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The continuous discharge of organic dyes into freshwater resources poses a long-term hazard to aquatic life. The advanced oxidation Fenton process is a combo of adsorption and degradation of pollutants to detoxify toxic effluents, such as anti-bacterial drugs, antibiotics, and organic dyes. In this work, an activated attapulgite clay-loaded iron-oxide (A-ATP@Fe3O4) was produced using a two-step reaction, in which attapulgite serves as an enrichment matrix and Fe3O4 functions as the active degrading component. The maximum adsorption capacity (qt) was determined by assessing the effect of temperature, pH H2O2, and adsorbent. The results showed that the A-ATP@Fe3O4 achieves the highest removal rate of 99.6% under optimum conditions: 40 °C, pH = 3, H2O2 25 mM, and 0.1 g dosage of the composite. The dye removal procedure achieved adsorption and degradation equilibrium in 120 and 30 min, respectively, by following the same processes as the advanced oxidation approach. Catalytic activity, kinetics, and specified surface characteristics suggest that A-ATP@Fe3O4 is one of the most promising candidates for advanced oxidation-enrooted removal of organic dyes.

Attapulgite (APT) is widely used in wastewater treatment due to its exceptional adsorption and colloidal properties, as well as its cost-effectiveness and eco-friendliness. However, low-grade APT generally limits its performance. Here, a colloid mill-assisted ultrasonic-fractional centrifugal purification method was developed to refine low-grade APT. This process successfully separated and removed impurity minerals such as quartz and dolomite from the raw ore, resulting in a refined APT purity increase from 16.9% to 60% with a specific surface area of 135.5 m2∙g−1. Further modifying of the refined APT was carried out through the hydrothermal method using varying dosages of cetyltrimethylammonium chloride (CTAC), resulting in the production of four different APT adsorbents denoted as QAPT-n (n = CTAC mole number) ranging from 0.5 to 5 mmol. Using Congo red (CR) as the target pollutant, the QAPT-5 sample exhibited the best adsorption capacity with the maximum quantity of 1652.2 mg∙g−1 in a neutral solution at 30 °C due to the highest surface charge (zeta potential = 8.25 mV). Moreover, the QAPT-5 pellets (~2.0 g adsorbent) shaped by the alginate-assisted molding method removed more than 96% of 200 mL aqueous solution containing 200 mg∙L−1 CR and maintained this efficiency in 10 adsorption–elution cycles, which exhibited the promising practical application.

The DNA virus responsible for monkeypox, transmitted from animals to humans, exhibits two distinct genetic lineages in central and eastern Africa. Beyond the zoonotic transmission involving direct contact with the infected animals’ bodily fluids and blood, the spread of monkeypox can also occur through skin lesions and respiratory secretions among humans. Both monkeypox and chickenpox involve skin lesions and can also be transmitted through respiratory secretions, but they are caused by different viruses. The key difference is that monkeypox is caused by an orthopox-virus, while chickenpox is caused by the varicella-zoster virus. In this study, the utilization of a patch-based vision transformer (ViT) model for the identification of monkeypox and chickenpox disease from human skin color images marks a significant advancement in medical diagnostics. Employing a transfer learning approach, the research investigates the ViT model’s capability to discern subtle patterns which are indicative of monkeypox and chickenpox. The dataset was enriched through carefully selected image augmentation techniques, enhancing the model’s ability to generalize across diverse scenarios. During the evaluation phase, the patch-based ViT model demonstrated substantial proficiency, achieving an accuracy, precision, recall, and F1 rating of 93%. This positive outcome underscores the practicality of employing sophisticated deep learning architectures, specifically vision transformers, in the realm of medical image analysis. Through the integration of transfer learning and image augmentation, not only is the model’s responsiveness to monkeypox- and chickenpox-related features enhanced, but concerns regarding data scarcity are also effectively addressed. The model outperformed the state-of-the-art studies and the CNN-based pre-trained models in terms of accuracy.

In response to the rapid advancements in facial manipulation technologies, particularly facilitated by Generative Adversarial Networks (GANs) and Stable Diffusion-based methods, this paper explores the critical issue of deepfake content creation. The increasing accessibility of these tools necessitates robust detection methods to curb potential misuse. In this context, this paper investigates the potential of Vision Transformers (ViTs) for effective deepfake image detection, leveraging their capacity to extract global features. Objective: The primary goal of this study is to assess the viability of ViTs in detecting multiclass deepfake images compared to traditional Convolutional Neural Network (CNN)-based models. By framing the deepfake problem as a multiclass task, this research introduces a novel approach, considering the challenges posed by Stable Diffusion and StyleGAN2. The objective is to enhance understanding and efficacy in detecting manipulated content within a multiclass context. Novelty: This research distinguishes itself by approaching the deepfake detection problem as a multiclass task, introducing new challenges associated with Stable Diffusion and StyleGAN2. The study pioneers the exploration of ViTs in this domain, emphasizing their potential to extract global features for enhanced detection accuracy. The novelty lies in addressing the evolving landscape of deepfake creation and manipulation. Results and Conclusion: Through extensive experiments, the proposed method exhibits high effectiveness, achieving impressive detection accuracy, precision, and recall, and an F1 rate of 99.90% on a multiclass-prepared dataset. The results underscore the significant potential of ViTs in contributing to a more secure digital landscape by robustly addressing the challenges posed by deepfake content, particularly in the presence of Stable Diffusion and StyleGAN2. The proposed model outperformed when compared with state-of-the-art CNN-based models, i.e., ResNet-50 and VGG-16.

Drug side effects (DSEs) or adverse drug reactions (ADRs) are a major concern in the healthcare industry, accounting for a significant number of annual deaths in Europe alone. Identifying and predicting DSEs early in the drug development process is crucial to mitigate their impact on public health and reduce the time and costs associated with drug development. Objective: In this study, our primary objective is to predict multiple drug side effects using 2D chemical structures, especially for COVID-19, departing from the conventional approach of relying on 1D chemical structures. We aim to develop a novel model for DSE prediction that leverages the CNN-based transfer learning architecture of ResNet152V2. Motivation: The motivation behind this research stems from the need to enhance the efficiency and accuracy of DSE prediction, enabling the pharmaceutical industry to identify potential drug candidates with fewer adverse effects. By utilizing 2D chemical structures and employing data augmentation techniques, we seek to revolutionize the field of drug side-effect prediction. Novelty: This study introduces several novel aspects. The proposed study is the first of its kind to use 2D chemical structures for predicting drug side effects, departing from the conventional 1D approaches. Secondly, we employ data augmentation with both conventional and diffusion-based models (Pix2Pix), a unique strategy in the field. These innovations set the stage for a more advanced and accurate approach to DSE prediction. Results: Our proposed model, named CHEM2SIDE, achieved an impressive average training accuracy of 0.78. Moreover, the average validation and test accuracy, precision, and recall were all at 0.73. When evaluated for COVID-19 drugs, our model exhibited an accuracy of 0.72, a precision of 0.79, a recall of 0.72, and an F1 score of 0.73. Comparative assessments against established transfer learning and machine learning models (VGG16, MobileNetV2, DenseNet121, and KNN) showcased the exceptional performance of CHEM2SIDE, marking a significant advancement in drug side-effect prediction. Conclusions: Our study introduces a groundbreaking approach to predicting drug side effects by using 2D chemical structures and incorporating data augmentation. The CHEM2SIDE model demonstrates remarkable accuracy and outperforms existing models, offering a promising solution to the challenges posed by DSEs in drug development. This research holds great potential for improving drug safety and reducing the associated time and costs.

Enhancers are short DNA segments (50–1500 bp) that effectively activate gene transcription when transcription factors (TFs) are present. There is a correlation between the genetic differences in enhancers and numerous human disorders including cancer and inflammatory bowel disease. In computational biology, the accurate categorization of enhancers can yield important information for drug discovery and development. High-throughput experimental approaches are thought to be vital tools for researching enhancers’ key characteristics; however, because these techniques require a lot of labor and time, it might be difficult for researchers to forecast enhancers and their powers. Therefore, computational techniques are considered an alternate strategy for handling this issue. Based on the types of algorithms that have been used to construct predictors, the current methodologies can be divided into three primary categories: ensemble-based methods, deep learning-based approaches, and traditional ML-based techniques. In this study, we developed a novel two-layer deep forest-based predictor for accurate enhancer and strength prediction, namely, NEPERS. Enhancers and non-enhancers are divided at the first level by NEPERS, whereas strong and weak enhancers are divided at the second level. To evaluate the effectiveness of feature fusion, block-wise deep forest and other algorithms were combined with multi-view features such as PSTNPss, PSTNPdss, CKSNAP, and NCP via 10-fold cross-validation and independent testing. Our proposed technique performs better than competing models across all parameters, with an ACC of 0.876, Sen of 0.864, Spe of 0.888, MCC of 0.753, and AUC of 0.940 for layer 1 and an ACC of 0.959, Sen of 0.960, Spe of 0.958, MCC of 0.918, and AUC of 0.990 for layer 2, respectively, for the benchmark dataset. Similarly, for the independent test, the ACC, Sen, Spe, MCC, and AUC were 0.863, 0.865, 0.860, 0.725, and 0.948 for layer 1 and 0.890, 0.940, 0.840, 0.784, and 0.951 for layer 2, respectively. This study provides conclusive insights for the accurate and effective detection and characterization of enhancers and their strengths.

The current study aimed to evaluate the impact of increasing cooling sessions from three to five times a day on milk yield and the welfare of dairy buffaloes during a semiarid summer in Pakistan. Eighteen Nili Ravi buffaloes were randomly assigned to three cooling strategies: (1) CTL, where buffaloes were cooled with a handheld hosepipe twice daily for 5 min each; (2) 3CS, where buffaloes were cooled using sprinklers three times daily; and (3) 5CS, where buffaloes were cooled using sprinklers five times daily. Each sprinkler cooling session lasted 1 h, with a 6 min cycle of 3 min of water on and 3 min off. Results showed that the 5CS group produced 1.6 and 3.2 kg more milk per day compared to the 3CS and CTL groups, respectively (p < 0.001). Both the 5CS and 3CS groups had consistently lower core body temperatures and respiration rates than the CTL group. Buffaloes in the 5CS group spent significantly more time eating (p < 0.001). Additionally, the 5CS group exhibited lower cortisol and blood urea nitrogen levels (p = 0.001) and higher glucose levels than the CTL group (p = 0.006). In conclusion, increasing cooling sessions to five times daily improved milk yield and welfare compared to the traditional cooling strategy (CTL) in dairy buffaloes during semiarid summers, highlighting the benefits of optimized cooling practices.

Skin cancer, particularly melanoma, has been recognized as one of the most lethal forms of cancer. Detecting and diagnosing skin lesions accurately can be challenging due to the striking similarities between the various types of skin lesions, such as melanoma and nevi, especially when examining the color images of the skin. However, early diagnosis plays a crucial role in saving lives and reducing the burden on medical resources. Consequently, the development of a robust autonomous system for skin cancer classification becomes imperative. Convolutional neural networks (CNNs) have been widely employed over the past decade to automate cancer diagnosis. Nonetheless, the emergence of the Vision Transformer (ViT) has recently gained a considerable level of popularity in the field and has emerged as a competitive alternative to CNNs. In light of this, the present study proposed an alternative method based on the off-the-shelf ViT for identifying various skin cancer diseases. To evaluate its performance, the proposed method was compared with 11 CNN-based transfer learning methods that have been known to outperform other deep learning techniques that are currently in use. Furthermore, this study addresses the issue of class imbalance within the dataset, a common challenge in skin cancer classification. In addressing this concern, the proposed study leverages the vision transformer and the CNN-based transfer learning models to classify seven distinct types of skin cancers. Through our investigation, we have found that the employment of pre-trained vision transformers achieved an impressive accuracy of 92.14%, surpassing CNN-based transfer learning models across several evaluation metrics for skin cancer diagnosis.

Background: In recent years, the use of the internet among children and adolescents has dramatically increased, leading to growing concerns regarding the potential risks of excessive internet use and addiction. Addressing these concerns, this systematic review aims to summarize current evidence on the effectiveness of treatment interventions for internet addiction among children and adolescents. Method: We performed a systematic review using PubMed, Web of Science, PsycInfo, and Google Scholar with search terms including “internet addiction”, “problematic internet use”, “children” or “adolescents”, “treatment” and “randomized controlled trial”. We found 10 Randomized Controlled Trials (RCTs) meeting the criteria and included them in this systematic review. Results: This systematic review analyzed 10 randomized controlled trials focused on treatment interventions for internet addiction in adolescents and young adults. The interventions used were diverse, including cognitive-behavioral therapy (CBT), medication, electro-acupuncture (EA), and solution-focused approaches. The measures used to assess the effectiveness of the interventions also varied, but most studies reported moderate to large effect sizes for at least some outcomes. Overall, the studies suggest that interventions such as CBT and EA can be effective in reducing symptoms of internet addiction, internet gaming disorder, and unspecified internet use disorders. School-based programs and brief manualized CBT programs also show promise, though more research is needed to determine their long-term effectiveness. Conclusion: Promising treatment approaches for internet addiction are emerging, but inconsistencies in conceptualization, language, and diagnostic criteria present some challenges. The growing recognition of problematic internet use, as shown by the DSM-5′s recognition of Internet Gaming Disorder, highlights the need for a multidisciplinary approach and standardized criteria to facilitate accurate reporting across studies. Continued research is needed to identify effective treatments and diagnostic criteria for internet addiction, with the potential to offer practical insights into effective medications and therapies.

Wastewater treatment is indispensable as wastewater can lead to adverse health effects and deteriorate the quality of life on earth. Photocatalysis is a facile methodology to address this issue. In this study, nanocomposites (NCs) of manganese oxide (Mn3O4) and nickel oxide (NiO) were synthesized in different weight ratios via the solid-state reaction route. Structural properties, optical properties, surface morphology, and functional group analysis of the synthesized nanomaterials were conducted using X-ray diffraction (XRD), UV– Vis spectroscopy, scanning electron microscopy (SEM) along with energy-dispersive X-ray (EDX) analysis, and Fourier-transform infrared (FTIR) spectroscopy, respectively. The bandgap of the nanocomposite decreases significantly from 2.35 eV for the Mn3O4 NPs to 1.65 eV for the Mn3O4/NiO nanocomposite (NC). Moreover, adsorption studies followed by the photocatalytic performance of the Mn3O4/NiO NCs were evaluated to determine the removal of methylene blue (MB) dye from wastewater. The photocatalytic performance of the nanocomposite enhances as the ratio of Mn3O4 in the composite increases from one weight percentage to three weight percentage. The photocatalytic degradation efficiency was calculated to be 95%. The results show that the synthesized NCs could play an important role in photocatalytic wastewater purification and environmental remediation.

Magneto-plasmonic Ag/Ni and Ag/Fe nanoparticles (NPs) were synthesized in this work using the environmentally safe and contaminant-free dual-pulsed Q-switched Nd:YAG 1064 nm laser ablation method. The optical and magnetic characteristics of synthesized nanomaterials were investigated using a vibrating sample magnetometer and an ultraviolet-visible absorption spectrometer. According to transmission electron microscopy (TEM), the shape of Ag/Ni and Ag/Fe NPs seems to be spherical, with mean diameters of 7.3 nm and 11.5 nm, respectively. X-ray diffraction (XRD) was used in order to investigate and describe the phase structures of the synthesized nanomaterials. The synthesized NPs reached maximum temperatures such as 48.9, 60, 63.4, 70, 75, and 79 °C for Ag/Ni nanofluid and 52, 56, 60, 68, 71, and 72 °C for Ag/Fe nanofluid when these nanofluids were subjected to an NIR 808 nm laser with operating powers of 1.24, 1.76, 2.36, 2.91, 3.5, and 4 W, respectively. Because of the plasmonic hyperthermia properties of nanoparticles, nanofluids display higher temperature profiles than pure water. According to these findings, plasmonic nanoparticles based on silver might be used to treat hyperthermia.

The wide variety of uses for nanoparticles (NPs) is due to their unique combination of features in a single assembly. The arc melted copper-cobalt ingot sample were qualitatively studied using laser induced breakdown spectroscopy (LIBS). Later, using the fabricated alloy as a target material for Nd:YAG laser ablation, CuCo2O4 NPs were synthesized. The magnetic properties of the synthesized NPs were studied using a vibrating sample magnetometer (VSM). To determine the composition and morphology of the synthesized NPs, X-ray diffraction (XRD), energy dispersive X-ray (EDX) analysis, transmission electron microscopy (TEM), scanning electron microscopy (SEM), and dynamic light scattering (DLS) techniques were used. The TEM and DLS showed that particles were spherical in shape with an average size of 32 nm and 28 nm, respectively. The antibacterial activity of the synthesized NPs was studied against S. aureus and E. coli strains as positive and negative controls using a standard approach. CuCo2O4 nanoparticles exhibited non-mutagenic potential against S. typhimurium TA-98 and TA-100 strains. Furthermore, the magnetic hyperthermia study of CuCo2O4 nanofluid was examined using a lab-made apparatus. The specific absorption rates (SAR) of 4.57 and 5.17 W/g were determined for the magnetic field strength of 230 μT and 247 μT, respectively. The study shows antibacterial activity and magnetic hyperthermia potential of the synthesized nanoparticles.

In this study, pEGFP-LUC was used as a model plasmid and three distinct cationic lipids (dioleyloxy-propyl-trimethylammonium chloride [DOTMA], dioleoyl trimethylammonium propane [DOTAP], and cetylpyridinium chloride [CPC]) were tested along with PEG 5000, as a nonionic surfactant, to prepare glyceryl monostearate (GMS)-based cationic solid lipid nanoparticles (cSLNs). Both the type and quantity of surfactant had an impact on the physicochemical characteristics of the cSLNs. Thermal analysis of the greater part of the endothermic peaks of the cSLNs revealed they were noticeably different from the individual pure compounds based on their zeta potential (ZP ranging from +17 to +56 mV) and particle size (PS ranging from 185 to 244 nm). The addition of cationic surfactants was required to produce nanoparticles (NPs) with a positive surface charge. This suggested that the surfactants and extensive entanglement of the lipid matrix GMS provided support for the behavioral diversity of the cSLNs and their capacity to interface with the plasmid DNA. Additionally, hemolytic assays were used to show that the cSLNs were biocompatible with the human colon cancer HCT-116 and human bronchial epithelial 16-HBE cell lines. The DOTMA 6-based cSLN was selected as the lead cSLN for further ex vivo and in vivo investigations. Taken together, these new findings might provide some guidance in selecting surfactants to prepare extremely efficient and non-toxic cSLN-based therapeutic delivery systems (e.g., gene therapy).

In the field of soft electronics, high-resolution and transparent structures based on various flexible materials constructed via various printing techniques are gaining attention. With the support of electrical stress-induced conductive inks, the electrohydrodynamic (EHD) jet printing technique enables us to build high-resolution structures compared with conventional inkjet printing techniques. Here, EHD jet printing was used to fabricate a high-resolution, transparent, and flexible strain sensor using a polydimethylsiloxane (PDMS)/xylene elastomer, where repetitive and controllable high-resolution printed mesh structures were obtained. The parametric effects of voltage, flow rate, nozzle distance from the substrate, and speed were experimentally investigated to achieve a high-resolution (5 µm) printed mesh structure. Plasma treatment was performed to enhance the adhesion between the AgNWs and the elastomer structure. The plasma-treated functional structure exhibited stable and long strain-sensing cycles during stretching and bending. This simple printing technique resulted in high-resolution, transparent, flexible, and stable strain sensing. The gauge factor of the strain sensor was significantly increased, owing to the high resolution and sensitivity of the printed mesh structures, demonstrating that EHD technology can be applied to high-resolution microchannels, 3D printing, and electronic devices.

Cloud computing is a rapidly growing paradigm which has evolved from having a monolithic to microservices architecture. The importance of cloud data centers has expanded dramatically in the previous decade, and they are now regarded as the backbone of the modern economy. Cloud-based microservices architecture is incorporated by firms such as Netflix, Twitter, eBay, Amazon, Hailo, Groupon, and Zalando. Such cloud computing arrangements deal with the parallel deployment of data-intensive workloads in real time. Moreover, commonly utilized cloud services such as the web and email require continuous operation without interruption. For that purpose, cloud service providers must optimize resource management, efficient energy usage, and carbon footprint reduction. This study presents a conceptual framework to manage the high amount of microservice execution while reducing response time, energy consumption, and execution costs. The proposed framework suggests four key agent services: (1) intelligent partitioning: responsible for microservice classification; (2) dynamic allocation: used for pre-execution distribution of microservices among containers and then makes decisions for dynamic allocation of microservices at runtime; (3) resource optimization: in charge of shifting workloads and ensuring optimal resource use; (4) mutation actions: these are based on procedures that will mutate the microservices based on cloud data center workloads. The suggested framework was partially evaluated using a custom-built simulation environment, which demonstrated its efficiency and potential for implementation in a cloud computing context. The findings show that the engrossment of suggested services can lead to a reduced number of network calls, lower energy consumption, and relatively reduced carbon dioxide emissions.

Recently, obesity-induced insulin resistance, type 2 diabetes, and cardiovascular disease have become major social problems. We have previously shown that Astaxanthin (AX), which is a natural antioxidant, significantly ameliorates obesity-induced glucose intolerance and insulin resistance. It is well known that AX is a strong lipophilic antioxidant and has been shown to be beneficial for acute inflammation. However, the actual effects of AX on chronic inflammation in adipose tissue (AT) remain unclear. To observe the effects of AX on AT functions in obese mice, we fed six-week-old male C57BL/6J on high-fat-diet (HFD) supplemented with or without 0.02% of AX for 24 weeks. We determined the effect of AX at 10 and 24 weeks of HFD with or without AX on various parameters including insulin sensitivity, glucose tolerance, inflammation, and mitochondrial function in AT. We found that AX significantly reduced oxidative stress and macrophage infiltration into AT, as well as maintaining healthy AT function. Furthermore, AX prevented pathological AT remodeling probably caused by hypoxia in AT. Collectively, AX treatment exerted anti-inflammatory effects via its antioxidant activity in AT, maintained the vascular structure of AT and preserved the stem cells and progenitor’s niche, and enhanced anti-inflammatory hypoxia induction factor-2α-dominant hypoxic response. Through these mechanisms of action, it prevented the pathological remodeling of AT and maintained its integrity.

Climate change is changing global weather patterns, with an increase in droughts expected to impact crop yields due to water scarcity. Crops can be provided with water via underground pumping systems to mitigate water shortages. However, the energy required to pump water tends to be expensive and hazardous to the environment. This paper explores different sites in Sudan to assess the crop water requirements as the first stage of developing renewable energy sources based on water pumping systems. The crop water requirements are calculated for different crops using the CROPWAT and CLIMWAT simulation tools from the Food and Agriculture Organization (FAO) of the United Nations. Further, the crop water requirements are translated into electrical energy requirements. Accurate calculations of the energy needed will help in developing cost-effective energy systems that can help in improving yields and reducing carbon emissions. The results suggest that the northern regions tend to have higher energy demands and that the potential for renewable energy should be explored in these regions, which are more susceptible to drought and where crops tend to be under higher stress due to adverse climate conditions.

Adulteration of meat products is a delicate issue for people around the globe. The mixing of lard in meat causes a significant problem for end users who are sensitive to halal meat consumption. Due to the highly similar lipid profiles of meat species, the identification of adulteration becomes more difficult. Therefore, a comprehensive spectral detailing of meat species is required, which can boost the adulteration detection process. The experiment was conducted by distributing samples labeled as “Pure (80 samples)” and “Adulterated (90 samples)”. Lard was mixed with the ratio of 10–50% v/v with beef, lamb, and chicken samples to obtain adulterated samples. Functional groups were discovered for pure pork, and two regions of difference (RoD) at wavenumbers 1700–1800 cm−1 and 2800–3000 cm−1 were identified using absorbance values from the FTIR spectrum for all samples. The principal component analysis (PCA) described the studied adulteration using three principal components with an explained variance of 97.31%. The multiclass support vector machine (M-SVM) was trained to identify the sample class values as pure and adulterated clusters. The acquired overall classification accuracy for a cluster of pure samples was 81.25%, whereas when the adulteration ratio was above 10%, 71.21% overall accuracy was achieved for a group of adulterated samples. Beef and lamb samples for both adulterated and pure classes had the highest classification accuracy value of 85%, whereas chicken had the lowest value of 78% for each category. This paper introduces a comprehensive spectrum analysis for pure and adulterated samples of beef, chicken, lamb, and lard. Moreover, we present a rapid M-SVM model for an accurate classification of lard adulteration in different samples despite its low-level presence.

Graphene as a material for optoelectronic design applications has been significantly restricted owing to zero bandgap and non-compatible handling procedures compared with regular microelectronic ones. In this work, nitrogen-doped reduced graphene oxide (N-rGO) with tunable optical bandgap and enhanced electrical conductivity was synthesized via a microwave-assisted hydrothermal method. The properties of the synthesized N-rGO were determined using XPS, FTIR and Raman spectroscopy, UV/vis, as well as FESEM techniques. The UV/vis spectroscopic analysis confirmed the narrowness of the optical bandgap from 3.4 to 3.1, 2.5, and 2.2 eV in N-rGO samples, where N-rGO samples were synthesized with a nitrogen doping concentration of 2.80, 4.53, and 5.51 at.%. Besides, an enhanced n-type electrical conductivity in N-rGO was observed in Hall effect measurement. The observed tunable optoelectrical characteristics of N-rGO make it a suitable material for developing future optoelectronic devices at the nanoscale.

Due to the extensive application of antibiotics in medical and farming practices, the continued diversification and development of antimicrobial resistance (AMR) has attracted serious public concern. With the emergence of AMR and the failure to treat bacterial infections, it has led to an increased interest in searching for novel antibacterial substances such as natural antimicrobial substances, including microbial volatile compounds (MVCs), plant-derived compounds, and antimicrobial peptides. However, increasing observations have revealed that AMR is associated not only with the use of antibacterial substances but also with tolerance to heavy metals existing in nature and being used in agriculture practice. Additionally, bacteria respond to environmental stresses, e.g., nutrients, oxidative stress, envelope stress, by employing various adaptive strategies that contribute to the development of AMR and the survival of bacteria. Therefore, we need to elucidate thoroughly the factors and conditions affecting AMR to take comprehensive measures to control the development of AMR.

Cooperative spectrum sensing (CSS) is a vital part of cognitive radio networks, which ensures the existence of the primary user (PU) in the network. However, the presence of malicious users (MUs) highly degrades the performance of the system. In the proposed scheme, each secondary user (SU) reports to the fusion center (FC) with a hard decision of the sensing energy to indicate the existence of the PU. The main contribution of this work deals with MU attacks, specifically spectrum sensing data falsification (SSDF) attacks. In this paper, we propose a correlation-based approach to differentiate between the SUs and the outliers by determining the sensing of each SU, and the average value of sensing information with other SUs, to predict the SSDF attack in the system. The FC determines the abnormality of a SU by determining the similarity for each SU with the remaining SUs by following the proposed scheme and declares the SU as an outlier using the box-whisker plot. The effectiveness of the proposed scheme was demonstrated through simulations.

Response Surface Methodology (RSM) was used to assess the optimal conditions for a Water/Oil/Water (W/O/W) emulsion for encapsulated nisin (EN). Nano-encapsulated nisin had high encapsulation efficiencies (EE) (86.66 ± 1.59%), small particle size (320 ± 20 nm), and low polydispersity index (0.27). Biodegradable polyvinyl alcohol (PVA) and polyacrylate sodium (PAAS) were blended with EN and prepared by electrospinning. Scanning electron microscopy (SEM) revealed PVA/PAAS/EN nanofibers with good morphology, and that their EN activity and mechanical properties were enhanced. When the ultrasonication time was 15 min and 15% EN was added, the nanofibers had optimal mechanical, light transmittance, and barrier properties. Besides, the release behavior of nisin from the nanofibers fit the Korsemeyer–Peppas (KP) model, a maximum nisin release rate of 85.28 ± 2.38% was achieved over 16 days. At 4 °C, the growth of Escherichia coli and Staphylococcus aureus was inhibited for 16 days in nanofibers under different ultrasonic times. The application of the fiber in food packaging can effectively inhibit the activity of food microorganisms and prolong the shelf life of strawberries, displaying a great potential application for food preservation.

Ultrasonication-assisted solution casting was used to prepare polyvinyl alcohol (PVA)/sodium carboxymethyl cellulose (CMC)/nano-ZnO/multilayer graphene nanoplatelet (xGnP) composite films; the performances (mechanical properties, water vapor permeability (WVP), biodegradability and antibacterial activity) of these films were investigated as a function of the ZnO NPs:xGnP mass ratio and ultrasonication time. Intermolecular interactions among ZnO NPs, xGnP and the PVA/CMC matrix were shown to improve WVP, while X-ray diffraction and scanning electron microscopy analyses revealed that the internal reticular structure of ultrasound-treated PVA/CMC/ZnO NPs/xGnP composite films was in a fluffier state than that of the untreated composite films and the PVA/CMC film. The incorporation of ZnO NPs and xGnP into the composite film reduced its tensile strength and elongation at break, and increased antibacterial activity and biodegradability. In addition, we carried out the experiment of strawberry preservation and measured weight loss ratio, firmness, content of total soluble solids and titration acid. Finally, the composite film of 7:3 had the best preservation effect on strawberries. Thus, the obtained results paved the way to develop novel biodegradable composite films with antimicrobial activity for a wide range of applications.

In recent years, the field of nanophotonics has progressively developed. However, constant demand for the development of new light source still exists at the nanometric scale. Light emissions from graphene-based active materials can provide a leading platform for the development of two dimensional (2-D), flexible, thin, and robust light-emitting sources. The exceptional structure of Dirac’s electrons in graphene, massless fermions, and the linear dispersion relationship with ultra-wideband plasmon and tunable surface polarities allows numerous applications in optoelectronics and plasmonics. In this article, we present a comprehensive review of recent developments in graphene-based light-emitting devices. Light emissions from graphene-based devices have been evaluated with different aspects, such as thermal emission, electroluminescence, and plasmons assisted emission. Theoretical investigations, along with experimental demonstration in the development of graphene-based light-emitting devices, have also been reviewed and discussed. Moreover, the graphene-based light-emitting devices are also addressed from the perspective of future applications, such as optical modulators, optical interconnects, and optical sensing. Finally, this review provides a comprehensive discussion on current technological issues and challenges related to the potential applications of emerging graphene-based light-emitting devices.

Plasmonic antennas are attractive optical components of the optoelectronic devices, operating in the far-infrared regime for sensing and imaging applications. However, low optical absorption hinders its potential applications, and their performance is limited due to fixed resonance frequency. In this article, a novel gate tunable graphene-metal hybrid plasmonic antenna with stacking configuration is proposed and investigated to achieve tunable performance over a broad range of frequencies with enhanced absorption characteristics. The hybrid graphene-metal antenna geometry is built up with a hexagon radiator that is supported by the Al2O3 insulator layer and graphene reflector. This stacked structure is deposited in the high resistive Si wafer substrate, and the hexagon radiator itself is a sandwich structure, which is composed of gold hexagon structure and two multilayer graphene stacks. The proposed antenna characteristics i.e., tunability of frequency, the efficiency corresponding to characteristics modes, and the tuning of absorption spectra, are evaluated by full-wave numerical simulations. Besides, the unity absorption peak that was realized through the proposed geometry is sensitive to the incident angle of TM-polarized incidence waves, which can flexibly shift the maxima of the absorption peak from 30 THz to 34 THz. Finally, an equivalent resonant circuit model for the investigated antenna based on the simulations results is designed to validate the antenna performance. Parametric analysis of the proposed antenna is carried out through altering the geometric parameters and graphene parameters in the Computer Simulation Technology (CST) studio. This clearly shows that the proposed antenna has a resonance frequency at 33 THz when the graphene sheet Fermi energy is increased to 0.3 eV by applying electrostatic gate voltage. The good agreement of the simulation and equivalent circuit model results makes the graphene-metal antenna suitable for the realization of far-infrared sensing and imaging device containing graphene antenna with enhanced performance.

‘BRS Isis’ is a new hybrid seedless table grape tolerant to downy mildew with a good adaptation to the tropical and subtropical climates. Gray mold, caused by Botrytis cinerea Pers. ex Fr. is known as the most important postharvest mold in table grapes, causing extensive losses worldwide. As the postharvest behavior of ‘BRS Isis’ is still unknown, the objective of this work was to evaluate the postharvest preservation and B. cinerea mold control of this new grape cultivar, grown under the double-cropping a year system. Grape bunches were purchased from a field of ‘BRS Isis’ seedless table grapes trained on overhead trellises located at Marialva, state of Parana (South Brazil). Grapes were subjected to the following treatments in a cold room at 1 ± 1 °C: (i) Control; (ii) SO2-generating pad; (iii) control with bunches inoculated with the pathogen suspension; (iv) SO2-generating pad with bunches inoculated with the pathogen suspension. The completely randomized experimental design was used with four treatments, each including five replicates. The incidence of gray mold and other physicochemical variables, including bunch mass loss, shattered berries, skin color index, soluble solids (SS), titratable acidity (TA), and SS/TA ratio of grapes, were evaluated at 50 days after the beginning of cold storage and at seven days at room temperature (22 ± 2 °C). The ‘BRS Isis’ seedless grape, packaged with SO2-generating pads and plastic liners, has a high potential to be preserved for long periods under cold storage, at least for 50 days, keeping very low natural incidence of gray mold, mass loss, and shattered berries.

Collagens were extracted from grass carp skin (GCC), grass carp scales (GSC), and crucian carp skin (CCC) using an acid-enzyme combination method, and their characteristics and self-assembly properties were analyzed. Electrophoretic patterns characterized all three as type I collagens. An ultraviolet analysis identified the optimal wavelengths for collagen detection, while a Fourier transform infrared spectroscopy analysis confirmed the triple-helical structure of the collagens. The GCC, GSC, and CCC had denaturation temperatures of 39.75, 34.49, and 39.05 °C, respectively. All three were shown to self-assemble into fibrils at 30 °C in the presence of NaCl, but the fibril formation rate of CCC (40%) was slightly higher than those of GCC (28%) and GSC (27%). The GSC were shown to form a more strongly intertwined fibril network with a characteristic D-periodicity. The fish collagens extracted in this study have potential applications in the development of functionalized materials.

Zein, corn starch (CS), and orange-peel oil (OPO) extracted from orange peels were used to prepare novel corn starch/orange-peel oil/zein nanocapsules (OZN) bio-active food packaging materials. The results showed that the OZN were round, smooth and in compact morphology with an average diameter of 102.7 ± 10.5 nm from OPO and zein (3:10, w/w). By testing the turbidity and atomic force microscopy (AFM) of OZN and the mechanical properties and water vapor permeability of the composite films, the comprehensive properties of composite films with different mass ratios were analyzed. It showed that the addition of OZN improved the mechanical and moisture barrier properties and extended the release time of OPO. When the ratio of OZN and CS is 5:5, the highest elongation at break and tensile strengths is achieved, at values of 30.91% ± 2.52% and 12.19 ± 1.97 MPa respectively. The relative release concentration of OPO was highest at a ratio of 5/5, and over time it would last longer to maintain a higher release concentration. Besides, the oxidation resistance of the composite film was good, especially when the ration of starch CS to OZN was 5/5, it had the highest DPPH radical scavenging activity (30.16% ± 1.69%). Thus, it can be used as a bio-active edible food packaging film to ensure the safety of food products and reduce environmental pressure to some extent.

In this experiment, we studied the effect of poly(lactic acid)/poly(butylene adipate-co-terephthalate) (PLA/PBAT) blend films on the efficiency of passion fruit preservation at 20 °C. The weight loss, shrinkage index, firmness, and total sugar of passion fruit packaged with PLA/PBAT films had no significant differences compared with PE films during 21 days (p > 0.05). PLA/PBAT films can more effectively reduce the rising of ethanol content and delay the total acid, ascorbic acid, and sensory evaluation. Compared with unpackaged (CK) and polyethylene (PE) films, PLA/PBAT films are more conducive to preserve the overall flavor of passion fruit during storage time, in agreement with sensory evaluation, tested by E-nose, E-tongue, and GC-MS, which also proved that it can effectively maintain the edible quality of passion fruit during storage time. We believe that our study makes a significant contribution to literature because it paves the way to the generalization and application of packaging films based on composite antibacterial polymers and facilitates the commercialization of fresh passion fruit as an important health food.

As one of the most diversified cyber-physical systems, the smart grid has become more decumbent to cyber vulnerabilities. An intelligently crafted, covert, data-integrity assault can insert biased values into the measurements collected by a sensor network, to elude the bad data detector in the state estimator, resulting in fallacious control decisions. Thus, such an attack can compromise the secure and reliable operations of smart grids, leading to power network disruptions, economic loss, or a combination of both. To this end, in this paper, we propose a novel idea for the reconstruction of sensor-collected measurement data from power networks, by removing the impacts of the covert data-integrity attack. The proposed reconstruction scheme is based on a latterly developed, unsupervised learning algorithm called a denoising autoencoder, which learns about the robust nonlinear representations from the data to root out the bias added into the sensor measurements by a smart attacker. For a robust, multivariate reconstruction of the attacked measurements from multiple sensors, the denoising autoencoder is used. The proposed scheme was evaluated utilizing standard IEEE 14-bus, 39-bus, 57-bus, and 118-bus systems. Simulation results confirm that the proposed scheme can handle labeled and non-labeled historical measurement data and results in a reasonably good reconstruction of the measurements affected by attacks.

Herein, we report the quantum chemical results based on density functional theory for the polarizability (α) and first hyperpolarizability (β) values of diacetylene-functionalized organic molecules (DFOM) containing an electron acceptor (A) unit in the form of nitro group and electron donor (D) unit in the form of amino group. Six DFOM 1−6 have been designed by structural tailoring of the synthesized chromophore 4,4′-(buta-1,3-diyne-1,4-diyl) dianiline (R) and the influence of the D and A moieties on α and β was explored. Ground state geometries, HOMO-LUMO energies, and natural bond orbital (NBO) analysis of all DFOM (R and 1−6) were explored through B3LYP level of DFT and 6-31G(d,p) basis set. The polarizability (α), first hyperpolarizability (β) values were computed using B3LYP (gas phase), CAM-B3LYP (gas phase), CAM-B3LYP (solvent DMSO) methods and 6-31G(d,p) basis set combination. UV-Visible analysis was performed at CAM-B3LYP/6-31G(d,p) level of theory. Results illustrated that much reduced energy gap in the range of 2.212−2.809 eV was observed in designed DFOM 1−6 as compared to parent molecule R (4.405 eV). Designed DFOM (except for 2 and 4) were found red shifted compared to parent molecule R. An absorption at longer wavelength was observed for 6 with 371.46 nm. NBO analysis confirmed the involvement of extended conjugation and as well as charge transfer character towards the promising NLO response and red shift of molecules under study. Overall, compound 6 displayed large <α> and βtot, computed to be 333.40 (a.u.) (B3LYP gas), 302.38 (a.u.) (CAM-B3LYP gas), 380.46 (a.u.) (CAM-B3LYP solvent) and 24708.79 (a.u.), 11841.93 (a.u.), 25053.32 (a.u.) measured from B3LYP (gas), CAM-B3LYP (gas) and CAM-B3LYP (DMSO) methods respectively. This investigation provides a theoretical framework for conversion of centrosymmetric molecules into non-centrosymmetric architectures to discover NLO candidates for modern hi-tech applications.

Composite films containing different amounts of potassium sorbate (KS) were prepared by using fish scale collagen (Col) and polyvinyl alcohol (PVA). Fourier transform infrared spectroscopy (FTIR), light transmittance, mechanical, water vapor transmission rate (WVTR), and the antibacterial properties of the composite films were analyzed. The results showed that the addition of Col significantly reduced the light transmittance of the composite film, but KS had no significant effect on the light transmission. The tensile strength decreased first and then increased with the addition of KS, while the WVTR increased first and then decreased. The composite film exhibited a certain degree of antibacterial properties against E. coli and S. aureus. In addition, we found that ultrasonic treatment reduced the WVTR, and also improved tensile strength and elongation at break of the composite films, but had no significant effect on other properties. The KS/Col/PVA films have the potential to be used as antimicrobial food packaging.