factors such as urbanization, obesity, poor nutrition, sedentary lifestyles, healthcare accessibility, diagnostic advancements, and genetic influences. Research on MAFLD and HCC risk factors, pathogenesis, and biomarkers has been conducted through a narrative review of relevant studies, with a focus on PubMed and Web of Science databases and exclusion criteria based on article availability and language. Steatosis marks the early stage of MASH advancement, commonly associated with factors of metabolic syndrome such as obesity and type 2 diabetes. Various mechanisms, including heightened lipolysis, hepatic lipogenesis, and consumption of high-calorie diets, contribute to the accumulation of lipids in the liver. Insulin resistance is pivotal in the development of steatosis, as it leads to the release of free fatty acids from adipose tissue. Natural compounds hold promise in regulating lipid metabolism and inflammation to combat these conditions. Liver fibrosis serves as a significant predictor of MASH progression and HCC development, underscoring the need to target fibrosis in treatment approaches. Risk factors for MASH-associated HCC encompass advanced liver fibrosis, older age, male gender, metabolic syndrome, genetic predispositions, and dietary habits, emphasizing the requirement for efficient surveillance and diagnostic measures. Considering these factors, it is important for further studies to determine the biochemical impact of these risk factors in order to establish targeted therapies that can prevent the development of HCC or reduce progression of MASH, indirectly decreasing the risk of HCC.

The increasing frequency of natural hazards, intensified by climate change, poses substantial challenges to sustainable development worldwide. Northern Pakistan, particularly the Hunza district, is highly susceptible to multiple hazards, including landslides, earthquakes, glacier-induced floods, debris flows, and Glacier Lake Outburst Floods (GLOFs), driven by both climatic and tectonic factors. A multi-hazard assessment is essential to understand the complex interactions between these hazards, offering a comprehensive perspective on risk and facilitating more effective disaster preparedness and mitigation strategies. This study addresses the existing gap in multi-hazard assessments, which are often confined to single-hazard evaluations, by developing an integrated multi-hazard susceptibility map for the Hunza district in Northern Pakistan. The region’s complex topography, active tectonics, and accelerated glacier melting contribute to its high vulnerability to cascading and co-occurring hazards. The integrated assessment utilizes diverse data sources, including topographic attributes, geological, hydro-meteorological, environmental variables, and literature-derived hazard map for multi-hazard susceptibility analysis. A Machine Learning (ML) Forest-Based Classification and Regression (FBCR) model, Analytical Hierarchy Process (AHP), and Vs30-based site characterization was employed to classify and generate hazards individually and as integrated multi-hazard susceptibility map. The model incorporates eighteen geo-environmental variables for individual hazards assessment. The resulting multi-hazard susceptibility map indicates that 23.11% of the area is prone to landslides, 6.07% to flash floods, 4.66% to debris flows and flash floods, and 3.98% to a combination of flash floods, landslides, and debris flows. The highest multi-hazard zone, comprising seismic hazard, debris flows, landslides, and flash floods, covers 2.88% of the area, whereas low-hazard zones constitute 56.84% of the region. The landslide susceptibility model classifies 20% of the area as very high susceptible, while the flash flood, debris flow, and seismic hazard models indicate 5, 2, and 13% of the area, respectively, fall under very high susceptibility/hazard. This integrated multi-hazard approach provides a comprehensive risk assessment framework, supporting evidence-based disaster risk reduction policies and infrastructure planning in hazard-prone regions. The findings identify critical high-hazard zones, offering data-driven insights for targeted mitigation strategies and disaster risk reduction efforts.

Purpose The halal food market is a large and fast-growing market. To maintain and boost the growth of the halal food industry, scholars have attempted to understand the behaviour of Muslims and non-Muslims towards halal food. To advance understating of previous studies on behaviour towards halal food and shedding light on future studies, this study aims to systematically review the literature. Design/methodology/approach A sample of 985 peer-reviewed papers was extracted from Scopus and Web of Science databases. A total of 96 articles related to customers' behaviour towards halal food by reviewing the titles, abstracts and contents of the extracted articles were identified and reviewed. Findings This study illustrates: (i) various research designs and methodology used in halal food context, (ii) theories that researchers used to explain customer behaviour towards halal food, (iii) most tested behaviours and (iv) determinants of customer behaviour towards halal food. Originality/value The findings provide deep insights into the current state of halal food literature. This paper highlights many gaps in the literature and suggests directions for future studies to advance the understanding of customer behaviour towards halal food. This study will help researchers to identify the new dimensions of research and contribute to the literature.