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In recent years, green nanomaterials are increasingly being utilized in horticulture and agriculture due to their potential to transform plant development, improve crop yield, combat diseases, and pest management, among other applications. Significantly, green nanomaterials enhance nutrient delivery by improving stability, uptake, and solubility efficiency. Utilizing non-toxic precursors, waste reduction strategies, renewable resources, and energy-efficient processes, they offer sustainable technology advancement. Moreover, tomatoes, the largest domestic and industrial crop, yield around 200 million tons globally. They are beneficial for blood cleansing, gastrointestinal secretion, and nutrient reduction. However, they are susceptible to diseases like early blight, septoria leaf spot, late blight, and buckeye rot. In this regard, green nanomaterials have the potential to revolutionize agriculture by improving disease management, detecting diseases quickly, enhancing nutrient uptake, safely delivering agrochemicals, and reducing pesticide dosage. So, impressed by this, in this current review paper, we explored the use of green nanomaterials for tomato growth, and development and investigated their unique features, utilization, challenges, and future prospects. Ultimately, this review will open a new research gateway for plant-based nanomaterial and their utilization in various disciplines particularly for crop protection, growth, and soil health.

Background: In coronavirus pandemic, misconception, distrust, uncertainty, and fear have emerged within the general public. Amidst this environment, health professionals were the primary bearers of accurate information regarding the causes of death, the severity of infections, and the intensity of disease transmission. Objective: To assess the attitude and responses of health professionals working in tertiary care hospitals of city Peshawar during pandemic regarding corona infection and related deaths. Study type,settings & duration:This descriptive cross sectional studywas conducted at tertiary care hospitals of Peshawar, from March to May2020. Methodology:The sample size of 126 health professionalswere takenat the peak of pandemic in Pakistan. A predesigned online questionnaire was shared with individual health professional after taking telephonic consent. Each health professional filled the online questionnaire and resubmitted. The data was collected, analyzed using SPSS version 21. Results:A total 126 health professionals participated in the study, 61 (48.4%) were doctors, 58 (46%) nurses and 7 (5.6%) were allied health professionals. Total46 (54.1%) health professionals agreed, that old age with comorbidities, stigma and late approach to health facilities, comorbidities and spread asymptomatic cases were associated with increased mortality of COVID patients in city Peshawar. About 54 (42.9%) health professionals agreed with the existence of false perception of public that corona do not exist, and 13 (10.3%) disclosed about lack of proper facilities and ignorance in provision of care in Hospitals. Conclusion:Health professionals were in agreement that comorbidities, older age, late approaching to Hospital, stigma, false perception and lack of health facilities were the factors behind the increased corona infected deaths in Peshawar.

Adopting low-carbon agricultural technologies (LCATs) is fundamental to reducing carbon emissions in agriculture. Our study explores the factors influencing the adoption of LCATs and the roles of production outsourcing and specialised farming within the framework of off-farm employment. In this regard, survey data were collected from 1 040 farmers in the Yellow River region of China in 2020 to examine the effect of production outsourcing on the farmers' adoption of LCATs. Potential mechanisms associated with specialised farming and off-farm employment are considered to comprehend this relationship. We also investigate the heterogeneous effects of production outsourcing on adopting LCATs, taking different education levels and arable land areas into account. The results show a positive association between production outsourcing and farmers' LCATs adoption behaviour, even after considering self-selection bias. Specifically, outsourcing production can significantly increase the likelihood of farmers adopting low-carbon tillage, low-carbon irrigation, and low-carbon fertilisation technologies by 7.2%, 8.1%, and 7.3%, respectively. This effect is more pronounced among farmers with higher levels of education and smaller areas of arable land. Furthermore, production outsourcing increases the LCATs adoption by promoting specialised farming. The findings suggest that outsourcing is vital to alleviating the lack of LCATs adoption resulting from off-farm employment.

One of the major obstacles to agriculture is global climate change, which damages crop productivity through increasing temperatures and irregular precipitation patterns. Wheat, one of the most vital staple crops in the world, is particularly susceptible to these weather Therefore, understanding the fundamental relationship between wheat yield and climate is critical in developing effective adaptation and mitigation plans. The study aims to determine how unirrigated wheat yield evolves depending on the temperature, humidity, rainfall, rainfed areas, and fertilizer in Khyber Pakhtunkhwa, Pakistan. The Kripfganz method was used for p-value calculation, and the DYNARDL simulation model and the autoregressive distributed lag bound testing were used to analyze data from the Development Statistics of Khyber Pakhtunkhwa and the Pakistan Meteorological Department. Unit root analysis was used to check the stationarity of the time series. Then, DYARDL-bound Testing was used for long-run cointegration analysis among variables. The short-run influence of precipitation is substantial, but the long-run impact is not. Meanwhile, the use of chemical fertilizers does not influence yields in either the short or long run. In addition, neither short-run nor long-run yields are statistically significantly connected with the highest temperature. The DYNARDL simulation model reveals precipitation, rainfed-area, and fertilization shocks in the long and short run. The 10 % positive and negative shocks in DYNARDL simulations are supplemented with graphical interpretations of the impact on yield. The study results may have significant implications for policymakers and management professionals in the agriculture sector in Khyber Pakhtunkhwa.These findings can inform the development of strategies to mitigate the impacts of climate change on wheat yields, ultimately enhancing food security and agricultural sustainability in the region.

The capacity of Compressive Sensing (CS) to recreate original data from a limited number of samples has led to a surge in attention in recent years. As Deep Neural Networks (DNN) have emerged, the performance level of CS has also increased with the deployment of data-driven Autoencoders in it. But the lack of optimal parameters and hyperparameters, and inconsistent Autoencoder structures for more complex datasets cause inefficient resource utilization and limit the true potential of Autoencoders. In this paper, we propose the optimally designed structures and parameters of deep Autoencoder subnetworks for CS-based sampling and reconstruction of speech and image datasets to align with the desired objectives and available constraints. Furthermore, the impact of optimal types, structures, and hyperparameters of Autoencoders are assessed to match with their deployment for speech and image CS. Apart from the optimized levels of structure and hyperparameters, our work emphasized the usage of other types of Autoencoders – other than the Stacked Autoencoders- which are employed for the sampling and reconstruction of speech and image datasets. As a result, the optimally designed Autoencoders have demonstrated 18% improved performances for speech signals when compared with the baseline model. In this work, the novelty is associated with re-engineering the internal structure and layers of Convolutional layers to provide an optimized level of hyperparameters and layer architecture for image compressive sensing, which better suits a variety of compression ratios to better preserve bandwidth, power, and computational resources. We found the compression ratio of 0.4 to be the optimum value, which exhibited the structural similarity value of 0.85, for Convolutional Autoencoders. The novelty of our work lies in developing AI-driven, data-adaptive models that leverage optimally trained autoencoders for CS, significantly enhancing resource efficiency in terms of storage, hardware complexity, and computational cost.

UMBRELLA (A Living Laboratory: https://www.umbrellaiot.com/) is an open, large-scale IoT ecosystem deployed across South Gloucestershire, UK. It is intended to accelerate innovation across multiple technology domains. UMBRELLA is built to bridge the gap between existing specialised testbeds and address holistically real-world technological challenges in a System-of-Systems (SoS) fashion. UMBRELLA provides open access to real-world devices and infrastructure, enabling researchers and the industry to evaluate solutions for Smart Cities, Robotics, Wireless Communications, Edge Intelligence, and more. Key features include over 200 multi-sensor nodes installed on public infrastructure, a robotics arena with 20 mobile robots, a 5G network-in-a-box solution, and a unified backend platform for management, control and secure user access. The heterogeneity of hardware components, including diverse sensors, communication interfaces, and GPU-enabled edge devices, coupled with tools like digital twins, allows for comprehensive experimentation and benchmarking of innovative solutions unviable in lab environments. This paper provides a comprehensive overview of UMBRELLA’s multi-domain architecture and capabilities, making it an ideal playground for Internet of Things (IoT) and Industrial IoT (IIoT) innovation. It discusses the challenges in designing, developing and operating UMBRELLA as an open, sustainable testbed and shares lessons learned to guide similar future initiatives. With its unique openness, heterogeneity, realism and tools, UMBRELLA aims to continue accelerating cutting-edge technology research, development and translation into real-world progress.

Modern industries rapid expansion has heightened energy needs and accelerated fossil fuel depletion, contributing to global warming. Additionally, organic pollutants present substantial risks to aquatic ecosystems due to their stability, insolubility, and non-biodegradability. Scientists are currently researching high-performance materials to address these issues. LaFeO3 nanosheets (LFO-NS) were synthesized in this study using a solvothermal method with polyvinylpyrrolidone (PVP) as a soft template. The LFO-NS demonstrate superior performance, large surface area and charge separation than that of LaFeO3 nanoparticles (LFO-NP). The LFO-NS performance is further upgraded by incorporating ZIF-67. Our results confirmed the ZIF-67/LFO-NS nanocomposite have superior performances than pure LFO-NP and ZIF-67. The integration of ZIF-67 has enhanced the charge separation and promote the surface area of LFO-NSwhich was confirmed by various characterization techniques including TEM, HRTEM, DRS, EDX, XRD, FS, XPS, FT-IR, BET, PL, and RAMAN. The 5ZIF-67/LFO-NS sample showed significant activities for CO2 conversion, malachite green degradation, and antibiotics (cefazolin, oxacillin, and vancomycin) degradation. Furthermore, stability tests have confirmed that our optimal sample very active and stable. Furthermore, based on scavenger experiments and the photocatalytic degradation pathways, it has been established that H+ and •O2− are vital in the decomposition of MG and antibiotics. Our research work will open new gateways to prepare MOFs-Perovskites nanocatalysts for exceptional CO2 conversion, organic pollutants and antibiotics degradation.

Objective: To determine the sero-prevalance of antibody against Hepatitis B virus surface antigen among EPI vaccinated children born between year 2009-2016. Study Design: Descriptive cross sectional study. Place and Duration of Study: Study was carried out among urban area children born in Peshawar city between 2009-2016 from 2 August, 2020 to 30, April 2021. Materials and Methods: This was descriptive cross-sectional study carried out among children born in city Peshawar “between” 2009-2016, using non-probability convenient sampling technique. After taking the written consent from the parents a predesigned questionnaire was filled. About 3 to 5 ml of blood was collected for anti HBs Ag test through using ELISA technique. Results were collected and analysed using SPSS version 21. Results: This study included 184 EPI HBV vaccinated children vaccinated “between” 2012 to 2016. Out of 184 children 118 (64.1%) were female belonging to the middle socio-economic class 170 (92.4%). The mean age of the study participants was 7.834 years ±2.045. Anti Hbs antibody titre revealed that out of 184 study participants 75.5% were vaccinated within last 10 years. It was observed that only 33 (17.9%) children out of 184 were immune against HBV (Antibody level > 100 mIU/ml). The study showed that there was no significant difference (p>0.05) in the immune status of the children with respect to the demography like age of child p=0.529, gender p= 0.461, place of vaccination p=0.918, economic status p=0.190. Conclusion: The EPI HBV vaccinated children lost protective anti Hbs antibody level after five years of vaccination.

Nanomaterials (NMs) have garnered significant attention in recent decades due to their versatile applications in a wide range of fields. Thanks to their tiny size, enhanced surface modifications, impressive volume-to-surface area ratio, magnetic properties, and customized optical dispersion. NMs experienced an incredible upsurge in biomedical applications including diagnostics, therapeutics, and drug delivery. This minireview will focus on notable examples of NMs that tackle important issues, demonstrating various aspects such as their design, synthesis, morphology, classification, and use in cutting-edge applications. Furthermore, we have classified and outlined the distinctive characteristics of the advanced NMs as nanoscale particles and hybrid NMs. Meanwhile, we emphasize the incredible potential of metal-organic frameworks (MOFs), a highly versatile group of NMs. These MOFs have gained recognition as promising candidates for a wide range of bio-applications, including bioimaging, biosensing, antiviral therapy, anticancer therapy, nanomedicines, theranostics, immunotherapy, photodynamic therapy, photothermal therapy, gene therapy, and drug delivery. Although advanced NMs have shown great potential in the biomedical field, their use in clinical applications is still limited by issues such as stability, cytotoxicity, biocompatibility, and health concerns. This review article provides a thorough analysis offering valuable insights for researchers investigating to explore new design, development, and expansion opportunities. Remarkably, we ponder the prospects of NMs and nanocomposites in conjunction with current technology.

The way we travel is changing rapidly and Cooperative Intelligent Transportation Systems (C-ITSs) are at the forefront of this evolution. However, the adoption of C-ITSs introduces new risks and challenges, making cybersecurity a top priority for ensuring safety and reliability. Building on this premise, this paper introduces an envisaged Cybersecurity Centre of Excellence (CSCE) designed to bolster researching, testing, and evaluating the cybersecurity of C-ITSs. We explore the design, functionality, and challenges of CSCE's testing facilities, outlining the technological, security, and societal requirements. Through a thorough survey and analysis, we assess the effectiveness of these systems in detecting and mitigating potential threats, highlighting their flexibility to adapt to future C-ITSs. Finally, we identify current unresolved challenges in various C-ITS domains, with the aim of motivating further research into the cybersecurity of C-ITSs.

The study was conducted to evaluate the emotional, cognitive and behavioral effects among COVID 19 survivors. The data was collected from April 2020 to June 2021. Simple face to face and online data collection methods were employed, a questionnaire using likart type questions was used. For data collection, five point Likert scale questionnaire was designed according to the objectives of the study. The questionnaire included questions related to cognitive, emotional and behavioral aspects of the COVID-19 survivors. Additionally, it also explored the perceptions of the participants about the disease. A total of 90 participants were recruited in this study. All these had confirmed diagnosis of COVID 19 on Polymerase Chain Reaction (PCR) positive on nasal swab. The findings of this study indicate that most of the covid-19 survivors feel weakness, and depression after recovering from this viral disease. A considerable number of COVID 19 survivors experienced loss taste and smell for a prolonged period. According to their perception social gatherings were responsible for spread of the disease thus they strongly agreed upon lockdown strategy adopted in many countries.

IntroductionThis study analyzes the influence of risk preference on the forage planting behavior of farmers in the agro-pastoral zone from three aspects-whether or not to plant forage, the scale of forage planting, and the duration of forage planting—and pays attention to the indirect effect of credit on the farmers' forage planting behavior, as well as the heterogeneity of the influence of risk preference on the forage planting behavior of farmers from the perspectives of different farming scales, types of farmers, and differences between generations.MethodsExperimental economics, a two-stage model, is used to analyze this problem.Results and conclusionThe results show that, first, risk preference can significantly promote farmers' forage planting probability, expand forage planting scale, and increase forage planting duration. Specifically, when risk preference increases by one unit, the probability of farmers choosing to plant forage increases by 7.8%, the planting scale increases by 0.205 hm2, and the planting duration increases by 0.519 years. This conclusion remained robust after changing the explanatory variables. Second, risk preference not only directly affects farmers' forage planting behavior but also indirectly affects farmers' forage planting behavior by influencing farmers' participation in credit. Third, heterogeneity analysis shows that risk preference has a significant effect on forage planting behavior among farmers with a medium breeding degree, among pure farmers and concurrent farmers, as well as middle-aged and elderly farmers.

The importance of understanding people's preferences and the well-being of river systems cannot be overstated when it comes to the effective restoration and long-term management of these systems. This study aimed to determine the preferences of people in the Hei River Basin of China regarding the conservation, restoration, and development of ecological systems and their associated services. The research utilized a combination of random parameter logit models for distance decay. The study collected data using a choice experiment method and separated the study area into three distance-based groups: group I (within 25 km), group II (25 to 50 km), and group III (beyond 50 km). The findings showed that there was spatial heterogeneity among the people, with a higher willingness to pay for high-quality agricultural production and the lowest willingness to pay for oasis. The results revealed a complex pattern of spatial heterogeneity and indicated the need for increased awareness programs, environmental education, and the promotion of social responsibility towards environmental protection. It is essential to implement environmental policies, restore properties, and preserve ecological systems to achieve sustainable development and a sustainable environment. The findings highlight the importance of considering spatial heterogeneity and the need for increased environmental awareness, education, and policy implementation. This research can contribute to developing effective strategies for the preservation of river systems and ensuring sustainable development and a sustainable environment.

Purpose – The main aim of this study is to investigate the impact of climate change and water salinity on farmer’s income risk with future outlook mitigation. Salinity and climate change are a threat to agricultural productivity worldwide. However, the combined effects of climate change and salinity impacts on farmers' income are not well understood, particularly in developing countries. Design/methodology/approach – The response-yield function and general maximum entropy methods were used to predict the impact of temperature, precipitation and salinity on crop yield. The target minimization of total absolute deviations (MOTAD)-positive mathematical programming model was used to simulate the impact of climate change and salinity on socioeconomic and environmental indicators. In the end, a multicriteria decision-making model was used, aiming at the selection of suitable climate scenarios. Findings – The results revealed that precipitation shows a significantly decreasing trend, while temperature and groundwater salinity (EC) illustrate a significantly increasing trend. Climate change and EC negatively impact the farmer's income and water shadow prices. Maximum reduction in income and water shadow prices was observed for A2 scenario (−12.4% and 19.4%) during 2050. The environmental index was the most important, with priority of 43.4% compared to socioeconomic indicators. Subindex amount of water used was also significant in study area, with 28.1% priority. The technique for order preference by similarity to ideal solution ranking system found that B1 was the best climatic scenario for adopting climate change adaptation in the research region. Originality/value – In this study, farmers' income threats were assessed with the aspects of different climate scenario (A1, A1B and B1) over the horizons of 2030, 2040 and 2050 and three different indicators (economic, social and environmental) in Northwestern region of Pakistan. Only in arid and semiarid regions has climate change raised temperature and reduced rainfall, which are preliminary symptoms of growing salinity.

A dataset of sensor measurements is presented. Our dataset contains discrete measurements of 8 IoT devices located in various places in a research lab at the University of Bristol. Nordic nRF52840 DK IoT devices periodically collects environmental data, such as temperature, humidity, pressure, gas, room light intensity, accelerometer; including also a measurement quality indicator. The measurements were taken every 10 seconds over a six-month period between February and September 2022. In addition, we provide Received Signal Strength Indicator (RSSI) of the IoT devices.The data files are formatted as CSV files. There are various software libraries available to access and read this file format. We provide “README.txt” file which explains the repository and how to use dataset. Each data file is named according to its creation date and, once it reaches a size of 1MB, it is compressed and archived. A new folder is created every week to store all the data files from that week automatically. The dataset can be used for drift detection such as malicious or anomaly detection algorithms. It can also be used for smart building applications like occupation detection. The dataset can be found at https://data.bris.ac.uk/data/dataset/fwlmb11wni392kodtyljkw4n2

Introduction: Recently, the public and policymakers have acquired knowledge of the detrimental effects of pesticide use in agriculture. These include the threat to the health of chemical applicators and the threat that pesticide residues pose to the safety of food. The present study focuses on the farmers’ social networks from a new perspective, along with the farmers’ concurrent agricultural business and their impact on the farmer’s safe production behavior.Methodology: The Endogenous Switching Probit Regression model and Binary Probit Group Regression model were employed for the empirical analysis of survey data collected from 585 households in the Xianyang, Yan’an, and Weinan districts of Shaanxi province, China.Results and Discussion: The results revealed that farmers’ social networks can greatly affect farmers’ safe production behavior. Additionally, we noted that the farmers’ social networks may play a positive role in promoting the farmers’ safe production behaviors of both concurrent agricultural business and non-concurrent agricultural business farmers. Moreover, their correlation coefficients were found significant at a confidence level of 5%. Our findings suggest that the government needs to construct social networks among farmers by setting up a communication platform and promoting the acquaintance of safe production through reciprocal culture.

A dataset of street light images is presented. Our dataset consists of ∼350 k images, taken from 140 UMBRELLA nodes installed in the South Gloucestershire region in the UK. Each UMBRELLA node is installed on the pole of a lamppost and is equipped with a Raspberry Pi Camera Module v1 facing upwards towards the sky and lamppost light bulb. Each node collects an image at hourly intervals for 24 h every day. The data collection spans for a period of six months.Each image taken is logged as a single entry in the dataset along with the Global Positioning System (GPS) coordinates of the lamppost. All entries in the dataset have been post-processed and labelled based on the operation of the lamppost, i.e., whether the lamppost is switched ON or OFF. The dataset can be used to train deep neural networks and generate pre-trained models providing feature representations for smart city CCTV applications, smart weather detection algorithms, or street infrastructure monitoring. The dataset can be found at 10.5281/zenodo.6046758.

Objective: To identify the dietary patterns and understand their association with sociodemographic characteristics among adults. Methods: The community-based cross-sectional study was conducted in Islamabad, Lahore, Karachi, Peshawar and Quetta cities of Pakistan from March to November 2018, after approval from the National Bioethics Committee, Islamabad, and comprised adults of either gender. Data was collected using the food frequency questionnaire, and dietary patterns were identified using factor analysis. Multivariate regression analysis was used to assess the association of socio-demographic determinants with dietary patterns. Data was analysed using SPSS 21. Parallel Analysis criterion (Eigen values) was determined along with Monte Carlo simulation. Results: Of the 448 subjects, 206(46%) were males and 242(54%) were females. The largest age group was 36-55 years 199(47.4%). Six dietary patterns were identified: “Vegetables”, “Fruits”, “Mixed Junk and Processed food”, “Dairy and Fast food”, “Discretionary” and “Fish”. Regression analysis showed that those aged 36-55 years had higher scores for vegetables, fruit and fish pattern (p

Multidrug resistance (MDR) is one of the major global threats of this century. So new innovative approaches are needed for the development of existing antibiotics to limit antibacterial resistance. The current study was aimed to utilize extracts of root, stem, and leaves of Persicaria hydropiper for the synthesis of silver nanoparticles (AgNPs) using standard procedure. Synthesis of AgNPs was evident from the change in color of the solution to dark brownish and then it was further revealed by UV–Vis and Fourier Transformed Infrared Spectroscopy (FTIR). UV–Vis spectroscopy has revealed absorbance peak at 370 nm while, FTIR spectrum displayed that aromatics amines were used as reducing agent in the fabrication of AgNPs. In addition, Scanning Electron Microscopy (SEM micrograph) displaying tetrahedron, spherical and oval shapes of synthesized AgNPs whereas, average size of synthesized AgNPs was found in the range of 32–77 nm. Beside this, it was also observed that the potency of antibiotics against MDR bacteria increased after coating with synthesized AgNPs i.e., the potency of Ceftazidime and Ciprofloxacin increased up to 450% and 500% against Bacillus respectively while, the potency of Gentamicin, Vancomycin and Linezolid increased up to 150%, 200% and 58% against Bacillus, Staphylococcus, and Proteus species respectively. Furthermore, it was concluded that utilizing AgNPs in combination with commercially available antibiotics would provide an alternate therapy for the treatment of infectious diseases caused by MDR bacteria.

Objectives: Tuberculosis (TB) is a significant public health concern, and the basis of successful anti-tuberculosis treatment (ATT) rests on the complete eradication of live bacilli from a patient. This study was conducted to detect the live TB bacilli in Lowenstein Jensen culture media among exit cases of TB who were declared successfully treated, either cured or treatment completed. Methods: This cross-sectional study was conducted across Pakistan. Fifty-eight active TB DOTS centers were selected. The sample size of 3355 TB cases were equally distributed in all DOTS facilities. A detailed questionnaire was developed to record the information from TB DOTS and patients. After successful treatment, the sputum was taken from TB cases and examined to detect live bacilli on L-J culture. Results: A total of 3355 TB cases were enrolled in the study. The male to female proportion was 1704(50.9%) and 1651(49.2%). Initially, 1993(59.4%) cases were cured, and 1362(40.6%) were declared as treatment completed cases. At exit, 324(9.65%) cases were again ZN smear-positive, and 328(9.77%) were positive on L-J culture, after being declared successfully treated for TB. Conclusions: To eradicate live TB bacilli, all TB cases should be subjected to L-J culture at the end of ATT.

The use of cardiovascular implantable electronic devices (CIED) is associated with improved quality of life and decreased fatal outcomes in patients with cardiac dysfunctions. As with all foreign devices that are inserted or implanted in the body, CIED also carries the risk of device-related infections. Infections account for

The present investigation envisages on the Mathematical modeling of waves propagating in a porous micropolar fibre-reinforced structure in a half-space and liquid interface. The harmonic method of wave analysis is utilized, such that, the reflection and transmission of waves in the media were modelled and it’s equations of motion analytically derived. It was deduced that incident longitudinal wave in the solid structure yielded four reflected waves given as; quasi–P wave (qLD), quasi–SV wave, quasi–transverse microrotational (qTM) wave and a wave due to voids and one transmitted wave known as the quasi-longitudinal transmitted (qLT) wave. The phase velocity in the liquid medium is independent of angle of propagation as observed. The corresponding amplitude ratios of propagations for both reflected and transmitted waves are analytically derived by employing Snell’s law. The model would prove to be of relevance in the understanding of modeling of the behavior of propagation phenomena of waves in micropolar fibre-reinforecd machination systems resulting in solid/liquid interfaces especially in earth sciences and in particular seismology, amongst others.

OBJECTIVES:. To describe the prevalence, associated risk factors, and outcomes of serious neurologic manifestations (encephalopathy, stroke, seizure, and meningitis/encephalitis) among patients hospitalized with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. DESIGN:. Prospective observational study. SETTING:. One hundred seventy-nine hospitals in 24 countries within the Society of Critical Care Medicine Discovery Viral Infection and Respiratory Illness Universal Study COVID-19 Registry. PATIENTS:. Hospitalized adults with laboratory-confirmed SARS-CoV-2 infection. INTERVENTIONS:. None. RESULTS:. Of 16,225 patients enrolled in the registry with hospital discharge status available, 2,092 (12.9%) developed serious neurologic manifestations including 1,656 (10.2%) with encephalopathy at admission, 331 (2.0%) with stroke, 243 (1.5%) with seizure, and 73 (0.5%) with meningitis/encephalitis at admission or during hospitalization. Patients with serious neurologic manifestations of COVID-19 were older with median (interquartile range) age 72 years (61.0–81.0 yr) versus 61 years (48.0–72.0 yr) and had higher prevalence of chronic medical conditions, including vascular risk factors. Adjusting for age, sex, and time since the onset of the pandemic, serious neurologic manifestations were associated with more severe disease (odds ratio [OR], 1.49; p < 0.001) as defined by the World Health Organization ordinal disease severity scale for COVID-19 infection. Patients with neurologic manifestations were more likely to be admitted to the ICU (OR, 1.45; p < 0.001) and require critical care interventions (extracorporeal membrane oxygenation: OR, 1.78; p = 0.009 and renal replacement therapy: OR, 1.99; p < 0.001). Hospital, ICU, and 28-day mortality for patients with neurologic manifestations was higher (OR, 1.51, 1.37, and 1.58; p < 0.001), and patients had fewer ICU-free, hospital-free, and ventilator-free days (estimated difference in days, –0.84, –1.34, and –0.84; p < 0.001). CONCLUSIONS:. Encephalopathy at admission is common in hospitalized patients with SARS-CoV-2 infection and is associated with worse outcomes. While serious neurologic manifestations including stroke, seizure, and meningitis/encephalitis were less common, all were associated with increased ICU support utilization, more severe disease, and worse outcomes.

Abstract The aim of this present investigation is establishing Minkowski fractional integral inequalities and certain other fractional integral inequalities by employing the Marichev–Saigo–Maeda (MSM) fractional integral operator. The inequalities presented in this paper are more general than the existing classical inequalities cited.

The principal aim of this article is to establish certain generalized fractional integral inequalities by utilizing the Marichev-Saigo-Maeda (MSM) fractional integral operator. Some new classes of generalized fractional integral inequalities for a class of n (n ∈ $\mathbb{N}$) positive continuous and decreasing functions on [a, b] by using the MSM fractional integral operator also derived.

Country olive forests are one of the major contributors with economic aspects. Spain is the leading country in the world which produced around 45% of olive oil compared to the total production in the world. The value of 2.4 million hectares of land is dedicated to olive cultivation in the country, which made it a huge olive oil producer in the world. Olive crop widely spread over large extensive areas; manual counting of trees is humanly infeasible. To address this problem, we propose an automatic scheme for the detection and enumeration of olive trees. The proposed technique comprises of multi-step image processing techniques applied over a single band of imagery. The single red band once extracted from the color spectrum of acquired images, is then sharpened and edges are detected. The closed edges formed by the tree boundaries are transformed into white blobs using morphological reconstruction. Resulting circular blobs are then filtered out based on their shape and size. Blobs with circular geometry and in-range radius are considered as olive trees, which are then mapped with the existing ground information. Results have been generated over the diverse images capturing the ground truth information with an estimation error of 1.27%.

OBJECTIVES:. To describe hospital variation in use of “guideline-based care” for acute respiratory distress syndrome (ARDS) due to COVID-19. DESIGN:. Retrospective, observational study. SETTING:. The Society of Critical Care Medicine’s Discovery Viral Infection and RESPIRATORY ILLNESS UNIVERSAL STUDY COVID-19 REGISTRY. PATIENTS:. Adult patients with ARDS due to COVID-19 between February 15, 2020, and April 12, 2021. INTERVENTIONS:. Hospital-level use of “guideline-based care” for ARDS including low-tidal-volume ventilation, plateau pressure less than 30 cm H2O, and prone ventilation for a Pao2/Fio2 ratio less than 100. MEASUREMENTS AND MAIN RESULTS:. Among 1,495 adults with COVID-19 ARDS receiving care across 42 hospitals, 50.4% ever received care consistent with ARDS clinical practice guidelines. After adjusting for patient demographics and severity of illness, hospital characteristics, and pandemic timing, hospital of admission contributed to 14% of the risk-adjusted variation in “guideline-based care.” A patient treated at a randomly selected hospital with higher use of guideline-based care had a median odds ratio of 2.0 (95% CI, 1.1–3.4) for receipt of “guideline-based care” compared with a patient receiving treatment at a randomly selected hospital with low use of recommended therapies. Median-adjusted inhospital mortality was 53% (interquartile range, 47–62%), with a nonsignificantly decreased risk of mortality for patients admitted to hospitals in the highest use “guideline-based care” quartile (49%) compared with the lowest use quartile (60%) (odds ratio, 0.7; 95% CI, 0.3–1.9; p = 0.49). CONCLUSIONS:. During the first year of the COVID-19 pandemic, only half of patients received “guideline-based care” for ARDS management, with wide practice variation across hospitals. Strategies that improve adherence to recommended ARDS management strategies are needed.

IMPORTANCE:. Even with its proclivity for older age, coronavirus disease 2019 has been shown to affect all age groups. However, there remains a lack of research focused primarily on the young adult population. OBJECTIVES:. To describe the epidemiology and outcomes of coronavirus disease 2019 and identify the risk factors associated with critical illness and mortality in hospitalized young adults. DESIGN, SETTINGS, AND PARTICIPANTS:. A retrospective cohort study of the Society of Critical Care Medicine’s Viral Infection and Respiratory Illness Universal Study registry. Patients 18–40 years old, hospitalized from coronavirus disease 2019 from March 2020 to April 2021, were included in the analysis. MAIN OUTCOMES AND MEASURES:. Critical illness was defined as a composite of mortality and 21 predefined interventions and complications. Multivariable logistic regression was used to assess associations with critical illness and mortality. RESULTS:. Data from 4,005 patients (152 centers, 19 countries, 18.6% non-U.S. patients) were analyzed. The median age was 32 years (interquartile range, 27–37 yr); 51% were female, 29.4% Hispanic, and 42.9% had obesity. Most patients (63.2%) had comorbidities, the most common being hypertension (14.5%) and diabetes (13.7%). Hospital and ICU mortality were 3.2% (129/4,005) and 8.3% (109/1,313), respectively. Critical illness occurred in 25% (n = 996), and 34.3% (n = 1,376) were admitted to the ICU. Older age (p = 0.03), male sex (adjusted odds ratio, 1.83 [95% CI, 1.2–2.6]), and obesity (adjusted odds ratio, 1.6 [95% CI, 1.1–2.4]) were associated with hospital mortality. In addition to the above factors, the presence of any comorbidity was associated with critical illness from coronavirus disease 2019. Multiple sensitivity analyses, including analysis with U.S. patients only and patients admitted to high-volume sites, showed similar risk factors. CONCLUSIONS:. Among hospitalized young adults, obese males with comorbidities are at higher risk of developing critical illness or dying from coronavirus disease 2019.

Abstract The present paper aims to establish certain new classes of integral inequalities for a class of n ( n∈N $n\in \mathbb{N}$) positive continuous and decreasing functions by utilizing the generalized fractional conformable integral operators (FCIO) recently defined by Khan and Khan. From these results, we also derive several particular cases.

Abstract In the article, we introduce the generalized proportional Hadamard fractional integrals and establish several inequalities for convex functions in the framework of the defined class of fractional integrals. The given results are generalizations of some known results.

Abstract In the last few years, various researchers studied the so-called conformable integrals and derivatives. Based on that notion some authors used modified conformable derivatives (proportional derivatives) to generate nonlocal fractional integrals and derivatives, called fractional proportional integrals and derivatives, which contain exponential functions in their kernels. Our aim in this paper is to establish some new integral inequalities by utilizing the fractional proportional-integral operators. In fact, certain new classes of integral inequalities for a class of n ( n∈N $n\in \mathbb{N}$) positive continuous and decreasing functions on [a,b] $[a,b]$ are presented. The inequalities presented in this paper are more general than the existing classical inequalities.

Abstract In this paper, we adopt conformable fractional integral to develop integral inequalities such as Minkowski and Hermite–Hadamard inequalities. Our results are the generalization of the inequalities obtained by Dahmani and Bougoffa cited in the literature.

Abstract Recent research has gained more attention on conformable integrals and derivatives to derive the various type of inequalities. One of the recent advancements in the field of fractional calculus is the generalized nonlocal proportional fractional integrals and derivatives lately introduced by Jarad et al. (Eur. Phys. J. Special Topics 226:3457–3471, 2017) comprising the exponential functions in the kernels. The principal aim of this paper is to establish reverse Minkowski inequalities and some other fractional integral inequalities by utilizing generalized proportional fractional integrals. Also, two new theorems connected with this inequality as well as other inequalities associated with the generalized proportional fractional integrals are established.

Blind image deconvolution/deblurring (BID) is a challenging task due to lack of prior information about the blurring process and image. Noise and ringing artefacts resulted during the restoration process further deter fine restoration of the pristine image. These artefacts mainly arise from using a poorly estimated point spread function (PSF) combined with an ineffective restoration filter. This paper presents a BID scheme based on the steepest descent in kurtosis maximization. Assuming uniform blur, the PSF can be modelled by a parametric form. The scheme tries to estimate the blur parameters by maximizing kurtosis of the deblurred image. The scheme is devised to handle any type of blur that can be framed into a parametric form such as Gaussian, motion and out-of-focus. Gradients for the blur parameters are computed and optimized in the direction of increasing kurtosis value using a steepest descent scheme. The algorithms for several common blurs are derived and the effectiveness has been corroborated through a set of experiments. Validation has also been carried out on various real examples. It is shown that the scheme optimizes on the parameters in a close vicinity of the true parameters. Results of both benchmark and real images are presented. Both full-reference and non-reference image quality measures have been used in quantifying the deblurring performance. The results show that the proposed method offers marked improvements over the existing methods.

Video recorders record the output of each security camera. After an incident, the video footage can be used for evidence by locating a suspect or criminal for a crime. A manual scan of the video footage requires a considerable amount of manpower and time, a luxury which cannot be afforded when tracking down a person of interest (POI). An automated system is proposed in this paper which aims at finding the desired POI through the available volume of video data quickly and accurately. It is visualized to go through all the available videos and detect the POI using facial recognition. Thereafter, it would create a video montage of all the desired frames and incorporate time and location information to produce a path map followed by the POI. The proposed system reduces the human burden, human error and reduces the time taken when searching the POI manually. Validation has been performed on various video data collected by ourselves as well. The results depict that the proposed system is able to correctly identify POI with an accuracy of 86% for video data captured in a constrained environment. Videos captured by a cell phone in an unconstrained environment result in an accuracy of around 80%. Real video tested in our university campus revealed the proposed system is capable of generating tracking information for POI effectively.

Cultivation of olive trees for the past few years has been widely spread across Mediterranean countries, including Spain, Greece, Italy, France, and Turkey. Among these countries, Spain is listed as the largest olive producing country with almost 45% of olive oil production per year. Dedicating land of over 2.4 million hectares for the olive cultivation, Spain is among the leading distributors of olives throughout the world. Due to its high significance in the country's economy, the crop yield must be recorded. Manual collection of data over such expanded fields is humanly infeasible. Remote collection of such information can be made possible through the utilization of satellite imagery. This paper presents an automated olive tree counting method based on image processing of satellite imagery. The images are pre-processed using the unsharp masking followed by improved multi-level thresholding-based segmentation. Resulting circular blobs are detected through the circular Hough transform for identification. Validation has been performed by evaluating the proposed scheme for the dataset formed by acquiring images through the “El Sistema de Información Geográfica de Parcelas Agrícolas”viewer over the region of Spain. The proposed algorithm achieves an accuracy of 96% in detection. Computation time was recorded as 24 ms for an image size of 300 × 300 pixels. The less spectral information is used in our proposed methodology resulting in a competitive accuracy with low computational cost in comparison to the state-of-the-art technique.

Background: Atrial fibrillation (AF) is the most common sustained arrhythmia with high risk for many cardiovascular (CV) complications. Adherence to recommended management guidelines is important to avoid complications. In India, there is little knowledge on how AF is managed in real world. Methods: This is a cross-sectional study of patients in India enrolled in RealiseAF survey between February 2010 and March 2010 with a diagnosis of AF within the last 12 months. Results: From 15 centers, 301 patients {mean age 59.9 years (14.4); 52.5% males} were recruited. AF was controlled in 50% of patients with 77 (26.7%) in sinus rhythm and 67 (23.3%) with heart rate

The spread of infectious diseases such as COVID-19, flu influenza, malaria, dengue, mumps, and rubella in a population is a big threat to public health. The infectious diseases spread from one person to another person through close contact. Without proper planning, an infectious disease can become an epidemic and can result in large human and financial losses. To better respond to the spread of infectious disease and take measures for its control, the public health authorities need models and simulations to study the spread of such diseases. In this paper, an agent-based simulation engine is presented that models the spread of infectious diseases in the population. The simulation takes as an input the human-to-human interactions, population dynamics, disease transmissibility and disease states and shows the spread of disease over time. The simulation engine supports non-pharmaceutical interventions and shows its impact on the disease spread across locations. A unique feature of this tool is that it is generic; therefore, it can simulate a wide variety of infectious disease models (SIR), susceptible-infectious-susceptible (SIS) and susceptible-infectious (SI). The proposed simulation engine will help the policy-makers and public health authorities study the behavior of disease spreading; thus, allowing for better planning.

Precisely measuring the work area of agriculture farm machinery is important for performing the authentication of machinery usage, better allocation of resources, measuring the effect of machinery usage on the yield, usage billing and driver’s behaviour. The manual measurement, which is a common practice is an error-prone and time-consuming process. The irregular fields make it even more difficult to calculate the work area. An automatic solution that uses smart technology and algorithms to precisely calculate the work area is crucial for the advancement of agriculture. In this work, we have developed a smart system that utilizes the Internet of Things (IoT), Global Positioning System (GPS) and Artificial Intelligence (AI) that records the movement of agriculture machinery and use it to measure the precise work area of its usage. The system couples the nearest neighbourhood algorithms with Contact-based mechanisms to find the precise work area for different shaped fields and activities. The system was able to record the movement of machinery and calculate its work area, regardless of how many times the machinery runs through a particular field. Our evaluation shows that the system was able to precisely find the work boundaries and calculate the area with a maximum of 9% error for irregular shapes.

Olive cultivation over the past few years has spread across Mediterranean countries with Spain being the world’s largest olive producer among them. Because olives are a major part of the economy for such countries keeping records of their tree count and crop yield is of high significance. Manual counting of trees over such large areas is humanly infeasible. To address this problem, we propose an automatic method for the detection and enumeration of olive trees. The algorithm is a multi-step classification system comprising pre-processing, image segmentation, feature extraction, and classification. RGB satellite images were acquired from the Spanish territory and pre-processed to suppress the additive noise. The region of interest was then segmented from the pre-processed images using K-Means segmentation, through which statistical features were extracted and classified. Promising results were achieved for all classifiers, namely Naive Bayesian, Support Vector Machines (SVMs), Random Forest and Multi-Layer Perceptrons (MLPs), at various division ratios of data samples. In a comparison of all the classification algorithms, Random Forest outperformed the rest by an overall accuracy of 97.5% at the division ratio of 70 to 30 for training to testing.

Reversible Colour Transforms (RCTs) in conjunction with Bi-level Burrows−Wheeler Compression Algorithm (BBWCA) allows for high-level lossless image compression as demonstrated in this study. The RCTs transformation results in exceedingly coordinated image information among the neighbouring pixels as compared to the RGB colour space. This aids the Burrows−Wheeler Transform (BWT) based compression scheme and achieves compression ratios of high degree at the subsequent steps of the program. Validation has been done by comparing the proposed scheme across a range of benchmarks schemes and the performance of the proposed scheme is above par the other schemes. The proposed compression outperforms the techniques exclusively developed for 2-D electrocardiogram (EEG), RASTER map and Color Filter Array (CFA) image compression. The proposed system shows no dependency over parameters like image size, its type or the medium in which it is captured. A comprehensive analysis of the proposed scheme concludes that it achieves a significant increase in compression and depicts comparable complexity similar to the various benchmark schemes.

Since an interesting functional by P.L. Chebyshev was presented in the year 1882, many results, which are called Chebyshev-type inequalities, have been established. Some of these inequalities were obtained by using fractional integral operators. Very recently, a new variant of the fractional conformable integral operator was introduced by Jarad et al. Motivated by this operator, we aim at establishing novel inequalities for a class of differentiable functions, which are associated with Chebyshev’s functional, by employing a fractional conformable integral operator. We also aim at showing important connections of the results here with those including Riemann–Liouville fractional and classical integrals.

The CMS experiment has an HTCondor Global Pool, composed of more than 200K CPU cores available for Monte Carlo production and the analysis of da.The submission of user jobs to this pool is handled by either CRAB, the standard workflow management tool used by CMS users to submit analysis jobs requiring event processing of large amounts of data, or by CMS Connect, a service focused on final stage condor-like analysis jobs and applications that already have a workflow job manager in place. The latest scenario canbring cases in which workflows need further adjustments in order to efficiently work in a globally distributed pool of resources. For instance, the generation of matrix elements for high energy physics processes via Madgraph5_aMC@NLO and the usage of tools not (yet) fully supported by the CMS software, such as Ten-sorFlow with GPUsupport, are tasks with particular requirements. A special adaption, either at the pool factory level (advertising GPU resources) or at the execute level (e.g: to handle special parameters that describe certain needs for the remote execute nodes during submission) is needed in order to adequately work in the CMS global pool. This contribution describes the challenges and efforts performed towards adaptingsuch workflows so they can properly profit from the Global Pool via CMS Connect.

The higher energy and luminosity from the LHC in Run 2 have put increased pressure on CMS computing resources. Extrapolating to even higher luminosities (and thus higher event complexities and trigger rates) beyond Run 3, it becomes clear that simply scaling up the the current model of CMS computing alone will become economically unfeasible. High Performance Computing (HPC) facilities, widely used in scientific computing outside of HEP, have the potential to help fill the gap. Here we describe the U.S.CMS efforts to integrate US HPC resources into CMS Computing via the HEPCloud project at Fermilab. We present advancements in our ability to use NERSC resources at scale and efforts to integrate other HPC sites as well. We present experience in the elastic use of HPC resources, quickly scaling up use when so required by CMS workflows. We also present performance studies of the CMS multi-threaded framework on both Haswell and KNL HPC resources.

The CMS Submission Infrastructure Global Pool, built on Glidein-WMS andHTCondor, is a worldwide distributed dynamic pool responsible for the allocation of resources for all CMS computing workloads. Matching the continuously increasing demand for computing resources by CMS requires the anticipated assessment of its scalability limitations. In addition, the Global Plmust be able to expand in a more heterogeneous environment, in terms of resource provisioning (combining Grid, HPC and Cloud) and workload submissi.A dedicated testbed has been set up to simulate such conditions with the purpose of finding potential bottlenecks in the software or its configuration. This report provides a thorough description of the various scalabilitydimensions in size and complexity that are being explored for the future Global Pool, along with the analysis and solutions to the limitations proposed with the support of the GlideinWMS and HTCondor developer teams.