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Understanding the dynamics of Fractional Vegetation Cover (FVC) is crucial for effective environmental monitoring and management, especially in regions like Pakistan that are sensitive to climate change. This study employs an innovative approach using MODIS NDVI data and the Pixel Dichotomy Model (PDM) to analyze the spatiotemporal dynamics of FVC across Pakistan from 2003 to 2020. Our findings reveal an overall increasing trend in FVC, with the highest value recorded in 2017 (0.37) and the lowest in 2004 (0.26). The Hurst exponent analysis (R/S ratio = 0.718) indicates a degree of long-term memory in the FVC time series. Rainfall was found to positively correlate with FVC (r = 0.6), while Land Surface Temperature (LST) and the Compounded Night Light Index (CNLI) exhibited negative correlations (r = −0.59 and r = −0.43, respectively). The Random Forest regression model highlighted CNLI as the most influential predictor (importance = 62.4%), emphasizing the need to consider human-induced factors in environmental management. These results provide critical insights for sustainable land management and contribute to understanding vegetation-climate interactions in arid and semi-arid environments.''

The Sub-tropical forests of Pinus roxburghii (chir pine) provide various ecosystem services and act as watershed for low lying regions. However, this species is prone to human induced fire primarily due to local communities' dependence for various resources exacerbated by the current dry conditions. The impact of fire across various vegetation layers and developmental stages has not been thoroughly studied. Bearing to this, the present study was conducted using composite burn index to assess the severity on various layers of vegetation and their long-term impact through a chronological approach. The impact of fire on 40 representative circular plots with a radius of 30 m, categorized into five forest strata: large and intermediate trees, seedlings/saplings, pole stage, shrubs, and soil were investigated and compared across four different time interval: unburnt (B0), burnt two years ago (B2), burnt five years ago (B5), and burnt 15 years ago (B15). The results were statistically proved using Kruskal–Wallis followed by Dunn's Post Hoc and Friedman test with the Holm correction in R Language. The study revealed significant variations in the average burn severity for each treatment, with shrubs having the highest average score of burn severity (average = 1.4) and soil showing the lowest (average = 0.408). The results of the Friedman test indicated non-uniform distribution of burn severity across different ecological treatments. This study is contributing significant insights into the effects of forest fires and their severity on different vegetation layers, which can be instrumental in devising and executing successful restoration strategies.

Elevation gradients significantly influence net primary productivity (NPP), but the relationship between elevation, climate variables, and vegetation productivity remains underexplored, particularly in diverse ecological zones. This study quantifies the impact of elevation and climatic variables on NPP in northern Pakistan, hypothesizing that elevation modulates NPP through its influence on temperature and precipitation patterns. Using remote sensing data (MODIS ERA5) and advanced ecological models like the Eddy Covariance-Light Use Efficiency (EC-LUE) model and the Thornthwaite Memorial Model (TMM), we analyzed Gross Primary Productivity (GPP) dynamics across various vegetation types and elevations from 2001 to 2023. Our findings show a mean annual NPP of 323.46 g C m-2 a-1, with an annual increase of 5.73 g C m-2 a-1. Significant elevation-dependent variations were observed, especially in mid-elevation zones (401 to 1600 meters), where NPP increased at a rate of 0.174 g C m-2 a-1 per meter (R² = 0.808, p < 0.01). In contrast, higher elevations (2800-5200 meters) exhibited a decline in NPP, decreasing by -0.171 g C m-2 a-1 per meter (R² = 0.905, p < 0.001). Temperature and precipitation were key drivers, with precipitation positively correlating with NPP across all vegetation types, particularly in Evergreen Needleleaf and Broadleaf Trees. The EC-LUE model's GPP estimates closely matched MODIS data (R² = 0.82), demonstrating the model's reliability. These findings highlight the critical role of elevation and climatic factors in vegetation productivity and underscore the need for targeted ecological management and conservation strategies. The insights from this research are vital for global climate adaptation policies and sustainable development goals, contributing to ecological resilience and carbon sequestration efforts worldwide.

Phenol resins (PRs) are considered as relatively inexpensive adsorbents synthesized from agricultural biomass via employing a variety of synthesized procedures. The performance of PR for heat transformation application is not widely investigated. In this regard, the present study aims to evaluate the four PR derivative/refrigerant pairs, namely (i) KOH6-PR/CO2, (ii) SAC-2/HFC, (iii) KOH4-PR/ethanol, and (iv) KOH6-PR/ethanol, for adsorption cooling and adsorption heating applications. Ideal cycle analyses and/or thermodynamic modelling approaches were utilized comprising governing heat and mass balance equations and adsorption equilibrium models. The performance of the AHP system is explored by means of specific cooling energy (SCE), specific heating energy (SHE), and coefficient of performance (COP), both for cooling and heating applications, respectively. It has been realized that KOH6-PR/ethanol could produce a maximum SCE of 1080 kJ/kg/cycle and SHE of 2141 kJ/kg/cycle at a regeneration temperature (Treg) and condenser temperature (Tcond) of 80 °C, and 10 °C, respectively, followed by KOH4-PR/ethanol, SAC-2/HFC-32, and KOH6-PR/CO2. The maximum COP values were estimated to be 1.78 for heating and 0.80 for cooling applications, respectively, at Treg = 80 °C and Tcond = 10 °C. In addition, the study reveals that, corresponding to increase/decrease in condenser/evaporator pressure, both SCE and SHE decrease/increase, respectively; however, this varies in magnitude due to adsorption equilibrium of the studied PR derivative/refrigerant pairs.

OBJECTIVE: To evaluate general dentists' perceptions, knowledge, and attitudes regarding the provision of dental services to patients with special health care needs (SHCN). METHODS: This cross-sectional descriptive study was conducted at the University Dental Hospital, University of Lahore, Pakistan, from May 2022 to October 2022, using non-probability purposive sampling. The study included house officers, postgraduate residents, and general dental practitioners aged up to 30 years. Self-administered questionnaires were distributed to 220 participants, gathering demographic data and assessing their knowledge, perceptions, and attitudes toward SHCN patients. Data analysis was performed using SPSS version 25. RESULTS: Out of 220 dental professionals, 85 (38.6%) were male and 135 (61.3%) were female. House officers comprised the majority (n=131, 59.5%), followed by postgraduate trainees (n=47, 21.4%) and general dental practitioners (n=42, 19.1%). The mean age of participants was 24.93±2.45 years. A significant portion (n=171; 77.7%) exhibited limited knowledge about SHCN patients, with only 2.7% (n=6) being well-informed. Frequent interactions with SHCN patients were reported by 23.6% (n=52), and 55.5% (n=122) believed there was insufficient experience among dental practitioners in treating these patients. CONCLUSION: The study highlighted a significant knowledge gap and perceived challenges in treating SHCN patients among general dentists. Addressing these issues requires reforms in dental education, including enhanced training and curriculum modifications to improve exposure to and management of SHCN patients.

This study explores a novel, eco-friendly approach to silver nanoparticle (AgNP) synthesis by using rose petal (RP) extracts. Unlike traditional chemical synthesis, this method reduces silver ions (Ag+) to elemental silver (Ag0) with RP, facilitating nanoparticle stabilization. RP-AgNPs were structurally characterized using UV/Vis (ultraviolet/visible) spectroscopy, Fourier transform infrared spectroscopy, dynamic light scattering, zeta potential, transmission electron microscopy, scanning electron microscopy, and energy dispersive X-ray spectroscopy which confirmed the synthesis of predominantly spherical to oval RP-AgNPs, with a distinct absorption peak at 410 nm as observed in UV/Vis spectra. The NPs demonstrated exceptional antibacterial efficacy against Escherichia coli and Staphylococcus aureus (inhibitory zones of 12.5 ± 0.20 and 14 ± 0.67 mm at 50 µg) and potent antibiofilm activity. Crucially, they also exhibited selective cytotoxicity toward HeLa cervical cancer cells when assessed by viability assays, fluorescence imaging, and flow cytometry. These findings underscore the potential of green synthesized AgNPs for future biomedical applications in areas such as infection control and cancer treatment.

Innovative approaches such as photodynamic therapy (PDT) and photothermal therapy (PTT) have made nanomedicines a promising frontier in cancer theranostics. The combination of nanocarriers with photothermal agents and photosensitizers (PSs) has shown excellent promise for the diagnosis and the treatment of cancer, primarily at the cellular, vascular, and tumor microenvironment level. Using nanocarriers in PDT has revolutionized precision and efficacy, allowing the drug to reach cancer cells faster and offering high enhancing PS accumulation. These agents are activated by light of specific wavelengths, leading to localized cytotoxicity, offering highly selective cancer therapy. Nanomaterials such as gold and silver nanoparticles have enabled remarkable progress in cancer hyperthermia using PTT. The unique optical properties of these nanoparticle-based nanomedicines make them ideal candidates for converting light energy into heat, selectively ablating the cancer cells. In this review, nanomedicine-integrated phototherapeutic agents are discussed and the most important recent developments in PDT and PTT are examined, as well as how nanoparticle-based formulations improve diagnosis and treatment. In addition, nanocarriers used in cancer phototherapy and their mode of action are discussed. Nanocarriers are useful for drug delivery as well as for imaging and diagnostic purposes during cancer treatment. In this review, we explore the role of nanoparticles in improving phototherapy precision and selectivity while minimizing collateral tissue damage. It specifies a comprehensive impression of the current research on cancer therapy, underscoring its potential to revolutionize the treatment paradigm by highlighting the current state of research.

Abstract This study assesses the relationships between vegetation dynamics and climatic variations in Pakistan from 2000 to 2023. Employing high-resolution Landsat data for Normalized Difference Vegetation Index (NDVI) assessments, integrated with climate variables from CHIRPS and ERA5 datasets, our approach leverages Google Earth Engine (GEE) for efficient processing. It combines statistical methodologies, including linear regression, Mann–Kendall trend tests, Sen's slope estimator, partial correlation, and cross wavelet transform analyses. The findings highlight significant spatial and temporal variations in NDVI, with an annual increase averaging 0.00197 per year (p

The increasing pressures of urban development and agricultural expansion have significant implications for land use and land cover (LULC) dynamics, particularly in ecologically sensitive regions like the Murree and Kotli Sattian tehsils of the Rawalpindi district in Pakistan. This study's primary objective is to assess spatial variations within each LULC category over three decades (1992–2023) using cross-tabulation in ArcGIS to identify changes in LULC and investigates into forest fragmentation analysis using the Landscape Fragmentation Tool (LFTv2.0) to classify forest into several classes such as patch, edge, perforated, small core, medium core, and large core. Utilizing remote sensing data from Landsat 5 and Landsat 9 satellites, the research focuses on the temporal dynamics in various land classes including Coniferous Forest (CF), Evergreen Forest (EF), Arable Land (AR), Buildup Area (BU), Barren Land (BA), Water (WA), and Grassland (GL). The Support Vector Machine (SVM) classifier and ArcGIS software were employed for image processing and classification, ensuring accuracy in categorizing different land types. Our results indicate a notable reduction in forested areas, with Coniferous Forest (CF) decreasing from 363.9 km2, constituting 45.0 % of the area in 1992, to 291.5 km2 (36.0 %) in 2023, representing a total decrease of 72.4 km2. Similarly, Evergreen Forests have also seen a significant reduction, from 177.9 km2 (22.0 %) in 1992 to 99.8 km2 (12.3 %) in 2023, a decrease of 78.1 km2. The study investigates into forest fragmentation analysis using the Landscape Fragmentation Tool (LFTv2.0), revealing an increase in fragmentation and a decrease in large core forests from 20.3 % of the total area in 1992 to 7.2 % in 2023. Additionally, the patch forest area increased from 2.4 % in 1992 to 5.9 % in 2023, indicating significant fragmentation. Transition matrices and a Sankey diagram illustrate the transitions between different LULC classes, providing a comprehensive view of the dynamics of land-use changes and their implications for ecosystem services. These findings highlight the critical need for robust conservation strategies and effective land management practices. The study contributes to the understanding of LULC dynamics and forest fragmentation in the Himalayan region of Pakistan, offering insights essential for future land management and policymaking in the face of rapid environmental changes.

Objective: Social media usage has been significantly increased in the twenty-first century. Social media causes different health-related issues and academic decline among students. This study aims to assess the level of social media use and depression and to observe the association between social media use, depression and demographic variables among nursing students. Methodology: A cross-sectional comparative study was performed on 120 nursing students of Sohail University, Karachi. Ethical permission was obtained from the Ethical Review Committee, at Sohail University. Descriptive statistics was performed on demographic variables. Chi-Square was performed to check the association between social media use, depression and demographic variables. Results: The majority of the nursing undergraduates (55.0%) were using social media from 0-4 hours. Most (44.2%) participants have moderate to extreme levels of depression. We found age, marital status, study program and years of study significantly associated with social media use. The association of age, study program and years of study with depression was found significant. Conclusion: The majority of participants use social media from 0-4 hours and mostly have moderate to extreme levels of depression. Age, study program, and year of study were found to be associated with depression and social media use. Educational programs need to be designed and implemented to reduce the use of social media and the level of depression and to increase healthy lifestyle behavior among nursing students.