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This article presents an image dataset of palm leaf diseases to aid the early identification and classification of date palm infections. The dataset contains images of 8 main types of disorders affecting date palm leaves, three of which are physiological, four are fungal, and one is caused by pests. Specifically, the collected samples exhibit symptoms and signs of potassium deficiency, manganese deficiency, magnesium deficiency, black scorch, leaf spots, fusarium wilt, rachis blight, and parlatoria blanchardi. Moreover, the dataset includes a baseline of healthy palm leaves. In total, 608 raw images were captured over a period of three months, coinciding with the autumn and spring seasons, from 10 real date farms in the Madinah region of Saudi Arabia. The images were captured using smartphones and an SLR camera, focusing mainly on inflected leaves and leaflets. Date palm fruits, trunks, and roots are beyond the focus of this dataset. The infected leaf images were filtered, cropped, augmented, and categorized into their disease classes. The resulting processed dataset comprises 3089 images. Our proposed dataset can be used to train classification deep learning models of infected date palm leaves, thus enabling the early prevention of palm tree-related diseases.

Rapid urbanization attributed to population growth is affecting the built environment's thermal and landscape dynamics. Using Landsat satellite datasets, this study investigated the complex interplay between urban Land Cover (LC) modification, fluctuation in Land Surface Temperature (LST) and severity of Urban Heat Island (UHI) from 1990 to 2020 in Peshawar City, Pakistan. Thermal bands were used to calculate LST and severity of UHI using the Urban Thermal Field Variance Index (UTFVI). Furthermore, through Cellular Automata (CA), Logistic Regression (LR), and Artificial Neural Network (ANN), future predictions on thermal characteristics associated with land use changes were made. The results showed that the urban areas expanded by ∼25 % from 1990 to 2020, while a ∼10 % decrease occurred in urban vegetation. The city is projected to expand by ∼45 % and ∼56 % in 2035 and 2050, respectively. Notably, the results also demonstrated that urban hotspots were found the warmest with the strongest UHI severity (∼34 °C), followed by the barren land (∼32 °C), and vegetation. The results further predicted an increase of LST (∼55 % and ∼82 %) and UTFVI (∼62 % and ∼83 %) in 2035 and 2050, respectively. These findings provide useful insights for policymakers and city planners to mitigate heat stress and create a sustainable urban environment through the development of effective urban land use policies and urban greening.

Abstract Crude oil, the backbone of modern industry, holds unparalleled significance as a global energy cornerstone. Unlocking its potential hinges on effective pretreatment techniques, ensuring purity, and maximizing efficiency. This study extends the established Spherical Fuzzy Set paradigm to explore the domain of Disc Spherical Fuzzy Sets (D-SFSs) in critical decision-making for crude oil preparation. Investigating D-SFSs within the Aczel Alsina norm, the research employs comparison rules, conversion rules, and distance metrics. Primary operations of the Aczel Alsina norm in D-SFSs are examined, laying the groundwork for introducing unique aggregation operations within this framework. The paper’s primary aim is to propose a hybrid method, termed MEREC-SWARA-MARCOS-D-SFSs Multiple Attribute Group Decision Making, which integrates the aforementioned aggregation procedures. A case study on crude oil pretreatment validates the effectiveness of the proposed method. Furthermore, a comprehensive comparison with CoCoSo underscores the reliability of the method. This study represents a significant stride in enhancing decision-making by providing a robust framework to tackle complex situations, particularly in the critical domain of crude oil pretreatment.

In the contemporary landscape of advanced manufacturing, there is an increasing demand for machining processes that excel in both quality and energy efficiency. One of the prime objectives is to excel in an era of machining practices that are environmentally conscious and economically sustainable. This research addresses this challenge by undertaking a comprehensive exploration of multi-objective optimization, specifically tailored to address the machining challenges presented by Titanium Grade 3 alloy. A robust Taguchi-Grey integrated approach was adopted with research aim to strike an optimum balance among specific cutting energy, tool wear, surface finish, and material removal rate. The impact of machining inputs i.e., feed rate, cutting speed, and depth of cut were analyzed. Best machining setting was identified using grey relational analysis. Feed rate was identified as the most influential member affecting grey relational grade having contribution ratio of 73.95%. Furthermore, surface plots and contour plots were developed for aid of machinists on job floor in selection of best cutting conditions. Harnessing the usefulness of response surface optimization, the machinability was further enhanced by 9% reduction in specific cutting energy and 7% improvement in tool wear. However, it is imperative to acknowledge the trade-offs as a result of MOO; surface roughness increases by 12%, potentially necessitating additional post-processing steps.

IntroductionIn 2020, Pakistan faced the formidable challenge of the COVID-19 pandemic with an existing yet disjointed healthcare infrastructure, that included by over 18,000 public and an estimated 75,000 private health facilities and some elements of an epidemic surveillance and response system. This descriptive study examines how Pakistan developed a COVID-19 response, driven by establishing a central coordination and decision-making mechanism to overcome these systemic challenges.MethodsThe study is based on interviews and interactions of the many actors in the response by the authors, who also participated in nearly all proceedings of the National Command and Operation Centre (NCOC) and many of the National Coordination Committee (NCC). This information is supplemented by reviewing documents, reports, news items, media and social media, and journal articles.ResultsThe study highlights the critical role of political arrangement, where the NCC, comprising of ministers, bureaucrats, and military personnel, facilitated federal and provincial integration. The NCC found resources and set policy. Its direction was implemented by the NCOC, a top-down yet inclusive platform, integrated political, military, and civil society actors, to ensure cohesive decision-making and implementation. It provided technical guidance, harnessed data for strategic decisions and held implementers accountable. At its peak, the NCOC boasted nearly 300 personnel, including high-ranking military officers, a stark contrast to the limited staffing in most ministries. In addition, the response’s success is attributed to the perception of COVID-19 as an existential threat, leading to unprecedented collaboration and decisive actions that were enforced authoritatively.ConclusionPakistan’s experience offers valuable insights for proactive management of health emergencies in resource-limited settings. It underscores the necessity for inter-sectoral dialog and data-driven policy implementation, especially in the context of political economies where activity-driven governance often overshadows objective-driven policy execution. However, the lessons from the COVID-19 response, including a blueprint for future epidemic responses and lessons for use of data and evidence in developing country health systems, if not institutionalized, risk being lost in the post-pandemic era.

Steviol glycoside sweeteners have been isolated and produced for hundreds of years in Bertoni, a South American Asteraceae plant prized for its sweetness. Stevia is becoming more popular among consumers and food manufacturers as a result of diabetes, rising obesity rates, and other related comorbidities, as well as global public policies calling for a reduction in intake of sugar to help control these issues. Low-calorie as well as no-calorie sweeteners, such as Stevia, have this appeal because they are plant-based, have no calories, and have a sugary flavour that is 50 to 350 times sweeter than table sugar, making them an excellent as well as suitable choice for use in foods as well as beverages with lower sugar and calorie content. This plant yields steviol glycoside sweeteners, which are isolated and processed. Current studies on Stevia rebaudiana extract, metabolism, tolerable intake, and safety, steviol glycoside extraction and purification, side effects, as well as the toxicity of Stevia extract are discussed in detail.

In a corrosive environment, it is crucial to increase the hardness and corrosion resistance of mild steel bucket excavators. Making a hard layer of nickel-chromium (NiCr) alloy by the deposition method is one of the many approaches to achieve this. In this research, a hard layer was deposited on the upper surface of mild steel using manual metal arc welding (MMAW). Starting with the addition of NiCr alloy during welding, the process continued with a quenching technique using different cooling media. In order to examine elemental composition, crystal structure, and microstructure, Optical Emission Spectroscopy (OES), X-ray diffraction (XRD), and optical microscopy (OM) are employed. The evaluation of hardness values, material deterioration, and electrochemical behaviour of the deposited layer involves hardness tests, digital balances, and potentiostats. Due to its improved hardness and corrosion resistance, NiCr has been successfully added to deposition layers. The increase was attributed to the presence of martensite and ferrite phases generated by the quenching technique and NiCr on the surface layer. The hardness value and corrosion resistance of samples coated with NiCr after being rapidly cooled in an oil medium are found to be suitable for excavator buckets, according to experimental findings.

Background: Stenotrophomonas maltophilia (S. maltophilia) is the first dominant ubiquitous bacterial species identified from the genus Stenotrophomonas in 1943 from a human source. S. maltophilia clinical strains are resistance to several therapies, this study is designed to investigate the whole genome sequence and antimicrobial resistance genes prediction in Stenotrophomonas maltophilia (S. maltophilia) SARC-5 and SARC-6 strains, isolated from the nasopharyngeal samples of an immunocompromised patient. Methods: These bacterial strains were obtained from Pakistan Institute of Medical Sciences (PIMS) Hospital, Pakistan. The bacterial genome was sequenced using a whole-genome shotgun via a commercial service that used an NGS (Next Generation Sequencing) technology called as Illumina Hiseq 2000 system for genomic sequencing. Moreover, detailed in-silico analyses were done to predict the presence of antibiotic resistance genes in S. maltophilia. Results: Results showed that S. maltophilia is a rare gram negative, rod-shaped, non sporulating bacteria. The genome assembly results in 24 contigs (>500 bp) having a size of 4668,850 bp with 65.8% GC contents. Phylogenetic analysis showed that SARC-5 and SARC-6 were closely related to S. maltophilia B111, S. maltophilia BAB-5317, S. maltophilia AHL, S. maltophilia BAB-5307, S. maltophilia RD-AZPVI_04, S. maltophilia JFZ2, S. maltophilia RD_MAAMIB_06 and lastly with S. maltophilia sp ROi7. Moreover, the whole genome sequence analysis of both SARC-5 and SARC-6 revealed the presence of four resistance genes adeF, qacG, adeF, and smeR. Conclusion: Our study confirmed that S. maltophilia SARC-5 and SARC-6 are one of the leading causes of nosocomial infection which carry multiple antibiotic resistance genes.

BackgroundRabies, caused by the rhabdovirus, is a fatal zoonotic disease with over 59,000 annual deaths globally. Asia and Africa account for 95%, with India leading, followed by China. In Pakistan, where it's endemic, over 50,000 cases are reported yearly. Controlling rabid dog populations through vaccination is crucial in curbing mortality. This research aimed to evaluate healthcare professionals' knowledge, attitudes, and practices concerning rabies in Peshawar, Pakistan.MethodsThe study was conducted at different tertiary care hospitals in Peshawar, Pakistan from 16 August 2021 to 15 February 2022. Cross-sectional research was conducted to gather data from a total of 100 healthcare workers representing different sections within the healthcare field, including Medical Officers, House Officers, Faculty Staff, Nurses, and Paramedics. Data on knowledge, attitudes, and practices about rabies were collected using a standardized questionnaire. The data analysis included using descriptive statistics and chi-square testing to ascertain potential correlations.ResultsAmong the healthcare professionals, 68 (68%) were males, and 32 (32%) were females. Profession-wise, the included professionals were Nurses 31 (31%), Medical Officers 27 (27%), House officers 26 (26%), paramedical staff 13 (13%), and faculty staff 3 (3%). 91 (91%) and 9 (9%) healthcare professionals responded that dogs and cats are responsible for rabies transmission, respectively. Moreover, 82 (82%) individuals responded that animal bite plays a vital role in the transmission of rabies, whilst 76 (76%) individuals responded that rabies transferred from human to human. 82 (82%) individuals replied that the anti-rabies vaccine (ARV) is the treatment of choice for rabies. Furthermore, 78 (78%) individuals responded that ARV is safe in pregnancy and lactation. Moreover, after being asked about the perception of the health care professionals about the failure in controlling rabies, their responses were unavailability of ARV/RIG 41 (41%), lack of control of stray dogs 34 (34%), lack of awareness 20 (20%). The study revealed statistically significant correlations between healthcare occupations and variables: knowledge of animals responsible for transmitting rabies (p = 0.024) and awareness of human-to-human transmission (p = 0.007). Significant disparities were noted in understanding rabies transmission through contaminated water (p = 0.002). There were variations in attitudes and practices seen across different positions, particularly about views about home treatments (p = 0.033) and the perceived effectiveness of cleansing bite wounds (p = 0.010). Disparities in perceptions of rabies treatment and the accessibility of anti-rabies vaccines and immunoglobulin were observed, with variations based on individual roles.ConclusionThe present research elucidates variations in rabies knowledge, attitudes, and practices among healthcare workers, specifically concerning their respective roles. Tailored training programs and standardized practices play a crucial role in mitigating these discrepancies, fostering a greater understanding of rabies, and enhancing the quality of patient treatment. It is recommended that future studies undertake an assessment of the efficacy of therapies and advocate for the adoption of collaborative One Health strategies in the realm of rabies management.

IntroductionPakistan's private sector caters to around 65% of family planning users. Private sector family planning was promoted in the Delivering Accelerated Family Planning in Pakistan (DAFPAK) program by UK's Foreign, Commonwealth & Development Office (FCDO) in 2019. We use data from DAFPAK to analyze the clientele and products distributed by two major NGOs, Marie Stopes Society (MSS) and DKT Pakistan, that support private providers in Pakistan. We also examined the effect of COVID-19 on client visits and contraceptives uptake at private facilities in Pakistan.MethodsDAFPAK used field validation surveys to analyze the volume of clients and products of 639 private facilities across three provinces (Punjab, KPK and Balochistan) of Pakistan. The data was collected in two phases (February 2020 and 2021) using multi-stage cluster sampling at 95% confidence level. Using a generalized negative binomial regression, facility-level characteristics and impact of COVID-19 was analyzed with the volume of clients and products given out at 95% confidence interval alongside descriptive analysis.ResultsDKT facilities covered 53% of the sample while MSS covered 47%, with 72% facilities in the rural areas. Average facility existence duration is 87 months (7.25 years). While the average experience of the facility staff is 52 months (4.33 years). MSS is serving more clients as compared to DKT during both phase 1 (IRR: 3.15; 95% CI: 2.74, 3.61) and phase 2 (IRR: 2.11; 95% CI: 1.79, 2.49). Similarly, MSS had a greater volume of products given out in both phases 1 (IRR: 1.89; 95% CI: 1.51, 2.38) and phase 2 (IRR: 2.57; 95% CI: 2.09, 3.14). In both phases, client visits and product distribution decreased when client privacy is invaded (IRR: 0.74; 95% CI: 0.67, 0.82 – phase 1) and (IRR: 0.83; 95% CI: 0.72, 0.97 – phase 2). Lastly, during COVID-19, products distribution decreased by a factor of 0.84 (IRR: 0.84; 95% CI: 0.72, 0.97) but client visits remain unaffected.ConclusionOverall, clientele is low for all facilities. At a facility, privacy is a determinant of client visits and products given out per visit. Transiently, during COVID-19, client volumes decreased, with a shift from oral pills to condoms and emergency contraceptive pills.

Radio-frequency electromagnetic field (RF-EMF) forecasting plays an important role in the evaluation of regulatory compliance, network planning and system optimization. The knowledge of RF-EMF levels is essential to ensure compliance with standards and avoid public health concerns, especially with the arrival of new frequencies and scenarios in fifth-generation (5G) and sixth generation (6G) wireless networks. This work provides a comprehensive study on time series forecasting for RF-EMF measured in frequency from 100 kHz - 3 GHz. The state-of-the-art deep learning model architectures consist of deep neural network (DNN), convolutional neural network (CNN), long-short term memory (LSTM), and transformer are applied for time series forecasting. The prediction performance is evaluated under three different scenarios - namely single-step input single-step output (SISO), multi-step input single-step output (MISO), and multi-step input multi-step output (MIMO). The findings from the simulation demonstrate that SISO forecasting is inadequate in predicting long-term radio-frequency electromagnetic fields (RF-EMF) data as it lacks accuracy while MISO and MIMO forecasting scenarios offer more precise predictions. Specifically, in these two scenarios where the input width and label width are both set to 20 steps, the LSTM and CNN models exhibit superior performance compared to other models. Nonetheless, as the input width and label width in a MIMO scenario increase, the accuracy of both CNN and LSTM models decline considerably, whereas the transformer model consistently maintains good performance. Additionally, the transformer model continues to deliver accurate predictions as the label width and shift length increase, which is not the case for DNN, CNN, and LSTM models.

This study focuses on optimizing offshore drilling operations through Multi-Criteria Decision-Making (MCDM). It recognizes MCDM as a crucial framework for evaluating alternatives in the complex offshore drilling landscape. The objective is to identify the most suitable alternative among four approaches: Advanced Drilling Technologies, Drilling Process Optimization, Human Factors and Safety Enhancement, and Environmental Impact Mitigation. Evaluation criteria include Operational Efficiency, Safety Performance, Environmental Impact, and Cost-effectiveness. The research aims to contribute insights and recommendations for stakeholders in the offshore oil and gas industry. In the field of information aggregation and fusion, there is a growing interest among researchers in the domain of probabilistic linguistic expression sets, which are particularly effective in consolidating uncertain data. This article aims to explore various methodologies for information aggregation using probabilistic linguistic expressions. To achieve this goal, we have introduced procedural principles based on the Dombi (D) framework, specifically designed for managing probabilistic linguistic term elements (PLTEs). These principles are firmly grounded in both the product and sum of Dombi operations. As a result, we have developed a range of techniques for probabilistic linguistic aggregation, including entities such as the Probabilistic Linguistic Dombi Average (PLDA) and the Probabilistic Linguistic Dombi Geometric (PLDG). Additionally, we have created weighted aggregation operators (AOs) such as PLDWA and PLDWG, along with ordered AOs like PLDOWA and PLDOWG. By utilizing the D $\tau $ -norm and $\tau $ -conorm, we have designed versatile aggregation tools that support information reinforcement in both ascending and descending directions. Furthermore, we provide a comparative analysis between our proposed methodologies and the MARCOS approach. Additionally, we offer a detailed explanation of the distinctive attributes associated with these operators. Through the application of PLDA, PLDG, PLDWA, PLDWG, PLDOWA, and PLDOWG, we present strategies for effectively integrating probabilistic linguistic term sets (PLTs) into the realm of MCDM.

Air pollution is a global challenge to human health and the ecological environment. Identifying the relationship among pollutants, their fundamental sources and detrimental effects on health and mental well-being is critical in order to implement appropriate countermeasures. The way forward to address this issue and assess air quality is through accurate air pollution prediction. Such prediction can subsequently assist governing bodies in making prompt, evidence-based decisions and prevent further harm to our urban environment, public health, and climate, all of which co-benefit our economy. In this study, the main objective is to explore the strength of features and proposed a two stage feature engineering approach, which fuses the advantage of influential factors along with the decomposition approach and generates an optimum feature combination for five major pollutants including Nitrogen Dioxide (NO2), Ozone (O3), Sulphur Dioxide (SO2), and Particulate Matter (PM2.5, and PM10). The experiments are conducted using a dataset from 2015 to 2020 which is publicly available and is collected from Belfast-based air quality monitoring stations in Northern Ireland, UK. In stage-1, using the dataset new features such as trigonometric and statistical features are created to capture their dependency on the target pollutant and generated correlation-inspired best feature combinations to improve forecasting model performance. This is further enhanced in stage-2 by an optimum feature combination which is an integration of stage-1 and Variational Mode Decomposition (VMD) based features. This study employed a simplified Long Short Term Memory (LSTM) neural network and proposed a single-step forecasting model to predict multivariate time series data. Three performance indicators are used to evaluate the effectiveness of forecasting model: 1) root mean square error (RMSE), 2) mean absolute error (MAE), and 3) R-squared (R2). The results demonstrate the effectiveness of proposed approach with 13% improvement in performance (in terms of R2) and the lowest error scores for both RMSE and MAE.

Unmanned aerial vehicles (UAVs) are envisioned to play a pivotal role in modern telecommunication and wireless sensor networks, offering unparalleled flexibility and mobility for communication and data collection in diverse environments. This paper presents a comprehensive investigation into the performance of supervised machine learning (ML) models for path loss (PL) prediction in UAV-assisted millimeter-wave (mmWave) radio networks. Leveraging a unique set of interpretable geometrical features, six distinct ML models–linear regression (LR), support vector regressor (SVR), K nearest neighbors (KNN), random forest (RF), extreme gradient boosting (XGBoost), and deep neural network (DNN)–are rigorously evaluated using a massive dataset generated from extensive raytracing (RT) simulations in a typical urban environment. Our results demonstrate that the RF algorithm outperforms other models showcasing superior predictive performance for the test dataset with a root mean square error (RMSE) of 2.38 dB. The proposed ML models demonstrate superior accuracy compared to 3GPP and ITU-R models for mmWave radio networks. This study thoroughly investigates the adaptability of these models to unseen environments and examines the feasibility of training them with sparse datasets to improve accuracy. The reduction in computation time achieved by using ML models instead of extensive RT computations for sparse training datasets is evaluated, and an efficient algorithm for training such models is proposed. Additionally, the sensitivity of ML models to noisy input features is analyzed. We also assess the importance of geometrical features and the impact of sequentially increasing the number of these features on model performance. The results emphasize the significance of the proposed geometrical features and demonstrate the potential of ML models to provide computationally efficient and relatively accurate PL predictions in diverse urban environments.

In this research, we introduce a significant advancement in decision-making methodologies by proposing the Combine Compromise Solution Approach. This innovative method addresses the complexities inherent in multi-criteria decision-making scenarios, particularly in optimizing logistics within the oil and gas supply chain. Through our exploration of circular spherical fuzzy sets, we investigate various algebraic operations without detailing the research methodology. Our primary contribution lies in the practical application and effectiveness of the Combine Compromise Solution Approach, exemplified through a case study on logistics optimization. By presenting insights gleaned from this approach and conducting a comparative analysis against alternative methods, we demonstrate its utility and potential impact in real-world contexts. This research not only offers immediate practical solutions but also paves the way for future investigations into the broader applications of circular spherical fuzzy sets in decision-making processes, thus advancing the field significantly.

To address the needs of tomorrow, Future Wireless Networks (FWNs) like 6G are anticipated to integrate terrestrial and non-terrestrial networks. Intelligent network coordination across several frequency bands, radio technologies, and end-user requirements are critical factors which will determine the success of such networks. However, there is a widespread concern about the growth in Radio Frequency Electromagnetic Fields (RF-EMF) exposure levels for end users from FWNs. It is critical to identify RF-EMF exposure levels and provide a framework for monitoring, managing, and optimization of RF-EMF levels. This study performs extensive band-selective RF-EMF measurements at two sporting events (a football and basketball match) in sub-6 GHz band. The data is collected every 6 seconds before and during the sporting events from different radio technologies using EME SPY evolution device. We provide a comprehensive analysis to understand exposure levels, contributions from different radio technologies and variations of total exposure levels over the sub-6 GHz band. In addition, RF-EMF levels are modelled using various statistical distributions and evaluated using the Anderson-Darling test. It is found that exposure levels increase in the range of 1.35-1.93 times compare to non-match day and Burr distribution provides the best fit to model RF-EMF exposure levels in both sporting events.

This study investigates magnetic flux density (B) and radiofrequency electromagnetic field (RF-EMF) measurements on electric buses operating in Samsun, Turkey, focusing on two bus routes (called E1 and E4) during the morning and evening hours. Measurements were taken under diverse operational conditions, including acceleration, cruising, and braking, at locations of peak passenger density. Along the E1 route, the magnetic field intensity varied significantly based on the bus position, road slope, and passenger load, with notable increases during braking. In contrast, the E4 route showed a lower magnetic field intensity and RF-EMF values due to its straighter trajectory and reduced operational stops. The highest RF-EMF measurement recorded was 6.01 V/m, which is below the maximum levels established by the ICNIRP guidelines. In 11 out of the 12 different band-selective RF-EMF measurements, the highest contribution came from the downlink band of the base stations, while in only one measurement, the highest contribution originated from the uplink bands of the base stations. All data were subject to the Anderson–Darling test, confirming the generalized extreme value distribution as the best fit for both B and RF-EMF measurements. Additionally, the study assessed B levels inside and outside the bus during charging, revealing heightened readings near the pantograph. These findings significantly contribute to our understanding of electromagnetic field exposure in electric bus environments, highlighting potential health implications and informing the development of targeted mitigation strategies.

Micro-expressions (ME) are rapidly occurring expressions that reveal the true emotions that a human being is trying to hide, cover, or suppress. These expressions, which reveal a person's actual feelings, have a broad spectrum of applications in public safety and clinical diagnosis. This study provides a comprehensive review of the area of ME recognition. A bibliometric and network analysis techniques is used to compile all the available literature related to ME recognition. A total of 735 publications from the Web of Science (WOS) and Scopus databases were evaluated from December 2012 to December 2022 using all relevant keywords. The first round of data screening produced some basic information, which was further extracted for citation, coupling, co-authorship, co-occurrence, bibliographic, and co-citation analysis. Additionally, a thematic and descriptive analysis was executed to investigate the content of prior research findings, and research techniques used in the literature. The year wise publications indicated that the published literature between 2012 and 2017 was relatively low but however by 2021, a nearly 24-fold increment made it to 154 publications. The three topmost productive journals and conferences included IEEE Transactions on Affective Computing (n = 20 publications) followed by Neurocomputing (n = 17) and Multimedia tools and applications (n = 15). Zhao G was the most proficient author with 48 publications and the top influential country was China (620 publications). Publications by citations showed that each of the authors acquired citations ranging from 100 to 1225. While publications by organizations indicated that the University of Oulu had the most published papers (n = 51). Deep learning, facial expression recognition, and emotion recognition were among the most frequently used terms. It has been discovered that ME research was primarily classified in the discipline of engineering, with more contribution from China and Malaysia comparatively.

Abstract Background COVID-19 mass vaccination is the only hopeful savior to curb the pandemic. Vaccine distribution to achieve herd immunity is hindered by hesitance and negative attitude of the public against COVID-19 vaccination. This study aims to evaluate the vaccine hesitancy and attitudes in major cities in Pakistan as well as their determinants. Methods A cross-sectional telephonic survey was conducted in June 2021 in major cities of Pakistan including Karachi, Lahore, Islamabad, Peshawar, and Gilgit, from unvaccinated urban population aged 18 years or older. Random Digit Dialing through multi-stage stratified random sampling was used to ensure representation of each target city and socio-economic classes. Questionnaire collected information on socio-demographics, COVID-19-related experiences, risk perception of infection, and receptivity of COVID-19 vaccination. Multivariate logistic regression analyses were performed to identify key determinants of vaccine hesitancy and acceptance. Results The prevalence of vaccinated population in this survey was 15%. Of the 2270 respondents, 65% respondents were willing to vaccinate, while only 19% were registered for vaccination. Factors significantly associated with vaccine willingness were older age (aOR: 6.48, 95% CI: 1.94–21.58), tertiary education (aOR: 2.02, 95% CI: 1.36, 3.01), being employed (aOR: 1.34, 95% CI: 1.01, 1.78), perceived risk of COVID-19 (aOR: 4.38, 95% CI: 2.70, 7.12), and higher compliance with standard operating procedures (aOR: 1.72, 95% CI: 1.26, 2.35). The most common vaccine hesitancy reasons were ‘no need’ (n = 284, 36%) and concerns with ‘vaccine safety and side effects’ (n = 251, 31%), while most reported vaccine motivation reasons were ‘health safety’ (n = 1029, 70%) and ‘to end the pandemic’ (n = 357, 24%). Conclusions Although our study found 35% hesitancy rate of COVID-19 vaccine, there were noticeable demographic differences that suggest tailored communication strategy to address concerns held by most hesitant subpopulation. Use of mobile vaccination facilities particularly for less mobile and disadvantaged, and implementation and evaluation of social mobilization strategy should be considered to increase overall COVID-19 vaccination acceptance and coverage.

Floods are among the most destructive natural disasters, causing extensive damage to human lives, property, and the environment. Pakistan is susceptible to natural calamities, such as floods, resulting in millions of people being impacted yearly. It has been demonstrated that flood severity is rising and may continue to escalate in the coming years because of climate change-induced changes in monsoon precipitation in the country. Given the country’s exposure to flooding, it is essential to assess the vulnerability to floods to prepare for and mitigate their impact in Pakistan. This study provides a new conceptual framework for assessing flood risk and vulnerability in Charsadda, a flood-prone district in Pakistan. It evaluates the vulnerability of settlements to floods based on four indicators: population density, the average gross domestic product (GDP) of land, the distance between settlements and rivers, and land use and cover (LULC). The analytical hierarchy process (AHP) technique was integrated with the geographical information system (GIS) to assess the level of vulnerability to floods in the study area. The results reveal a higher degree of vulnerability to floods in the region. The spatial pattern of vulnerable areas reveals a significant connection between high-risk flood areas and densely populated areas during different flood seasons. The results further reveal that more than 60% of the area is arable land and is highly susceptible to flood. The population and their land-use setup show high and extremely high values of vulnerability in the normalized threshold of 0.3–0.4, respectively. The study provides an in-depth and comprehensive analysis of the chosen indicators, evaluation methods, and results, making this a valuable contribution to the field of flood vulnerability assessment. The findings of this study also include thematic maps and related information to the stakeholders for effective vulnerability management in the study area.

IntroductionChild immunization, though cost-beneficial, experiences varying costs influenced by individual facility-level factors. A real-time solution is to optimize resources and enhance vaccination services through proper method to measure immunization facility efficiency using existing data. Additionally, examine the impact of COVID-19 on facility efficiency, with the primary goal of comprehensively assessing child immunization facility efficiency in Pakistan.MethodsUtilizing survey data collected in four rounds from May 2018 to December 2020, the research focuses on doses administered and stock records for the preceding six months in each phase. In the initial stage, Data Envelopment Analysis (DEA) is utilized to compute facility efficiency, employing two models with varied outputs while maintaining consistent inputs. Model 1 assesses doses administered, encompassing three outputs (pentavalent vaccine 1, 2, and 3). Meanwhile, Model 2, focuses on stock used featuring a single output (total doses used). The inputs considered in both models include stock availability, staff members, cold chain equipment, vaccine carriers, and vaccine sessions. The second stage involves the application of two competing regression specifications (Tobit and Simar-Wilson) to explore the impact of the COVID-19 pandemic and external factors on the efficiency of these facilities.ResultsIn 12 districts across Punjab and Sindh, we assess 466 facilities in Model 1 and 455 in Model 2. Model 1 shows 59% efficiency, and Model 2 shows 70%, indicating excess stock. Stock of vaccines need to be reduced by from 36% to 43%. In the stage, COVID-19 period reduced efficiency in Model 1 by 10%, however, insignificant in Model 2.ConclusionsThe proposed methodology, utilizing DEA, emerges as a valuable tool for immunization facilities seeking to improve resource utilization and overall efficiency. Model 1, focusing on doses administered indicates facilities low efficiency at average 59% and proves more pertinent for efficiency analysis as it directly correlates with the number of children vaccinated. The prevalent issue of overstocking across all facilities significantly impacts efficiency. This study underscores the critical importance of optimizing resources through the redistribution of excess stock with low efficiency.

The groundwater resources in different areas of Pakistan are heading towards depletion along with the deterioration of quality due to over-abstraction and urbanization. The main focus of this study is to map the current hydrostratigraphical and hydraulic conditions of the late Quaternary aquifers in the central part of Thal Doab of Punjab Plains. To achieve the target, a comprehensive approach was employed combining geophysical investigations using electrical resistivity surveys (ERS) and physiochemical analysis of groundwater specimens collected from the study area. Careful calibration of resistivity models was performed by comparing them with lithologs to ensure their accuracy. The current groundwater conditions were assessed through thirty vertical electrical soundings (VES) using the Schlumberger electrode configuration up to 300m of AB/2. The interpreted results revealed the presence of four to six geo-electric sublayers comprising the intermixing layers of clay, silt, sand, gravel, and kankar inclusions. These layers exhibited very low (230 Ω-m) resistivity zones at various depth intervals. The developed 2D/3D models of aquifer systems identify the promising areas of good/fresh quality groundwater in the regions characterized by medium to very high resistivity mainly within the sand with gravel layers. However, lower resistivity values indicate the presence of marginally suitable/fair and saline/brackish groundwater showing the existence of fine sediments such as clays/silts. Additionally, twenty groundwater samples were collected to assess various parameters including pH, TDS, arsenic, fluoride, iron, nitrate, and nitrite. The spatial distribution of these parameters was visualized using 2D maps. The suitability of the groundwater for drinking consumption was evaluated in accordance with WHO guidelines.

This study seeks to assess the hydrogeochemical characteristics of groundwater in the southern part of Thal Desert of Pakistan. The primary focus lies in identifying potential sources of contamination and evaluating their impact on groundwater and the ecosystem. Groundwater samples were collected from diverse sources including shallow hand pumps, tubewells, and dug wells, with depths ranging from 11 to 28 m. A comprehensive analysis was performed to scrutinize the physical, chemical, and microbial attributes of the samples. Utilizing visual aids like the Piper, Durov, and Gibbs diagrams, as well as Pearson correlation, scatter plots, Schoeller diagrams, and pie charts, the study evaluated the groundwater quality and its suitability for consumption. Results indicate that mineral infiltration from rainfall, domestic waste, and industrial effluents significantly affects groundwater quality, leading to widespread salinity. Weathering processes and ion exchange were identified as key factors contributing to elevate levels of bicarbonates, sodium, magnesium, and chloride ions. Employing the Water Quality Index (WQI) on 40 groundwater samples, findings reveal that 52.5% of samples demonstrated poor to not suitable quality, with 27.5% categorized as poor, 2.5% as very poor, and 22.5% not suitable consumption. Conversely, 47.5% of samples showcased good to excellent quality, with 25% rated as good and 22.5% as excellent. These findings provide valuable insights for hydrogeologists to develop appropriate strategies for water treatment and address any concerns related to groundwater quality.

Thermal diffusion is a phenomenon where the concentration gradient or diffusive flux is created due to the temperature gradient. Thermal diffusion is induced because of the higher temperature and uneven distribution of the mixture. Formally, thermal diffusion is called the Soret effect, and it is a crucial factor in a number of natural occurrences like the separation of isotopes technique of purification. In this research paper, Maxwell fluid’s flow in the vicinage of a flat plate is discussed by considering the effect of the thermodiffusion subject to the first-order slip at the boundary with the application of a constant proportional Caputo (CPC) fractional derivative. The effect of heat generation and radiation is also taken into consideration, as well as the effect of a magnetic field of constant magnitude. The generalized heat and mass fluxes are considered, and this generalization of heat and mass fluxes is done by utilizing the CPC fractional derivative. After converting the current model’s governing equations into a dimensionless form, the temperature, concentration, and velocity fields’ analytical solutions are found. By drawing graphs of the temperature, concentration, and velocity fields for the parametric modifications, the results are graphically illustrated. It becomes clear from the results discussion that the outcomes produced by the constant proportional derivative are more decaying than those obtained with the classical differential operator of order one.

ABSTRACTGlaciers in the Hindu Kush-Karakoram-Himalayas (HKH) are a major source of freshwater in the agriculture-dependent economy of Pakistan. In recent decades, mountain glaciers have been threatened by global warming. In this study, we estimated the Equilibrium Line Altitude (ELA) and geodetic mass balance of fifteen representative glaciers from Hunza (Karakoram), Chitral (Hindu Kush), and Astore (Himalaya) using Landsat satellite images and Advanced Space-borne Thermal Emission and Reflection Radiometer (ASTER) DEMs between 2000 and 2018. The climatic trends (temperature and precipitation) in the 19 years (1995–2013) time series of the three regions were assessed using the non-parametric Mann-Kendall test and Sen’s Slope. The ELA of the observed glaciers except for two Atrak (Chitral) and Gulkin (Hunza) is shifted upward, while the mass balance indicates a heterogeneous pattern ranging from −0.23 ± 0.05 to −0.01 ± 0.015 m w.e a−1, −0.13 ± 0.05 to+0.17 ± 0.11 m w.e a−1 and −0.03 ± 0.02 to+0.23 ± 0.09 m w.e a−1 in Chitral, Hunza, and Astore basin, respectively. A significant or slight increasing or decreasing trend was found in the mean annual temperature, mean total precipitation, and mean discharge of the studied basins. This study will be a good contribution to understanding snow and glacier dynamics in HKH, the factors that influence them, and their interaction with the environment.

The increasing concern about the environment has enhanced the utilization of sustainable products in every field of life, from common household items to fabrics. Although cotton is a plant-based fiber, its cultivation requires large amounts of water and pesticides. Therefore, the future of product development demands environmental friendliness. To overcome the problems of cotton related to the environment, a novel sustainable fabric was developed by using Tencel and jute fiber for the apparel industry to reduce the usage of cotton. In this current study, six blended yarns of two counts (10 & 20 Ne) of Tencel and jute (90:10, 80:20, and 70:30) were developed by ring spinning to weave fabric in two weave designs (Plain & Twill). These developed fabrics were assessed for mechanical, comfort, and hand properties. The results revealed that the blended fabric with a high content of Tencel is good in terms of mechanical, comfort, and hand properties, except for stiffness, pilling resistance, resilience, and drapeability. Additionally, fabric woven with a fine yarn count is also good in the above-stated properties, except for stiffness, resilience, and wrinkle recovery. However, fabrics woven with a twill weave design have good comfort and hand properties, except for smoothness and drapability. On the other hand, fabric with a plain weave design is good in terms of mechanical properties, except for tear strength. The comparison of TJ blended fabric with PC blended fabric shows that the tensile strength, air permeability and OMMC of JT blended fabric is 8.31%, 96%, and 29.46% higher than PC blended fabric but tear strength is 99% less. Furthermore, statistical analysis was performed to check the effect of the input variables on the response factors, and it was found that the effect of yarn count on all output responses is statistically the most significant.

Rapid urban sprawl adversely impacts the local climate and the ecosystem components. Islamabad, one of South Asia's green and environment-friendly capitals, has experienced major Land Use Land Cover (LULC) changes over the past three decades consequently, elevating the seasonal and annual Land Surface Temperature (LST) in planned and unplanned urban areas. The focus of this study was to quantify the fluctuations in LULC and LST in planned and unplanned urban areas using Landsat data and Machine Learning algorithms involving the Support Vector Machine (SVM) over the 1990–2020 data period. Moreover, hybrid Cellular Automata-Markov (CA-Markov) and Artificial Neural Network (ANN) models were employed to project the future changes in LULC and annual LST, respectively, for the years 2035 and 2050. The findings of the study reveal a distinct difference in seasonal and annual LST in planned and unplanned areas. Results showed an increase of ∼22 % in the built-up area but vegetation and bare soil decreased by ∼10 % and ∼12 %, respectively. Built-up land showed a maximum annual mean LST followed by bare-soil and vegetative surfaces. Seasonal analysis showed that summer months experience the highest LST, followed by spring, autumn and winter. Future projections revealed that the built-up areas (∼27 % in 2020) are likely to increase to ∼37 % and ∼50 %, and the areas under the highest annual mean LST class i.e., ≥28 °C are likely to increase to ∼19 % and ∼21 % in planned, and ∼38 % and ∼42 % in unplanned urban areas for the years 2035 and 2050, respectively. Planned areas have better temperature control with urban green spaces, and controlled infrastructure. The Capital Development Authority of Islamabad may be advised to control the expansion of built-up areas, grow urban forests, and thus mitigate the possible Urban Heat Island (UHI) effect.

This study investigates 2D bioconvection magneto-hydrodynamic (MHD) flow and heat transfer of the non-Newtonian (Casson) nanofluid model. The phenomenon of Brownian motion and thermophoresis containing gyrotactic microorganisms over a nonlinear surface is demonstrated pictorially under the simultaneous impact of thermal radiation and velocity slip. Herein, the flow is electrically conducting where different cases and the effect of convergence parameters such as chemical reactions and heat generation/absorption are studied. The transformed ODEs are tackled numerically by employing the Bvp4c scheme. This method contains three-stage Lobatto IIIa collocation formula that provides continuous solutions up to fifth-order accuracy. The salient features of relevant flow parameters are illustrated through tables and graphs, and the current results are compared with the previous ones, which claim considerable agreement. The main finding reveals an increase in the thermophoresis parameter (Nt) and radiation (R) parameters, uprising the temperature profile which leads to enhancement in the thermal boundary layer. Also, the impact of the magnetic parameter (M) shows decrement in the velocity profile because there exists a Lorentz force that suppresses fluid motion. The friction factor and local Nusselt number decrease for higher values of the Casson parameter (β), whereas increment is illustrated for the suction parameter (S).

Abstract Background Pakistan is the fifth most populous country in the world, with a population that is growing at 2.4% annually. Despite considerable political will, including a national commitment that was endorsed by the president to raise the contraceptive prevalence rate (CPR) to 50% by 2025, it has stagnated at around 30–35%. Much of the dialogue on raising CPR is hypothetical and revolves around percentage point change rather than an actual number of women that must be served. Methods The Demographic and Health Survey 2017–18 (DHS 2017–18) provides information about the channels through which users receive family planning (FP) services and disaggregates this information at the provincial level. Proportions of users from each of these channels were multiplied by the Pakistan Census-2017 populations to arrive at the number of users. These users were compared with the total FP users and the number of women that had used any FP service in the past 12 months. Linear estimations of population were applied to calculate population numbers in 2025. Results The national target of 50% CPR by 2025 translates to a population of 20.02 million users. Currently, 11.26 million married women of reproductive age (MWRA) use any method, 8.22 million use a modern method and 4.94 million received this service in the past 12 months. Of these, 2.7 million did so from social marketing outlets, 0.76 million from public sector outreach through lady health workers (LHWs), 0.55 million from private sector and 0.88 million from public sector facilities. However, arriving at the CPR target means expanding annual service delivery from 4.94 to 13.7 million users. Since social marketing and LHW outreach may have become saturated, only public and private health facilities are the likely channels for such an expansion. Conclusions We demonstrate triangulation of the survey data with the census data as a simple policy analysis tool that can help decision-makers estimate the quantum of services they must provide. Such an analysis also allows an understanding of the utilization patterns of each of these channels. In Pakistan’s context, underutilization of funds and existing facilities suggests that increased funding or more providers will likely not be helpful. The policy changes that will likely be most effective include adding outreach to support existing public and private sector facilities while ensuring that procurement of commodities is prioritized.

The advancement in automation and medical care technologies in recent decade has changed the traditional medical treatment of patients. Although, these technologies have increased the treatment’s precision but the growing number and the complexity of IoT healthcare devices are impacting accuracy along with several other challenges. Moreover, the use of different programming languages, operating platforms and data management methodologies are creating restrictions in safe exchange, integration and reuse of information across different applications. However, with the advent of the semantic web, the semantic technologies are growing in healthcare systems due to the capability of machine interpretation and processing by overcoming the restriction of languages and data heterogeneity. The most common shortcoming in the existing systems are the context-awareness and quality of services and the absence of rich patient ontology; leading towards low accuracy of results. To this aim, this paper provides a smart health framework, consisting on the collection and processing of IoT data (related to patient conditions and context). The framework is supported by the patient ontology along with SWRL rules for better decision making that consider different features (context-awareness and quality of services) differently that results in the improved accuracy. In the evaluation process the proposed work has achieved an accuracy of 89.81%. This work will help the practitioners to treat the patients in a better way.

Abstract Background Urban slums are home to a significant number of marginalized individuals and are often excluded from public services. This study explores the determinants of willingness and uptake of COVID-19 vaccines in urban slums in Pakistan. Methods The study uses a cross-sectional survey of 1760 respondents from five urban slums in twin cities of Rawalpindi and Islamabad carried out between June 16 and 26, 2021. Pairwise means comparison tests and multivariate logistic regressions were applied to check the associations of socio-demographic factors and COVID-19 related factors with willingness to get vaccinated and vaccination uptake. Results Only 6% of the sample was fully vaccinated while 16% were partially vaccinated at the time of survey. Willingness to receive vaccination was associated with higher education (aOR: 1.583, CI: 1.031, 2.431), being employed (aOR: 1.916, CI: 1.423, 2.580), prior infection in the family (but not self) (aOR: 1.646, CI: 1.032, 2.625), family vaccination (aOR: 3.065, CI: 2.326, 4.038), knowing of and living close to a vaccination center (aOR: 2.851, CI: 1.646, 4.939), and being worried about COVID-19 (aOR: 2.117, CI: 1.662, 2.695). Vaccine uptake was influenced by the same factors as willingness, except worriedness about COVID-19. Both willingness and vaccination were the lowest in the two informal settlements that are the furthest from public facilities. Conclusions We found low lived experience with COVID-19 infection in urban slums, with moderate willingness to vaccinate and low vaccination uptake. Interventions that seek to vaccinate individuals against COVID-19 must account for urban poor settlement populations and overcome structural barriers such as distance from vaccination services, perhaps by bringing such services to these communities.

Atopic dermatitis (AD) is a long-lasting dermatological disease that is associated with ophthalmic complications in the long run. Those having AD are more likely at risk of developing comorbidities in eye as compared to normal persons. This review encompasses the clinical manifestations, pathophysiology and treatment of common ophthalmic complications presented with AD i.e., blepharitis, kerato-conjunctivitis, corneal ectasias, glaucomatous eyes, lens opacification, detachment of retina, herpetic eye disease (HED) and dupilumab associated eye toxicities. It is necessary for dermatology colleagues to be vigilant enough not to miss ophthalmic problems associated with AD, as an early detection and management can save the vision.

OBJECTIVE: To evaluate the therapeutic effects of intra-vitreal injection Bevacizumab combined with Diclofenac-Na versus intra-vitreal Bevacizumab alone in the treatment of naïve diabetic macular edema. METHODS: In this prospective, randomized interventional clinical trial, 40 eyes of 40 participants were enrolled for trial conducted at an Ophthalmology department of Qazi Hussain Medical Complex, Nowshera. Twenty eyes each included in the intra-vitreal Bevacizumab and bevacizumab plus diclofenac group via random sampling technique. The main outcome variable was a change in best-corrected visual acuity (BC-VA) in log MAR at 4th, 12th and 24th week. The secondary outcomes included mean change in central subfield thickness (CSFT) of macula and possible injection-related side effects. RESULTS: Marked improvement in BC-VA was observed in both therapeutic groups (mean change in Log MAR: 0.324±0.411 and 0.562±0.388 for bevacizumab alone and combination group, respectively). The difference in BC-VA change was in favor of combination group; however, the level didn’t achieve statistical significance (p = 0.08). Significant decrease in CSFT was noted in both groups (mean reductions: 178.02 ± 166.42, 214.55 ± 132.65) for bevacizumab and combination, respectively). Comparison of CSFT changes between groups revealed that combination decreased CSFT more than bevacizumab, but the difference was statistically insignificant (p = 0.07). Neither injection related side effects nor any marked change in intraocular pressure was observed in either groups. CONCLUSION: In diabetic macular edema, superiority of combination therapy over Bevacizumab alone was evident, esp. with regard to structural improvement in macula.

Textile auxetic materials with negative Poisson’s ratio (NPR) have potential applications in protective textile, filtration, and energy absorbance. However, cotton core-spun based auxetic textile is rarely investigated. In this study, cotton core-spun (Lycra)-based helical auxetic yarn (HAY) was developed, and the effects of core filament linear density on the auxetic behavior were studied. Core-spun cotton yarns of 8/1 Nec containing core filament (180, 260, and 420 D) were developed. Then, these core-spun yarns were wrapped with 50 D Dyneema filament at 393 twist/m to make the HAY. The diameter, NPR, and tensile strength of HAY were increased with increasing linear density of Lycra filament. Image analysis of HAY containing 420 D Lycra showed 57% diameter increase with the change in axial strain from 0 to 15 mm. The maximum auxetic behavior with NPR of −3.97 and tensile strength of 26.71 cN/tex was observed in 420 D Lycra-based HAY. This study proved that the cotton core spun-based HAY can be produced with significant auxetic properties for practically potential use in wearable protective textiles.

حمل التّكوين النّسقيّ للغةِ النّقائض دلالاتٍ خرجت من رحم القيم التي وُظِّفَت في الشّعر؛ ذلك أنّ الشّعر صوت المجتمع الذي ينقل أحواله وأخباره وأيّامه وكلّ ما يجول في وعي شعرائه؛ أبناء المجتمع. ومن هنا حضرت القيم في متون النّقائض على نحو واضح، سواء أكانت جاهليّة المنشأ أم إسلاميّة المنشأ أم أمويّة التّجلّي. وقد سلّطت ميادين النّقد الأدبيّ الضّوء على القيم الدّلاليّة التي حملتها النّقائض، فركّزت في جماليّات الكرم، والفروسيّة، والافتخار بالنّسب، والاعتزاز بالولاء والانتماء، والتّغنّي بمركزيّة الأخذ بالثّأر. إلّا أنّ النّقد الثّقافيّ يتابع العيوب التي أُضمِرت خلف عباءة تلك الجماليّات؛ بما في ذلك من تكسّب، ونفاق شعريّ، ونسب وولاء متذبذب، وغدر وخيانة قد يضمرها طلب الثّأر. وعليه، فإنّ النّقائض، بغزارتها، كانت مادّة خصبة لتتبّع القيم بما فيها من جماليّات أدبيّة وعيوب ثقافيّة.

Inducing roughness to achieve superhydrophobic surfaces through nanoparticlesʹ inclusion is a well-known concept; however, the consistency and secondary pollutants are challenges to be addressed. As a potential solution, we proposed a superhydrophobic nanofibrous membrane through the electrospinning of polyacrylonitrile and hydrophobic polydimethylsiloxane (PAN/H-PDMS) blended solution and post-heat treatment process. During carbonization, a drastic differential shrinkage between PAN and H-PDMS induces a hierarchically nanorough surface of the electrospun nanofiber. Thanks to the synergistic combination of micro-nano scale hierarchical roughness, a significant improvement in superhydrophobicity was observed with the water contact angle (WCA) of 163.48° and sliding angle (SA) of 4.2°. The proposed composite superhydrophobic nanofibrous membrane (CSN-M) exhibited excellent robustness against the tape peel, abrasion, and bending cycles by maintaining WCA higher than 158° and SA less than 6.5°. The outstanding self-healing feature recovered the WCA to 162.25° and lowered the SA to 5.0° after heat treatment at 60 °C. In addition, the CSN-M revealed a tensile modulus of 12.11 Mpa, a hydrostatic pressure of 39.18 cmH2O, and excellent breathability. The developed CSN-M is strong, with high permeability and outstanding mechano-chemical durability, making it a suitable choice for water/oil separation and self-cleaning applications.

Seismic numerical modeling technique provide most suitable way for the simulation of wave propagation through subsurface rocks and play an essential role in seismic interpretation, seismic inversion and in evaluating and designing a seismic survey. In order to delineate subsurface more precisely, different approaches are proposed for numerical modeling in which the solutions of Biot’s poroelastic equation are given by using reflectivity, finite element and finite difference algorithms. In current study, a finite difference forward modeling method is proposed to quantify the variation in wave properties during its propagation in a poroelastic media exhibiting mesoscopic heterogeneity due to the existence of three phase fluids. The Biot’s poroelastic wave equations are resolved and a generalized finite difference technique in time domain (FDTD) is used to examine seismic responses in a poroelastic media saturated with three phase fluids. So that the ability to detect geo-fluids through forward modeling technique can be improved. The accuracy of the simulation will lay a good foundation for the follow up work and will enhance our understandings about wave propagation through multi-phase poroelastic media and will also assist in detection and discrimination between fluids of different nature. Also, the proposed approach is of great significance for imaging and prediction of subsurface structures and distribution of fluids.

This article describes a novel experimental study considering a multiband fibre–wireless system for constructing the transport network for fifth-generation (5G) networks. This study describes the development and testing of a 5G new radio (NR) multi-input multi-output (MIMO) hybrid fibre–wireless (FiWi) system for enhanced mobile broadband (eMBB) using digital pre-distortion (DPD). Analog radio over fibre (A-RoF) technology was used to create the optical fronthaul (OFH) that includes a 3 GHz supercell in a long-range scenario as well as a femtocell scenario using the 20 GHz band. As a proof of concept, a Mach Zehnder modulator with two independent radio frequency waveforms modifies a 1310 nm optical carrier using a distributed feedback laser across 10 km of conventional standard single-mode fibre. It may be inferred that a hybrid FiWi-based MIMO-enabled 5G NR system based on OFH could be a strong competitor for future mobile haul applications. Moreover, a convolutional neural network (CNN)-based DPD is used to improve the performance of the link. The error vector magnitude (EVM) performance for 5G NR bands is predicted to fulfil the Third Generation Partnership Project’s (3GPP) Release 17 standards.

Objective: To assess the efficacy of topical azithromycin drops versus oral doxycycline therapy in meibomian gland dysfunction. Method: The prospective randomised trial was conducted from December 2019 to June 2020 at the Qazi Hussain Ahmad Medical Complex, Nowshera, Pakistan, and comprised patients of either gender aged 26-42 years having long-standing posterior blepharitis / meibomian gland dysfunction. The subjects were randomised into two equal groups. Both the groups were advised to do warm compresses and lid massage three times a day for 5min each for 4 weeks. In addition, group A group received azithromycin 1% drops 2 times/day for 1 week, followed by once a day for 3 weeks, while group B received oral doxycycline 100mg once a day for 4 weeks. Baseline, midstream at 2 weeks and post-intervention status, including subjective symptoms, were compared. Results: Of the 60 subjects enrolled, there were 30(50%) in each of the two groups; 32(53.3%) males and 28(46.4%) females. While all 30(100%) the participants in group A completed the trial without any adverse reaction to medication, 8(26.7%) in group B quit midstream owing to anorexia/nausea and gastrointestinal discomfort. Compared to baseline, reduction in both subjective and objective features of the disease in both groups was noted regardless of gender (p=0.08). No significant difference was evident in symptoms healing rate and improvement in foreign body sensation between the groups (p>0.05). Group A improved eye redness, while group B proved better in respect of meibomian glands obstruction healing and corneal staining p

Land Surface Temperature (LST) affects exchange of energy between earth surface and atmosphere which is important for studying environmental changes. However, research on the relationship between LST, Land Use Land Cover (LULC), and Normalized Difference Vegetation Index (NDVI) with topographic elements in the lower Himalayan region has not been done. Therefore, the present study explored the relationship between LST and NDVI, and LULC types with topographic elements in the lower Himalayan region of Pakistan. The study area was divided into North-South, West-East, North-West to South-East and North-East to South-East directions using ArcMap 3D analysis. The current study used Landsat 8 (OLI/TIRS) data from May 2021 for LULC and LST analysis in the study area. The LST data was obtained from the thermal band of Landsat 8 (TIRS), while the LULC of the study areas was classified using the Maximum Likelihood Classification (MLC) method utilizing Landsat 8 (OLI) data. TIRS collects data for two narrow spectral bands (B10 and B11) with spectral wavelength of 10.6 μm–12.51 μm in the thermal region formerly covered by one wide spectral band (B6) on Landsat 4–7. With 12-bit data products, TIRS data is available in radiometric, geometric, and terrain-corrected file format. The effect of elevation on LST was assessed using LST and elevation data obtained from the USGS website. The LST across LULC types with sunny and shady slopes was analyzed to assess the influence of slope directions. The relationship of LST with elevation and NDVI was examined using correlation analysis. The results indicated that LST decreased from North-South and South-East, while increasing from North-East and South-West directions. The correlation coefficient between LST and elevation was negative, with an R-value of −0.51. The NDVI findings with elevation showed that NDVI increases with an increase in elevation. Zonal analysis of LST for different LULC types showed that built-up and bare soil had the highest mean LST, which was 35.76 °C and 28.08 °C, respectively, followed by agriculture, vegetation, and water bodies. The mean LST difference between sunny and shady slopes was 1.02 °C. The correlation between NDVI and LST was negative for all LULC types except the water body. This study findings can be used to ensure sustainable urban development and minimize urban heat island effects by providing effective guidelines for urban planners, policymakers, and respective authorities in the Lower Himalayan region. The current thermal remote sensing findings can be used to model energy fluxes and surface processes in the study area.

The emphasis on clean and green technologies to curtail greenhouse gas emissions due to fossil fuel-based economies has originated the shift towards electric mobility. As on-road electric vehicles (EVs) have shown exponential growth over the last decade, so have the charging demands. The provision of charging facilities from the low-voltage network will not only increase the distribution system’s complexity and dynamics but will also challenge its operational capabilities, and large-scale upgrades will be required to meet the inevitably increasing charging demands. An ultra-fast (UF) charging infrastructure that replicates the gasoline refueling network is urgently needed to facilitate a seamless transition to EVs and ensure smooth operation. This paper presents a review of state-of-the-art DC fast chargers, the charging infrastructure’s current status, motivation, and challenges for medium-voltage (MV) UF charging stations (UFCS). Furthermore, we consider the possible UFCS architectures and suitable power electronics topologies for UF charging applications. To address the peak formation issues in the daily load profile and high operational expenses of UFCSs, integration of renewable energy sources and energy storage systems due to their technological and economic benefits is being considered. The benefits of line frequency transformer (LFT) replacement with a solid-state transformer (SST), SST models, SST-based UF chargers, and MV SST-based UFCS architectures, as well as related MV active front-end and back-end power electronics topologies, are presented. Finally, the application of microgrids’ hierarchical control architecture is considered for chargers and system-level control and management of UFCSs.

Introduction: Oilfield development’s primary objective has changed in recent years as a result of a deeper focus on oilfield exploration and possible reservoir oil extraction. These days, the distribution and characteristics of residual oil are hot topics.Methodology: This research study provides a physical simulation of the remaining oil distribution in the third-order architectural unit in the beach reservoir. Based on the reservoir geometry and compositional sequence, the third-order architecture unit in a beach sand reservoir can be divided into three types: layered, plate-like, and trough-like architecture units.Results and Discussion: A water-flooding simulation experiment is performed to find the distribution pattern of remaining oil (shortened as RO and used hereafter) and the controlling effect of the mudstone interlayer. The simulation results revealed that in the layered architecture unit with reverse-graded bedding, RO is mainly distributed between interlayers and accumulates at the bottom in fine-grain sands. The horizontal distribution of the mudstone interlayer has a profound effect on blocking the longitudinal migration of fluid. Second, in the plate-like architecture unit with uniform grain size, RO is mainly found in the middle portion of the model, separated by clay interlayers, with irregular presence of RO in the upper and lower part of the model. The oblique distribution of the clay interlayer has a significant effect on blocking the lateral migration of the fluid. Thirdly, in the trough-like architectural unit with normal-graded bedding, the RO is mainly distributed on top of the model in fine-grain sands and on the ridge-like parts formed by the interlayer’s intersection.Conclusion: A trough-like clay interlayer can promote fluid movement. RO distribution patterns from the current experiment can be used to explore the remaining oil in beach sand reservoirs of similar oilfields.

ObjectivesThe COVID-19 pandemic showed distinct waves where cases ebbed and flowed. While each country had slight, nuanced differences, lessons from each wave with country-specific details provides important lessons for prevention, understanding medical outcomes and the role of vaccines. This paper compares key characteristics from the five different COVID-19 waves in Pakistan.MethodsData was sourced from daily national situation reports (Sitreps) prepared by the National Emergency Operations Centre (NEOC) in Islamabad. We use specific criteria to define COVID-19 waves. The start of each COVID-19 wave is marked by the day of the lowest number of daily cases preceding a sustained increase, while the end is the day with the lowest number of cases following a 7-days decline, which should be lower than the 7 days following it. Key variables such as COVID-19 tests, cases, and deaths with their rates of change to the peak and then to the trough are used to draw descriptive comparisons. Additionally, a linear regression model estimates daily new COVID-19 deaths in Pakistan.ResultsPakistan saw five distinct waves, each of which displayed the typical topology of a complete infectious disease epidemic. The time from wave-start to peak became progressively shorter, and from wave-peak to trough, progressively longer. Each wave appears to also be getting shorter, except for wave 4, which lasted longer than wave 3. A one percent increase in vaccinations decreased deaths by 0.38% (95% CI: -0.67, -0.08) in wave 5 and the association is statistically significant.ConclusionEach wave displayed distinct characteristics that must be interpreted in the context of the level of response and the variant driving the epidemic. Key indicators suggest that COVID-19 preventive measures kept pace with the disease. Waves 1 and 2 were mainly about prevention and learning how to clinically manage patients. Vaccination started late during wave 3 and its impact on hospitalizations and deaths became visible in wave 5. The impact of highly virulent strains Alpha/B.1.1.7 and Delta/B.1.617.2 variants during wave 3 and milder but more infectious Omicron/B.1.1.529 during wave 5 are apparent.

Nowadays, heart disease is the leading cause of death. The high mortality rate and escalating occurrence of heart diseases worldwide warrant the requirement for a fast and efficient diagnosis of such ailments. The purpose is to design an automated system for the classification of abnormal heartbeat audio signals to assist cardiologists. To the best of our knowledge, this is the first study that uses a single neural network model for the classification of eight different types of heartbeat audio signals. The proposed recurrent neural network (RNN) model using Long short-term memory (LSTM) is developed on two publically available databases such as the PASCAL challenge and the 2017 PhysioNet challenge. Mel frequency cepstrum coefficient (MFCC) is applied to extract the dominant features, and a bandpass filter is used to remove the noise from both of the datasets. Afterward, the downsampling technique is used to fix the size of the sampling rate of each sound signal to 20KHz and 300 Hz for the Pascal and PhysioNet database, respectively. The proposed model is compared with multi-layer perceptron (MLP) in terms of different performance evaluation matrices. Furthermore, the outcomes of five machine learning (ML) models are also analyzed. The proposed model has achieved the highest classification accuracy of 0.9971 on the Pascal database, and 0.9870 accuracy on the PhysioNet challenge dataset, which is consistently superior to its competitor approaches. The proposed model provides significant assistance to the cardiac consultant in detecting heart valve diseases.

The early Eocene carbonate reservoir, Sui Main Limestone (SML), is the largest gas reservoir in Pakistan. In the Qadirpur area, more than 30 wells have been drilled, some of which have been declared dry or abandoned due to poor reservoir characteristics or facies variation. The aim of this work is to re-evaluate the reservoir characteristics and facies identification of Sui Main Limestone by using petrophysical analysis and rock physics modeling in the Qadirpur field in the Middle Indus Basin of Pakistan. The reservoir characterization of carbonate rocks is difficult because of their complex pore networking. The well data on five exploratory wells drilled in this gas field are used. The log-derived porosity varies in a very large range of 2%–36%, with an average of 14%–34%. The average porosity of the clean carbonate intervals is 10%–14% and thus has the capacity to accumulate hydrocarbon. The high porosity value indicates the presence of micro-fractures in Sui Main Limestone. These micro-fractures and secondary pores are well interconnected and allow the pore fluids to communicate. The secondary porosity is mainly due to the presence of vuggs and fractures in Sui Main Limestone. The volume of shale varies from 11%–38% in the wells. The net pay zones have hydrocarbon saturation in the range of 40%–70%, which is mainly gas. Using wireline log response, the formation is divided into three facies: limestone, shale, and shaly limestone. This comprehensive work may help improve the prediction of the reservoir quality in heterogeneous carbonate reservoirs and optimize field development.

Dog bites are a major cause for transmission of rabies virus to humans. Pakistan ranks fifth among most rabies affected countries in the world. There are a few regional (ecological) studies that investigated factors that explain geographic disparities in incidence of dog bite injuries. The main objective of this research was to document findings of spatial exploratory data analysis of incidence of reported cases of dog bite in Punjab province of Pakistan (2016–2019). In addition, we have quantified the association between incidence of dog bites and a set of selected socio-economic and demographic variables. District-wise data about reported cases of dog bites from 2016 to 2019 were used to map annual crude incidence per 100,000 of population. There was an obvious spatial variation in incidence of dog bites but there was no evidence of spatial autocorrelation. The risk of dog bite attacks was relatively higher in districts with low human population density (per sq. km), poor literacy rate, more rural population (% of total population), and lower median nighttime lights.