Search

Hemolytic anemia is a blood disorder whose incidence is increasing in the world in recent years especially after the pandemic. Conventional treatments include use of steroids and immunosuppresants that are accompanied by numerous adverse effects. With growing interest in using complex multi-component formulations for multi-targeted therapy, the present study aims to investigate the therapeutic efficacy of a traditional Ayurvedicherbomineral preparation, Punarnavadi Mandura, which has been traditionally used as a supplement in iron-deficiency anemia, against phenylhydrazine-induced hemolytic anemia in rodent models.

Hedonistic smartphone usage refers to smartphone activities for instant gratification and pleasure; increased hedonistic use is associated with perceived stress, problematic smartphone usage, and smartphone addiction among adolescents. Parents naturally intervene in adolescents’ screen media use through various mediation strategies, including active mediation of safety, restrictive mediation, monitoring mediation, and technical mediation, to reduce potential negative impacts and online risks. This study aims to identify the relationship between parental mediation strategies and adolescents’ hedonistic smartphone use using the data from 447 adolescents and 432 parents. This study also explores the consideration of a new mediation technique, participatory learning mediation, in the parental mediation model. Multiple regression results revealed positive and negative associations between different parental mediation strategies and adolescents’ hedonistic smartphone use, with restrictive mediation having no effect.

In this paper, we investigate the existence, uniqueness and stability of solutions to the mixed sequential pantograph fractional integro-differential equations with non-local boundary conditions. The solution of the problem is obtained and the existence and uniqueness of the solution is tested by means of Krasnosel’skii’s fixed point theorem and the Banach contraction principle respectively. Moreover, the Ulam–Hyers and Ulam–Hyers–Rassias stability of the solution is determined. An example emphasising our findings is provided.

IMPORTANCE: Because mentorship is critical for professional development and career advancement, it is essential to examine the status of mentorship and identify challenges that junior surgical faculty (assistant and associate professors) face obtaining effective mentorship. OBJECTIVE: To evaluate the mentorship experience for junior surgical faculty and highlight areas for improvement. DESIGN, SETTING, AND PARTICIPANTS: This qualitative study was an explanatory sequential mixed-methods study including an anonymous survey on mentorship followed by semistructured interviews to expand on survey findings. Junior surgical faculty from 18 US academic surgery programs were included in the anonymous survey and interviews. Survey responses between “formal” (assigned by the department) vs “informal” (sought out by the faculty) mentors and male vs female junior faculty were compared using χ2 tests. Interview responses were analyzed for themes until thematic saturation was achieved. Survey responses were collected from November 2022 to August 2023, and interviews conducted from July to December 2023. EXPOSURE: Mentorship from formal and/or informal mentors. MAIN OUTCOMES AND MEASURES: Survey gauged the availability and satisfaction with formal and informal mentorship; interviews assessed broad themes regarding mentorship. RESULTS: Of 825 survey recipients, 333 (40.4%) responded; 155 (51.7%) were male and 134 (44.6%) female. Nearly all respondents (319 [95.8%]) agreed or strongly agreed that mentorship is important to their surgical career, especially for professional networking (309 respondents [92.8%]), career advancement (301 [90.4%]), and research (294 [88.3%]). However, only 58 respondents (18.3%) had a formal mentor. More female than male faculty had informal mentors (123 [91.8%] vs 123 [79.4%]; P = .003). Overall satisfaction was higher with informal mentorship than formal mentorship (221 [85.0%] vs 40 [69.0%]; P = .01). Most male and female faculty reported no preferences in gender or race and ethnicity for their mentors. When asked if they had good mentor options if they wanted to change mentors, 141 (47.8%) responded no. From the interviews (n = 20), 6 themes were identified, including absence of mentorship infrastructure, preferred mentor characteristics, and optimizing mentorship. CONCLUSIONS AND RELEVANCE: Academic junior surgical faculty agree mentorship is vital to their careers. However, this study found that few had formal mentors and almost half need more satisfactory options if they want to change mentors. Academic surgical programs should adopt a framework for facilitating mentorship and optimize mentor-mentee relationships through alignment of mentor-mentee goals and needs.

In this present study, a novel environment-friendly bio-polymeric hybrid ultrafiltration membrane was fabricated by incorporating poly(methyl vinyl ether maleic anhydride) (PMVEAMA) into the poly(lactic acid) (PLA) polymer matrix. The integration of PMVEAMA into PLA significantly altered the membrane's structural morphology, functional characteristics, and filtration performance. As a result, key functional parameters like membrane porosity and water content were increased, which exhibiting the improved hydrophilicity. Furthermore, the molecular weight cut-off of the PLA/PMVEAMA membrane reached up to a maximum of 66.5 KDa with an average pore size of 9.14 nm, subsequently improving the flux of the membrane up to 2.8 times than PLA membrane. The functional capacity of the synthesized membrane was further assessed by testing its ability to filter toxic chlorophenolic compounds. Rejection studies reveal that the PLA/PMVEAMA membrane achieved a rejection efficiency of 44.64 % pentachlorophenol (PCP) and 51.85 % 2,4-dichlorophenol (DCP). Moreover, fouling studies revealed enhanced PCP and DCP flux recovery ratios of 50.94 % and 51.40 %, respectively, with a significant reduction in the flux decline rate. Additionally, the synthesized PLA/PMVEAMA membranes improved stability and regenerative properties up to 12 filtration cycles and antifouling properties highlight its potential to filter chlorophenolic compounds from wastewater.

Soybean is the prime leguminous oilseed crop in India. Collar rot of soybean (CRS) caused by Sclerotium rolfsiiis one of the major diseases in India and is of significant economic importance in recent years. No commercial soybean cultivar is reported to be resistant to this disease. In the current study, twenty-six advanced breeding lines were evaluated under epiphytotic field conditions, and thirty-four genotypes, including advanced breeding lines, commercial cultivars, and germplasm lines, were evaluated for resistance to S. rolfsiiunder controlled glasshouse conditions. Most of the genotypes were found susceptible to S. rolfsii;however, PS 16–70, PS 16–61, NRC 137, NRC SL-1, JS 20–34, NRC 7, and AGS 25 showed moderate resistance reaction to S. rolfsii. Furthermore, agronomic traits were evaluated after inoculating S. rolfsiiin the glass house study. Area under disease progress curve (AUDPC) was positively correlated with root-and-shoot polyphenol oxidase and negatively correlated with root-and-shoot phenols and peroxidase. Moderate resistance cultivars were further evaluated for initiation of defense reaction against S. rolfsii.All the genotypes produced substantial defense enzymes and compounds after inoculation. Principal component analysis showed that the moderate resistance genotype PS 16–70 was positively associated with root and biochemical traits. In contrast, other moderate resistant genotypes showed a positive association with shoot traits. These moderately resistant genotypes could be used in breeding programs to develop high-yielding and collar rot-resistant varieties and mapping populations to map genes/quantitative trait loci (QTL) governing resistance.

There is a paucity of literature guiding trauma surgeons in the care of patients with active metastatic cancer (MC). Even less is known regarding outcomes for MC patients requiring emergent surgery after trauma. We hypothesized that trauma patients with active Metastatic Cancer (MC) have an increased mortality rate and undergo increased rates of withdrawal of care (WoC) within 72-hours following emergent operations, compared to similarly matched patients without MC.

Garlic (Allium sativum)exhibit well defined therapeutic properties due to presence of distinct bioactive compounds, specifically organosulphur compounds. Spectral analysis by UV–Vis and FT-IR confirmed interaction of different functional groups and free electrons with the electromagnetic waves showed presence of different bioactive compounds formulated silver nanoparticles (AgNPs) using diallyl sulphide (DAS) also done for aqueous garlic extract. Different assays performed for confirming the presence of antibacterial and anticancer properties of DAS-AgNPs and garlic extract. The minimum inhibitory concentration required to inhibit Gram negative (Escherichia coli, V. cholera) and Gram positive (S. aureus, B. cereus) were found lesser in DAS-AgNPs followed by garlic extract. Cytotoxicity effect confirmed by DAS-AgNPs and A. sativumtowards HeLa cell line by MTT assay. The IC50values obtained for MTT assay were at 24.09 μg/ml in DAS-AgNPs and 129.22 μg/ml in garlic extract. Anticancer efficacy was found more potent in DAS-AgNPs as compared with aqueous garlic extract.

ABSTRACTThe proposed research work is to develop the brick by incorporating steel industry industrial waste to achieve sustainable environment. Aluminium dross (AD) is the one of the major waste produced during aluminium ingots production. Particle size and chemical compositions of aluminium dross are analysed using Scanning Electron Microscopy (SEM). To effectively utilise the aluminium dross, five mix composition of bricks were produced to determine the influence of AD on mechanical properties of bricks such as the compressive strength, water absorption, efflorescence and bulk density. Mix B gives the maximum compressive strength of 11.54 N/mm2, water absorption of 5.6% and bulk density of 1658.4 kg/m3for the mix of 5% dross. Analysis of variance (ANOVA) is employed to analyse the influence of dross and fly ash concentrations on the quadratic model.

This study introduces a blockchain-based lightweight encryption strategy with federated learning to address the scalability and trust concerns of electronic health records (EHR). After implementing lightweight encryption, the EHR data is stored in a decentralized cloud system. The importance of protecting the privacy and security of distant patients’ health records is explored. Now that stakeholders have a secure portal and cloud data is inaccessible, assaults on electronic healthcare records should decrease. The study guarantees full encryption throughout the whole conversation with federated learning. Deprived of the essential for a trusted third party, the system sets up active smart contracts at runtime between the sensor and the data user to facilitate the transfer of EHR data. To ensure that the data is private between the owner and user during the contract’s execution, it employs a very effective proxy re-encryption mechanism with federated learning. To examine the performance of the proposed system, it has been built and deployed on an Ethereum-based testbed. It is observed that the PSNR and MSE of the proposed model are 39 ( $1.07\times $ ) and 229.6 ( $1.02\times$ ) respectively. The entropy of the image is assessed to be 7.8 for the proposed model. This is also compared with existing algorithms and proved to be a secured model.

The speedy growth of human population, the issues arising out of pollution, demand of invertase yield and dumping of wastes from agro-industry have been influenced to find new solutions for microbial enzymes. In this study, an attempt was made to produce the invertase by yeast isolated from honey bees. Influence of various process parameters like nutritional sources and environmental conditions was studied using waste material. The enzyme was precipitated by using the acetone to get the product for further experimental analysis. Subsequently, it was carried out for further purification by Sephadex G-50 column chromatography and dialysed. At that moment, optimization of culture conditions for the yield of invertase with agro waste was determined by process parameters. It was followed by the immobilization of microbial cells for increasing the yield of invertase by various matrices. In order to check the bioactive potential of invertase, the bread was prepared with and without the amendment of enzyme. The existing result could be supportive to future researchers for industrial applications of microbial invertase by immobilization approach.

Many people all across the globe have been utilising social media to exchange information. Numerous firms apply social data mining to extract numerous exciting insights from social data which is represented as a sophisticated network structure. However, publishing social data has a direct and indirect influence on the privacy of many of its users. Recently, numerous anonymisation methods have been invented and deployed to preserve sensitive information about users and their interactions on social media. This paper presents a complete assessment of several data anonymisation algorithms for social network data and evaluates their pros and downsides. It also tackles the primary research problems surrounding the effectiveness of anonymisation technologies.

Background: Some of the new challenges that consumers are confronted with include safeguarding data and privacy. Data retrieved from the cloud's external sources and the calculations that follow aren't always accurate. Addressing the risk of adversaries utilizing various exploitation techniques to compromise transactional data privacy is a basic concern when establishing big private networks. The profession of criminal investigation is quickly embracing new technologies, and one of these is blockchain. Every sector, from banking and supply chain management to smart apps and the Internet of Things (IoT), has been increasingly vulnerable to security threats in recent years.Methods: An effective solution to the "data island" problem, federated-learning (FL) has recently been a hot and broad concern topic. But as FL technology finds more practical uses, training management gets more complicated, and the trade-off of multi-tasking gets more difficult, due to the increasing quantity of FL tasks. A privacy-preserving FL framework with multi-tasks using a partitioned blockchain is proposed in this study to address this shortcoming. The framework may execute several FL tasks by separate requesters. To start, an FL task force is established to help with the visualization, organization, and administration of security aggregation.Results: In order to safeguard users' privacy and guarantee the accuracy of the global model, the suggested framework incorporates both Paillier-homomorphic-encryption (PHE) and Pearsoncorrelation- coefficient (PCC). Lastly, a novel incentive system based on the blockchain is introduced to encourage individuals to provide their valuable data.Conclusion: Our suggested framework achieves a global model accuracy of 99.2% according to the experimental data. Specifically, in the realm of industrial applications, the suggested framework is clearly more suited to real-world settings.

Reliable data on the composition and structure of forests at various spatial scales is necessary for the conservation and monitoring of forest biodiversity. However, because field sampling techniques can be challenging, comprehensive data regarding the features of forest habitat over wide areas are frequently lacking. In order to get over this restriction, we created variables that describe the structure of the forest landscape over a wide environmental gradient using a nationally accessible light detection and ranging (LiDAR) remote sensing dataset. This research proposes a novel technique in forest management based on biodiversity analysis using LiDAR image processing by deep learning techniques. Here, the input LiDAR image has been collected as temperature pattern dataset in major deforestation regions and processed for noise removal and normalization. This image feature has been extracted using recurrent graph learning-based SegNet component analysis. Then, this extracted feature has been classified using Gaussian deep belief region-based adversarial neural networks. The experimental analysis has been carried out for various temperature pattern datasets in terms of random accuracy, average precision, recall, NSE, and AUC. The results of this study will help determine policy objectives for the restoration of mountain areas and enhance the effectiveness of monitoring damaged mountain forests. With the help of the suggested method, 96% random accuracy, 94% average precision, 49% NSE, 93% recall, and 95% AUC were achieved.

Weather significantly influences agricultural productivity. Plant biotic and abiotic stressors are primarily induced by climate change, resulting in a detrimental effect on worldwide agricultural productivity. These two components are closely interconnected. This paper presents an innovative approach in smart agriculture for climate change combining remote sensing and a deep learning algorithm. The input is obtained as a multispectral environmental image and then subjected to noise reduction and normalisation processing. The image has been retrieved using a primary convolutional component with a stacked encoder model. The retrieved features are identified using ResNet graph reinforcement neural networks. The resulting classification output displays environmental imagery depicting climate variations. The agriculture sector has been analysed based on the classified climate analysis. The experimental results have been conducted on diverse farm datasets related to climate change, evaluating the detection accuracy, recall, mean average precision, normalized correlation, and F-1 score. The proposed method achieved a detection accuracy of 98%, a normalised correlation of 95%, a mean average precision of 92%, a recall of 97%, and an F-MEASURE of 94%. Machine learning can assist in monitoring and predicting the impact of climate change on food security, as indicated by the findings.

The study aims to analyse the impact of shopping applications on the shopping behaviour of the working women community in Bangalore, a city known as the IT hub. The research uses a quantitative analysis with SPSS version 23 software and a structured questionnaire survey technique to gather data from the working women community. The study uses descriptive statistics, ANOVA, regression, and Pearson correlation analysis to evaluate the perception of working women regarding the significance of online shopping applications. The results show that digital shopping applications are more prevalent among the working women community in Bangalore. The study also evaluates the socio-economic and psychological factors that influence their purchasing behaviour. The findings suggest that online marketers should enhance their strategies to improve their business on digital platforms. The research provides valuable insights into the shopping habits of the working women community in Bangalore.

This paper proposes a novel sorted level-shifted U-shaped carrier-based pulse width modulation (SLSUC PWM) strategy combined with an input power control approach for a 13-level cascaded H-bridge multi-level inverter designed for grid connection, specifically tailored for photovoltaic (PV) systems, which avoids a double-stage power conversion configuration. In this methodology, every inverter generates a quasi-square output voltage waveform with a width that is intricately linked to the output power of its corresponding PV panel. The application of this SLSUC pulse width modulation technique with input power control in a solar energy-based 13-level grid-tied inverter facilitates precise maximum power point (MPP) tracking for each of the PV panels under uniform and non-uniform irradiation conditions and ensures the consistent maintenance of capacitor voltage balance. Moreover, this novel SLSUC PWM method for 13-level inverters offers a range of benefits, including a low total harmonic distortion (THD) in the output voltage of the multi-level inverter and higher inverter and MPPT efficiencies over the existing PWM techniques. To verify the efficacy of the proposed control method over existing techniques, a PV-based grid-connected multi-level inverter with the proposed control strategy undergoes modeling and simulation using MATLAB/Simulink. Then, experimental hardware-in-the-loop (EHIL) testing is conducted to confirm and evaluate its effectiveness.

Two-dimensional (2D) molybdenum disulfide (MoS2) has attracted significant attention due to its unique structure and properties, especially following the discovery of graphene. This is mainly attributed due to its flexibility which facilitates the MoS2phase and properties exploration. MoS2mainly exists in three phases, 2H, 1T and 3R, where the naturally occurring 2H and 3R phase of MoS2acts as a semiconductor, with active edge sites and inactive basal planes. Conversely, the metallic 1T phase of MoS2features active edge sites and basal planes, making it more favorable for various applications. Numerous efforts have been made to modify bulk MoS2which exists as a 2H phase. By utilizing intercalation, which facilitates the conversion from the 2H to the 1T phase, also improves storage capacity and stabilizes metastable 1T phase, etc. To achieve these unique properties, various intercalation methods have been extensively studied and are thoroughly reviewed in this study. The intercalation process involves introducing foreign materials into a host material with adjustable van der Waals gaps. This allows for the insertion of guest material between the layers, making them excellent candidates for intercalation. In this review, we will explore various methods of intercalating MoS2material, including the electrochemical, solution-based, wet chemical, and ion-exchange methods.

Resource conservation technologies (RCTs) are recommended to address the rainfall deficits of dryland crops in semi-arid regions. Understanding the effects of RCTs on mung bean (Vigna radiataL. Wilczek) plant roots and thereby yields is crucial for drought tolerance, and few studies are reported regarding this. A field study was conducted during 2015 and 2016 at Indian Council of Agricultural Research - Central Research Institute for Dryland Agriculture (ICAR-CRIDA), India to evaluate root dynamics of Drought Tolerant (DTV) and Drought Susceptible (DSV) varieties of mung bean under Organic Amendment at the rate of 3 t ha-1(OA), resource conservation measure (in-situ Conservation Furrow for every three rows of the crop) as RCM, the combination RCM + OA under normal and deficit rainfall conditions. An innovative field root architectural sampling was followed to extract roots from different soil depths with minimal root losses. RCM increased the rooting depth by 5 cm while OA increased root length density (RLD). Together these measures improved root plasticity as early increased root length, high RLD at deeper soil depths (≥ 0.5 cm cm− 3), and fine roots of < 1.08 mm diameter in the drought-tolerant mung bean cultivar. The estimated potential carbon additions of mung bean root to soil is 7.1–16.3 kg ha− 1for a single crop. Integration of both application of organic amendment and in-situ conservation furrow for every three mung bean rows to drought tolerant mung bean cultivar resulted in increased rooting depth (RD), RLD and more absorptive roots mitigated moisture stress through root adaptation under Semi-Arid Tropic (SAT) rainfed ecosystems. Soil carbon addition is the co-benefit associated with these RCTs.

This paper is concerned with the exponential H∞filtering problem for a class of discrete-time switched neural networks with random time-varying delays based on the sojourn-probability-dependent method. Using the average dwell time approach together with the piecewise Lyapunov function technique, sufficient conditions are proposed to guarantee the exponential stability for the switched neural networks with random time-varying delays which are characterized by introducing a Bernoulli stochastic variable. Based on the derived H∞performance analysis results, the H∞filter design is formulated in terms of Linear Matrix Inequalities (LMIs). Finally, two numerical examples are presented to demonstrate the effectiveness of the proposed design procedure.