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Reverse takotsubo cardiomyopathy (rTTC), also known as inverted takotsubo syndrome, is an acute cardiac syndrome that mimics myocardial infarction and accounts for a smaller proportion of cases compared to the classic takotsubo syndrome. While the exact prevalence is not well-established, studies suggest that the proportion of patients with the rTTC variant out of all Takotsubo Cardiomyopathy (TTC) according to different literature ranges from 1-23%. This variant of TTC is characterized by basal and/or mid-akinesis/hypokinesis associated with apical hyperkinesis that resolves spontaneously. We report a case of a 30-year-old male patient who presented with complaints of sudden crushing chest pain and shortness of breath. After conducting a thorough examination, the patient’s diagnostic workup confirmed reverse takotsubo cardiomyopathy, elucidating the underlying cardiac pathology

Abstract Sociocultural determinants affect the physical and mental well-being and coping of individuals with chronic illnesses. However, no studies have examined the relationship of sociocultural determinants with psychological distress and coping. The purpose of this study was to determine the levels of psychological distress and coping among individuals with chronic illness and identify sociocultural determinants affecting distress and coping. A cross-sectional design was used. Data were collected from a convenience sample of 384 individuals admitted to inpatient settings and attending outpatient clinics at two tertiary care hospitals. The validated Urdu-translated version of the Hospital Anxiety and Depression Scale, and the Brief COPE scales were used for data collection. The mean HADS-Depression score was (11.38 ± 2.53), and the HADS-Anxiety score was 13.42 ± 2.34), indicating high levels of depression and anxiety. The most commonly used coping strategies were problem-focused coping (15.95 ± 4.95), followed by emotion-focused coping (15.01 ± 2.33), and avoidance coping (13.89 ± 4.77). Higher levels of psychological distress in individuals with chronic illnesses underscore the importance of implementing community-based support approaches. Varied use of coping strategies was influenced by the type of chronic illness, living conditions, educational level, years of living with a chronic illness, family dynamics, and available support systems.

Improving patient outcomes and treatment efficacy requires effective early detection of breast cancer. Recently, medical diagnostics has been transformed by merging the Internet of Things (IoT) technology with AI and ML methods. Better and faster diagnoses have been made possible by this revolutionary synergy, which allows the study of both real-time and historical data. Unfortunately, many people still die from breast cancer because modern diagnostics are not good enough to catch the disease in its early stages, even though medical science has come a long way. To overcome this obstacle, this study introduces a new medical diagnostic system that utilizes IoT to accurately distinguish between patients with and without tumors. The proposed model achieved 95% classification accuracy in differentiating between non-tumor and tumor instances by utilizing a Convolutional Neural Network (CNN) with hyperparameter adjustment. This approach can improve the accuracy and efficiency of breast cancer diagnosis by integrating medical devices with AI applications and healthcare infrastructure. In the long run, this study could help reduce breast cancer deaths by increasing early detection rates. This study can revolutionize healthcare delivery and improve patient outcomes on a global scale through continued innovation and collaboration with medical IoT technology.

The complex anti-fuzzy set (CAFS) is an extension of the traditional anti-fuzzy set with a wider range for membership function beyond real numbers to complex numbers with unit disc aims to address the uncertainty of data. The complex anti-fuzzy set is more significant because it provides two dimensional information and versatile representation of vagueness and ambiguity of data. In terms of the characteristics of complex anti-fuzzy sets, we proposed the concept of $ (\epsilon, \delta) $-CAFSs that offer a more comprehensive representation of the uncertainty of data than CAFSs by considering both the magnitude and phase of the membership functions and explain the $ \left(\epsilon, \delta \right) $-complex anti fuzzy subgroups (CAFS) in the context of CAFSs. Moreover, we showed that everyCAFSGis a $ (\epsilon, \delta) $-CAFSG. Also, we used this approach to define $ (\epsilon, \delta) $-complex anti-fuzzy(CAF) cosets and $ (\epsilon, \delta) $-CAF normal subgroups of a certain group as well as to investigate some of their algebraic properties. We elaborated the $ (\epsilon, \delta) $-CAFSG of the classical quotient group and demonstrated that the set of all $ (\epsilon, \delta) $-CAF cosets of such a particular CAFs normal subgroup formed a group. Furthermore, the index of $ \left(\epsilon, \delta \right) $-CAFSG was demonstrated and $ (\epsilon, \delta) $-complex anti fuzzification of Lagrange theorem corresponding to the Lagrange theorem of classical group theory was briefly examined.

In the realm of gas turbine engine technology, turbine blades are persistently subjected to extreme thermal conditions due to prolonged exposure to high-temperature gases. Given this operating environment, devising effective cooling strategies is paramount for ensuring the turbine’s safety and longevity. Despite the critical nature of this issue, existing literature scarcely addresses cooling channels that incorporate factors like variable cross-sectional attributes and rotational effects. In this study, Computational Fluid Dynamics (CFD) and numerical methodologies were employed to analyze the flow mechanism and performance of three distinct cooling channels characterized by variable cross-sectional features. The results indicate a marginal thermal performance improvement of up to 3% for U-shaped cooling channels, specifically at a Reynolds number of 20,000, in comparison to Net cooling channels. Conversely, jet impingement channels outperform U-shaped channels by an impressive margin of over 11%. Additionally, at a Reynolds number of 60,000, jet impingement cooling channels manifest a significant upsurge of nearly 25% in the ratio between average Nusselt number, and the reference Nusselt number when compared with U-shaped and Net cooling channels.

Assistance for doctors in disease detection can be very useful in environments with scarce resources and personnel. Historically, many patients could have been cured with early detection of the disease. The application of deep learning techniques in the fields of medical imaging, on large datasets, has allowed computer algorithms to produce as effective results as medical professionals. To assist doctors, it is essential to have a versatile system that can timely detect multiple diseases in the lungs with high accuracy. Over time, although many classifiers and algorithms have been implemented, however, deep learning models (i.e., CNN, Deep-CNN, and R-CNN) are known to offer better results. After a thorough literature review of the state-of-the-art techniques, this work applies various models such as MobileNet, DenseNet, VGG-16, EfficientNet, Xception, and InceptionV3 to the selected large dataset. The goal is to enhance the accuracy of these algorithms by experimenting with parameter optimizations. We observe that MobileNet produces better results as compared to other models. We implement a deep convolutional GAN to produce synthetic X-ray images containing various pathologies already included in the chosen imbalanced dataset namely NIH Chest X-ray containing 14 classes. The synthetic dataset contains 1193 samples belonging to five classes. We test the suggested model using evaluation measures like recall, precision, and F1-score, along with binary accuracy. The suggested deep learning model produces recall as high as 57%, binary accuracy as 93.4%, F1-Score as 0.553, and AUC as 81. After the inclusion of generated synthetic samples, the value of the F1-score becomes 0.582 resulting in a 5% increase. Though, Generative Adversarial Network (GAN) shows lower performance, however, we encourage further research and experiments to find the versatility of GANs in the field of medical imaging.

Saline water triggers the inevitable progression of corrosion risk in marine equipment, driven by spontaneous electrochemical reactions. This study used dip coating technique to prepare copper (Cu) substrates coated with epoxy resin. The study aimed to determine the optimal coating curing temperature and the epoxy-hardener ratio to exhibit maximum corrosion resistance for the epoxy-coated Cu. A comprehensive analysis of the curing temperature within the designated 40 to 120°C range and molarity of 0.6M-1.8M was conducted. The corrosion response of Cu substrates was evaluated using the electrochemical measurements of linear polarization resistance (LPR), potentiodynamic curve, and electrochemical impedance spectroscopy (EIS). The study reveals that the corrosion resistance (Rc) notably increases until reaching its peak at the optimal temperature value of 90°C and epoxy-hardener ratio of 2:1. Furthermore, a correlation between corrosion rate and molarity increased results in the deterioration of the coated surface.

Introduction: The perinatal mortality rate of Neonates of Diabetic Mothers (NDMs) over the years showed a reducing trend. Polycythaemia is common in NDMs, and such neonates have a risk of hyperviscosity, renal vein thrombosis, cardiac failure, metabolic abnormalities, and necrotising enterocolitis. Aim: To assess the association between cord blood haematocrit and neonatal outcomes among NDMs. Materials and Methods: The present study was a cohort study that included 130 neonates, which was conducted at Amala Institute of Medical Sciences (AIMS), Thrissur, Kerala, India, from December 2019 to June 2021. The primary inclusion criteria were singleton neonates of Gestational Diabetic Mothers (GDM) (diabetes detected after 20 weeks of gestation) and Overt Diabetes Mellitus (ODM) defined under White’s classification (known to be diabetic before the onset of pregnancy or detected in the initial visits). The parameters assessed were the frequency of distribution of hypoglycaemia, hypocalcaemia, and hyperbilirubinemia among the neonates of DM. Statistical analysis was done using Fisher’s exact test and Student’s t-test. Results: Among the 130 neonates, the majority of the mothers had GDM 111 (85.4%) than ODM 19 (14.6%). The majority were delivered by normal vaginal delivery 67 (51.5%). 10 (7.7%) of NDMs had a birth weight > 4000 g. Significant associations were noted between cord Packed Cell Volume (PCV) and the presence of hypoglycaemia (p-value=0.003), hypocalcaemia (p-value=0.0001), and Neonatal Hyperbilirubinemia (NNH) (p-value=0.0001) among NDMs. Conclusion: Hypoglycaemia, hyperbilirubinemia, and hypocalcaemia were common complications noted in NDMs.

Endophytic bacteria, living inside plants, are competent plant colonizers, capable of enhancing immune responses in plants and establishing a symbiotic relationship with them. Endophytic bacteria are able to control phytopathogenic fungi while exhibiting plant growth-promoting activity. Here, we discussed the mechanisms of phytopathogenic fungi control and plant growth-promoting actions discovered in some major groups of beneficial endophytic bacteria such as Bacillus, Paenibacillus, and Pseudomonas. Most of the studied strains in these genera were isolated from the rhizosphere and soils, and a more extensive study of these endophytic bacteria is needed. It is essential to understand the underlying biocontrol and plant growth-promoting mechanisms and to develop an effective screening approach for selecting potential endophytic bacteria for various applications. We have suggested a screening strategy to identify potentially useful endophytic bacteria based on mechanistic phenomena. The discovery of endophytic bacteria with useful biocontrol and plant growth-promoting characteristics is essential for developing sustainable agriculture.

IntroductionAlthough it is widely acknowledged that biodiversity maintains plant community assembly processes, exploring the patterns and drivers of beta-diversity (β-diversity; species variation among local plant communities) has received much less attention compared to alpha-diversity (α-diversity; species variation within a local plant community). Here, we aim to examine the patterns and spatial–environmental drivers of taxonomic and phylogenetic β-diversity, and their components such as species turnover and nestedness, in large-scale Leymus chinensis grassland communities.MethodsWe collected plant community data from 166 sites across widely distributed L. chinensis communities in northern China, and then calculated the taxonomic and phylogenetic β-diversity indices (overall, turnover and nestedness) using a pairwise dissimilarity approach. To assess the effects and to explain the variation in the patterns of β-diversity, we collected data on geospatial, climate and soil conditions. We applied descriptive statistics, Mental correlations, and multiple linear regression models to assess the patterns and spatial–environmental drivers of β-diversity.ResultsThe β-turnover, as compared to β-nestedness, exhibited a predominant influence, constituting 92.6% of the taxonomic β-diversity and 80.4% of the phylogenetic β-diversity. Most of the spatial–environmental variables were significantly positively correlated with the overall taxonomic and phylogenetic β-diversity and β-turnover, but not with β-nestedness. Climatic factors such as MAP and MAT were the strongest predictors of both taxonomic and phylogenetic β-diversity and β-turnover. The variance partitioning analysis showed that the combined effects of spatial and environmental factors accounted for 19% and 16% of the variation in the taxonomic and phylogenetic β-diversity (overall), 17% and 12% of the variation in the β-turnover, and 7% and 1% of the variation in the β-nestedness, respectively, which were higher than independent effects of either spatial or environmental factors.DiscussionAt larger spatial scales, the turnover component of β-diversity may be associated with the species complementarity effect, but dominant or functionally important species can vary among communities due to the species selection effect. By incorporating β-diversity into grassland management strategies, we can enhance the provision of vital ecosystem services that bolster human welfare, serving as a resilient barrier against the adverse effects of climate change at regional and global scales.

Defect detection is very important for guaranteeing the quality and pricing of fabric. A considerable amount of fabric is discarded as waste because of defects, leading to substantial annual losses. While manual inspection has traditionally been the norm for detection, adopting an automatic defect detection scheme based on a deep learning model offers a timely and efficient solution for assessing fabric quality. In real-time manufacturing scenarios, datasets lack high-quality, precisely positioned images. Moreover, both plain and printed fabrics are being manufactured in industries simultaneously; therefore, a single model should be capable of detecting defects in all kinds of fabric. So training a robust deep learning model that detects defects in fabric datasets generated during production with high accuracy and lower computational costs is required. This study uses an indigenous dataset directly sourced from Chenab Textiles, providing authentic and diverse images representative of actual manufacturing conditions. The dataset is used to train a computationally faster but lighter state-of-the-art network, i.e., YOLOv8. For comparison, YOLOv5 and MobileNetV2-SSD FPN-Lite models are also trained on the same dataset. YOLOv8n achieved the highest performance, with a mAP of 84.8%, precision of 0.818, and recall of 0.839 across seven different defect classes.

Internet of Things (IoT) devices are leading to advancements in innovation, efficiency, and sustainability across various industries. However, as the number of connected IoT devices increases, the risk of intrusion becomes a major concern in IoT security. To prevent intrusions, it is crucial to implement intrusion detection systems (IDSs) that can detect and prevent such attacks. IDSs are a critical component of cybersecurity infrastructure. They are designed to detect and respond to malicious activities within a network or system. Traditional IDS methods rely on predefined signatures or rules to identify known threats, but these techniques may struggle to detect novel or sophisticated attacks. The implementation of IDSs with machine learning (ML) and deep learning (DL) techniques has been proposed to improve IDSs’ ability to detect attacks. This will enhance overall cybersecurity posture and resilience. However, ML and DL techniques face several issues that may impact the models’ performance and effectiveness, such as overfitting and the effects of unimportant features on finding meaningful patterns. To ensure better performance and reliability of machine learning models in IDSs when dealing with new and unseen threats, the models need to be optimized. This can be done by addressing overfitting and implementing feature selection. In this paper, we propose a scheme to optimize IoT intrusion detection by using class balancing and feature selection for preprocessing. We evaluated the experiment on the UNSW-NB15 dataset and the NSL-KD dataset by implementing two different ensemble models: one using a support vector machine (SVM) with bagging and another using long short-term memory (LSTM) with stacking. The results of the performance and the confusion matrix show that the LSTM stacking with analysis of variance (ANOVA) feature selection model is a superior model for classifying network attacks. It has remarkable accuracies of 96.92% and 99.77% and overfitting values of 0.33% and 0.04% on the two datasets, respectively. The model’s ROC is also shaped with a sharp bend, with AUC values of 0.9665 and 0.9971 for the UNSW-NB15 dataset and the NSL-KD dataset, respectively.

In this paper, first, we find locally rotationally symmetric (LRS) Bianchi type I solutions in symmetric teleparallel gravity, which is so-called f(Q) gravity, where f(Q) is the function of non-metricity scalar Q. To explore the solutions, we assume both linear as well as nonlinear f(Q) gravity models, along with some additional constraints on the components of the spacetimes. We came across twelve distinct classes of solutions for the said f(Q) gravity models. We also classify the extracted solutions according to their conformal vector fields (CVFs). The CVFs, being a generalization of the Killing vector fields (KVFs), also inherit some conservation laws. We observe the conservation of (linear, angular, or generalized) momentum, which consequently allows us to solve dynamical systems by conserved momenta. We also note the existence of proper CVFs in four cases (conformally non-flat), leaving the remaining classes of solutions to admit either homothetic vector fields (HVFs) or the KVFs. The overall dimension of CVFs for the obtained classes of solutions in f(Q) gravity is either four, five, six, or fifteen. In order to model the dynamics of extracted solutions, we find the energy density and pressure of fluid elements in each case.

In order to remain competitive in a post-COVID-19 scenario, manufacturing organizations should use the pandemic scenario as a lever to make IoT adoption more agile and faster by reinventing their business models. Although, IoT has vast potential in multiple sensors’ direct integration without human intervention, its adoption has observed significant barriers during COVID-19. In this context, the current research aims to identify, prioritize, categorize, and investigate inter-dependencies among the significant barriers to adopting IoT technologies in the manufacturing industry. The analysis used two-stage integrated AHP-TOPSIS (Analytical Hierarchy Process-Technique for Order Preference by Similarity to Ideal Solution) and ISM (Interpretative Structural Modeling) techniques. For AHP and ISM techniques, a group of 8 experts from academia and industry with at least 10 years of experience was surveyed to capture their perceptions. Whereas, for TOPSIS, data were collected from 433 operational-level users. The research discloses that the significant driver barriers that hamper the adoption of IoT technologies are: poor internet infrastructure, high adoption & operating costs and lack of government regulations. This study will assist practitioners, decision-makers and organizations in understanding the significant barriers to seamless IoT implementation.

Due to the lack of symmetry property for the quasi metrics, we have considered left and right versions of best proximity points of multivalued mappings of quasi metric spaces. Further we consider the problem of existence of semi (left and right) best proximity points of generalized multivalued contractions of quasi metric spaces via various versions of so called $ p- $property. Some examples are given to explain the results.

Snow leopards (Panthera uncia) are elusive predators inhabiting high-altitude and mountainous rugged habitats. The current study was conducted in the Yanchiwan National Nature Reserve, Gansu Province, China, to assess the habitat suitability of snow leopards and identify key environmental factors inducing their distribution. Field data collected between 2019 and 2022 through scat sampling and camera trapping techniques provided insights into snow leopard habitat preferences. Spatial distribution and cluster analyses show distinct hotspots of high habitat suitability, mostly concentrated near mountainous landscapes. While altitude remains a critical determinant, with places above 3300 m showing increased habitat suitability, other factors such as soil type, human footprint, forest cover, prey availability, and human disturbance also play important roles. These variables influence ecological dynamics and are required to assess and manage snow leopard habitats. The MaxEnt model has helped us to better grasp these issues, particularly the enormous impact of human activities on habitat suitability. The current study highlights the importance of altitude in determining snow leopard habitat preferences and distribution patterns in the reserve. Furthermore, the study underscores the significance of considering elevation in conservation planning and management strategies for snow leopards, particularly in mountainous regions. By combining complete environmental data with innovative modeling tools, this study not only improves local conservation efforts but also serves as a model for similar wildlife conservation initiatives around the world. By understanding the environmental factors driving snow leopard distribution, conservation efforts can be more efficiently directed to ensure the long-term survival of this endangered species. This study provides valuable insights for evidence-based conservation efforts to safeguard the habitats of snow leopards amidst emerging anthropogenic pressure and environmental fluctuations.

In the present research, we consider a biological model of serum hepatitis disease. We carry out a detailed analysis of the mentioned model along with a class with asymptomatic carriers to explore its theoretical and numerical aspects. We initiate the study by using the piecewise fractal–fractional derivative (FFD) by which the crossover effects within the model are examined. We split the time interval into subintervals. In one subinterval, FFD with a power law kernel is taken, while in the second one, FFD with an exponential decay kernel of the proposed model is considered. This model is then studied for its disease-free equilibrium point, existence, and Hyers–Ulam (H-U) stability results. For numerical results of the model and a visual presentation, we apply the Lagrange interpolation method and an extended Adams–Bashforth–Moulton (ABM) method, respectively.

The study assesses the performance of a developing economy’s educational sector by comparing some fundamental educational statistics from Pakistan to those of ten better performing nations, and it outlines the required levels of education for the following decade. It presents a road map for the educational sector in Pakistan for the next ten years after identifying the gap between the existing and required levels of educational statistics. For each level of education, projections for the following variables are created. The required levels of Gross enrolment ratios, student-teacher ratios, the number of enrolments and the number of teachers. The required distribution of government spending across educational levels. Normed Planning approach is followed for the purpose by using panel data of 10 selected countries over the time period 2003-2017. It has also been observed that enrolments at each level of education are far below the required level based on international norms. In primary and secondary education, there are significant disparities between actual and necessary student-teacher ratios. In Pakistan, educational funding appears to be unbalanced, with primary and secondary education receiving more public funds than tertiary education. The analysis also demonstrates that the percentage of capital spending in total government expenditure on education is lower than what is required.

Introduction: Patient satisfaction is an important indicator of the quality of care given to patients in the health care system. It helps the clinician to know about the exact and objective feedback of the services they provide and to improve the quality and outcome of patient care. Patient satisfaction in physical therapy has not been closely monitored, and limited research exists in the field of health services in this area. The purpose of this study was to measure the patient's satisfaction at the physiotherapy OPD of Hayatabad medical complex Peshawar. Material & Methods: A cross-sectional survey was conducted from October 2018 to January 2019 to measure patient satisfaction. MedRisk patient satisfaction questionnaire by Beattie et al (2005) was used for data collection. The questionnaire was distributed among 125 participants at the physiotherapy OPD of Hayatabad medical complex Peshawar by non-probability convenient sampling. SPSS version 20 was used for data analysis. Results: A total of 125 participants were recruited of which more than half were female (n=70, 56%) and male (n=55,44%) with a mean age of 41.88±12.23. The chief complaint participants presented with was shoulder pain (n=29, 23.2%) followed by low back pain (n=24,19.2%). Patients were satisfied and responded positively to all the MedRisk patient satisfaction questionnaire statements. When asked the participants “My therapist treats me respectfully” (n=4,3.2%) disagree, (n=4,3.2%) neutral responses, and (n=117,93.6%) agree. When inquired about the time therapist spend with participants (n=111, 88.8%) disagree, (n=14, 11.2%) agree. “My therapist thoroughly explains the treatment(s) I receive” (n=3,2.4%) disagree, (n=3,2.4%) neutral response and (n=119,95.2%) agree. “Overall, I am completely satisfied with the services I received from my therapist” (n=1,0.8%) disagree, (n=7,5.6%) neutral response, (n=117,93.6%) agree. No significant correlation was found between gender and patient satisfaction as the p-value was greater than 0.05. Conclusion: This cross-sectional survey showed a high level of patient satisfaction with physiotherapy and good quality care to the patient by a professional therapist in physiotherapy OPD of Hayatabad medical complex Peshawar.

The aim of this paper is to optimize turbine blade cooling channels by applying jet impingement Method. The selection of experiment data for NASA 3CX turbine blade, and 3D model using solidworks software and create computational fluid dynamics (CFD) simulations used to model the coolant flow and temperature distribution in the vane, while experimental testing can validate the CFD results and provide additional insights into the cooling system's performance., ANSYS FLUENT code was used as a CFD solver, and ANSYS ICEM-CFD was used for mesh generation. MATLAB code is used for calculation using experiment data and this was helpful for simulations. Heat transfer conjugation analysis bases SST shear stress analyses K-ω turbulent model. The results conclude that providing additional information about the cooling channels and how they differ in the studies being compared. The results demonstrate that the cooling channels' hydraulic diameter decreases by a significant percentage (up to 49.70%–69.55%) as they are drawn to the trailing edge of the blade. This can have a significant impact on the heat transfer coefficients and the performance of the cooling system. The pressure side of the turbine blade is observed to follow the Hylton Model, while the current study predicts a large over-anticipated heat transfer coefficient around the Turbine blade head and on the bulk of the suction side. In terms of average heat transfer coefficient, the two models differ by 23.36%. The authors found that the cooling effectiveness for the Optimized jet impingement model is 0.4892 for whole blade and compared it with the cooling effectiveness for the optimized jet impingement model, which is 0.6936, The results of the comparison between the base model and the optimized jet impingement model suggest that the optimized model has a significantly higher cooling effectiveness. The increase in cooling effectiveness of 29.46% for the whole blade and 28.823% for the trailing edge indicates that the optimized jet impingement design provides improved cooling performance. These results highlight the importance of considering optimized cooling designs for turbine blades to maintain efficient and safe operation.

This study investigated the potential benefits and challenges of using translanguaging as a pedagogical tool in higher education in Pakistan. The research used an explanatory sequential design, collecting data from 90 final year BS English students and 22 English teachers through a questionnaire and semi-structured interview questionnaire, respectively. The quantitative data were analyzed using descriptive statistics, while qualitative data were analyzed using Atlas.ti software to identify dominant themes. The results indicate that there is a need to revise language policies and practices in higher education institutions in Pakistan to better reflect the linguistic and cultural diversity of the student population. The use of translanguaging was found to enhance learning outcomes and promote more inclusive and effective teaching and learning practices. However, the study also highlighted the need for teacher training in translanguaging techniques to ensure effective implementation in the classroom. The current study's contribution to existing literature is significant as it provides insights into the perceptions of Pakistani students regarding the use of translanguaging in higher education and identifies the challenges and opportunities associated with its implementation. The findings may inform the development of language policies and practices that promote more inclusive and effective teaching and learning approaches, especially in multilingual contexts like Pakistan.

This study aims to examine the errors committed by Urdu EFL learners in acquiring English Relative Clauses (henceforth RCs) by putting to test the predictions of hypothesis proposed by Keenan and Comrie’s (1977) Noun Phrase Accessibility Hierarchy (NPAH). It aims to inspect whether the NPAH constraint for the acquisition of English relative clauses be applicable to Urdu L2 learners or not. In order to accomplish the purpose of the study total 80 participants were selected from the University of Azad Jammu & Kashmir. Data were collected through questionnaire that was consisted of two tasks: grammaticality judgment task and sentence combining task. The study uncovered the fact that Urdu learners commit a number of errors in the production of English RCs. These errors are pronoun retention, head noun retention, shift of noun function, non-adjacency to head noun and relative pronoun, preposition stranding, and deletion, selection of wrong relativizer, change of lexical item, change of syntactic pattern, and deletion of comparative particle. Urdu EFL learners’ hierarchy of acquiring English RCs is partially consistent with NPAH. The present study confirms the difficulty of direct objective relative clauses compared to subject relative clauses and indirect relatives in Pahari, which strongly supports Comrie (2007) hierarchy that shows that the performance of Urdu EFL Learners for first three positions of hierarchy is consistent with NPAH while their performance for the remaining three positions is not consistent with NPAH. Urdu learners find it easy to perform on OCOMP RCs which is placed at the end in NPAH. Urdu EFL learners find Oblique RCs more difficult compared to genitive RCs.

This study investigates how R&D affects firm performance and examines whether and how state ownership moderates the R&D–firm performance nexus. Moreover, this study investigates whether and how product market competition moderates the association between R&D and performance across the different natures of firms. Employing a sample of Chinese listed firms from 2000 to 2020, the study performs estimations by applying pooled ordinary least squares regressions, fixed-effect panel regressions, and a two-step system GMM model to account for endogeneity problems. The findings reveal that firm performance reduces as R&D expenses rise, with the impact being less pronounced for state-owned enterprises. The adverse impact of R&D on corporate performance is also alleviated under intense competition. The results have important implications for policymakers, investors, and corporations seeking to understand how R&D spending influences corporate performance in competitive industries. JEL classification: L25, G32, O3

The q-rung orthopair fuzzy atmosphere is an innovative approach for handling unclear circumstances in a range of decision making problems. As compare to intuitionistic fuzzy sets, this one is more appropriate and adaptable because it evaluates the significance of ring theory while retaining the features of q-rung orthopair fuzzy sets. In this study, we characterize q-rung orthopair fuzzy subring as a modification of the pythagorean fuzzy subring. We introduce the novel idea of q-rung orthopair fuzzy subring and investigate the algebraic characteristics for the q-rung orthopair fuzzy subrings. Furthermore, we establish the concept of q-rung orthopair fuzzy quotient ring and q-rung orthopair fuzzy left and right ideals. Also, we describe the q-rung orthopair fuzzy level subring and associate axioms. Finally, we investigate how ring homomorphism influences the q-rung orthopair fuzzy subring and investigate there pre-images homomorphism on q-ROFSR and different aspects of images.