In the modern era of digitization, the analysis in the Internet of Things (IoT) environment demands a brisk amalgamation of domains such as high-dimension (images) data sensing technologies, robust internet connection (4 G or 5 G) and dynamic (adaptive) deep learning approaches. This is required for a broad range of indispensable intelligent applications, like intelligent healthcare systems. Dynamic image classification is one of the major areas of concern for researchers, which may take place during analysis under the IoT environment. Dynamic image classification is associated with several temporal data perturbations (such as novel class arrival and class evolution issue) which cause a massive classification deterioration in the deployed classification models and make them in-effective. Therefore, this study addresses such temporal inconsistencies (novel class arrival and class evolution issue) and proposes an adapted deep learning framework (ameliorated adaptive convolutional neural network (CNN) ensemble framework), which handles novel class arrival and class evaluation issue during dynamic image classification. The proposed framework is an improved version of previous adaptive CNN ensemble with an additional online training (OT) and online classifier update (OCU) modules. An OT module is a clustering-based approach which uses the Euclidean distance and silhouette method to determine the potential new classes, whereas, the OCU updates the weights of the existing instances of the ensemble with newly arrived samples. The proposed framework showed the desirable classification improvement under non-stationary scenarios for the benchmark (CIFAR10) and real (ISIC 2019: Skin disease) data streams. Also, the proposed framework outperformed against state-of-art shallow learning and deep learning models. The results have shown the effectiveness and proven the diversity of the proposed framework to adapt the new concept changes during dynamic image classification. In future work, the authors of this study aim to develop an IoT-enabled adaptive intelligent dermoscopy device (for dermatologists). Therefore, further improvements in classification accuracy (for real dataset) is the future concern of this study.
The increasing interest in online shopping in recent years has increased the importance of understanding customer engagement valence (CEV) in a virtual service network. There is yet a comprehensive explanation of the CEV concept, particularly its impact on multi-actor networks such as web stores. Therefore, this study aims to fill this research gap. In this study, past literature in the marketing and consumer psychology field was critically reviewed to understand the concept of CEV in online shopping, and the propositional-based style was employed to conceptualize the CEV within the online shopping (web stores) context. The outcomes demonstrate that the valence of customer engagement is dependent on the cognitive interpretation of signals that are prompted by multiple actors on a web store service network. If the signals are positively interpreted, positive outcomes such as service co-creation are expected, but if they are negatively interpreted, negative outcomes such as service co-destruction are predicted. These notions create avenues for future empirical research and practical implications.
To synthesize lithium ferrite with various Gd concentrations (Li0.5Fe2.5-xGdxO4), x = 0.00, 0.025, 0.05, 0.075, 0.1, solutes were dissolved in glycol, i.e. by using the without water and surfactant (WOWS) sol-gel method. X-ray diffraction (XRD) analysis confirmed that the material possessed an inverse spinel cubic structure and is single phase. Pellets of all samples were sintered at 700 °C and XRD confirmed that samples were crystalline, phase pure and had an inverse spinel cubic lattice. Scanning electron microscopy indicated that the grains were agglomerated and had a predominantly spherical shape. It is concluded that Gd acts as a grain refiner in lithium ferrite up to a Gd concentration of 0.05. AC conductivity and dielectric constant increased by increasing Gd concentration. The Maxwell-Wagner model and Johnsher's power law were used to explain the dielectric properties. DC conductivity was measured from 100 to 600 °C. DC conductivity was explained by the hopping mechanism. It is concluded that DC resistivity and dielectric constant values are related reciprocally in the prepared sample. AC electrical properties were also measured at a constant frequency of 1 MHz in the temperature range from 400 to 600 °C. Gd-substituted lithium ferrite showed high AC conductivity, high DC resistivity and constant dielectric values, but low dielectric loss values as compared to pure lithium ferrite.
The staggering growth in smartphone and wearable device use has led to a massive scale generation of personal (user-specific) data. To explore, analyze, and extract useful information and knowledge from the deluge of personal data, one has to leverage these devices as the data-mining platforms in ubiquitous, pervasive, and big data environments. This study presents the personal ecosystem where all computational resources, communication facilities, storage and knowledge management systems are available in user proximity. An extensive review on recent literature has been conducted and a detailed taxonomy is presented. The performance evaluation metrics and their empirical evidences are sorted out in this paper. Finally, we have highlighted some future research directions and potentially emerging application areas for personal data mining using smartphones and wearable devices.
The study of biofilms has skyrocketed in recent years due to increased awareness of the pervasiveness and impact of biofilms. It costs the USA literally billions of dollars every year in energy losses, equipment damage, product contamination and medical infections. But biofilms also offer huge potential for cleaning up hazardous waste sites, filtering municipal and industrial water and wastewater, and forming biobarriers to protect soil and groundwater from contamination. The complexity of biofilm activity and behavior requires research contributions from many disciplines such as biochemistry, engineering, mathematics and microbiology. The aim of this review is to provide a comprehensive analysis of emerging novel antimicrobial techniques, including those using myriad organic and inorganic products as well as genetic engineering techniques, the use of coordination complex molecules, composite materials and antimicrobial peptides and the use of lasers as such or their modified use in combination treatments. This review also addresses advanced and recent modifications, including methodological changes, and biocide efficacy enhancing strategies. This review will provide future planners of biofilm control technologies with a broad understanding and perspective on the use of biocides in the field of green developments for a sustainable future.
Keyword extraction is essential in determining influenced keywords from huge documents as the research repositories are becoming massive in volume day by day. The research community is drowning in data and starving for information. The keywords are the words that describe the theme of the whole document in a precise way by consisting of just a few words. Furthermore, many state-of-the-art approaches are available for keyword extraction from a huge collection of documents and are classified into three types, the statistical approaches, machine learning, and graph-based methods. The machine learning approaches require a large training dataset that needs to be developed manually by domain experts, which sometimes is difficult to produce while determining influenced keywords. However, this research focused on enhancing state-of-the-art graph-based methods to extract keywords when the training dataset is unavailable. This research first converted the handcrafted dataset, collected from impact factor journals into n-grams combinations, ranging from unigram to pentagram and also enhanced traditional graph-based approaches. The experiment was conducted on a handcrafted dataset, and all methods were applied on it. Domain experts performed the user study to evaluate the results. The results were observed from every method and were evaluated with the user study using precision, recall and f-measure as evaluation matrices. The results showed that the proposed method (FNG-IE) performed well and scored near the machine learning approaches score.
The over usage and over dependency on digital devices, like smartphones, has been considered as a growing international epidemic. The increased dependency on gadgets, especially smartphones for personal and official uses, has also brought many detrimental effects on individual users. Hence it is vital to understand the negative effects of smartphone usage on human. Therefore, this study aims to investigate the effects of bedtime smartphone usage on work performances, interpersonal conflicts, and work engagement, via the mediating role of sleep quality among employees. Using a cross-sectional study design, a questionnaire-based field survey was conducted on 315 employees who participated as respondents. The results confirmed the negative effects of bedtime smartphone usage on sleep quality. Along with it, the effects of sleep quality on work performances, work engagements and interpersonal conflicts were also proven to be statistically significant. Regarding the mediating role of sleep quality, it was empirically evident that sleep quality mediates the relationship between bedtime smartphone usage with work performances and interpersonal conflicts. The findings revealed that bedtime smartphone usage reduces sleep quality among the employees, resulting in lower work performances and engagements while contributing to higher interpersonal conflicts. The findings concluded that smartphone usage before sleep increases the prospects of employees to be less productive, less engaged, and have more workplace conflicts. The findings warrant the continued managerial as well as academic research attention, as the smartphones are now used by many organisations to run businesses as well.
Electrocardiography (ECG) sensors play a vital role in the Internet of Medical Things, and these sensors help in monitoring the electrical activity of the heart. ECG signal analysis can improve human life in many ways, from diagnosing diseases among cardiac patients to managing the lifestyles of diabetic patients. Abnormalities in heart activities lead to different cardiac diseases and arrhythmia. However, some cardiac diseases, such as myocardial infarction (MI) and atrial fibrillation (Af), require special attention due to their direct impact on human life. The classification of flattened T wave cases of MI in ECG signals and how much of these cases are similar to ST-T changes in MI remain an open issue for researchers. This article presents a novel contribution to classify MI and Af. To this end, we propose a new approach called deep deterministic learning (DDL), which works by combining predefined heart activities with fused datasets. In this research, we used two datasets. The first dataset, Massachusetts Institute of Technology-Beth Israel Hospital, is publicly available, and we exclusively obtained the second dataset from the University of Malaya Medical Center, Kuala Lumpur Malaysia. We first initiated predefined activities on each individual dataset to recognize patterns between the ST-T change and flattened T wave cases and then used the data fusion approach to merge both datasets in a manner that delivers the most accurate pattern recognition results. The proposed DDL approach is a systematic stage-wise methodology that relies on accurate detection of R peaks in ECG signals, time domain features of ECG signals, and fine tune-up of artificial neural networks. The empirical evaluation shows high accuracy (i.e., ≤99.97%) in pattern matching ST-T changes and flattened T waves using the proposed DDL approach. The proposed pattern recognition approach is a significant contribution to the diagnosis of special cases of MI.
Objectives: The aim of the study was to assess the safety and efficacy of the Amplatzer ductal occluder (ADO) in transcatheter occlusion of patent ductus arteriosus (PDA).
Background: Transcatheter closure of small to moderate sized PDAs is an established procedure. The ADO is a self-expandable device with a number of salutary features, notably its retrievability, ease of delivery via small 5F to 7F catheters and a range of sizes suitable even for the larger PDAs.
Methods: Between November 1997 and August 1999, the ADO was successfully implanted in 205 of 209 patients with PDA. The inclusion criteria for this device occlusion method were patients with clinical and echocardiographic features of moderate to large PDA, weighing > or =3.5 kg as well as asymptomatic adolescents and adults with PDA measuring > or =5.0 mm on two-dimensional (2D) echocardiogram. Occlusion was achieved via the antegrade venous approach. Follow-up evaluations were performed with 2D echocardiogram, color-flow mapping and Doppler measurement of the descending aorta and left pulmonary artery velocity at 24 h and 1, 3, 6 and 12 months after implantation.
Results: Two hundred and five patients had successful PDA occlusion using this device. The patients were between two months and 50 years (median 1.9) and weighed between 3.4 kg and 63.2 (median 8.4). Infants made up 26% of the total patients. The PDA measured from 1.8 to 12.5 mm (mean 4.9) at the narrowest diameter. Forty-four percent of patients achieved immediate complete occlusion. On color Doppler the closure rates at 24 h and 1 month after implant were 66% and 97%, respectively. At 6 and 12 months all except one patient attained complete occlusion. Device embolization occurred in three patients; in two this was spontaneous, and in the other it was due to catheter manipulation during postimplant hemodynamic measurement. Mild aortic narrowing was seen in an infant.
Conclusions: Patent ductus arteriosus occlusion using ADO is safe and efficacious. It is particularly useful in symptomatic infants and small children with relatively large PDA. Embolization can be minimized by selection of appropriate sized devices, and caution should be exercised in infants <5 kg.
This article presents an optimized ultrasound-assisted ethanolic extraction (UAEE) and characterization of selected high-value components from Gemlik olive fruit (GOF) harvested from Potohar region of Pakistan. Response surface methodology (RSM), involving central composite design (CCD), was applied to optimize the extraction variables i. e., temperature (25-65 °C), extraction time (15-45 min) and aqueous ethanol concentration (60-90 %) for optimal recovery of bioactives extract, total phenolic contents (TPC) and DPPH free radical scavengers. Under the optimized set of conditions such as 43 °C temperature, 32 min extraction time and 80 % aqueous ethanol, the best extract yield (218.82 mg/g), TPC (19.87 mg GAE/g) and DPPH scavenging activity (63.04 %) were recorded. A quadratic polynomial model was found to be reasonably fitted to the observed results for extract yield (p<0.0001 and R2 =0.9941), TPC (p<0.0001 and R2 =0.9891), and DPPH radical scavenging activity (p<0.0001 and R2 =0.9692). Potent phenolic compounds were identified by GC/MS in GOF extract and considerable amount of essential fatty acids were also detected. The current findings support the use of UAEE as an effective green route for optimized recovery of high-value components from GOF and hence its applications can be extended to functional food and nutra-pharmaceutical developments.
Advances in financial inclusions have contributed to economic growth and poverty alleviation, addressing environmental implications and implementing measures to mitigate climate change. Financial inclusions force advanced countries to progress their policies in a manner that does not hinder developing countries' current and future development. Consequently, this research examined the asymmetric effects of information and communication technology (ICT), financial inclusion, consumption of primary energy, employment to population ratio, and human development index on CO2 emissions in oil-producing countries (UAE, Nigeria, Russia, Saudi Arabia, Norway, Kazakhstan, Kuwait, Iraq, USA, and Canada). The study utilizes annual panel data spanning from 1990 to 2021. In addition, this study investigates the validity of the Environmental Kuznets Curve (EKC) trend on the entire sample, taking into account the effects of energy consumption and population to investigate the impact of financial inclusion on environmental degradation. The study used quantile regression, FMOLS, and FE-OLS techniques. Preliminary outcomes revealed that the data did not follow a normal distribution, emphasizing the need to use quantile regression (QR). This technique can effectively detect outliers, data non-normality, and structural changes. The outcomes from the quantile regression analysis indicate that ICT consistently reduces CO2 emissions in all quantiles (ranging from the 1st to the 9th quantile). In the same way, financial inclusion, and employment to population ratio constrains CO2 emissions across each quantile. On the other side, primary energy consumption and Human development index were found to increase CO2 emissions in each quantile (1st to 9th). The findings of this research have implications for both the academic and policy domains. By unraveling the intricate interplay between financial inclusion, ICT, and environmental degradation in oil-producing nations, the study contributes to a nuanced understanding of sustainable development challenges. Ultimately, the research aims to guide the formulation of targeted policies that leverage financial inclusion and technology to foster environmentally responsible economic growth in oil-dependent economies.
Synthesis of new Cefpodoxime derivatives via Schiff Bases mechanism and the efficiency of their antimicrobial and antiviral activities were addressed. They were analyzed for structural validation by using spectroscopic techniques using FTIR, 1HNMR, and 13CNMR. Molecular docking against IBV Virus papain-like protease (PLPro) was done with Auto dock tools against compounds having excellent IC50 values against IBV (Corona Class) virus. All derivatives showed strong zone of inhibition ranges from (55 ± 2.0 to 70 ± 0.8 mm) against E. coli. Compounds 1,2,4 and 6 derivatives showed remarkable activity against Stenotrophomonas maltophilia and Serratia marcescens. But For most the newly synthesized derivatives C 1 (64 ± 1.60), C 3 (32 ± 0.80), and C 8 (64 ± 1.60) showed potential IC50 values against two variants of Corona class viruses i.e. Avian Influenza (H9) and Avian corona (IBV) viruses. The current study revealed that newly synthesized Schiff Bases possessed strong anti-viral potential. Further studies may make a breakthrough in medical sciences to tackle latest challenges such as Corona Virus Diseases.
This research aims to establish the MHD radiating convective nanofluid flow properties with the viscous dissipation across an exponentially accelerating vertical plate. As the plate accelerates, its temperature progressively increases. There are two separate types of water-based nanofluids that include copper ([Formula: see text]) and titanium dioxide ([Formula: see text]) nanoparticles, respectively. The most crucial aspect of this investigation is finding a closed-form solution to a nonlinear coupled partial differential equations scheme. Galerkin finite element method (G-FEM) is used to figure out the initial managing equations. Utilizing graphs, the effect of the flow phenomenon's contributing variables as well as the influence of other factors is determined and depicted. In the part dedicated to the findings and discussion, the properties of these emergent parameters are described in more depth. Nonetheless, the thermal radiation and heat sink factors increase the thermal profile. In addition, the greater density of the copper nanoparticles cause the nanoparticle volume fraction to lessen the velocity delineation.
Copper oxide and Zinc (Zn)-doped Copper oxide nanostructures (CuO-NSs) are successfully synthesized by using a hydrothermal technique. The as-obtained pure and Zn-doped CuO-NSs were tested to study the effect of doping in CuO on structural, optical, and antibacterial properties. The band gap of the nanostructures is calculated by using the Tauc plot. Our results have shown that the band gap of CuO reduces with the addition of Zinc. Optimization of processing conditions and concentration of precursors leads to the formation of pine needles and sea urchin-like nanostructures. The antibacterial properties of obtained Zn-doped CuO-NSs are observed against Gram-negative (Pseudomonasaeruginosa,Klebsiellapneumonia,Escherichiacoli) and Gram-positive (Staphylococcusaureus) bacteria via the agar well diffusion method. Zn doped s are found to have more effective bacterial resistance than pure CuO. The improved antibacterial activity is attributed to the reactive oxygen species (ROS) generation.
This study was designed to seek the phytochemical analysis, antioxidant, enzyme inhibition, and toxicity potentials of methanol and dichloromethane (DCM) extracts of aerial and root parts of Crotalaria burhia. Total bioactive content, high-performance liquid chromatography-photodiode array detector (HPLC-PDA) polyphenolic quantification, and ultra-high performance liquid chromatography-mass spectrometry (UHPLC-MS) analysis were utilized to evaluate the phytochemical composition. Antioxidant [including 2,2-diphenyl-1-picryl-hydrazyl-hydrate (DPPH)], 2,2'-azino-bis[3-ethylbenzothiazoline-6-sulfonic acid (ABTS), ferric reducing antioxidant power assay (FRAP), cupric reducing antioxidant capacity CUPRAC, phosphomolybdenum, and metal chelation assays] and enzyme inhibition [against acetylcholinesterase (AChE), butyrylcholinesterase (BChE), α-glucosidase, α-amylase, and tyrosinase] assays were carried out for biological evaluation. The cytotoxicity was tested against MCF-7 and MDA-MB-231 breast cell lines. The root-methanol extract contained the highest levels of phenolics (37.69 mg gallic acid equivalent/g extract) and flavonoids (83.0 mg quercetin equivalent/g extract) contents, and was also the most active for DPPH (50.04 mg Trolox equivalent/g extract) and CUPRAC (139.96 mg Trolox equivalent /g extract) antioxidant assays. Likewise, the aerial-methanol extract exhibited maximum activity for ABTS (94.05 mg Trolox equivalent/g extract) and FRAP (64.23 mg Trolox equivalent/g extract) assays. The aerial-DCM extract was noted to be a convincing cholinesterase (AChE; 4.01 and BChE; 4.28 mg galantamine equivalent/g extract), and α-glucosidase inhibitor (1.92 mmol acarbose equivalent/g extract). All of the extracts exhibited weak to modest toxicity against the tested cell lines. A considerable quantities of gallic acid, catechin, 4-OH benzoic acid, syringic acid, vanillic acid, 3-OH-4-MeO benzaldehyde, epicatechin, p-coumaric acid, rutin, naringenin, and carvacrol were quantified via HPLC-PDA analysis. UHPLC-MS analysis of methanolic extracts from roots and aerial parts revealed the tentative identification of important phytoconstituents such as polyphenols, saponins, flavonoids, and glycoside derivatives. To conclude, this plant could be considered a promising source of origin for bioactive compounds with several therapeutic uses.