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Streptococcus pneumoniae (S. pneumoniae), which is a Gram-positive diplococcus, has emerged as a significant human pathogen. It is a primary cause of bacterial pneumonia, otitis media, meningitis, and septicemia, leading to a considerable impact on global morbidity and mortality. The investigation of S. pneumoniae and its virulence factors has resulted in the identification of surface endonuclease A (EndA). EndA functions in DNA uptake during natural transformation and plays a significant role in gene transfer. The ability of S. pneumoniae to degrade neutrophil extracellular traps (NETs) enhances its virulence and invasive potential in pneumococcal infections. NETosis occurs when neutrophils release chromatin into the extracellular space to form NETs, capturing and neutralizing pathogens. Currently, NETosis can be induced by several microbes, particulate matter, and sterile stimuli through distinct cellular mechanisms, and this includes the involvement of EndA in S. pneumoniae. Here, we reviewed the cellular functions of EndA, its role in S. pneumoniae as a virulence factor in relation to NETosis, its relationship to immunogenicity, and its involvement in several diseases. The discovery of this relationship would significantly impact therapeutic technology in reducing disease burden, especially pneumococcal infections.

Pakistan has significant geothermal potential but due to poor energy management and low utilization of rich geothermal energy, the country is ranked among the energy deficient nations in the world. In this study, integrated geophysical techniques were used to assess the geothermal energy potential of various hot springs in the Sindh province. The study revealed that Naing Shareef and Gaji Shah hot springs have remarkable potential for geothermal energy having pool water temperature of 43 °C and 45 °C, respectively. Geophysical resistivity survey was carried out at both Naing Shareef and Gaji Shah hot spring to determine depth, thickness and quality of thermal water. At Naing Shareef hot spring, only a single thermal aquifer zone has been pinpointed within the shale composition of the Oligocene Nari Formation, and it is on average 55.07 m depth and 29.8 m thick. At an average depth of 100.66 m and an aquifer thickness of 77.03 m, the Gaji Shah hot springs exclusively possess a thermal aquifer region established within the sandstone composition of the Oligocene Nari Formation.

Abstract Limiting cellular plasticity is of key importance for the therapeutic targeting of metastatic breast cancer (MBC). Fibroblast growth receptor (FGFR) is a critical molecule in cellular plasticity and potent inhibitors of FGFR enzymatic activity have been developed, but kinase independent functions for this receptor also contribute to MBC progression. Herein, we evaluated several FGFR inhibitors and find that while FGFR-targeted kinase inhibitors are effective at blocking ligand-induced cell growth, dormant cells persist eventually giving rise to MBC progression. To more broadly target FGFR and cellular plasticity, we examined the FGFR1 proximal promoter, and found several sequences with potential to form G-quadruplex secondary structures. Circular dichroism was used to verify formation of G-quadruplex in the FGFR1 proximal promoter. Importantly, use of the clinical G-quadruplex-stabilizing compound, CX-5461, stabilized the FGFR1 G-quadruplex structures, blocked the transcriptional activity of the FGFR1 proximal promoter, decreased FGFR1 expression, and resulted in potent inhibition of pulmonary tumor formation. Overall, our findings suggest G-quadruplex-targeted compounds could be a potential therapeutic strategy to limit the cellular plasticity of FGFR1 overexpressing MBC.

This systematic review explores the impact of artificial intelligence (AI) on the development of computational thinking and self-efficacy within educational environments. Computational thinking, a foundational skill in problem-solving, is increasingly recognized as essential for navigating a technology-driven world. AI tools and methodologies have been integrated into educational practices to enhance learning experiences and outcomes. This review synthesizes current literature to examine how AI technologies contribute to the cultivation of computational thinking skills among students of various ages and educational levels. Furthermore, it investigates the role of AI in bolstering students' self-efficacy confidence in one's ability to achieve goals in learning computational concepts. By analyzing empirical studies and educational interventions, this review identifies key mechanisms through which AI supports the development of these critical skills. Insights from this synthesis underscore the potential of AI to transform educational paradigms, providing opportunities for personalized learning, adaptive feedback, and collaborative problem-solving. The findings highlight implications for educators, policymakers, and researchers aiming to leverage AI effectively in educational settings to foster computational thinking and enhance students' self-efficacy, thereby preparing them for future challenges in a digital age.

Abstract The main objective of this study is to provide a comprehensive and inclusive review of current research on sustainable entrepreneurship (SE). One of the aims of this study is to navigate the understanding of SE practices by investigating the prospects and trends in research on sustainable entrepreneurship. It seeks to present an efficient portrayal of the latest research on sustainable entrepreneurship, serving as a valuable reference for scholars, practitioners, and policymakers. By applying the PRISMA approach, this study is based on a systematic literature review (SLR), and a substantial pool of 363 Scopus results and 291 WOS results was obtained using the keyword “sustainable entrepreneurship,” yielding a total of 654 articles that may be deemed possibly relevant. After the methodological screening, 234 mainly chosen studies using databases, including Scopus and WOS. This SLR utilizes thorough descriptive, Network, and thematic analysis, research tendencies, and methodological approaches. The findings of this study discussed the possible research gaps and future trends, suggesting the significant need for a stable framework for SE concepts. Empirical and exploratory studies are linked with social, economic, and technological scope, specifically sustainability, and are related to artificial intelligence (AI) studies. It contributes to laying the groundwork for future investigations into the critical connection between entrepreneurship and sustainability. Furthermore, given the continuously changing landscape of sustainable business, certain conclusions may be outdated.

Abstract Background Spontaneous pneumothorax (PTX) is more prevalent among COVID-19 patients than other critically ill patients, but studies on this are limited. This study compared clinical characteristics and in-hospital outcomes among COVID-19 patients with concomitant PTX to provide insight into how PTX affects health care utilization and complications, which informs clinical decisions and healthcare resource allocation. Methods The 2020 Nationwide Inpatient Sample was used analyze patient demographics and outcomes, including age, race, sex, insurance status, median income, length of hospital stay, mortality rate, hospitalization costs, comorbidities, mechanical ventilation, and vasopressor support. Propensity score matching was employed for additional analysis. Results Among 1,572,815 COVID-19 patients, 1.41% had PTX. These patients incurred significantly higher hospitalization costs ($435,508 vs. $96,668, p

Abstract Background Co-morbidity with respiratory viruses including influenza A, cause varying degree of morbidity especially in TB patients compared to general population. This study estimates the risk factors associated with influenza A (H1N1)pdm09 in TB patients with ILI. Methods A cohort of tuberculosis (TB) patients who were admitted to and enrolled in a TB Directly Observed Therapy Program (DOTs) in tertiary care hospitals of Lahore (Mayo Hospital and Infectious Disease Hospital) were followed for 12 weeks. At the start of study period, to record influenza-like illness (ILI), a symptom card was provided to all the participants. Every participant was contacted once a week, in person. When the symptoms were reported by the participant, a throat swab was taken for the detection of influenza A (H1N1)pdm09. A nested case control study was conducted and TB patients with ILI diagnosed with influenza A (H1N1)pdm09 by conventional RT-PCR were selected as cases, while those who tested negative by conventional RT-PCR were enrolled as controls. All cases and controls in the study were interviewed face-to-face in the local language. Epidemiological data about potential risk factors were collected on a predesigned questionnaire. Logistic analysis was conducted to identify associated risk factors in TB patients with ILI. Results From the main cohort of TB patients (n = 152) who were followed during the study period, 59 (39%) developed ILI symptoms; of them, 39 tested positive for influenza A (H1N1)pdm09, while 20 were detected negative for influenza A (H1N1)pdm09. In univariable analysis, four factors were identified as risk factors (p

This paper presents a critical analysis of the imagery used in William Wordsworth's poem My Heart Leaps Up, examining its role in the context of Ecocriticism. By offering a close reading of the poem and drawing on various eco-critical perspectives, the researcher explores the relationship between the poem's imagery and concepts such as biocentrism, anthropocentrism, eco-feminism, and deep ecology. Additionally, the researcher compares My Heart Leaps Up with other nature poems by Wordsworth and other Romantic poets, which helps us to identify particular stylistic and thematic features of the poem. This study aims to demonstrate how the use of imagery in My Heart Leaps Up connects to contemporary discourses on climate change and the anthropogenic epoch. This paper highlights the significance of My Heart Leaps Up within the broader context of Wordsworth's poetry and offers new theoretical and argumentative extensions to the reading of his nature poetry. Overall, this research contributes to our understanding of how language and imagery can be used to convey the beauty and importance of the natural world and our role in it.

This research introduces an innovative methodology for the integrated modeling, simulation, and analysis of two fingers, with particular emphasis on their fundamental roles in everyday tasks. In this study, we provide two nonlinear control strategies, specifically Sliding Mode Control (SMC) and Feedback Linearization Control (FLC), to achieve accurate and stable finger movements. As mentioned earlier, the controllers are utilized in the context of a biomechanical model consisting of two fingers, each possessing two degrees of freedom. These controllers enable the coordination of flexion and extension movements. The research conducted in our study emphasizes the coordinated regulation of finger movements, enabling the achievement of flexion through the utilization of two nonlinear controllers. By implementing these sophisticated control mechanisms, we can effectively showcase our model’s fidelity in adhering to the physiological limitations inherent to human fingers in their natural state. In addition, the proposed controllers demonstrate sound mitigation of non-linearities, such as load variations, different velocities, positional changes, and damping forces. This approach presents several advantages, such as handling non-linearities, guaranteeing robustness, choosing suitable parameters, and conducting comparative analysis. In order to substantiate our findings, we develop the nonlinear model utilizing the MATLAB/Simulink software. The findings of our study demonstrate effective regulation and control of the two-finger model’s position. In our study, we were able to get flexion angles of θ 1 = 1 . 221 rad and θ 2 = 1 . 396 rad using the sliding mode control (SMC) technique, and flexion angles of θ 1 = 1 . 047 rad and θ 2 = 1 . 134 rad using the fuzzy logic control (FLC) technique, all within a time frame of 5 s. These results serve to illustrate the applicability and significance of our proposed methodology.

Bone fractures are a critical medical condition requiring a thorough and accurate diagnosis to ensure proper treatment and healing. The physical examination of X-ray images by radiologists is time-consuming and subject to human error, indicating the need for an automated solution. This study proposes an advanced deep-learning approach to detect and classify bone fractures from X-ray images. This research aims to improve detection accuracy and streamline the diagnostic process using cutting-edge computer vision methods. Our approach starts with preprocessing the dataset, encompassing image resizing to 50x50 pixels, enhancement, and augmentation to improve diversity. Canny Edge detection is used to emphasize structural edges in the augmented dataset. The dataset is classified into training, testing, and validation subsets. A Convolutional Neural Network (CNN) automatically extracts deep learning features, obtaining complex patterns that show bone fractures. The Gray Level Co-occurrence Matrix (G.L.C.M) is utilized for texture features. These features are arranged to form a comprehensive feature set. The principal component analysis (PCA) is applied to this fused feature set to shrink dimensionality while preserving critical information, which is then used for fracture prediction. Experiments were conducted using a dataset of 14,718 X-ray images covering various bone fractures. The proposed method achieved an incredible classification accuracy of 98%, significantly outperforming traditional diagnostic methods and other contemporary models.

The study aimed to quantify carcass fat and protein retention, and the efficiency of carcass energy utilization (Kre) resulting from feeding broiler chickens diets containing wheat, maize or mixtures of both as the major cereal ingredient. The apparent metabolizable energy (AME) of the four cereal samples was determined in adult cockerels. There was a linear (p < 0.001) increase in AME with increasing amounts of maize within the four cereal mixtures, with analyses indicating that the AME of maize was 1.4 MJ/kg greater than that of wheat. A second bioassay with growing chickens was used to determine Kre in each cereal, measured as carcass fat and protein from 7 to 21d age. Increasing proportions of maize resulted in linear increases in carcass fat and energy retained from fat (p < 0.001). However, the carcass protein and energy retained from protein did not follow the same pattern as fat (p = 0.121), but rather decreased numerically (L = 0.032). The Kre tended (p = 0.060) to increase with greater proportion of maize in a linear fashion (L = 0.009). Although AME values of cereals were confirmed to be additive, this could not be confirmed for Kre. This data can be used for optimizing energy utilization models for growing broilers.

Background: Social media has become an influential tool with its usage increasing immensely in recent years. This study aimed to assess the impact of social media on the perception of dentofacial aesthetics and its gender-wise comparison among general population of Pakistan. Methods: This cross-sectional study was conducted from November 2022 to March 2023 using an online questionnaire developed on Google Forms, distributed among 500 participants via WhatsApp and Facebook groups among males and females with 18 years or above operating any social media account, using non-probability convenience sampling. SPSS version 25 was used, and categorical variables were compared using Chi-Square Test. p< 0.05 was significant. Results: Out of a total of 455 people who responded, 125 (27.5%) were male, and 330 (72.5%) were female. Females were very frequent (n=244, 53.6%) users of social media (p=0.029), spending 4 to 6 hours every day (p=0.01). There was a significant gender disparity in social media usage, with females predominantly using Instagram (n=308, 93.3%) (p

Bacteriophages are viruses that infect bacterial cells and use their machinery to reproduce. This unique characteristic holds immense promise for combating antibiotic-resistant bacterial infections, a growing global threat. There are two types: one of them is named temperate phages, which inject their genomic material into bacteria and integrate into the host’s genome, while the second one is entitled as lytic phages that subdue the entire metabolism of the bacterium for the synthesis of its genome and proteins, including lytic proteins involved in breaking bacterial cell membrane and release of novel phages. In addition, phage therapy can be expressed through anti-biofilm activity and by triggering innate and adaptive immune cells responses. Moreover, no adverse effects of phage therapy have been reported. However, phage therapy is still grim for many and could influence some interpretations related to immune response, bacteriophage selections, and phage resistance in the future.

Abstract The emergence of plasmid-mediated tigecycline resistance gene tet(X4) among clinically relevant bacteria has promoted significant concerns, as tigecycline is considered a last-resort drug against serious infections caused by multidrug-resistant bacteria. We herein focused on the isolation and molecular characterization of tet(X4)-positive Klebsiella pneumoniae (K. pneumoniae) and Escherichia coli (E. coli) in wild bird populations with anthropogenic interaction in Faisalabad, Pakistan. A total of 150 birds including black kites (Milvus migrans) and house crows (Corvus splendens) were screened for the presence of tigecycline resistance K. pneumoniae and E. coli. We found two K. pneumoniae and one E. coli isolate carrying tet(X4) originating from black kites. A combination of short- and long-read sequencing strategies showed that tet(X4) was located on a broad host range IncFII plasmid family in K. pneumoniae isolates whereas on an IncFII-IncFIB hybrid plasmid in E. coli. We also found an integrative and conjugative element ICEKp2 in K. pneumoniae isolate KP8336. We demonstrate the first description of tet(X4) gene in the WHO critical-priority pathogen K. pneumoniae among wild birds. The convergence of tet(X4) and virulence associated ICEKp2 in a wild bird with known anthropogenic contact should be further investigated to evaluate the potential epidemiological implications. The potential risk of global transmission of tet(X4)-positive K. pneumoniae and E. coli warrant comprehensive evaluation and emphasizes the need for effective mitigation strategies to reduce anthropogenic-driven dissemination of AMR in the environment.

Abstract Background Ocular inflammatory diseases, including scleritis and uveitis, have been widely treated with immunomodulatory therapies (IMTs) as a steroid-sparing approach. Such strategy includes conventional therapies (antimetabolites, alkylating agents, and calcineurin inhibitors) as well as biologic agents like adalimumab, infliximab, rituximab, and tocilizumab. Cyclophosphamide (CP) is an alkylating agent and mainly inhibits the functioning of both T and B cells. Though known to have potential adverse events, including bone marrow suppression, hemorrhagic cystitis, and sterility, CP has been shown to be efficacious, especially in recalcitrant cases and when used intravenous (IV) for a limited period. Main findings We conducted a retrospective case-series to assess the safety and efficacy of CP therapy for patients with severe ocular inflammatory diseases who failed other IMTs. Medical records of 1295 patients who presented to the Uveitis Clinic at the Byers Eye Institute at Stanford between 2017 and 2022 were reviewed. Seven patients (10 eyes) who received CP therapy for ocular inflammatory diseases with at least one year of follow-up were included. The mean age of the patients (4 males, 3 females) was 61.6 ± 14.9 (43.0–89.0) years. Clinical diagnoses included necrotizing scleritis (5 eyes), peripheral ulcerative keratitis (2 eyes), orbital pseudotumor (1 eye), HLA-B27 associated panuveitis and retinal vasculitis (2 eyes). Ocular disease was idiopathic in 3 patients, and was associated with rheumatoid arthritis, IgG-4 sclerosing disease, dermatomyositis, and ankylosing spondylitis in 1 patient each. All the patients had history of previous IMT use including methotrexate (5), mycophenolate mofetil (3), azathioprine (1), tacrolimus (1), adalimumab (2), infliximab (4), and rituximab (1). The mean follow-up time was 34.4 ± 11.0 (13–45) months, and mean duration of CP therapy was 11.9 ± 8.8 (5–28) months. Remission was achieved in 5 patients (71.4%). Four patients (57.1%) experienced transient leukopenia (white blood cell count

In this research, a novel explainable multi-level ensemble learning framework has been developed to accurately monitor the greenhouse gas (GHG) emission drivers of the Atlantic Canada's potato crop system i.e., Carbon dioxide (CO2), nitrous oxide (N2O), and water vapour (H2O). For this purpose, alongside the GHG emission drivers, the hydro-meteorological and soil properties information was collected from three Canadian sites, two in Prince Edward Island (PEI) and one in New Brunswick. This advanced framework includes a transparent multi-level pre-processing module and a Runge-Kutta optimizer (RUN), integrated with an eXplainable gradient-boosted decision Tree (GBDT) machine learning (ML) technique. The preprocessing scheme meticulously selects the most effective input combinations from the hydro-meteorological and soil properties datasets using hybridization of Boruta-GBDT for feature selection, Best Subset Lasso Regression (BSLR), and Weighted Aggregated Sum Product Assessment (WASPAS). The optimal combinations were then analyzed using the GBDT-RUN and compared against two algorithms: LightGBM coupled with RUN optimizer (LightGBM-RUN) and classical GBDT. The explainability of the primary model was enhanced using SHapley Additive exPlanations (SHAP). Model validation employed various metrics, such as the correlation coefficient (R), squared deviation (SquD), and a range of sophisticated statistical graphics. Results demonstrated that the GBDT-RUN model exhibited superior performance in monitoring GHG emissions (CO2|R = 0.8431, SquD=17.1759, WASPAS=1.88E-07; N2O|R = 0.8431, SquD=17.1759, WASPAS=1.88E-07; H2O| R = 0.8431, SquD=17.1759, WASPAS=1.88E-07), outperforming both LightGBM-RUN and classical GBDT. Furthermore, the explainability analysis identified dew point and soil temperature as the most influential factors in the CO2, N2O, and H2O emissions scenarios.

The enzyme α-glucosidase in the small intestine regulates blood glucose levels and stimulates the hydrolysis of oligosaccharides and polysaccharides, increasing glucose levels in the body. Inhibiting this enzyme slows glucose digestion and absorption and as a result post-prandial blood glucose levels remain low, causing decreased insulin demand. Here, we investigated the ayurvedic antidiabetic plants and virtually screened an in-house library of 478 phytochemicals of these plants against the human α-glucosidase. We identified 11 secondary metabolites, including palmitic acid α-monoglyceride, (+)-(2 R)-6-propionyloxyethyl-4′,5,7-trihydroxyisoflavanone, Abruquinone E, and Aurantiamide Acetate, among others, showed stronger interactions with the receptor than the native ligand N-acetyl cysteine. Surprisingly, except one, all of these metabolites were from Abrus precatorius L. [Fabaceae] affirming its ethnopharmacological use against diabetes. The stability of the interactions between the ligands and receptor protein was evaluated through Molecular Dynamic (MD) simulation trajectories including root mean square deviation (RMSD), root mean square fluctuation (RMSF), radius of gyration (Rg), H bonds, β-factor analysis, and binding energy calculation through MM/GBSA method. The efficacy of top metabolites in inhibiting α-glucosidase is depicted in pharmacophore analysis. A comprehensive pharmacokinetics analysis confirmed the druggability, safety, and efficiency of top drug candidates. Additionally, we predicted the interactions of these top metabolites within the biological system. The medicinal properties described in this study will help develop active drug candidates for therapeutic purposes. Further experiments are recommended to prove the effectiveness of these metabolites in inhibiting the α-glucosidase enzyme for exploring their potential in the treatment of diabetes.

In this study, three novel machine learning algorithms of additive regression-random forest (AR-RF), Iterative Classifier Optimizer (ICO-AR-RF), and multi-scheme (MS-RF) were explored for carbon dioxide (CO2) flux rate prediction from three agricultural fields. To build the dataset, 401 samples were collected from two fields in Prince Edward Island (PEI) and 122 samples from the New Brunswick (NB), Canada. In addition, soil moisture (SM), temperature (ST), and electrical conductivity (EC), alongside eight climatic variables including wind speed (WS), solar radiation (SR), relative humidity (RH), precipitation (PCP), air temperature (AT), dew point (DP), vapour pressure difference (VPD) and reference evapotranspiration (ETo) were also collected. Greedy stepwise (GS) approach was implemented for feature selection. Finally, different qualitative (scatter plot, line graph, Taylor diagram, box plot, and Rug plot), and quantitative (uncertainty analysis, root mean square error (RMSE), percent of BIAS (PBIAS), Nash Sutcliff efficiency (NSE) and RMSE-observations standard deviation ratio (RSR)) techniques were used for model evaluation and comparison. Results of feature selection approaches revealed that DP, AT, SM, and ST are the four most effective variables at CO2 prediction in PEI, while AT, RH, DP, and ST are the most effective in the NB study area. For optimum input scenario, the GS algorithm was applied, and results showed that a combination of DP, AT, ST, SM, and ETo was the best for the PEI study area, while for NB, all input variables should be involved. Our analysis, for prediction of CO2 fluxes, confirmed that the ICO-AR-RF model performed the best at both PEI (RMSE=0.70, NSE=0.76, PBIAS=-5.11, RSR=0.48) and NB (RMSE=0.74, NSE=0.75, PBIAS=3.23, RSR=0.50), followed by MS-RF and AR-RF. Uncertainty analysis showed that CO2 prediction is more sensitive to input scenario selection than models in both study areas. Results revealed that climatic variables are more effective in CO2 prediction than soil characteristics and the developed hybrid model ICO-AR-RF can be a promising tool for decision-makers and beneficial for stakeholders.

This paper investigates the thermal stability of metakaolin/fly ash (MK/FA) based geopolymers at elevated temperature of 500℃ and 800℃. The thermal stability of geopolymers is evaluated from the following aspects: apparent morphology, residual compressive strength, volumetric shrinkage, and microstructure. The results show that the incorporation of FA contributes to the reduction of specimen cracking while the increasing of equivalent Na2O leads to severe cracking. After experiencing 800°C, the compressive strength of all specimens remains above 70 % of that at room temperature, and the specimen with high equivalent Na2O exhibits higher compressive strength. The XRD and FTIR results indicate that the chemical component of most specimens remains stable at elevated temperatures without decomposition and formation of new crystalline phases. However, the MK/FA based geopolymer exhibit considerable volume shrinkage at elevated temperatures. The shrinkage ranges from 3.2 % to 4.9 % at 500°C and 5.1–14.2 % at 800°C for different specimens. The increase of equivalent Na2O from 8 % to 16 % results in a 174.8 % increase of shrinkage. Overall, MK/FA based geopolymers exhibit convincing thermal performance, and the specimens exhibit higher residual compressive strength and chemical stability after exposure to elevated temperatures.

AbstractFinancial risk has received increasing attention from policymakers and financial institutions. Therefore, the present study examines the relationship between capital and risk for Jordanian banks by using data from 2010–2019. The study employs fixed effect, random effect, GMM, and 3SLS. Our findings show that the capital requirement regulation has a positive impact on capital and risk rates. Moreover, the study also concludes that Jordanian banks hold more than the minimum regulatory capital requirements laid down by Basel II, III, and the CBJ. The banking sector increases its capital adequacy by raising its liquidity and reducing its tendency to take risks. Our results indicate a highly significant negative relationship between Jordanian commercial bank capital and risk. Liquidity risk, ROA and stock market capitalization are positively related to bank capital. The results of the study suggest that Jordanian banks should be involved in higher-risk lending actions and help increase competition in the banking sector.

The measles virus is an RNA virus belonging to the Paramyxoviridae family. It leads to an acute communicable illness that primarily involves the respiratory tract. Vaccination has significantly reduced the overall incidence and mortality worldwide; however, outbreaks still occur globally each year due to several factors. The SARS-CoV-2 pandemic has been a major hurdle since 2020. Despite the World Health Organization's goal to eradicate measles by 2023, there has been an increase in measles incidence in India, with 61,562 cases in 2022. Vaccination is a crucial preventive measure, and coverage needs to be increased through education, advocacy, and outreach to isolated communities.

Background: Pakistan is one of the largest host of refugees. People in Pakistan have bad access to healthcare not only due to bad facilities but also due to discrimination based on their personal characteristics and possessions. The behavior not only damages the dignity of the people being targeted but also creates hindrances in further seeking professional medical help and follow-ups thus deteriorating the health status of the community as a whole. Health providers possess a moral commitment to encourage dignity and treat their patients without any kind of discrimination in their behavior. Patient experiences of discrimination result in delay in seeking healthcare, they do not stick to advice of professionals and ultimately poor health. The objective of this study is to identify sources of discrimination and its effects on patients' health status and their level of satisfaction. Methods: This descriptive study took place in Outdoor Patient Department, Mayo Hospital, Lahore. 170 patients were selected from all the patients coming to OPD with equal male to female ratio. Each patient was given a questionnaire containing relevant questions to know whether they faced any sort of discriminatory behavior or not. Results: 66% females and 49% males of the study sample experienced discrimination at some stage of their hospital visit. Conclusion: Our study concluded that females experienced more discrimination than males. Therefore, gender is the most important factor when it comes to discriminatory experiences. Moreover, people with the higher income had fewer incidents of discrimination than their counterparts.

Water is a vital component of life that is abundantly available on planet Earth. However, a significant amount of the world’s water is not fit for direct use and drinking due to salinity and contaminants. Salinity coupled with the impacts of population growth, industrialization, climate change, and pollution has led to the decreasing supplies of safe drinking water. The desalination process has emerged as a decisive solution to meet the increasing demand for potable water. The distillation process particularly through a solar still, has gained importance for the purity of salty and contaminated water using solar energy. Solar stills have the potential to solve the problem of drinking water, especially in remote areas and villages. A simulation-based study was conducted to analyze the desalination process in a single slope solar still using ANSYS Fluent in Peshawar, Pakistan (coordinates: 34.0151°N, 71.5249°E). The study was focused on investigating the various parameters including glass temperature, water temperature, heat transfer coefficient, and volume fraction of water and vapors. The simulation results illustrate the significance of various parameters influencing the performance of the solar still and provide crucial information about the temperature profile and condensation formation, which plays a vital role in the overall efficiency of the desalination process within the solar still.

Introduction: Malignancy-related hypercalcemia is commonly observed in patients with advanced stages of cancer. It is intricately linked with an unfavourable prognosis among oncology patients. This study aimed to evaluate survival outcomes among individuals diagnosed with hypercalcemia associated with malignancy. Materials and Methods: This retrospective analysis of 173 cancer patients with hypercalcemia who sought treatment at Shaukat Khanum Memorial Cancer Hospital and Research Centre, Lahore, Pakistan, between July 2019 and June 2020. This cohort of patients underwent a longitudinal follow-up for 2.5 years. To assess survival outcomes, the Kaplan-Meier tool was used to construct survival curves and estimate the survival probability over time. The significance of potential survival factors was evaluated using the log-rank test. Results: All patients exhibited elevated levels of calcium. At admission, the cohort demonstrated varying degrees of hypercalcemia severity attributable to malignancy: mild hypercalcemia was observed in approximately 61.3% of patients, moderate hypercalcemia in 23.7%, and severe hypercalcemia in 15% of cases. Among the total sample, most patients were female (54.9%), with a median age of 54. The primary tumour site most frequently observed was in cases of breast cancer (35.3%), wherein the prevalent histological subtype was lobular/ductal invasive carcinoma (34.1%). Most of the patients (93.6%) had an Eastern Cooperative Oncology Group (ECOG) performance status (ECOG) > 1. Additionally, the median overall survival for patients diagnosed with hypercalcemia was 51 days. Notably, there was a significant association between survival factors, including the primary site of malignancy (P=0.001), bone metastasis (P=0.04), severity and symptoms of hypercalcemia (P=0.001), altered mental state (P=0.001), albumin levels (P=0.001), and ECOG (P=0.001). Conclusion: Malignancy-related hypercalcemia in patients with cancer is a significant predictor of an unfavourable prognosis. The aforementioned survival factors may have the potential to influence patient survival outcomes. Further studies on larger cohorts are warranted.

COVID-19 has badly affected the psychological well-being of people all over the globe, especially in developing countries, due to the loss of jobs, social capital and stress. The present study is also designed to analyze the impact of government quality on subjective well-being (SWB) after the pandemic through the mediating role of financial stress. This study uses the data of 599 respondents in Pakistan and applies mixed methodology in Generalized Structure Equation Model (GSEM) structure with Tobit and order logit. Results reveal that people perceived more financial and economic stress and perceived poor satisfaction with the government’s role in fighting the pandemic. Regression analyses confirmed that least economic stress, good perceived governance, and high level of income are negatively associated with financial stress and positively relate to happiness and life satisfaction. Moreover, a significant relationship exists between the mediator (financial stress) and SWB. Our results suggest government bodies should encourage investment for business start-ups. This will not only reduce unemployment and economic and financial stress but also expand the size of the economy, which leads to achieving the wellbeing of people.

The Global Pandemic introduced new challenges in pedagogy, methodology, and curriculum. However, it offered some novel solutions for academia to rapidly shift from traditional teacher-centered classrooms to student-oriented interactive classrooms using online tools. Hence, this study aims to identify the technical, institutional, and academic problems English language teachers face during online classes for undergraduate students at a public sector university in Karachi, Pakistan. For this purpose, the study utilized a mixed-method approach, and the target population included sixteen teachers teaching virtually. Data was collected in two stages using purposive sampling through an online teaching form from all the respondents, and to know teachers' experiences while conducting the classes, interviews were conducted. Semi-structured interviews were conducted with five participants. The findings suggest that teachers face problems with ICT skills, digital resources, content development, assessment, and using different online platforms for tasks and activities. The results indicate that teachers also met multiple economic, technological, interaction, attention, motivational, and well-being issues of learners during the online classes. Comparatively, some young teachers reflected the opposite trends and were motivated to explore this new education system. Therefore, the higher education institutes in Pakistan should immediately develop new curricula in alignment with the technological needs. Also, sufficient training for ELT practitioners and teaching language skills must be provided for the betterment of future generations.

This research aims to provide a comprehensive analysis of the 'A More Perfect Union' speech by Barack Obama using a pragmatic approach. The paper is qualitative and descriptive and uses textual analysis for interpretation. This study utilizes the principles of relevance theory developed by Dan Sperber and Deirdre Wilson to unlock the intricate ways through which Obama powerfully crafted his message to connect, persuade, and resonate with his audience. Through a careful and purposeful examination of the context, implication, and cognitive effects, the paper intends to make known the cognitive mechanisms that Obama employs to establish relevance and get his message across effectively. The presented research work contributes to a deeper knowledge of the pragmatic aspects of political rhetoric and offers insights into the impact of linguistic strategies on the formation of public opinion and debates nowadays.

Objective: To appraise the overall outcomes of microsurgical resection of low-grade arteriovenous malformations (AVMs) in a low-middle-income country. Methods: Data was collected prospectively from three different neurosurgical centres in Pakistan for this study and it lasted for two years. According to the Spetzler-Martin (S-M) grading system, patients diagnosed with cerebral AVMs were divided into three classes A, B, and C. AVMs of grade 1 and 2 were included in Class A. Class B contained grade 3 AVMs, while Class C contained grade 4 and 5 AVMs. All male and female patients in Class A were eligible for this study. Patients were evaluated postoperatively for seizures, haemorrhage, focal neurological deficits, and AVM cure rates. Morbidity, mortality, and functional recovery were used to evaluate the outcome. Functional recovery and cure rate were observed after 6 months of follow-up. Results: There were a total of 22 patients. Mean age was 36.41 ± 14.32 SD years. There were 12 (54.5 %) male patients and 10 (45.5 %) female patients. 13 patients (59.1 %) presented with spontaneous intracerebral haemorrhage, while 9 patients (40.9 %) presented with seizures. 14 patients (63.6 %) had S-M grade 1 and 8 patients (36.4 %) had S-M grade 2. All patients underwent microsurgical resection. We discovered 4.5 % morbidity in our study. There was no postoperative mortality. At 6 months, an excellent functional outcome of 95.5 % (according to the Glasgow outcome scale) and 100 % cure rate was observed. Conclusion: Microsurgery is a safe and effective treatment for low-grade AVMs in terms of morbidity, mortality, functional outcome and cure rates in our country.

Background: Histoplasmosis is a fungus disease caused by a dimorphic organism called Histoplasma capsulatum, it is endemic in United States, Asia, and Africa. Inoculation is through inhalation of infectious spores. Histoplasmosis is missed in diagnosis because it mimics tuberculosis in clinical presentations and not all patients suffering from tuberculosis are tuberculosis patients. The aim is to determine the prevalence of disseminated histoplasmosis among patients with pulmonary infections. Method: This is cross sectional and hospital based. Samples were collected from patients and questionnaires were administered. Three hundred and twenty-two (322) urine samples were aseptically collected in urine tubes and analyzed by enzyme linked immunosorbent assay to detect the presence of galactomannan in the urine samples. Results: Out of 322 samples analyzed, 12 (3.74 %) were positive for the presence of galactomannan while 310 (96.27 %) were negative. Participants of the geriatric age group had the highest prevalence of 12.5 %. Male 8.07 % were more prone to histoplasmosis disease than their female 3.10 % counterpart. The married displayed a high prevalence value of 6.25 % than singles 2.25 % and the divorced 3.57 % and framer were demonstrated a high value of 7.69 % prevalence. Among the risk factors trader had an odd ratio of 3.800 and chest discomfort 1.037 % is a disease symptom associated with histoplasmosis. Conclusion: Disseminated histoplasmosis occur among the study participants through low in prevalence, awareness of its presence should be known to the general public and clinician, diagnosis of histoplasmosis is be done along that of Tuberculosis because it can be co-infection with Tuberculosis for proper treatment regime.

In this paper, we propose the design, operation, and implementation of an Internet of Things-based hybrid structural health monitoring. This innovative system leverages the capabilities of both fog and cloud layers in computing and monitoring. The system architecture consists of leaf nodes deployed on a target structure. These nodes, synchronously, collect acceleration signals from accelerometers attached directly to the structure and transmit the data to an on-site central node via a short-range communication protocol. At the fog layer, the central node, applies damage detection algorithms on the collected data. If a damage is detected, it forwards the acceleration signals to a cloud-based monitoring server using cellular internet connectivity, where more complex algorithms are used to identify and locate the damage. We provide detailed information about the design of the different system nodes, the implementation of damage detection algorithms, and the architecture of the monitoring server. To evaluate the effectiveness of the proposed system, several practical experiments were conducted. The results demonstrate that the hybrid system presented in this paper provides an efficient, reliable and cost-effective approach to damage detection and identification in civil infrastructures.

Abstract Background Hepatocellular carcinoma (HCC) is a highly aggressive and rapidly progressing form of cancer with a poor prognosis. Recent advances in the management of HCC focused on the novel immunotherapeutic modalities for patients with advanced disease. PD-L1 has emerged as a promising immunotherapeutic approach for HCC. The evaluation of PD-L1 expression aids in identifying patients who can derive maximum benefits from these therapies. This study aims to examine and compare the expression of PD-L1 using two clones (22C3 and 28-8) in HCC patients. Methods Forty-six patients with HCC were selected between 2005 and 2022 from the Shaukat Khanum Memorial Cancer Hospital and Research Centre (SKMCH&RC) in Lahore, Pakistan. The patients' formalin-fixed paraffin-embedded (FFPE) tissue samples were retrieved from the department of pathology to conduct immunohistochemical analysis. Moreover, the clinicopathological data of these patients were gathered from the hospital information system (HIS). To assess the relationship between variables, bivariate analysis was carried out using either the chi-square test or Fisher exact test when necessary. Results Among the 46 tissue specimens analyzed, the presence of clone 22C3 was detected in 20 HCC patients, with 10 patients showing high expression (21.7%) and another 10 patients showing low expression (21.7%). 22C3 expression was not observed in 26 patients (56.5%). On the other hand, clone 28-8 was expressed in 10 patients, all of whom exhibited low expression (21.7%), while no expression of clone 28-8 was observed in 36 patients (78.3%). An association was found between the expression of 22C3 and 28-8 PD-L1 clones (p-value 0.01). Furthermore, upon closer examination, it was revealed that 12 cases exhibited positive results for 22C3 but negative results for 28-8. Interestingly, two cases displayed positive results for 28-8 but negative results for 22C3. Conclusion We obserevd that the PD-L1 clones, 22C3 and 28-8, are comparable. If PD-L1 expression using 22C3 is negative, considering the use of 28-8 for evaluating expression in HCC patients may be beneficial. However, further validation in a larger cohort is necessary.

Purpose – Cybersecurity vulnerabilities are often due to human users acting according to their own ethical priorities. With the goal of providing tailored training to cybersecurity professionals, the authors conducted a study to uncover profiles of human factors that influence which ethical principles are valued highest following exposure to ethical dilemmas presented in a cybersecurity game. Design/methodology/approach – The authors’ game first sensitises players (cybersecurity trainees) to five cybersecurity ethical principles (beneficence, non-maleficence, justice, autonomy and explicability) and then allows the player to explore their application in multiple cybersecurity scenarios. After playing the game, players rank the five ethical principles in terms of importance. A total of 250 first-year cybersecurity students played the game. To develop profiles, the authors collected players' demographics, knowledge about ethics, personality, moral stance and values. Findings – The authors built models to predict the importance of each of the five ethical principles. The analyses show that, generally, the main driver influencing the priority given to specific ethical principles is cultural background, followed by the personality traits of extraversion and conscientiousness. The importance of the ingroup was also a prominent factor. Originality/value – Cybersecurity professionals need to understand the impact of users' ethical choices. To provide ethics training, the profiles uncovered will be used to build artificially intelligent (AI) non-player characters (NPCs) to expose the player to multiple viewpoints. The NPCs will adapt their training according to the predicted players’ viewpoint.

Today many young members of the Sindhi community are migrating from villages to cities in Pakistan, where the national language, Urdu and the co-official language, English, dominates. This study investigates the daily language patterns of transplanted Sindhi speakers and the impact of such patterns on their mother tongue. Furthermore, as these speakers frequently switch from one language to another due to exposure to multilingualism in urban settings, this study determines the dominant language being used by young Sindhis in their mixed discourse, compared to the discourse of older Sindhis. Reasons for adopting other languages in academic institutions and home-settings were also investigated. A qualitative case study was conducted to obtain detailed responses about the linguistic choices of twenty young Sindhi speakers. Two Sindhi families were also observed to study the language choices across generations in home-settings, and semi-structured interviews were conducted to determine reasons for the mixed languages which emerged. The data were analysed using frequency analysis for linguistic choices and thematic analysis of the daily life discourse. Results show that the young Sindhis in the city of Karachi are using dominant languages Urdu and English in their daily life and have higher proficiency in Urdu and English as compared to their mother tongue Sindhi. Moreover, there are several social, economic, and cultural reasons for switching from one language to another among the younger Sindhi generation, in contrast to the older Sindhi generation.

This study aims to identify the employability skills of graduates passionate to join the banking industry in Pakistan. Eleven factors related to employability skills are included explicit knowledge, tacit knowledge, hard skills, soft skills, intellectual abilities, physical abilities, conscientiousness, agreeableness, emotional stability, openness, and extroversion. Managers are requested to indicate the importance of these skills for graduates willing to join the banking industry and share their satisfaction related to the tasks done by the fresh banking graduates under their supervision. The study uses Mean, Standard deviation, One Sample T-test and Coefficient of variation to rank each skill by managers. Results of the study reveal that 'openness', 'tacit knowledge and 'consciousness' ranked in the top three positions as far as the employability skills required in the banking sector, where as 'physical ability’ is ranked lowest. This study's results are imperative for bank managers and educational institutions to guide and train graduate according to the market needs and skills requirements.

Abstract In the current industrial scenario, cadmium (Cd) as a metal is of great importance but poses a major threat to the ecosystem. However, the role of micronutrient − amino chelates such as iron − lysine (Fe − lys) in reducing Cr toxicity in crop plants was recently introduced. In the current experiment, the exogenous applications of Fe − lys i.e., 0 and10 mg L − 1, were examined, using an in vivo approach that involved plant growth and biomass, photosynthetic pigments, oxidative stress indicators and antioxidant response, sugar and osmolytes under the soil contaminated with varying levels of Cd i.e., 0, 50 and 100 µM using two different varieties of canola i.e., Sarbaz and Pea − 09. Results revealed that the increasing levels of Cd in the soil decreased plant growth and growth-related attributes and photosynthetic apparatus and also the soluble protein and soluble sugar. In contrast, the addition of different levels of Cd in the soil significantly increased the contents of malondialdehyde (MDA) and hydrogen peroxide (H2O2), which induced oxidative damage in both varieties of canola i.e., Sarbaz and Pea − 09. However, canola plants increased the activities of superoxide dismutase (SOD), peroxidase (POD), catalase (CAT), and non-enzymatic compounds such as phenolic, flavonoid, proline, and anthocyanin, which scavenge the over-production of reactive oxygen species (ROS). Cd toxicity can be overcome by the supplementation of Fe − lys, which significantly increased plant growth and biomass, improved photosynthetic machinery and sugar contents, and increased the activities of different antioxidative enzymes, even in the plants grown under different levels of Cd in the soil. Research findings, therefore, suggested that the Fe − lys application can ameliorate Cd toxicity in canola and result in improved plant growth and composition under metal stress.

Quadrupedal robots are confronted with the intricate challenge of navigating dynamic environments fraught with diverse and unpredictable scenarios. Effectively identifying and responding to obstacles is paramount for ensuring safe and reliable navigation. This paper introduces a pioneering method for 3D object detection, termed viewpoint feature histograms, which leverages the established paradigm of 2D detection in projection. By translating 2D bounding boxes into 3D object proposals, this approach not only enables the reuse of existing 2D detectors but also significantly increases the performance with less computation required, allowing for real-time detection. Our method is versatile, targeting both bird’s eye view objects (e.g., cars) and frontal view objects (e.g., pedestrians), accommodating various types of 2D object detectors. We showcase the efficacy of our approach through the integration of YOLO3D, utilizing LiDAR point clouds on the KITTI dataset, to achieve real-time efficiency aligned with the demands of autonomous vehicle navigation. Our model selection process, tailored to the specific needs of quadrupedal robots, emphasizes considerations such as model complexity, inference speed, and customization flexibility, achieving an accuracy of up to 99.93%. This research represents a significant advancement in enabling quadrupedal robots to navigate complex and dynamic environments with heightened precision and safety.

Acid value (AV) serves as an important indicator to assess the quality of oil, which can be used to judge the deterioration of edible oil. In order to realize the quantitative prediction of the AV of camellia seed oil, which was made from camellia oleifolia, hyperspectral data of 168 camellia seed oil samples were collected using a hyperspectral imaging system, which were related to their AV content measured via classical chemical titration. On the basis of hyperspectral full wavelengths, characteristic wavelengths, and fusing spectral and image features, the quantitative prediction AV models for camellia seed oil were established. The results demonstrating the 2Der-SPA-GLCM-PLSR model fusing spectral and image features stood out as the optimal choices for the AV prediction of camellia seed oil, with the correlation coefficient of calibration set (Rc2) and the correlation coefficient of prediction set (Rp2) at 0.9698 and 0.9581, respectively. Compared with those of 2Der-SPA-PLSR, the Rc2 and Rp2 were improved by 2.11% and 2.57%, respectively. Compared with those of 2Der-PLSR, the Rc2 and Rp2 were improved by 5.02% and 5.31%, respectively. Compared with the model based on original spectrum, the Rc2 and Rp2 were improved by 32.63% and 40.11%, respectively. After spectral preprocessing, characteristic wavelength selection, and fusing spectral and image features, the correlation coefficient of the optimal AV prediction model was continuously improved, while the root mean square error was continuously decreased. The research demonstrated that hyperspectral imaging technology could precisely and quantitatively predict the AV of camellia seed oil and also provide a new environmental method for detecting the AV of other edible oils, which is conducive to sustainable development.

To achieve the rapid grade classification of camellia seed oil, hyperspectral imaging technology was used to acquire hyperspectral images of three distinct grades of camellia seed oil. The spectral and image information collected by the hyperspectral imaging technology was preprocessed by different methods. The characteristic wavelength selection in this study included the continuous projections algorithm (SPA) and competitive adaptive reweighted sampling (CARS), and the gray-level co-occurrence matrix (GLCM) algorithm was used to extract the texture features of camellia seed oil at the characteristic wavelength. Combined with genetic algorithm (GA) and support vector machine algorithm (SVM), different grade classification models for camellia seed oil were developed using full wavelengths (GA-SVM), characteristic wavelengths (CARS-GA-SVM), and fusing spectral and image features (CARS-GLCM-GA-SVM). The results show that the CARS-GLCM-GA-SVM model, which combined spectral and image information, had the best classification effect, and the accuracy of the calibration set and prediction set of the CARS-GLCM-GA-SVM model were 98.30% and 96.61%, respectively. Compared with the CARS-GA-SVM model, the accuracy of the calibration set and prediction set were improved by 10.75% and 12.04%, respectively. Compared with the GA-SVM model, the accuracy of the calibration set and prediction set were improved by 18.28% and 18.15%, respectively. The research showed that hyperspectral imaging technology can rapidly classify camellia seed oil grades.

Recently, the research community has taken great interest in human activity recognition (HAR) due to its wide range of applications in different fields of life, including medicine, security, and gaming. The use of sensory data for HAR systems is most common because the sensory data are collected from a person’s wearable device sensors, thus overcoming the privacy issues being faced in data collection through video cameras. Numerous systems have been proposed to recognize some common activities of daily living (ADLs) using different machine learning, image processing, and deep learning techniques. However, the existing techniques are computationally expensive, limited to recognizing short-term activities, or require large datasets for training purposes. Since an ADL is made up of a sequence of smaller actions, recognizing them directly from raw sensory data is challenging. In this paper, we present a computationally efficient two-level hierarchical framework for recognizing long-term (composite) activities, which does not require a very large dataset for training purposes. First, the short-term (atomic) activities are recognized from raw sensory data, and the probabilistic atomic score of each atomic activity is calculated relative to the composite activities. In the second step, the optimal features are selected based on atomic scores for each composite activity and passed to the two classification algorithms: random forest (RF) and support vector machine (SVM) due to their well-documented effectiveness for human activity recognition. The proposed method was evaluated on the publicly available CogAge dataset that contains 890 instances of 7 composite and 9700 instances of 61 atomic activities. The data were collected from eight sensors of three wearable devices: a smartphone, a smartwatch, and smart glasses. The proposed method achieved the accuracy of 96.61% and 94.1% by random forest and SVM classifiers, respectively, which shows a remarkable increase in the classification accuracy of existing HAR systems for this dataset.

Radial artery is mostly used for arterial cannulation because of its location, collateral circulation, and less complications. Dorsalis pedis artery can be an alternative for arterial cannulation in difficult radial or brachial arteries cannulation situations as it is mostly overlooked. We present a case of a 45-year-old female planned for supratentorial craniotomy for excision of meningioma. After induction of anesthesia, the invasive access couldn’t be attained after multiple attempts under ultrasound guidance by five senior anesthesiologists. The surgery was abandoned, and the patient awakened. The case was rescheduled after 2 days. The new anesthesia team attained the arterial access in the right dorsalis pedis artery and the central venous access in the right internal jugular vein in the first attempt. No complications were noted post-operatively. The dorsalis pedis artery can be safely used for arterial cannulation when radial artery cannulation is not possible.

Water is an essential requirement for agricultural productivity. In the agriculture sector, electricity generated by conventional sources contributes to a substantial amount of carbon footprints for pumping water through tube wells. Over the past few decades, a transitional shift towards renewable resources has increased leading to decarbonizing the environment and is considered as a viable solution for electricity production. To assist and provide a road map for this paradigm shift, the proposed study presents a techno-economic and environmental analysis of irrigation systems by carrying comparative analysis of both standalone and grid-connected systems based on four independent sites in a developing country. PV system integrated with grid enabling both energy purchase and sale (PV + G(P+S)), proved to be the most optimal configuration with cost of energy (COE) of $0.056/kWh, $0.059/kWh, $0.061/kWh, and $0.068/kWh while having net present cost (NPC) of $7,908, $20,186, $25,826, and $34,487 for Peshawar, Khyber Agency, Mardan, and Charsadda respectively, over a useful life span of 25 years. Furthermore, sensitivity analysis has been carried out based on uncertain variables such as Grid power purchase (GPP) and average solar radiation (GHI) to check the optimality behavior of the system. Results from environmental analysis revealed that (PV+ G(P+S)) system has a relatively low carbon impact as compared with conventional sources. This configuration also has the ability to prevent excess water extraction by selling any excessive solar PV energy to the grid. This study provides a policy framework insight for the entities for future optimization.

Chinese language teaching in universities was introduced as part of China-Pakistan Economic Corridor initiative (CPEC) signed in 2013 for economic, entrepreneurial, and social reasons. Over a decade of introducing Chinese as a foreign language, Pakistani language learners invested in Chinese language for scholarships, higher education, jobs, and business purposes. However, many CFL learners were unable to acquire Chinese language and had low proficiency in the language. Therefore, this study aims to investigate the beliefs about difficulties that foreign language learners face about learning Chinese as a foreign language in a public sector university. For this purpose, quantitative research using BALLI questionnaire was conducted within a public sector university. The beliefs about language learning inventory (BALLI) questionnaire inquired about the general and specific language learning difficulties that students face while learning CFL. Data was collected using stratified sampling from two hundred undergraduate students who have completed CFL-I and CFL-II language courses at the university. Data was analyzed using descriptive statistics through SPSS. The findings of the study showed that learners considered Chinese as a difficult language to learn as compared to other foreign languages. Also, when inquired about the success of their spoken Chinese language in future, few of the participants believed that they would be able to succeed with Chinese language. Similarly, learners reported about the four language skills in Chinese and considered listening as easier than speaking. Additionally, the learners reported that reading and writing are equally difficult in acquiring Chinese.

Key Clinical Message Desmoplastic fibroma presents similar to other soft tissue tumors to such an extent that even a gold standard investigation can miss. Abstract This is to report a mass in a 47‐year‐old male arising from the chest wall, which was first thought to be a hemangioma but was later diagnosed as a case of desmoplastic fibroblastoma with the help of a biopsy.

The Cattle Biometrics Dataset is the result of a rigorous process of data collecting, encompassing a wide range of cattle photographs obtained from publicly accessible cattle markets and farms. The dataset provided contains a comprehensive collection of more than 8,000 annotated samples derived from several cow breeds. This dataset represents a valuable asset for conducting research in the field of biometric recognition. The diversity of cattle in this context includes a range of ages, genders, breeds, and environmental conditions. Every photograph is taken from different quality cameras is thoroughly annotated, with special attention given to the muzzle of the cattle, which is considered an excellent biometric characteristic. In addition to its obvious practical benefits, this dataset possesses significant potential for extensive reuse. Within the domain of computer vision, it serves as a catalyst for algorithmic advancements, whereas in the agricultural sector, it augments practises related to cattle management. Machine learning aficionados highly value the use of machine learning for the construction and experimentation of models, especially in the context of transfer learning. Interdisciplinary collaboration is actively encouraged, facilitating the advancement of knowledge at the intersections of agriculture, computer science, and data science. The Cattle Biometrics Dataset represents a valuable resource that has the potential to stimulate significant advancements in various academic disciplines, fostering ground breaking research and innovation.

Pakistan is a developing country with more than a quarter of its population living under the poverty line. Data relating to the socioeconomic conditions of the population is scarce and sporadic. Government, NGOs, and other interna- tional organizations perform door-to-door surveys to collect data, but these can be expensive and time-consuming to conduct. Currently, statistics about poverty in Pakistan exist only as estimates from field surveys, which are often unofficial and limited. This lack of reliable information leads to ineffective policy decisions. This is one of the biggest challenges in fighting issues such as poverty. Reliable information is thus needed for the unified and concerted direction of development activities. This thesis aims to build a computer vision system that can automat- ically extract information about poverty from publicly available high-resolution satellite imagery. The proposed system output is heat maps indicating poverty and development levels. These heat maps can be overlaid on digital maps for easy visualization. We propose to use transfer learning techniques to extract indica- tors such as geographical and man-made structural features from high-resolution satellite imagery. The trained model learns to filter and distinguish between vari- ous terrains and man-made features, such as highways, buildings, and farmlands. We show that these learned traits are quite useful for mapping socioeconomic variables and even come close to matching the prediction data with field survey data. For a developing country like Pakistan, this thesis is useful because the approach uses publicly available data and is a scalable and inexpensive alterna- tive to traditional surveys. The results from our thesis can aid policymakers and NGOs in distributing their funds to areas that are most deserving and enacting and assessing policies more effectively.

The call to try civilians in military courts in 2022 reignited the debate surrounding the legitimacy of quasi-judicial tribunals, and their exercise of jurisdiction over civilians. Regarding trials of civilians in military tribunals, there have been many judgments and academic discourses. There are diverging views either endorsing or condemning this sui generis practice. The higher courts have not decided the matter conclusively. Pakistan does not subscribe to the idea of the direct effect of international treaties. Yet, like any other dualist state, many rights guaranteed in such treaties have been incorporated into its constitution. There is a stark contrast, however, between the case law interpretation of these rights and their international corresponding equivalents in the transnational regimes. The international mechanisms for the protection of rights still pose many questions on the legitimacy of civilians being tried in military courts. More confusion arises with the proliferation of contemporary idea of sustainable justice, which seems divergent and somewhat in contravention of such Pakistani state practices. This qualitative research paper juxtaposes the traditional constitutional interpretations with contestations in relevant international instruments and explores the possibility of harmonization. It lays out a futuristic proposal to predict a direction for proposed future amendments. The same would be done with the increasingly important idea of sustainable justice which directly relates to the United Nations (UN) Sustainable Development Goal (SDG) 16. Moreover, as indispensable themes, the paper explores the following: whether the trial of civilians in military courts is a constitutionally sound practice. Does trying civilians in military courts infringe upon Pakistan’s international obligations? And is the expediency behind these specialized courts still prevalent in the country?

The stilling basin of the Taunsa barrage is a modified form of the United States Bureau of Reclamation (USBR) Type-III basin, which consists of baffle and friction blocks. Studies revealed uprooting of baffle blocks due to their vertical face. Additionally, the literature highlighted issues of rectangular face baffle blocks: less drag, smaller wake area, and flow reattachment. In contrast, the use of wedge-shaped baffle blocks (WSBBs) is limited downstream of open-channel flows. Therefore, this study developed numerical models to investigate the effects of USBR and WSBB basins on the hydraulic jump (HJ) downstream of the Taunsa barrage under lower tailwater conditions. Surface profiles in WSBB and modified USBR basins showed agreement with previous studies, for which the coefficient of determination (R2) reached 0.980 and 0.970, respectively. The HJ efficiencies reached 57.9 and 58.6% in WSBB and modified USBR basins, respectively. The results of sequent depths, roller length, and velocity profiles in the WSBB basin were found more promising than the modified USBR basin, which further confirmed the suitability of the WSBB basin for barrages. Furthermore, WSBB improved flow behaviors in the basin, which showed no fluid reattachment on the sides of WSBB, increased wake regions, and decreased turbulent kinetic energies. HIGHLIGHTS Investigation of hydraulic jumps in the WSBB basin at lower tailwater using FLOW-3D was studied.; Also, comparison of hydraulic jumps and energy dissipating index between WSBB and modified USBR baffle block basins was studied.; Performance assessment of different hydraulic parameters was carried out by employing regression analysis.;