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Herein, we report an electrochemical scaffold consisting of functionalized multiwalled carbon nanotubes (COOH-fMWCNTs) and iron-doped zinc oxide nanoparticles (Fe-ZnO) for the detection of a hazardous textile dye safranin T (ST) and monitoring of its photocatalytic degradation. Prior to the detection and degradation analysis, Fe-ZnO NPs were synthesized by the sol-gel method and characterized by a number of structural and morphological techniques. The carboxyl moiety of COOH-fMWCNTs possessing a strong affinity for the amino functionality of ST led to significant enhancement of the current response at the designed electrochemical platform, whereas the electrocatalytic role, surface area enhancement, and the provision of binding sites of Fe-ZnO led to a further increase in the peak current intensity of ST. Electrochemical impedance spectroscopy showed that the sensing scaffold made of the glassy carbon electrode modified with COOH-fMWCNTs and Fe-ZnO efficiently transfers charge between the transducer and the redox probe. Under optimized conditions, the developed sensor showed a 2.3 nM limit of detection for ST. Moreover, recovery experiments and anti-interference tests qualified the sensing platform for practical applications. The dye was photocatalytically degraded using Fe-ZnO NPs up to 99% in 60 min with a rate constant of 0.068 min−1. The designed sensor was used to probe the degradation kinetics of the target dye, and the results were found consistent with the findings obtained from electronic absorption method. To the best of our knowledge, the present work is the first approach for the efficient detection and almost absolute degradation of ST. [ABSTRACT FROM AUTHOR]

Due to their efficiency and control capabilities, induction motors fed with inverters have become prevalent in various industrial applications. However, ensuring the reliable operation of the motor and diagnosing faults on time are crucial for preventing unexpected failures and minimizing downtime. This paper systematically analyzes condition monitoring and practical diagnostic techniques for inverter-fed motor drive systems. This study encompasses a thorough evaluation of different methods used for condition monitoring and diagnostics of induction motors, with the most crucial faults in their stator, rotor, bearings, eccentricity, shaft currents, and partial discharges. It also includes an assessment of their applicability. The presented analysis includes a focus on the challenges associated with inverter-fed systems, such as high-frequency harmonics, common-mode voltages causing the bearing currents, and high voltage gradients (dv/dt) due to fast switching frequency, which can impact the motor operation, as well as its faults analysis. Furthermore, this research explores the usefulness and efficiency of various available diagnostic methods, such as motor current signature analysis and other useful analyses using advanced signal processing techniques. This study aims to present findings that provide valuable insights for developing comprehensive condition monitoring strategies, and practical diagnostic techniques that enable proactive maintenance, enhanced system performance, and improved operational reliability of inverter-fed motor drive systems. [ABSTRACT FROM AUTHOR]

The pandemic has significantly affected many countries including the USA, UK, Asia, the Middle East and Africa region, and many other countries. Similarly, it has substantially affected Malaysia, making it crucial to develop efficient and precise forecasting tools for guiding public health policies and approaches. Our study is based on advanced deep-learning models to predict the SARS-CoV-2 cases. We evaluate the performance of Long Short-Term Memory (LSTM), Bi-directional LSTM, Convolutional Neural Networks (CNN), CNN-LSTM, Multilayer Perceptron, Gated Recurrent Unit (GRU), and Recurrent Neural Networks (RNN). We trained these models and assessed them using a detailed dataset of confirmed cases, demographic data, and pertinent socio-economic factors. Our research aims to determine the most reliable and accurate model for forecasting SARS-CoV-2 cases in the region. We were able to test and optimize deep learning models to predict cases, with each model displaying diverse levels of accuracy and precision. A comprehensive evaluation of the models' performance discloses the most appropriate architecture for Malaysia's specific situation. This study supports ongoing efforts to combat the pandemic by offering valuable insights into the application of sophisticated deep-learning models for precise and timely SARS-CoV-2 case predictions. The findings hold considerable implications for public health decision-making, empowering authorities to create targeted and data-driven interventions to limit the virus's spread and minimize its effects on Malaysia's population. [ABSTRACT FROM AUTHOR]

The progress in nanotechnology has effectively tackled and overcome numerous global issues, including climate change, environmental contamination, and various lethal diseases. The nanostructures being a vital part of nanotechnology have been synthesized employing different physicochemical methods. However, these methods are expensive, polluting, eco-unfriendly, and produce toxic byproducts. Green chemistry having exceptional attributes, such as cost-effectiveness, non-toxicity, higher stability, environment friendliness, ability to control size and shape, and superior performance, has emerged as a promising alternative to address the drawbacks of conventional approaches. Plant extracts are recognized as the best option for the biosynthesis of nanoparticles due to adherence to the environmentally benign route and sustainability agenda 2030 of the United Nations. In recent decades, phytosynthesized nanoparticles have gained much attention for different scientific applications. Eucalyptus globulus (blue gum) is an evergreen plant belonging to the family Myrtaceae, which is the targeted point of this review article. Herein, we mainly focus on the fabrication of nanoparticles, such as zinc oxide, copper oxide, iron oxide, lanthanum oxide, titanium dioxide, magnesium oxide, lead oxide, nickel oxide, gold, silver, and zirconium oxide, by utilizing Eucalyptus globulus extract and its essential oils. This review article aims to provide an overview of the synthesis, characterization results, and biomedical applications of nanoparticles synthesized using Eucalyptus globulus. The present study will be a better contribution to the readers and the students of environmental research. [ABSTRACT FROM AUTHOR]

Pathogenic bacteria present a major threat to human health, causing various infections and illnesses, and in some cases, even death. The accurate identification of these bacteria is crucial, but it can be challenging due to the similarities between different species and genera. This is where automated classification using convolutional neural network (CNN) models can help, as it can provide more accurate, authentic, and standardized results.In this study, we aimed to create a larger and balanced dataset by image patching and applied different variations of CNN models, including training from scratch, fine-tuning, and weight adjustment, and data augmentation through random rotation, reflection, and translation. The results showed that the best results were achieved through augmentation and fine-tuning of deep models. We also modified existing architectures, such as InceptionV3 and MobileNetV2, to better capture complex features. The robustness of the proposed ensemble model was evaluated using two data splits (7:2:1 and 6:2:2) to see how performance changed as the training data was increased from 10 to 20%. In both cases, the model exhibited exceptional performance. For the 7:2:1 split, the model achieved an accuracy of 99.91%, F-Score of 98.95%, precision of 98.98%, recall of 98.96%, and MCC of 98.92%. For the 6:2:2 split, the model yielded an accuracy of 99.94%, F-Score of 99.28%, precision of 99.31%, recall of 98.96%, and MCC of 99.26%. This demonstrates that automatic classification using the ensemble model can be a valuable tool for diagnostic staff and microbiologists in accurately identifying pathogenic bacteria, which in turn can help control epidemics and minimize their social and economic impact. [ABSTRACT FROM AUTHOR]

The construction industry is among the most hazardous industries, with workers routinely involved in critical accidents. Following proper safety procedures can help reduce these accidents. Incident reporting is widely regarded as an effective approach to improving worker safety on construction sites, as it allows relevant stakeholders to learn from past events. Despite the acknowledged significance of incident reporting in reducing accident rates, several researchers have reported that the construction industry still lacks effective reporting and investigation practices. This study aims to explore the barriers that inhibit the reporting of incidents in the Pakistani construction industry. Through a detailed literature review, 29 barriers that inhibit effective incident reporting were identified. A questionnaire survey was then carried out to include the perspective of construction industry professionals on the identified barriers. A total of 214 responses were collected. Further, for dimension reduction, exploratory factor analysis (EFA) was utilised and resultantly, four components were revealed and discussed. These components include organisational, individual, environmental and technical. The findings of this study will help the relevant stakeholders in the construction industry improve safety performance by mitigating these reporting challenges at the construction sites, resulting in a better working environment for workers with fewer risks and accidents. [ABSTRACT FROM AUTHOR]

Background: Epidemiological studies show a dose–response association between cannabis use and the risk of psychosis. This review aimed to determine whether there are identifiable risk-thresholds between the frequency of cannabis use and psychosis development. Methods: Systematic search of Embase, MEDLINE, PsycINFO, CINAHL, and Web of Science for relevant studies (1 January 2010–26 April 2021). Case–control or cohort studies that investigated the relationship between cannabis use and the risk of psychosis development that reported effect estimates [odds ratios (OR), hazard ratios (HR), risk ratios (RR)] or the raw data to calculate them, with information on the frequency of cannabis consumption were included. Effect estimates were extracted from individual studies and converted to RR. Two-stage dose–response multivariable meta-analytic models were utilized and sensitivity analyses conducted. The Newcastle Ottawa Scale was used to assess the risk of bias of included studies. Results: Ten original (three cohorts, seven case–control) studies were included, including 7390 participants with an age range of 12–65 years. Random-effect model meta-analyses showed a significant log-linear dose–response association between cannabis use frequency and psychosis development. A restricted cubic-splines model provided the best fit for the data, with the risk of psychosis significantly increasing for weekly or more frequent cannabis use [RR = 1.01, 95% confidence interval (CI) 0.93–1.11 yearly; RR = 1.10, 95% CI 0.97–1.25 monthly; RR = 1.35, 95% CI 1.19–1.52 weekly; RR = 1.76, 95% CI 1.47–2.12 daily] Conclusion: Individuals using cannabis frequently are at increased risk of psychosis, with no significant risk associated with less frequent use. Public health prevention messages should convey these risk-thresholds, which should be refined through further work. [ABSTRACT FROM AUTHOR]

Purpose: Given the importance of environmental protection and the crucial role of manufacturing firms in environmental degradation, the purpose of this research is to investigate the impact of green intellectual capital (GIC) on firms' green performance (GP), mediating effects of ambidextrous green innovation (GI) and moderating role of technological turbulence (TT). Design/methodology/approach: The study employed a quantitative research approach with the partial least square structural equation modeling (PLS-SEM) methodology to assess the proposed relationships among the constructs on a sample of 334 executives from 134 medium and large-sized manufacturing firms. Findings: The findings show that GIC significantly impacts different aspects of GP, including green management, green process and green product performance. Moreover, exploitative and exploratory GI serves as mediators between GIC and firms' GP. Finally, the findings demonstrate that TT moderation enhanced the effects of GIC on exploratory GI, while decreasing the effects of GIC on exploitative GI. Practical implications: The research offers valuable insights and a novel strategy for manufacturing firms and policymakers to mitigate environmental degradation and attain sustainable GP by stimulating ambidextrous GI through green intangible resources. Originality/value: This research adds to the current GIC, GI and GP literature by focusing on green environmental issues using the resource-based view (RBV) theory. This research also provides a significant theoretical and practical justification for explaining the relationships by differentiating ambidextrous GI between exploitative and exploratory GI's mediating effects and TT's moderating effects. [ABSTRACT FROM AUTHOR]

The comparative advantage theory perspective has been extended to the public innovation and trade openness of OECD and Non-OECD countries by focusing on the role of tariff and asymmetric effect of trade openness by comparing imports and exports growth with the causal relationship among the respective factors. The study's findings indicate that trade openness, innovation, and tariffs are linked in developing countries more than in developed countries due to the difference in economic perspectives and policies, which disprove the idea of the tariff being ineffective for developed nations. The causality effectiveness in developed and underdeveloped countries has resulted among public innovation toward openness of trade and tariff but not in case of tariff to trade openness. Additionally, the results hold up to sub-indicators and measures of public innovation, evaluating that input sub-index is more impactful than output sub-index, and both are even stronger in developing countries than developed countries. Finally, this study implied the usefulness of monetary policy to encourage the governments to lower import tariffs, paved the way to increased investment in research and development and collaboration between foundations, businesses, and academia. [ABSTRACT FROM AUTHOR]

In this research, injector, cleaner and camphor were blended with premium motor spirit (PMS). The physicochemical properties and eff ects on the performance and exhaust emission levels of spark ignition engines were investigated. Samples with injector cleaner showed the best physicochemical properties relative to the sole petrol and camphor blended samples; these specimens had densities of 0.72706 kg/m2 (I5), flashpoints of 47 °C for I5, I10, and I15 and 48 °C by I25, and viscosities of 1.7. A performance analysis showed that I25 at 3 Nm had specific fuel consumption levels of 45.51 kgkwh-1 and 27.2 kgkwh-1, which were the best recorded among all the samples; additionally, the sample I25 showed the lowest emissions of CO and CO2 at 0.2% and 0.13% at 3 Nm and 6 Nm, respectively, for CO emissions and at 1.22 and 1.47% at 3 Nm and 6 Nm, respectively, for CO2 emissions. Concerning NOx emissions, sample I25 showed the best results, with the highest NOx emission reductions of 312 and 342 ppm at 3 and 6 Nm engine torques, respectively. With this information, it was concluded that cleaner injector blends were better than camphor blends. [ABSTRACT FROM AUTHOR]

Incident reporting has drawn the attention of safety experts in the construction industry to reduce the likelihood of future hazards by learning from previous events. A number of barriers have been identified that inhibit the effective reporting of safety incidents. Adopting an electronic incident reporting system (E-IRS) has immense potential to overcome these barriers and enhance incident reporting frequency in the construction industry. Nonetheless, the successful implementation of E-IRS requires an in-depth understanding of users' acceptance of the system and the factors influencing its usage. This research employed a cross-sectional design based on the technology acceptance model (TAM) to quantitatively examine the factors influencing the adoption of E-IRS in the construction industry of developing countries. Data were collected using an online questionnaire survey to test the 11 hypothesized relationships. A total of 128 completed responses are retrieved and the results are analyzed using partial least squares-structural equation modeling. The findings indicate that perceived ease of use and trust are critical determinants that influence the acceptance behavior of E-IRS. Facilitating conditions and technological self-efficacy have a significant positive effect on perceived ease of use. In addition, social influence is found to have a significant impact on trust. The outcomes of this study provide a theoretical basis for all the concerned stakeholders to take effective measures to encourage the use of E-IRS in the construction industry, thereby reducing the likelihood of construction-related incidents. [ABSTRACT FROM AUTHOR]

Organic solar cells (OSCs) made of electron-acceptor and electron-donor materials have significantly developed in the last decade, demonstrating their enormous potential in cutting-edge optoelectronic applications. Consequently, we designed seven novel non-fused ring electron acceptors (NFREAs) (BTIC-U1 to BTIC-U7) using synthesized electron-deficient diketone units and reported end-capped acceptors, a viable route for augmented optoelectronic properties. The DFT and TDDFT approaches were used to measure the power conversion efficiency (PCE), open circuit voltage (Voc), reorganization energies (λh, λe), fill factor (FF), light harvesting efficiency (LHE) and to evaluate the potential usage of proposed compounds in solar cell applications. The findings confirmed that the photovoltaic, photophysical, and electronic properties of the designed molecules BTIC-U1 to BTIC-U7 are superior to those of reference BTIC-R. The TDM analysis demonstrates a smooth flow of charge from the core to the acceptor groups. Charge transfer analysis of the BTIC-U1:PTB7-Th blend revealed orbital superposition and successful charge transfer from HOMO (PTB7-Th) to LUMO (BTIC-U1). The BTIC-U5 and BTIC-U7 outperformed the reference BTIC-R and other developed molecules in terms of PCE (23.29% and 21.18%), FF (0.901 and 0.894), normalized Voc (48.674 and 44.597), and Voc (1.261 eV and 1.155 eV). The proposed compounds enclose high electron and hole transfer mobilities, making them the ideal candidate for use with PTB7-Th film. As a result, future SM-OSC design should prioritize using these constructed molecules, which exhibit excellent optoelectronic properties, as superior scaffolds. [ABSTRACT FROM AUTHOR]

Background Methods Results Conclusion This prospective experimental study was conducted on 40 eyes of 40 patients with complicated uveitic cataracts to compare a single intraoperative intravitreal injection of triamcinolone acetonide (IVTA) with oral steroids for inflammation control after cataract extraction.The IVTA group (19 eyes) received an intravitreal injection of 4 mg/0.1 ml triamcinolone acetonide intraoperatively, and the oral steroid group (19 eyes) received oral steroids peri-operatively. Two patients had to be excluded from the study because they underwent extracapsular cataract extraction (ECCE), instead of phacoemulsification.A statistically significant difference was observed in the anterior chamber cell and flare response between both groups initially, but by the last follow-up, this became insignificant. None of the patients in each group experienced a recurrence of uveitis nor developed cystoid macular edema. Five patients in the oral steroid group had IOP >21 mmHg post-operatively.A single injection of 4 mg IVTA has similar efficacy to peri-operative oral steroid administration. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has increased the usage of personal protective equipment (PPE) all round the world and, in turn, it has also increased the waste caused by disposable PPE. This has exerted a severe environmental impact, so in our work, we propose the utilization of a sustainable electrospun nanofiber based on poly lactic acid (PLA), as it is biobased and conditionally degradable. We optimized the weight percentage of the PLA-precursor solution and found that 19% PLA produces fine nanofibers with good morphology. We also introduced carbon nanodots (CNDs) in the nanofibers and evaluated their antibacterial efficiency. We used 1, 2, 3, and 4% CNDs with 19% PLA and found increased antibacterial activity with increased concentrations of CNDs. Additionally, we also applied a spunbond-nanofiber layered assembly for the medical face masks and found that with the addition of only 0.45 mg/cm2 on the nonwoven sheet, excellent particle filtration efficiency of 96.5% and a differential pressure of 39 Pa/cm2 were achieved, meeting the basic requirements for Type I medical face masks (ASTM-F2100). [ABSTRACT FROM AUTHOR]

Objective To determine the efficacy of intradermal tranexamic acid (TA) in the patients of melasma. Methods This study was conducted in Dermatology OPD, Punjab Rangers Teaching Hospital over a period of 4 months from 14-08-2022 to 14-12-2022. A total number of 32 patients of melasma were enrolled after informed written consent. 2mL of concentrated tranexamic acid (50mg/ml) was injected intradermally, 1 ml on either side of face 1cm apart over the affected area. A total of 4 sessions were carried out, followed after every 2 weeks. Results were interpreted on the basis of MASI scoring before, during and after completion of the sessions. A mild topical steroid application was advised for 2 days. Sunscreen application was essentially prescribed. Results Out of 32 enrolled patients of melasma, 56% showed 61-100% reduction in MASI scoring, which constituted a majority. Conclusion We concluded that un-diluted intra dermal tranexamic acid is an excellent treatment modality for treatment of melasma without any side effects. [ABSTRACT FROM AUTHOR]

The abundant availability of natural fibres around the globe seeks researchers' attention on their potential use as construction materials. There is a need to explore the structural integrity of cement concrete reinforced with such natural fibres. To start with, wheat straw is considered. In this study, the structural performance of wheat straw reinforced cement concrete (WSRC) road is evaluated. The properties of plain concrete pavement are taken as a reference. Wheat straw, of approximately 18 mm in length and 1% content (by mass of wet concrete), is considered to make the WSRC road test section. The laying technique of WSRC, at a larger scale, is explained. The laboratory and field investigations are made. The compressive properties, deflection measurements, and cracks progression are determined after the exposure of test sections to vehicular loading for 18 months. Micro-structural analysis of straw–concrete matrix is also done to verdict the uniform dispersion of straw with proper bonding and its failure mechanism. The study is concluded with 34% and 16% more energy absorption capacity and load transfer efficiency, respectively, in the WSRC test section compared to that of the controlled section. Also, the structural performance of the WSRC road test section, with 7% less design thickness than the plain concrete road test section, is comparable for having economical and sustainable concrete roads. [ABSTRACT FROM AUTHOR]

The comparative advantage theory perspective has been extended to the public innovation and trade openness of OECD and Non-OECD countries by focusing on the role of tariff and asymmetric effect of trade openness by comparing imports and exports growth with the causal relationship among the respective factors. The study's findings indicate that trade openness, innovation, and tariffs are linked in developing countries more than in developed countries due to the difference in economic perspectives and policies, which disprove the idea of the tariff being ineffective for developed nations. The causality effectiveness in developed and underdeveloped countries has resulted among public innovation toward openness of trade and tariff but not in case of tariff to trade openness. Additionally, the results hold up to sub-indicators and measures of public innovation, evaluating that input sub-index is more impactful than output sub-index, and both are even stronger in developing countries than developed countries. Finally, this study implied the usefulness of monetary policy to encourage the governments to lower import tariffs, paved the way to increased investment in research and development and collaboration between foundations, businesses, and academia. [ABSTRACT FROM AUTHOR]

In this article, a novel technique to evaluate and compare the neighborhood degree molecular descriptors of two variations of the carbon nanosheet C 5 C 7 (a , b) is presented. The conjugated molecules follow the graph spectral theory, in terms of bonding, non-bonding and antibonding Ruckel molecular orbitals. They are demonstrated to be immediately determinable from their topological characteristics. The effort of chemical and pharmaceutical researchers is significantly increased by the need to conduct numerous chemical experiments to ascertain the chemical characteristics of such a wide variety of novel chemicals. In order to generate novel cellular imaging techniques and to accomplish the regulation of certain cellular mechanisms, scientists have utilized the attributes of nanosheets such as their flexibility and simplicity of modification, out of which carbon nanosheets stand out for their remarkable strength, chemical stability, and electrical conductivity. With efficient tools like polynomials and functions that can forecast compound features, mathematical chemistry has a lot to offer. One such approach is the M-polynomial, a fundamental polynomial that can generate a significant number of degree-based topological indices. Among them, the neighborhood M-polynomial is useful in retrieving neighborhood degree sum-based topological indices that can help in carrying out physical, chemical, and biological experiments. This paper formulates the unique M-polynomial approach which is used to derive and compare a variety of neighborhood degree-based molecular descriptors and the corresponding entropy measures of two variations of pent-heptagonal carbon nanosheets. Furthermore, a regression analysis on these descriptors has also been carried out which can further help in the prediction of various properties of the molecule. [ABSTRACT FROM AUTHOR]

Economic interventions have social consequences. In this paper, we explore one such relationship, between microfinance intensity and social distrust levels reported by the low-income people. We find a significant association between microfinance intensity in a country and distrust among the poor as well as ultra-poor in cross-section using World Values Survey & European Values Survey (WVS-EVS) Wave 7 (2017–2022). We supplement these findings using empirical Bayes on a panel extending back from 7th to the 4th WVS wave (1999–2004). To deal with potential endogeneity, we run 2SLS as well as weak instruments-robust conditional instrumental variable tests and find evidence showing microfinance prevalence intensity affects distrust levels among the poor and ultra-poor households. We find no association between microfinance and distrust levels in the rich in any of the tests, potentially because the rich are not exposed to microfinance. [ABSTRACT FROM AUTHOR]

Atrial flutter (AFL) is a common arrhythmia with two significant mechanisms, namely, focal (FAFL) and macroreentry (MAFL). Discrimination of the AFL mechanism through noninvasive techniques can improve radiofrequency ablation efficacy. This study aims to differentiate the AFL mechanism using a 12-lead surface electrocardiogram. P-P interval series variability is hypothesized to be different in FAFL and MAFL and may be useful for discrimination. 12-lead ECG signals were collected from 46 patients with known AFL mechanisms. Features for a proposed classifier are extracted through descriptive statistics of the interval series. On the other hand, the class ratio of MAFL and FAFL was 41 : 5, respectively, which was highly imbalanced. To resolve this, different data augmentation techniques (SMOTE, modified-SMOTE, and smoothed-bootstrap) have been applied on the interval series to generate synthetic interval series and minimize imbalance. Modification is introduced in the classic SMOTE technique (modified-SMOTE) to properly produce data samples from the original distribution. The characteristics of modified-SMOTE are found closer to the original dataset than the other two techniques based on the four validation criteria. The performance of the proposed model has been evaluated by three linear classifiers, namely, linear discriminant analysis (LDA), logistic regression (LOG), and support vector machine (SVM). Filter and wrapper methods have been used for selecting relevant features. The best average performance was achieved at 400% augmentation of the FAFL interval series (90.24% sensitivity, 49.50% specificity, and 76.88% accuracy) in the LOG classifier. The variation of consecutive P-wave intervals has been shown as an effective concept that differentiates FAFL from MAFL through the 12-lead surface ECG. [ABSTRACT FROM AUTHOR]

A topological index, which is a number, is connected to a graph. It is often used in chemometrics, biomedicine, and bioinformatics to anticipate various physicochemical properties and biological activities of compounds. The purpose of this article is to encourage original research focused on topological graph indices for the drugs azacitidine, decitabine, and guadecitabine as well as an investigation of the genesis of symmetry in actual networks. Symmetry is a universal phenomenon that applies nature's conservation rules to complicated systems. Although symmetry is a ubiquitous structural characteristic of complex networks, it has only been seldom examined in real-world networks. The M ¯ -polynomial, one of these polynomials, is used to create a number of degree-based topological coindices. Patients with higher-risk myelodysplastic syndromes, chronic myelomonocytic leukemia, and acute myeloid leukemia who are not candidates for intense regimens, such as induction chemotherapy, are treated with these hypomethylating drugs. Examples of these drugs are decitabine (5-aza-20-deoxycytidine), guadecitabine, and azacitidine. The M ¯ -polynomial is used in this study to construct a variety of coindices for the three brief medicines that are suggested. New cancer therapies could be developed using indice knowledge, specifically the first Zagreb index, second Zagreb index, F-index, reformulated Zagreb index, modified Zagreb, symmetric division index, inverse sum index, harmonic index, and augmented Zagreb index for the drugs azacitidine, decitabine, and guadecitabine. [ABSTRACT FROM AUTHOR]

Mupirocin (MUP) is a topical antibacterial agent used to treat superficial skin infections but has limited application due to in vivo inactivation and plasma protein binding. A nanoemulsion formulation has the potential to enhance the delivery of mupirocin into the skin. MUP-loaded nanoemulsions were prepared using eucalyptus oil (EO) or eucalyptol (EU), Tween® 80 (T80) and Span® 80 (S80) as oil phase (O), surfactant (S) and cosurfactant (CoS). The nanoemulsions were characterised and their potential to enhance delivery was assessed using an in vitro skin model. Optimised nanoemulsion formulations were prepared based on EO (MUP-NE EO) and EU (MUP-NE EU) separately. MUP-NE EO had a smaller size with mean droplet diameter of 35.89 ± 0.68 nm and narrower particle size index (PDI) 0.10 ± 0.02 nm compared to MUP-NE EU. Both nanoemulsion formulations were stable at 25 °C for three months with the ability to enhance the transdermal permeation of MUP as compared to the control, Bactroban® cream. Inclusion of EU led to a two-fold increase in permeation of MUP compared to the control, while EO increased the percentage by 48% compared to the control. Additionally, more MUP was detected in the skin after 8 h following MUP-NE EU application, although MUP deposition from MUP-NE EO was higher after 24 h. It may be possible, through choice of essential oil to design nanoformulations for both acute and prophylactic management of topical infections. [ABSTRACT FROM AUTHOR]

β-cyclodextrin-based novel pH-responsive hydrogels were prepared by grafting 2-acrylamido-2-methylpropane sulphonic acid (AMPS) and methacrylic acid (MAA) through free radical polymerization technique using N,N′-methylene-bis-acrylamide as cross-linker for the controlled drug delivery. Captopril was loaded as a model drug. The influence of acrylamide sulphonate and methacrylate-modified β-cyclodextrin was investigated on the properties of preformed hydrogels. FTIR, TGA, DSC, SEM and Sol–gel fraction analysis were performed for the characterization of these polymeric composites. pH-responsive properties of β-CD-co-poly (AMPS) and β-CD-co-poly (MAA) hydrogels were studied by evaluating swelling dynamics at different pH. FTIR, TGA and DSC analysis confirmed the formation of new polymeric network. β-CD-co-poly (MAA) gels with less monomeric contents showed maximum swelling, drug loading and release at pH 7.4, while β-CD-co-poly (AMPS) hydrogels exhibited pH independent swelling and drug release was observed maximum at pH 7.4. Sol–gel analysis showed increase in gel fraction on increasing β-cyclodextrin, AMPS and MAA contents. Results indicated non-Fickian release of captopril from β-CD-based-hydrogel. Thereby, concluding that β-cyclodextrin properties can be functionalized differently by cross-linking with different monomers, moreover, it forms stable polymeric network with both monomers. Polymeric hydrogels with such excellent mouldable modalities can be effectively employed as carrier system for the controlled drug delivery. [ABSTRACT FROM AUTHOR]

Plants are excellent food and medicinal resources because of their unique composition. Researchers have discovered that thousands of plants can help in achieving the desired health status through advanced research. This research article reveals the nutritional composition and significance of Tribulus terrestris. In addition, it also analyzed the physio-chemical evolution of nutraceutical products from Tribulus terrestris. From the proximate analysis, it was found that T. terrestris is rich in fiber and fat 24.28 ± 0.93% and 14.49 ± 1.42%, respectively. The rheological properties of the product were observed. The results reveal that water activity, stability of product, and dough development time increased 59.0 ± 0.06%, 15.0 ± 0.06 min, and 4.3 ± 0.07 min, respectively, by increasing the powder percentage. The functional behavior of the samples was examined, and significant results were depicted. Bulk density and oil absorption increased by increasing the percentage of powder 83.86 ± 1.36% and 69.16 ± 3.61%. The results of emulsifying stability were recorded as non-significant. Results regarding color analysis showed non-significant behavior of supplemented biscuits. The textural behavior of the product increased from 1.47 Nm−2 to 2.27 Nm−2. Results regarding the spread factor increased from 3.01 ± 0.04 to 3.83 ± 0.01 from T1 to T4. The sensory results indicated that T1 (2% Tribulus powder), T2 (3% Tribulus powder), T3 (4% Tribulus powder), and T4 (5% Tribulus powder) were acceptable after the control group. Thus, Tribulus terrestris can be used as a nutraceutical and functional ingredient in baked goods. [ABSTRACT FROM AUTHOR]

Following its initial identification on December 31, 2019, COVID-19 quickly spread around the world as a pandemic claiming more than six million lives. An early diagnosis with appropriate intervention can help prevent deaths and serious illness as the distinguishing symptoms that set COVID-19 apart from pneumonia and influenza frequently don't show up until after the patient has already suffered significant damage. A chest X-ray (CXR), one of many imaging modalities that are useful for detection and one of the most used, offers a non-invasive method of detection. The CXR image analysis can also reveal additional disorders, such as pneumonia, which show up as anomalies in the lungs. Thus these CXRs can be used for automated grading aiding the doctors in making a better diagnosis. In order to classify a CXR image into the Negative for Pneumonia, Typical, Indeterminate, and Atypical, we used the publicly available CXR image competition dataset SIIM-FISABIO-RSNA COVID-19 from Kaggle. The suggested architecture employed an ensemble of EfficientNetv2-L for classification, which was trained via transfer learning from the initialised weights of ImageNet21K on various subsets of data (Code for the proposed methodology is available at: https://github.com/asadkhan1221/siim-covid19.git). To identify and localise opacities, an ensemble of YOLO was combined using Weighted Boxes Fusion (WBF). Significant generalisability gains were made possible by the suggested technique's addition of classification auxiliary heads to the CNN backbone. The suggested method improved further by utilising test time augmentation for both classifiers and localizers. The results for Mean Average Precision score show that the proposed deep learning model achieves 0.617 and 0.609 on public and private sets respectively and these are comparable to other techniques for the Kaggle dataset. [ABSTRACT FROM AUTHOR]

Coxiella burnetii (Cb) is a hardy, stealth bacterial pathogen lethal for humans and animals. Its tremendous resistance to the environment, ease of propagation, and incredibly low infectious dosage make it an attractive organism for biowarfare. Current research on the classification of Coxiella and features influencing its presence in the soil is generally confined to statistical techniques. Machine learning other than traditional approaches can help us better predict epidemiological modeling for this soil-based pathogen of public significance. We developed a two-phase feature-ranking technique for the pathogen on a new soil feature dataset. The feature ranking applies methods such as ReliefF (RLF), OneR (ONR), and correlation (CR) for the first phase and a combination of techniques utilizing weighted scores to determine the final soil attribute ranks in the second phase. Different classification methods such as Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), Logistic Regression (LR), and Multi-Layer Perceptron (MLP) have been utilized for the classification of soil attribute dataset for Coxiella positive and negative soils. The feature-ranking methods established that potassium, chromium, cadmium, nitrogen, organic matter, and soluble salts are the most significant attributes. At the same time, manganese, clay, phosphorous, copper, and lead are the least contributing soil features for the prevalence of the bacteria. However, potassium is the most influential feature, and manganese is the least significant soil feature. The attribute ranking using RLF generates the most promising results among the ranking methods by generating an accuracy of 80.85% for MLP, 79.79% for LR, and 79.8% for LDA. Overall, SVM and MLP are the best-performing classifiers, where SVM yields an accuracy of 82.98% and 81.91% for attribute ranking by CR and RLF; and MLP generates an accuracy of 76.60% for ONR. Thus, machine models can help us better understand the environment, assisting in the prevalence of bacteria and decreasing the chances of false classification. Subsequently, this can assist in controlling epidemics and alleviating the devastating effect on the socio-economics of society. [ABSTRACT FROM AUTHOR]

Without deploying face anti‐spoofing countermeasures, face recognition systems can be spoofed by presenting a printed photo, a video, or a silicon mask of a genuine user. Thus, face presentation attack detection (PAD) plays a vital role in providing secure facial access to digital devices. Most existing video‐based PAD countermeasures lack the ability to cope with long‐range temporal variations in videos. Moreover, the key‐frame sampling prior to the feature extraction step has not been widely studied in the face anti‐spoofing domain. To mitigate these issues, this paper provides a data sampling approach by proposing a video processing scheme that models the long‐range temporal variations based on Gaussian weighting function (GWF). Specifically, the proposed scheme encodes the consecutive t frames of video sequences into a single RGB image based on a Gaussian‐weighted summation of the t frames. Using simply the data sampling scheme alone, it is demonstrated here that state‐of‐the‐art performance can be achieved without any bells and whistles in both intra‐database and inter‐database testing scenarios for the three public benchmark datasets; namely, replay‐Attack, MSU‐MFSD, and CASIA‐FASD. In particular, the proposed scheme provides a much lower error (from 15.2% to 7.6% on CASIA‐FASD and 5.9% to 4.9% on replay‐attack) compared to baselines in cross‐database scenarios. [ABSTRACT FROM AUTHOR]

Seaweed-based films have emerged as a promising solution for sustainable food packaging due to their renewable sourcing, biodegradability, and functional properties. This review provides an in-depth analysis of seaweed-based films, focusing on their properties, incorporation of essential oils, applications in food packaging, and future directions. The advantages of seaweed-based films include their renewable and abundant source, biodegradability, and favorable barrier properties. The review explores the physical and mechanical properties, barrier properties, and safety considerations of seaweed-based films. Additionally, it discusses the incorporation of essential oils into seaweed-based films and their potential benefits. Current and potential applications of seaweed-based films in food packaging, ranging from fresh produce to dairy products, are examined, along with the advantages and challenges associated with their use. A comparison with other sustainable packaging options is provided. Furthermore, the review highlights future research directions in developing seaweed-based films, such as improving mechanical properties, extending shelf life, scaling up production, reducing costs, and innovation in formulation. Overall, seaweed-based films offer a promising and sustainable alternative for food packaging, with ongoing research and development driving their advancement and potential for a more environmentally friendly packaging industry. [ABSTRACT FROM AUTHOR]

Entropy is a measure of a system's molecular disorder or unpredictability since work is produced by organized molecular motion. Shannon's entropy metric is applied to represent a random graph's variability. Entropy is a thermodynamic function in physics that, based on the variety of possible configurations for molecules to take, describes the randomness and disorder of molecules in a given system or process. Numerous issues in the fields of mathematics, biology, chemical graph theory, organic and inorganic chemistry, and other disciplines are resolved using distance-based entropy. These applications cover quantifying molecules' chemical and electrical structures, signal processing, structural investigations on crystals, and molecular ensembles. In this paper, we look at K-Banhatti entropies using K-Banhatti indices for C 6 H 6 embedded in different chemical networks. Our goal is to investigate the valency-based molecular invariants and K-Banhatti entropies for three chemical networks: the circumnaphthalene ( C N B n ), the honeycomb ( H B n ), and the pyrene ( P Y n ). In order to reach conclusions, we apply the method of atom-bond partitioning based on valences, which is an application of spectral graph theory. We obtain the precise values of the first K-Banhatti entropy, the second K-Banhatti entropy, the first hyper K-Banhatti entropy, and the second hyper K-Banhatti entropy for the three chemical networks in the main results and conclusion. [ABSTRACT FROM AUTHOR]

Entropy is a thermodynamic function in physics that measures the randomness and disorder of molecules in a particular system or process based on the diversity of configurations that molecules might take. Distance-based entropy is used to address a wide range of problems in the domains of mathematics, biology, chemical graph theory, organic and inorganic chemistry, and other disciplines. We explain the basic applications of distance-based entropy to chemical phenomena. These applications include signal processing, structural studies on crystals, molecular ensembles, and quantifying the chemical and electrical structures of molecules. In this study, we examine the characterisation of polyphenylenes and boron ( B 12 ) using a line of symmetry. Our ability to quickly ascertain the valences of each atom, and the total number of atom bonds is made possible by the symmetrical chemical structures of polyphenylenes and boron B 12 . By constructing these structures with degree-based indices, namely the K Banhatti indices, R e Z G 1 -index, R e Z G 2 -index, and R e Z G 3 -index, we are able to determine their respective entropies. [ABSTRACT FROM AUTHOR]

Introduction: Acute kidney injury (AKI) is known to be a marker of mortality in patients with cirrhosis and variceal hemorrhage. Aim: To study the effect of AKI on hospital-based outcomes in patients with variceal hemorrhage. Material and methods: We obtained data from the National Inpatient Sample for the years 2016-2018. Study inclusion criteria comprised adult variceal hemorrhage patients who also had AKI. The primary outcome of interest was in-hospital mortality. Secondary outcomes were length of stay, hospital charge, shock, blood transfusion, and ICU admission. We also determined the independent predictors of mortality in variceal hemorrhage patients using multivariate regression analysis. We used 2 different methods: multivariate logistic regression and propensity matching to adjust for confounders. Results: The number of people included in this study was 124,430, of whom 32,315 (26%) had AKI. Mortality in variceal hemorrhage patients with AKI was 30.4% in comparison to 4.8% without AKI. The presence of AKI was associated with increased odds of mortality (AOR = 8.28, 95% CI: 7.45-9.20, p < 0.01), ICU admissions (AOR = 4.76, 95% CI: 4.42-5.13, p < 0.01), blood transfusion (AOR = 1.24, 95% CI: 1.15-1.32, p < 0.01), and shock (AOR = 3.41, 95% CI 3.07-3.79, p < 0.01). The patients with AKI also had increased length of stay and hospital charges. Higher Charlson co-morbidity index, African American race, and being admitted to large sized hospital were independently associated with increased mortality. Conclusions: After analyzing the combined NIS dataset of 2016-2018, we concluded that patients admitted with variceal hemorrhage who has AKI are prone to adverse hospital outcomes. [ABSTRACT FROM AUTHOR]

Simple Summary: Different chest diseases badly affect the human respiration system. The chest radiographs of the lungs are used to classify these diseases. Identifying diseases is essential, but the most important thing is explaining the reason behind classification results. This research provides an explanation of the classification results of different lung pulmonary diseases so that doctors can understand the reason that causes these diseases. This work achieved 97% classification accuracy. This research also evaluated the highlighted regions in the input image, during the explanation of classification results with the manifest file, where the doctor highlighted the same regions with red arrows. The automatic disease explanation and identification will help doctors to diagnose these diseases at a very early stage. Explainable Artificial Intelligence is a key component of artificially intelligent systems that aim to explain the classification results. The classification results explanation is essential for automatic disease diagnosis in healthcare. The human respiration system is badly affected by different chest pulmonary diseases. Automatic classification and explanation can be used to detect these lung diseases. In this paper, we introduced a CNN-based transfer learning-based approach for automatically explaining pulmonary diseases, i.e., edema, tuberculosis, nodules, and pneumonia from chest radiographs. Among these pulmonary diseases, pneumonia, which COVID-19 causes, is deadly; therefore, radiographs of COVID-19 are used for the explanation task. We used the ResNet50 neural network and trained the network on extensive training with the COVID-CT dataset and the COVIDNet dataset. The interpretable model LIME is used for the explanation of classification results. Lime highlights the input image's important features for generating the classification result. We evaluated the explanation using radiologists' highlighted images and identified that our model highlights and explains the same regions. We achieved improved classification results with our fine-tuned model with an accuracy of 93% and 97%, respectively. The analysis of our results indicates that this research not only improves the classification results but also provides an explanation of pulmonary diseases with advanced deep-learning methods. This research would assist radiologists with automatic disease detection and explanations, which are used to make clinical decisions and assist in diagnosing and treating pulmonary diseases in the early stage. [ABSTRACT FROM AUTHOR]

Development of novel materials for organic solar cells is a booming area of current research. Fused-ring electron accepters are the potential agents of revolution in organic photovoltaic devices and revealing high efficiency in organic solar cells. This study highlights the novel quad-rotor-shaped molecules as first example of efficient fused-ring non-fullerene acceptor materials with proficient photovoltaic parameters for their utilization in high-performance organic solar cells. First time, eight quad-rotor-shaped fused-ring electron accepters (QRFR-1–QRFR-8) are developed via modulating end-caps of experimentally synthesized (BFTT-TN) molecule (QRFR). Optoelectronic properties of proposed molecules are determined using frontier molecular orbitals (FMO), UV–Visible, density of state (DOS), overlap DOS (ODOS), transition density matrix (TDM) heat maps, open circuit voltage (Voc), binding energies (Eb), reorganization energy of electron (λe), hole (λh), charge transfer analysis, and compared with reference QRFR. All proposed fused-ring electron accepters disclose less energy gap and λmax in near IR region than QRFR after end-capped engineering. Highest Voc with respect to HOMOPM6–LUMOacceptor is found 1.66 V in QRFR-6 than QRFR (1.63 V). Eb values of QRFR-1–QRFR-8 are found better and comparable with QRFR. The λe is found smaller than QRFR in all molecules except QRFR-5. The proposed quad-rotor-shaped molecules exhibit proficient photovoltaic features and can serve as best candidate for organic solar cells when blended with PM6 film. This study not only enlightens the researchers to use end-capped reforms as effective tactic for designing materials, but also provides novel quad-rotor-shaped materials to experimentalist for synthesis and their usage in future application of organic solar cells. [ABSTRACT FROM AUTHOR]

Phytophthora infestans is a destructive pathogen in the tropical and the sub-tropical areas of the world. Identification of pathogens is very important to find successful disease control strategies. Here, we isolated and identified P. infestans, as well as, determined the morphological characteristics, on different media. The phenotypic characteristic of the isolates were observed as fluffy cottony growth with striated pattern. The average length of sporangia among the isolates collected during 2017 from potato growing areas of Punjab ranged from 14.94 to 47.89 µm, and mean breadth of sporangia varied from 10.44 0 to 23.67 µm. During 2018, among all districts, isolate Jhg-22 showed the highest length (46.64 µm) and breadth (22.17 µm) followed by Shl-25 (46.59 µm) and breadth (22.61 µm), Oka-24 (46.31 x 21.72 µm) while the lowest value measured by Cht-14 (16.05 x 13.39 µm). The best growth and sporulation of P. infestans revealed on Rye agar media compared with other media, while the minimus growth was observed on carrot agar media across most isolates. Therefore, the determination of morphological characteristics for plant-pathogen may add knowledge of the taxonomic behavior of pathogen. The usage of various growth media is providing a useful pattern of the growth that can be utilized in determining the best possible control of late blight disease. [ABSTRACT FROM AUTHOR]

Entropy is essential. Entropy is a measure of a system's molecular disorder or unpredictability, since work is produced by organized molecular motion. Entropy theory offers a profound understanding of the direction of spontaneous change for many commonplace events. A formal definition of a random graph exists. It deals with relational data's probabilistic and structural properties. The lower-order distribution of an ensemble of attributed graphs may be used to describe the ensemble by considering it to be the results of a random graph. Shannon's entropy metric is applied to represent a random graph's variability. A structural or physicochemical characteristic of a molecule or component of a molecule is known as a molecular descriptor. A mathematical correlation between a chemical's quantitative molecular descriptors and its toxicological endpoint is known as a QSAR model for predictive toxicology. Numerous physicochemical, toxicological, and pharmacological characteristics of chemical substances help to foretell their type and mode of action. Topological indices were developed some 150 years ago as an alternative to the Herculean, and arduous testing is needed to examine these features. This article uses various computational and mathematical techniques to calculate atom–bond connectivity entropy, atom–bond sum connectivity entropy, the newly defined Albertson entropy using the Albertson index, and the IRM entropy using the IRM index. We use the subdivision and line graph of the H 3 B O 3 layer structure, which contains one boron atom and three oxygen atoms to form the chemical boric acid. [ABSTRACT FROM AUTHOR]

Diabetic Retinopathy affects one-third of all diabetic patients and may cause vision impairment. It has four stages of progression, i.e., mild non-proliferative, moderate non-proliferative, severe non-proliferative and proliferative Diabetic Retinopathy. The disease has no noticeable symptoms at early stages and may lead to chronic destruction, thus causing permanent blindness if not detected at an early stage. The proposed research provides deep learning frameworks for autonomous detection of Diabetic Retinopathy at an early stage using fundus images. The first framework consists of cascaded neural networks, spanned in three layers where each layer classifies data into two classes, one is the desired stage and the other output is passed to another classifier until the input image is classified as one of the stages. The second framework takes normalized, HSV and RGB fundus images as input to three Convolutional Neural Networks, and the resultant probabilistic vectors are averaged together to obtain the final output of the input image. Third framework used the Long Short Term Memory Module in CNN to emphasize the network in remembering information over a long time span. Proposed frameworks were tested and compared on the large-scale Kaggle fundus image dataset EYEPAC. The evaluations have shown that the second framework outperformed others and achieved an accuracy of 78.06% and 83.78% without and with augmentation, respectively. [ABSTRACT FROM AUTHOR]

The research focus in remote sensing scene image classification has been recently shifting towards deep learning (DL) techniques. However, even the state-of-the-art deep-learning-based models have shown limited performance due to the inter-class similarity and the intra-class diversity among scene categories. To alleviate this issue, we propose to explore the spatial dependencies between different image regions and introduce patch-based discriminative learning (PBDL) for remote sensing scene classification. In particular, the proposed method employs multi-level feature learning based on small, medium, and large neighborhood regions to enhance the discriminative power of image representation. To achieve this, image patches are selected through a fixed-size sliding window, and sampling redundancy, a novel concept, is developed to minimize the occurrence of redundant features while sustaining the relevant features for the model. Apart from multi-level learning, we explicitly impose image pyramids to magnify the visual information of the scene images and optimize their positions and scale parameters locally. Motivated by this, a local descriptor is exploited to extract multi-level and multi-scale features that we represent in terms of a codeword histogram by performing k-means clustering. Finally, a simple fusion strategy is proposed to balance the contribution of individual features where the fused features are incorporated into a bidirectional long short-term memory (BiLSTM) network. Experimental results on the NWPU-RESISC45, AID, UC-Merced, and WHU-RS datasets demonstrate that the proposed approach yields significantly higher classification performance in comparison with existing state-of-the-art deep-learning-based methods. [ABSTRACT FROM AUTHOR]

Phytophthora infestans is a destructive pathogen in the tropical and the sub-tropical areas of the world. Identification of pathogens is very important to find successful disease control strategies. Here, we isolated and identified P. infestans, as well as, determined the morphological characteristics, on different media. The phenotypic characteristic of the isolates were observed as fluffy cottony growth with striated pattern. The average length of sporangia among the isolates collected during 2017 from potato growing areas of Punjab ranged from 14.94 to 47.89 µm, and mean breadth of sporangia varied from 10.44 0 to 23.67 µm. During 2018, among all districts, isolate Jhg-22 showed the highest length (46.64 µm) and breadth (22.17 µm) followed by Shl-25 (46.59 µm) and breadth (22.61 µm), Oka-24 (46.31 x 21.72 µm) while the lowest value measured by Cht-14 (16.05 x 13.39 µm). The best growth and sporulation of P. infestans revealed on Rye agar media compared with other media, while the minimus growth was observed on carrot agar media across most isolates. Therefore, the determination of morphological characteristics for plant-pathogen may add knowledge of the taxonomic behavior of pathogen. The usage of various growth media is providing a useful pattern of the growth that can be utilized in determining the best possible control of late blight disease. [ABSTRACT FROM AUTHOR]

Duloxetine is indicated for the treatment of chemotherapy-induced neuropathic pain in adults. It is also indicated for anxiety, depression, and fibromyalgia in children. A rare side effect of syndrome of inappropriate antidiuretic hormone (SIADH) secretion has been reported in adults, but not in pediatrics or pediatric oncology patients. We present the case of a 10-year-old child with acute lymphoblastic leukemia, who developed SIADH after duloxetine was given for chemotherapy-induced neuropathic pain and comorbid anxiety. The SIADH resolved after duloxetine was stopped. This case highlights a rare side effect of duloxetine and caution should be taken when prescribing duloxetine to children. [ABSTRACT FROM AUTHOR]

A 35-year-old female presented with episodes of frequent dizziness, ear fullness, and right ear tinnitus for 12 months. Head imaging revealed a right glomus tympanicum tumor. She underwent pre-operative endovascular embolization of the glomus tympanicum tumor with surgical, cyanoacrylate-based glue. Immediately after the procedure, she developed drowsiness and severe pain in the right temporal region. Further investigations revealed a right cerebellar stroke in the posterior inferior cerebellar artery territory. She was treated with intravenous heparin, followed by one year of oral anticoagulation. With rehabilitation, she significantly recovered from her post embolization stroke. However, the tumor was resected at another institution. Ten years later, follow-up imaging indicated a gradual increase in the size of the glomus jugulare tumor compressing the nearby critical vascular structures. She subsequently received radiation therapy to treat the residual tumor. Currently, she has no neurological deficit, but her mild dizziness, right ear tinnitus, and hearing impairment persist. [ABSTRACT FROM AUTHOR]

Objective: To determine the etiological pattern, clinical presentation and outcome of patients with proptosis in a tertiary care hospital. Study Design: Cross-sectional study. Place and Duration of Study: Ophthalmology Department, Khyber Teaching Hospital, Peshawar Pakistan, from Jan 2019 to Jun 2020. Methodology: This study was conducted on 60 patients having proptosis. Patients were treated either medically, surgically or both. The demographic profile included age, gender and type of proptosis. The outcome included recovery, re-treatment, referral to the relevant speciality and loss to follow-up, were measured. Results: Out of 60 patients with proptosis, 39 were males, and 21 were female. Fifty-two patients had unilateral, and 8 had bilateral proptosis, with the majority suffering from non-axial proptosis. On aetiology exhibited tumours (45%), infectious (25%), inflammatory (16.6%), vascular (6.66%) and injury (6.66%). The surgical procedure was indicated in 27(45%) patients, while medical treatment was given in 28(46.6%) patients. Five patients (8.4%) received both surgical and medical treatment. Out of 60 patients, 26 patients (46.33%) fully recovered and 11 patients (18.33%) did not recover, 17(28.33%) patients were referred to other specialities for management, and 5(8.3%) patients lost to follow-up. Conclusion: In our study, tumours were the main cause of proptosis, followed by infective and inflammatory causes, with the paediatric age group (<18 years) at more risk. [ABSTRACT FROM AUTHOR]

Bearing elements under rolling contact fatigue (RCF) exhibit microstructural features, known as white etching bands (WEBs) and dark etching regions (DERs). The formation mechanism of these microstructural features has been questionable and therefore warranted this study to gain further understanding. Current research describes mechanistic investigations of standard AISI 52100 bearing steel balls subjected to RCF testing under tempering conditions. Subsurface analyses of RCF-tested samples at tempering conditions have indicated that the microstructural alterations are progressed with subsurface yielding and primarily dominated by thermal tempering. Furthermore, bearing balls are subjected to static load tests in order to evaluate the effect of lattice deformation. It is suggested from the comparative analyses that a complete rolling sequence with non-proportional stress history is essential for the initiation and progression of WEBs, supported by the combination of carbon flux, assisted by dislocation and thermally activated carbon diffusion. These novel findings will lead to developing a contemporary and new-fangled prognostic model applied to microstructural alterations. [ABSTRACT FROM AUTHOR]

Pyrolysis and hydrocracking of plastic waste can produce valuable products with manageable effects on the environment as compared to landfilling and incineration. This research focused on the process simulation and life cycle assessment of the pyrolysis and hydrocracking of high-density polyethylene. Aspen Plus was used as the simulator and the Peng-Robinson thermodynamic model was employed as a fluid package. Additionally, sensitivity analysis was conducted in order to optimize product distribution. Based on the simulation, the hydrocracking process produced value-added fuels, i.e., gasoline and natural gas. In contrast, pyrolysis generated a significant quantity of pyrolysis oil with a high number of cyclo-compounds and char, which are the least important to be utilized as fuels. Moreover, in the later part of the study, life cycle assessment (LCA) was adopted in order to investigate and quantify their impact upon the environment using simulation inventory data, which facilitates finding a sustainable process. Simapro was used as a tool for LCA of the processes and materials used. The results demonstrate that hydrocracking is a better process in terms of environmental impact in 10 out of the 11 impact categories. Overall, the present study proposed a promising comparison based on energy demands, product distribution, and potential environmental impacts, which will help to improve plastic waste management. [ABSTRACT FROM AUTHOR]

A topological index is a numerical parameter that is derived mathematically from a graph structure. In chemical graph theory, these indices are used to quantify the chemical properties of chemical compounds. We compute the first and second temperature, hyper temperature indices, the sum connectivity temperature index, the product connectivity temperature index, the reciprocal product connectivity temperature index and the F temperature index of a molecular graph silicate network and silicate chain network. Furthermore, a QSPR study of the key topological indices is provided, and it is demonstrated that these topological indices are substantially linked with the physicochemical features of COVID-19 medicines. This theoretical method to find the temperature indices may help chemists and others in the pharmaceutical industry forecast the properties of silicate networks and silicate chain networks before trying. [ABSTRACT FROM AUTHOR]

Over the years, several forms of energy resources have been used for a variety of purposes; however, the over use of energy supplies has resulted in a variety of problems. Renewable energy sources are an excellent approach for addressing these challenges. In recent years, it has become possible to modify photoelectrochemical cells with titanium dioxide, cadmium sulfide, and graphitic nitride to create clean hydrogen. It has been discovered that the doping method enhances the photocatalytic activity of the catalyst. Metal-oxide nanoparticles are common dopants; one example of this is iron-doped TiO2, which exhibits remarkable quantum efficiency due to the presence of iron. Nanomaterials with excellent crystallinity, three-dimensional structure, and tiny size are needed for obtaining high photocatalytic efficiency. The focus of the current research is on clean fuel production (hydrogen synthesis) using a doped titanium dioxide photocatalyst. In addition to clean fuel generation, attention is being paid to the development of titanium dioxide doping technologies and the doping of titanium dioxide. Experimental manufacturing approaches have been investigated to achieve this goal. Both theoretical (computational) and experimental methodologies have been discussed that aid in reducing band gap of pure Anatase TiO2 and may help in achieving better photocatalytic water splitting (PWS) for hydrogen production. [ABSTRACT FROM AUTHOR]

Entropy is a thermodynamic function in chemistry that reflects the randomness and disorder of molecules in a particular system or process based on the number of alternative configurations accessible to them. Distance-based entropy is used to solve a variety of difficulties in biology, chemical graph theory, organic and inorganic chemistry, and other fields. In this article, the characterization of the crystal structure of niobium oxide and a metal–organic framework is investigated. We also use the information function to compute entropies by building these structures with degree-based indices including the K-Banhatti indices, the first redefined Zagreb index, the second redefined Zagreb index, the third redefined Zagreb index, and the atom-bond sum connectivity index. [ABSTRACT FROM AUTHOR]

Groundwater is more prone to contamination due to its extensive usage. Different methods are applied to study vulnerability of groundwater including widely used DRASTIC method, SI and GOD. This study proposes a novel method of mapping groundwater vulnerability using machine learning algorithms. In this study, point extraction method was used to extract point values from a grid of 646 points of seven raster layer in the Al Khatim study area of United Arab Emirates. These extracted values were classified based on nitrate concentration threshold of 50 mg/L into two classes. Machine learning models were developed, using depth to water (D), recharge (R), aquifer media (A), soil media (S), topography (T), vadose zone (I) and hydraulic conductivity (C), on the basis of nitrate class. Classified 'groundwater vulnerability class values' were trained using 10-fold cross-validation, using four machine learning models which were Random Forest, Support Vector Machine, Naïve Bayes and C4. 5. Accuracy showed the model developed by Random Forest gained highest accuracy of 93%. Four groundwater vulnerability maps were developed from machine learning classifiers and was compared with base method of DRASTIC index. The efficiency, accuracy and validity of machine learning based models were evaluated based on Receiver Operating Characteristics (ROC) curve and Precision-Recall curve (PRC). The results proved that machine learning is an efficient tool to access, analyze and map groundwater vulnerability. [ABSTRACT FROM AUTHOR]

BACKGROUND & OBJECTIVE: The most common cause of low back pain is disc prolapse. Disc prolapse occurs in both gender, but females are more prone to disc degenerative changes. The aim of this study was to determine the correlation between clinical findings and magnetic resonance imaging (MRI) findings of lumbar disc prolapse patients. METHODOLOGY: This study recruited 32 participants of either gender, between the ages of 19 to 65, from the Radiology department of Shalamar Hospital. This study was carried out in six months with follow-up. Only diagnosed patients with lumbar disc prolapse on MRI were recruited. Numeric pain rating scale (NPRS) was used to identify the intensity of pain. History and neurological examinations were done in all diagnosed patients. All the findings of MRI were correlated with the clinical findings at different levels of lumbar vertebrae using Pearson correlation. RESULTS: There was a statistically significant positive correlation of MRI findings with clinical findings in the presence of nerve root compression at the L1-L2 level of lumbar vertebrae. All abnormal findings on MRI did not correlate with clinical findings. There was a significant positive (p-value >0.05) correlation between clinical features (sensations, power and reflexes) and MRI findings at L1, L2, right L3 myotomes and L2 dermatome only. CONCLUSION: There was a no correlation between MRI findings and clinical findings of patients with lumbar disc prolapse except L2 and L3. [ABSTRACT FROM AUTHOR]

• Ten novel compounds with naphthalene-fused octacyclic core are designed for solar cell applications. • Electronic, photophysical and photovoltaic features are explored using computational tools. • Augmented optoelectronic properties are predicted in proposed molecules. • The designed compounds are recommended for stable and efficient organic solar cells. To meet the growing need for high-efficiency photovoltaic materials in today's high-tech applications, ten acceptor molecules (SH01-SH10) with naphthalene-fused octacyclic electron-donating central core NITT are proposed. End-capped structural-alterations on reference R (NITT-2F) with strong electron-drawing groups (A01-A10) are performed for attaining improved optoelectronic characteristics and possible applications in organic solar cells (OSCs). In comparison to the reference R (NITT-2F), key parameters such as FMO analysis, energy gap (E gap), open-circuit voltage (V OC), absorption spectra (λ max), excitation energy (E x), transition density matrix (TDM) with binding energy (E b), the density of states (DOS), reorganization energy of the electron (λ e) and hole (λ h), and charge transfer with PM6 donor substance are perceived. It is found that all proposed molecules (SH01-SH10) have a reduced energy gap (2.104–1.901 eV), a broader absorption spectrum (707–820 nm), and smaller excitation energy (1.75–1.51 eV) as compared to reference molecule NITT-2F values 2.132 eV, 675 nm and 1.79 eV respectively. The significant charge transfers and 0.23 eV decrease in the E gap is noted for the designed molecule SH05. Results confirmed that all proposed molecules, especially SH05 could be an excellent choice for OSCs candidate due to the combination of strong electron-withdrawing effect in end-capped acceptor unit and extended conjugation, as well as its promising photovoltaic properties, which include the lowest E gap (1.901 eV), highest λ max (820 nm (in chloroform) and 729 nm (in gas phase)) with E x values of 1.51 and 1.70 eV respectively, slightest electron mobility (λ e = 0.0090 eV) and hole mobility (λ h = 0.0073 eV), highest ionization potential (6.826 eV) and electron affinity (3.384 eV), and fine value of V OC =1.02 V with respect to (ǀEPM6 HOMO ǀ - ǀESH05 LUMO ǀ). End-capped acceptor alterations are an acceptable way to attain the desired optoelectronic characteristics demonstrated in this research work. This study can be essential in achieving efficient novel non-fullerene OSCs with higher performance, low cost, and higher optical absorption coefficient. Thus, proposed molecules (SH01-SH10) with proficient electron and hole transfer mobilities and optoelectronic features are recommended to develop higher-efficiency solar cells. [Display omitted] [ABSTRACT FROM AUTHOR]

The article titled "Poly (N-vinylcaprolactam-grafted-sodium alginate) Based Injectable pH/Thermo Responsive In Situ Forming Depot Hydrogels for Prolonged Controlled Anticancer Drug Delivery; In Vitro, In Vivo Characterization and Toxicity Evaluation" has been retracted by the journal Pharmaceutics. Concerns were raised about overlapping figures with a previously published article that had no common authorship and represented different experimental conditions. An investigation was conducted, and the authors were unable to explain the overlapping figures or meet the required quality standards for raw images. As a result, the paper was retracted due to concerns about the reliability of the findings. The authors disagree with this retraction. [Extracted from the article]