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This study investigated the impact of incorporating a CrFeNiTiZn High Entropy Alloy (HEA) into the A356 aluminum matrix through the friction stir processing (FSP) technique. The CrFeNiTiZn HEA, renowned for its compositional complexity and high-performance potential, was incorporated into the A356 alloy with different weight percent combinations to enhance its mechanical and tribological characteristics. The results revealed a refined microstructure characterized by solid solution phases and potential intermetallic compound formation due to the HEA addition for A356/2 %Cr2 %Fe2 %Ni2 %Ti2 %Zn composition. Strong interfacial bond strength was also observed among the matrix and reinforcement particles for the A356/2 %Cr2 %Fe2 %Ni2 %Ti2 %Zn composition. The number of grains was found to be about 1820.34 (average grain size is 686 µm) for A356/2 %Cr2 %Fe2 %Ni2 %Ti2 %Zn processed composite with FSP per square inch at 500 magnifications. The A356/2 %Cr2 %Fe2 %Ni2 %Ti2 %Zn composite exhibited improved tensile strength (35.70 %) and hardness (63.33 %) after the addition of 2 %Cr2 %Fe2 %Ni2 %Ti2 %Zn into A356 alloy, attributed to the strengthening effect of HEA particles. Furthermore, wear resistance is notably enhanced, likely due to the synergistic effects of the HEA’s inherent hardness and the modified microstructure.

The approach emphasizes dispersing Ag@CdS (ACS) core@shell (CS) nanocrystals (NCs) in polyvinyl alcohol (PVA) and activating them at room temperature via a straightforward wet chemical co-precipitation process. The improved performance of ACS NCs is due to their CS shape, surface plasmon resonance (SPR), and quantum confinement phenomena. The UV-Vis and PL spectroscopy revealed variations in optical properties, including a tunable band gap in CdS quantum dots, indicating their potential as nanoelectronic (NE) devices. The XRD, TEM, and SEM measurements validated the nanoscale development of ACS NCs on the PVA surface. Findings NCs have excellent dielectric characteristics in capacitors, which improves energy storage efficiency as capacitance falls with frequency due to increased polarization. The assemblies outperform those with simply core or shell NCs, with ACS CS NCs producing a peak dielectric constant of 106, capacitance of 200 nF, conductivity of 0.1, and a voltage gain of 91% at specific frequencies. The incorporation of ACS NCs into PVA has major implications for future NE applications, including low-pass frequency filters, energy conversion, energy storage, memristive devices, and sensors. This study enhances energy-efficient technologies by embedding ACS in a stable PVA matrix, improving nanocrystals' functionality and stability for NEs devices.

The heat transfer augmentation, solar power systems, medical equipment, semiconductor cooling, aerospace, and automotive industries all use ternary hybrid nanofluids (THNFs). The current study is mainly about a magnetized THNF flow that cannot be squished around a spinning sphere that has different viscosity, thermal conductivity, and shape (brick, platelets, cylinder, and blade). The heat transport simulation incorporates the principles of viscous dissipation and joule heating. Water is mixed with silver, magnesium oxide, and iron trioxide to make the THNF. Similarity substitution converts model equations to ordinary differential equations (ODEs). Runge–Kutta fourth order numerically estimates the non-dimensional set of ODEs. For certain emergent parameters, velocity, temperature, entropy generation, Nusselt number, and skin friction are computed and analyzed. The research shows that entropy generation increases with brinkman number, nanoparticle volume fraction and magnetic parameters and reduces with temperature difference parameter. Increasing the unsteadiness parameter upsurges velocity in the x-direction, but decreases it in the z-direction and temperature curve. Skin friction upsurges in the x-direction and declines in the z-direction with rotation. Platelet-shaped nanoparticles usually outperform blade, brick, and cylinder shapes. When mass suction $( S )$is elevated from 1.0 to 2.0, the heat transfer rate increases by 47.25% for the brick form, 47.26% for the platelets shape, 35.08% for the cylinders shape, and 37.65% for the blades shape. Comparing the results to prior literature shows excellent agreement.Graphical Abstract

This article presents a novel multimodal Rényi cascade mask regional convolutional neural network (RCMRCNN) diagnostic system for breast cancer detection and localization. It consists of a reformed anisotropic diffusion high-boost filter (RADHBF) that is proposed to restore and enhance the multimodal database from the various multiplicative noises. Furthermore, multiscale feature learning is employed in the proposed method. It concatenates the single-scale features and provides accurate mapping of abnormalities. Apart from this, at the architectural level a cascade module of bounding box (BB) and a Rényi loss function have been proposed for the precise multi-instance localization of breast abnormalities. It is deployed on the NVIDIA Jetson Nano developer kit for the industrial applications. It exhibits the following values of accuracy, true positive rate (TPR), true negative rate (TNR), precision (PC), F-score (FS), balanced classification rate (BCR), Youden’s index (YI), Matthew’s correlation coefficient (MCC), Jaccard index (JC), and dice coefficient (DC): 98.50%, 99.00%, 98.02%, 0.98, 0.98, 0.98, 0.97, 0.97, 0.97, and 0.98, respectively.

Global manufacturing of high-performance manufactured products made from natural resources is increasing as a result of renewable and environmental concerns. This study investigated the mechanical and morphological properties of PLA and kenaf short fiber (3–4 mm). The kenaf fibers were treated with different chemical concentrations (10% and 20%) before being used to make composites with an injection molding machine. SEM analysis was performed on the treated and mechanically tested bio-composites. Mechanical analysis of the generated composites was performed utilizing tensile, flexural, and impact tests to investigate the influence of treatment on interfacial adhesion. Fibers treated with a 20% concentration of sodium acetate provided the most desirable tensile and flexural characteristics in the composites. Apart from impact strength, chemical treatment has a significant impact on the properties of green composites. X-ray diffraction (XRD) and Fourier transform infrared (FTIR) spectroscopy were used to examine the crystallinity and infrared spectra of bio-composites. The chemical treatment of natural fiber with sodium acetate is an environmentally friendly approach for producing long-lasting PLA-based bio-composites.

This study focused on the synthesis and purification of nano-sized titanium carbide (TiC) particles through vacuum carbothermal reduction under a hydrogen/argon atmosphere. The process involved the production of carbon-coated titanium precursors, followed by heating under vacuum conditions at 1500 °C for 2.5 h. The resulting products underwent additional treatment in either a hydrogen/argon (1:1) mixed gas or pure hydrogen gas. Experimental findings revealed that TiC powders exhibiting a single phase are obtained within a molar ratio of Ti to C ranging from 1:2 to 1:4. Adjusting the molar ratio of Ti/C in the precursors allowed for controlled variation in the particle size of the synthesized TiC powders, ranging from 50 to 80 nm. Treatment in hydrogen/argon mixed gas at 850 °C for 3 h resulted in TiC products with an impressive purity of 99.40%. The subsequent incorporation of 5% Nano-TiC-3 (Ti to C ranging from 1:3) as reinforcement into an aluminum alloy by Friction stir process technique demonstrated a remarkable 19.41% improvement in tensile strength, highlighting the efficacy of this specific reinforcement. The nano-TiC-3 reinforced composite exhibited uniform distribution without porosity. Additionally, a notable 50.81% improvement in hardness and the best wear resistance were observed for the 5% Nano-TiC-3 reinforced aluminum composite. This comprehensive study contributes valuable insights into the synthesis, purification, and application of nano-sized TiC particles, paving the way for the development of high-performance aluminum-based composites with enhanced mechanical and tribological properties.

Precisely predicting the Specific Wear Rate (SWR) of AlSi10Mg components produced using Laser Powder Bed Fusion (LPBF) at high temperatures, which is an essential concern in additive manufacturing. This study aims to address the gap in literature by developing accurate predictive models for SWR via machine learning regression techniques. Experiments using a dry sliding wear rig shown that wear loss in AlSi10Mg print specimens increased with temperature and load, reaching a maximum wear rate of 1.5444E-06 mm3/Nm at 200 °C, 10 N, and 1.2 m/s, compared to the minimum wear rate of 6.4672E-07 mm3/Nm seen at 100 °C, 20 N, and 1.4 m/s. However, to accurately predict the wear rate at high temperatures, six different machine learning regression algorithms were used, namely Support Vector Machine (SVM), Linear Regression (LR), Random Forest Regression (RFR), Gaussian Process Regression (GPR), XGBoost regression (XGB) and Decision Tree (DT). R-squared values and various error functions were employed to validate these strategies against the anticipated outcomes. Within this set of models, GPR model has a lower Mean Absolute Error of 0.3177, Root Mean Square Error of 0.6704 and higher R2value of 0.9686, resulting a prediction accuracy of 96.86%. From these findings, it is suggested that GPR is a very useful model for predicting the rate at which LPBFed AlSi10Mg printed parts wear under high temperature conditions in comparison with other developed models. These machine learning methods are anticipated to be beneficial for additive manufacturing enterprises.

The issue of waste generation and its management is rising nowadays. However, despite controlling waste generation, significant efforts were diverted towards identifying alternative ways of waste management. Research, encouraging the use of waste materials as a construction material, has been going on for several years. The present study will focus on utilizing the waste PET plastic in the form of Fibre with a specified aspect ratio in the matrix. The effect of coated Fibre on the various properties of concrete (Mechanical Properties) will be evaluated. The effect of distinct mineral admixture i.e., Ground Granulated Blast Furnace Slag, Silica Fume, and Sugarcane Bagasse Ash with the plastic on the matrix will be studied. PET-Fs in concrete added in proportion 0.36%, 2%, 6%, 10% and 11.46 in aspect ratios 3.85, 10, 25, 40 and 46.15. Results indicated that the 7-day and 28-day Compressive Strength of the concrete samples with PET-Fs were observed to be lower than the control samples. However, on coating the PET-Fs with SF, GBFS and SBA, a significant increase in the 7-day and 28-day Compressive Strength was observed. Coating of PET-Fs with mineral admixtures further led to an increase in their split tensile strength. Maximum increase in flexural strength of concrete was observed in the order of SF-coated < GBFS-coated < SBA-coated PET-Fs. Overall research concludes that the addition of PW in existing concrete roads can manage approximately 18,847 kg of the city's plastic waste.

Breast cancer is a significant global health concern, with invasive ductal carcinoma (IDC) being a significant subtype. Detecting IDC is a challenging task that can be hindered by the oversight of important contextual cues within whole slide images (WSIs). To address this issue, we present the Sequential-LSTM Invasive Ductal Carcinoma Detection with EfficientNet (SLIDE-Net) framework. SLIDE-Net synchronizes tissue image patch locations within WSIs, allowing for comprehensive and subtle identification of IDC. The inherent issue of data imbalance in IDC datasets, particularly the variable density of positive IDC patches concerning WSI size, is effectively tackled through the introduction of adaptive fuzzy C-Mean as a data balancing policy. This novel approach enhances model efficacy and robustness, resulting in superior performance across key metrics, such as accuracy (86%), balanced accuracy (BAC) (86%), sensitivity (87%), specificity (86%), and GMean (86%). Our findings were substantiated by a comprehensive analysis revealing the significant cross-patch dependencies observed among patches of similar classes. When tested on the balanced PatchCAM dataset, SLIDE-Net once again showcased superiority with accuracy (89%), BAC (89%), F1-Score (87%), sensitivity (87%), and specificity (90%). This underscores the efficient utilization of shared contextual information within WSIs. These results firmly establish SLIDE-Net as a robust and reliable solution for accurate IDC detection. Our work not only advances the precision of IDC detection, but also contributes valuable insights into the intricate dynamics of histopathology image analysis, paving the way for enhanced diagnostic accuracy in the ongoing battle against breast cancer.

Nanomaterial-reinforced particle gel systems have attracted significant attention in the oil and gas industry for managing undesirable water production in mature oil fields. However, controlling excessive water production in challenging reservoirs, such as high-salinity, high-temperature reservoirs, remains a formidable task for the petroleum industry. This research paper provides a detailed account of the fabrication, chemical characterization, and performance assessment of a novel alumina nanomaterial reinforced particle gel (Al-RPG) designed for use in high-temperature reservoirs. The synthesis involved using alumina nanomaterial as a physical cross-linker or reinforcing agent, along with NaSS (styrene-4-sulfonic acid sodium salt) and DMA (N,N-dimethyl acrylamide) as monomers, NMBA (N,N-methylene bis (acrylamide)) as a chemical cross-linker, and 2,2-azobis(2-methylpropionamidine) dihydrochloride as an initiator. Chemical characterizations, including FT-IR, P-XRD FESEM, EDX, TGA (Thermal Gravimetric Analysis), DTG (Differential Thermogravimetric Analysis), and DSC (Differential Scanning Calorimetry), confirm the effective reinforcement of alumina nanomaterial in the polymeric matrix of Al-RPG. The developed Al-RPG can swell up to 18.74 times its dry weight in a 30000 ppm NaCl saline solution at room temperature conditions. A thorough assessment of the swelling characteristics has been performed, taking into account diverse factors including salinity, pH, temperature, composition of nanomaterials, and the presence of both monovalent and multivalent ions. Furthermore, a long-term thermal stability test of Al-RPG (0.30 wt % alumina nanomaterial reinforced particle gel) demonstrates excellent thermal resistance for 300 days at 150 °C in a 30000 ppm NaCl brine. Rheological studies indicate that Al-RPG possesses higher mechanical strength/toughness compared to a particle gel without reinforcement. Additionally, a sandpack flooding experiment assesses the water shutoff performance of the optimized Al-RPGs (0.30 wt % alumina-reinforced particle gel). The test data reveal that the plugging capacity (Ø) of Al-RPG is 89.45, 88.32, 90.79, and 90.73 and the residual resistance factor (RRF) is 9.48, 8.56, 10.86, and 10.79, respectively. These results authenticate that the novel Al-RPG may be implemented for water control/shutoff treatment applications in the high-temperature reservoirs of mature oil fields.

Skin cancer poses a significant burden on mankind and healthcare systems globally, necessitating the development of effective diagnostic and treatment strategies. This paper introduces FusionEXNet, an innovative and interpretable fused deep-learning model for skin cancer detection. FusionEXNet leverages the strengths of EfficientNetV2S and XceptionNet architectures to extract robust features from dermoscopic images, achieving superior performance compared to individual models. While XceptionNet and EfficientNetV2S attained accuracies of 88.82% and 88.01%, respectively, FusionEXNet surpassed them with an impressive accuracy of 90.83%. To enhance model interpretability, Explainable Artificial Intelligence (XAI) techniques, such as SmoothGrad and Faster Score-CAM, are integrated, providing valuable insights into the decision-making process. The model was trained and evaluated using the extensive HAM10000 dataset, consisting of over 10,000 high-resolution images across seven skin lesion categories. The proposed FusionEXNet model offers a reliable, accurate, and interpretable system for skin cancer detection, contributing to improved patient outcomes and more efficient diagnostic processes. This study underscores the potential of combining advanced CNN architectures with XAI methods to create powerful tools for dermatological diagnostics.

Purpose: Chitosan is a biopolymer obtained from the exoskeletons of crustaceans and the cell walls of fungi. Due to its biocompatibility and capacity for tissue regeneration, this substance exhibits considerable potential as a candidate for wound healing. The objective of this article is to provide a detailed review of the properties and medical applications of chitosan in wound healing. The investigation also encompasses ethical questions pertaining to patient safety, informed consent, healthcare access, and sustainable sourcing of chitosan. Method: This review pertains to the characteristics and medical applications of chitosan, while concurrently exploring the ethical considerations associated with its use. Result: The review presents a comprehensive examination of chitosan, emphasizing its biocompatibility, absorbability, polycationic properties, film-forming ability, hemostatic properties, antimicrobial effects, tissue regeneration capabilities, and angiogenic potential. The usefulness of chitosan encompasses a wide range of areas, including wound dressings, tissue scaffolds, medication delivery systems, and medical devices. The ethical considerations and challenges pertaining to patient safety, informed consent, equitable provision of healthcare, and sustainable sourcing are duly acknowledged and attended to. Conclusion: Chitosan demonstrates diverse medicinal capabilities, particularly in the context of wound healing. Additionally, it has the potential to yield advantages in the context of combinational medicines. Nevertheless, ethical issues take precedence. It is of utmost importance to ensure that the applications of chitosan are in accordance with ethical norms, encompassing aspects such as safety, consent, equitable access, and sustainability. This review sheds light on the scientific potential and ethical challenges associated with the use of chitosan in medical applications, hence providing guidance for the development of responsible healthcare advancements. Lay Summary: The article investigates the potential of chitosan, a versatile substance obtained from fungus and crustaceans, in the field of medicine, with a specific focus on its application in wound healing. The distinctive attributes of chitosan, such as its biocompatibility, absorbency, and antibacterial properties, render it well-suited for various applications, including wound dressings and drug delivery systems. The essay additionally addresses significant ethical aspects, including patient safety, informed consent, and sustainable sourcing. It underscores the importance of striking a balance between innovation and the adoption of responsible and ethical medical practices.

This research aims to develop a machine learning-based phishing attack detection framework. Phishing attacks have become one of the most prevalent cybersecurity threats, potentially compromising sensitive information such as login credentials, financial details, and personal data. A machine learning (ML)–based approach for phishing URL detection can help improve the effectiveness and efficiency of phishing detection. The healthcare industry received multiple attacks recently, including phishing attacks. There may be multiple reasons behind the attacks, including outdated resources and weak security mechanisms. The proposed ML-based approach trained on a large dataset to learn the characteristics that distinguish Phishing and Non-phishing URLs to prevent the healthcare industry from phishing attacks, The features were taken from the URLs and provided to the model for training. By detecting phishing URLs in real time, individuals and organizations can take proactive measures to protect themselves from the damaging effects of phishing attacks. The proposed ML-based model detected the phishing URL with 99.00% accuracy, indicating that most attacks were detected.

Assessing the solubility of pharmaceutical compounds in supercritical carbon dioxide (scCO2) is a fundamental prerequisite for advancing the application of supercritical technologies in the pharmaceutical sector. The supercritical solubility of Febuxostat, a drug with limited solubility often prescribed for gout patients to lower blood uric acid levels, has previously been studied within the pressure and temperature ranges of 12–27 MPa and 308–338 K, exhibiting a mole fraction range of 0.05 × 10–4–7.42 × 10–4. This study explores the influence of various cosolvents, namely, ethanol, acetone, and dimethyl sulfoxide (DMSO), on the supercritical solubility of Febuxostat under comparable conditions. Results indicate solubility within the mole fraction range of 0.180 × 10–4–26.658 × 10–4for ethanol, 0.120 × 10–4–14.810 × 10–4for acetone, and 0.108 × 10–4–14.366 × 10–4for DMSO, respectively. Ethanol exhibits the most substantial impact, enhancing the supercritical solubility of Febuxostat by approximately 2.4–3.8 times, while acetone and DMSO contribute to an increase of approximately 2–2.5 times. Furthermore, the study employs empirical models and an artificial neural network (ANN) approach to theoretically investigate the supercritical solubility of Febuxostat with these cosolvents. Among the empirical models, the Jouyban model demonstrates the most accurate correlation for the solubility data of all cosolvents. Moreover, the ANN model demonstrates exceptional accuracy in forecasting the solubility of Febuxostat, with a mean AARD % of 3.207% and Radjof 0.993 across all experimental measurements for Febuxostat solubility in scCO2+ ethanol, scCO2+ acetone, and scCO2+ DMSO solvents.

ABSTRACTSeveral researchers have modified the original Social Force Model (SFM) for pedestrians by including vehicles and simulating interaction behaviors in shared space layouts. The present study is conducted at a signalized intersection where the performance and functionality are defined by spontaneous order. The SFM was modified, and two-wheelers were added along with cars and pedestrians to analyze their interactions. The Hybrid Calibration Method was adopted to calibrate the interaction parameters utilizing an optimization technique and real-time data from the study site. It can be concluded from the parameter values that the strength of interaction reduced as the range between the road agents increased. The findings can aid urban planners in visualizing the interaction behavior of pedestrians, cars, and two-wheelers. Moreover, the feasibility of introducing the shared space concept in India as an alternative to the conventional traffic planning system can also be determined during the designing phase.

Electrical Discharge Machining(EDM)-machined maraging steel components are promising in the application field of aerospace and production tools. However, the unavoidable adverse effects of recast/white layer formation, high tensile residual stress, and micro-crack on the machined surface necessitate post-treatment techniques. The current study proposed Electropolishing(EP) as a post-treatment process to eliminate EDM’s adverse effects. The performance of EP on surface residual stress and morphology of ED machined surface was evaluated for an optimum combination of parameters, including applied voltage, polishing time, agitation, and temperature. Residual stress has been analyzed using the “sin2ψmethod” of X-ray Diffraction (XRD). After EDM, the generated surface residual stress was 407.17 MPa which had been relaxed to 35.27 MPa after EP. The surface morphology of maraging steel surfaces after EDM and EP were compared using an Atomic Force Microscope(AFM) and Field Emission Scanning Electron Microscope(FESEM). It is observed that micro-cracks, globules, voids, and thermal pitting marks were entirely eradicated following EP. It can also be seen that the EP has successfully removed the formed recast layer during EDM after 6 mins of polishing time. An average of 7.94 µm layer has been removed from the surface, removing all the surface defects. The optical profilometer analyzed the surface roughness profiles, and a 78% improvement in surface roughness was observed after EP.

As cryptocurrencies become more popular as investment vehicles, bitcoin draws interest from businesses, consumers, and computer scientists all across the world. Bitcoin is a computer file stored in digital wallet applications where each transaction is secured using strong cryptographic algorithms. It was challenging to forecast the future price of bitcoin due to its nonlinearity and extreme volatility. Several recent classic parametric models have been found with limited accuracy. To address the limitations and fill the existing research gaps, there is a need for a good prediction model which will provide the desired accuracy in the case of uncertainty and dynamism. This research suggested a deep learning-based framework for predicting and forecasting Bitcoin price. The research will be helpful for worldwide consumers and industries to take their decision on whether to invest or not. The research utilizes Yahoo! finance dataset for the period of 01-03-2016 to 26-02-2021 having 1828 samples. The experimental outcomes of the proposed Long Short-Term Memory (LSTM) model outperformed similar deep learning models by securing minimum loss and confirming that it can be used for future price prediction of the cryptocurrencies, which is helpful for the buyer to take their decision.

The industrial sector has experienced a tremendous advancement in deep supervised learning due to its representation ability, but it comes with high computing and labeled data demands. Recently, the demand for intelligent IoT devices on assembly and disassembly lines has surged. This necessitates algorithms that can use data to make intelligent decisions and a framework that can enable multiple IoT devices to learn collaboratively. Further, huge image generation using IoTs also needs an efficient data annotation scheme for classification problems. WeCollab is one such framework in federated learning that significantly reduces human efforts in data annotation with breakthroughs in self-supervised learning. The proposed framework is generic and can be adapted to any specific image data generated by industrial robots involved in assembly and disassembly lines. Our method outperforms supervised learning by 25% and 20% on the CIFAR-10 and CINIC-10 datasets, respectively, for the labeling task. We generate pseudo labels for the unlabeled part of the data and train a model to achieve 30% better test accuracy on CIFAR-10 and 20% better test accuracy on the CINIC-10 dataset as compared to supervised learning. Extensive experiments unveil the effectiveness of the method and proposed combination of loss functions used by WeCollab.

Low hemolytic therapeutic peptides have gained an edge over small molecule-based medicines. However, finding low hemolytic peptides in laboratory is time-consuming, costly and necessitates the use of mammalian red blood cells. Therefore, wet-lab researchers often perform in-silico prediction to select low hemolytic peptides before proceeding with in-vitro testing. The in-silico tools available for this purpose have following limitations: (i) They do not provide predictions for peptides having N/C terminal modifications. (ii) Data is food for AI; however, datasets used to create existing tools do not contain peptide data generated over past eight years. (iii) Performance of available tools is also low. Therefore, a novel framework has been proposed in current work, which utilizes recent dataset and uses ensemble learning technique to combine the decisions produced by bidirectional long short-term memory, bidirectional temporal convolutional network, and 1-dimensional convolutional neural network deep learning algorithms. Deep learning algorithms are capable of extracting features themselves from data. However, instead of relying solely on deep learning-based features (DLF), handcrafted features (HCF) were also provided so that deep learning algorithms can learn features that are missing from HCF, and a better feature vector can be constructed by concatenating HCF and DLF. Additionally, ablation studies were carried out to understand the roles of an ensemble algorithm, HCF, and DLF in the proposed framework. Ablation studies found that the ensemble algorithm, HCF and DLF are crucial components of proposed framework, and there is a decrease in performance on eliminating any of them. Mean value of performance metrics, namely $A_{cc}$, $S_{n}$, $P_{r}$, $F_{s}$, $S_{p}$, $B_{a}$, and $M{cc}$ obtained by proposed framework for test data is $\approx$ 87, 85, 86, 86, 88, 87, and 73, respectively. To aid scientific community, model developed from proposed framework has been deployed as a web server at https://endl-hemolyt.anvil.app/.

In response to environmental threats, pathogens make several changes in their genome, leading to antimicrobial resistance (AMR). Due to AMR, the pathogens do not respond to antibiotics. Amongst drug-resistant pathogens, the ESKAPEE group of bacteria poses a major threat to humans, and therefore World Health Organization has given them the highest priority status. Antibacterial peptides (ABPs) are a family of peptides found in nature that play a crucial role in the innate immune systems of organisms. These ABPs offer several advantages over widely used antibiotics. As a result, they have recently received a lot of attention as potential replacements for currently available antibiotics. But it is expensive and time-consuming to identify ABPs from natural sources. Thus, wet lab researchers employ various tools to screen promising ABPs rapidly. However, the main limitation of the existing tools is that they do not provide the minimum inhibitory concentration values against the ESKAPEE pathogens for the identified ABP. To address this, in the current work, we developed ESKAPEE-MICpred, a two-input model that utilizes transfer learning and ensemble learning techniques. The concept of ensemble learning was realized by combining the decisions provided by deep learning algorithms, whereas the concept of transfer learning was realized by utilizing pretrained amino acid embeddings. The proposed model has been deployed as a web server at https://eskapee-micpred.anvil.app/ to aid the scientific community.

Diabetes mellitus is an exponentially growing chronic metabolic disease identified by prolonged hyperglycemia that leads to a plethora of health problems. It is well established that the skin of diabetic patients is more prone to injury, and hence, wound healing is an utmost critical restorative process for injured skin and other tissues. Diabetes patients have problems with wound healing at all stages, which ultimately results in delays in the healing process. Therefore, it is vital to find new medications or techniques to hasten the healing of wounds. Metal–organic frameworks (MOFs), an assorted class of porous hybrid materials comprising metal ions coordinated to organic ligands, can display great potential in accelerating diabetic wound healing due to their good physicochemical properties. The release of metal ions during the degradation of MOFs can promote the differentiation of fibroblasts into myofibroblasts and subsequently angiogenesis. Secondly, similar to enzyme-like active substances, they can eliminate reactive oxygen species (ROS) overproduction (secondary to the bio-load of wound bacteria), which is conducive to accelerating diabetic wound healing. Subsequently, MOFs can support the slow release of drugs (molecular or gas therapeutics) in diabetic wounds and promote wound healing by regulating pathological signaling pathways in the wound microenvironment or inhibiting the expression of inflammatory factors. In addition, the combination of photodynamic and photothermal therapies using photo-stimulated porphyrin-based MOF nanosystems has brought up a new idea for treating complicated diabetic wound microenvironments. In this review, recent advances affecting diabetic wound healing, current means of rapid diabetic wound healing, and the limitations of traditional approaches are discussed. Further, the diabetic wound healing applications of MOFs have been discussed followed by the future challenges and directions of MOF materials in diabetic wound healing.

Some physical phenomena and various chemical substances newly introduced in nanotechnology have allowed scientists to develop valuable devices in the field of food sciences. Regarding such progress, the identification of foodborne pathogenic microorganisms is an imperative subject nowadays. These bacterial species have been found to cause severe health impacts after food ingestion and can result in high mortality in acute cases. The rapid detection of foodborne bacterial species at low concentrations is in high demand in recent diagnostics. CRISPR/Cas-mediated biosensors possess the potential to overcome several challenges in classical assays such as complex pretreatments, long turnaround time, and insensitivity. Among them, colorimetric nanoprobes based on the CRISPR strategy afford promising devices for POCT (point-of-care testing) since they can be visualized with the naked eye and do not require diagnostic apparatus. In this study, we briefly classify and discuss the working principles of the different CRISPR/Cas protein agents that have been employed in biosensors so far. We assess the current status of the CRISPR system, specifically focusing on colorimetric biosensing platforms. We discuss the utilization of each Cas effector in the detection of foodborne pathogens and examine the restrictions of the existing technology. The challenges and future opportunities are also indicated and addressed.

Safety in conjunction with production is a reality achieved in underground mining, where roof or side falls can have devastating effects on operations. A precise understanding of the roof structure is crucial for designing effective support systems that mitigate ground-fall risks. A key finding underscores the significance of this understanding. Sub Surface Profiler Ground-Penetrating Radar (SSPGPR) technology, utilizing real-time data and wirelessly transmitted signals, plays a pivotal role in achieving accurate knowledge of the roof structure. Geotechnical approaches, incorporating SSPGPR algorithms, facilitate continuous recording of sub-horizontal reflections through the lithology, optimizing roof support with accurate images of unexplored rock structures. The technology’s practical application in the Saoner group of underground mines highlights its effectiveness in mapping various zones within the roof rock strata, aiding excavation and support methods. SSPGPR is instrumental in detecting unmined strata profiles not evident in borehole data during exploration, emphasizing its transformative impact on efficiency and safety in underground mining. The correlation between fault zones mapped by SSP and ground faults further validates its effectiveness.

Nowadays in our society, lung cancer is exhibiting a high mortality rate and threat to human health. Conventional diagnostic techniques used in the field of lung cancer often necessitate the use of extensive instrumentation, exhibit a tendency for false positives, and are not suitable for widespread early screening purposes. Conventional approaches to treat lung cancer primarily involve surgery, chemotherapy, and radiotherapy. However, these broad-spectrum treatments suffer from drawbacks such as imprecise targeting and significant side effects, which restrict their widespread use. Metal–organic frameworks (MOFs) have attracted significant attention in the diagnosis and treatment of lung cancer owing to their tunable electronic properties and structures and potential applications. These porous nanomaterials are formed through the intricate assembly of metal centers and organic ligands, resulting in highly versatile frameworks. Compared to traditional diagnostic and therapeutic modalities, MOFs can improve the sensitivity of lung cancer biomarker detection in the diagnosis of lung cancer. In terms of treatment, they can significantly reduce side effects and improve therapeutic efficacy. Hence, this perspective provides an overview concerning the advancements made in the field of MOFs as potent biosensors for lung cancer biomarkers. It also delves into the latest research dealing with the use of MOFs as carriers for drug delivery. Additionally, it explores the applications of MOFs in various therapeutic approaches, including chemodynamic therapy, photodynamic therapy, photothermal therapy, and immunotherapy. Furthermore, this review comprehensively analyses potential applications of MOFs as biosensors in the field of lung cancer diagnosis and combines different therapeutic approaches aiming for enhanced therapeutic efficacy. It also presents a concise overview of the existing obstacles, aiming to pave the way for future advancements in lung cancer diagnosis and treatment.

The main goal of this research is to meet the demanding need for developing lightweight materials with improved properties that are appropriate for several engineering applications. The investigators proceeded to considerable efforts to assess the mechanical, micro-structural, and tribological characteristics of Al-0.7Fe-0.6Si-0.375Cr-0.25Zn/10 wt%SiCP/3 wt%GrP-MMC, Al-0.7Fe-0.6Si-0.375Cr-0.25Zn/15 wt%SiCP/5 wt%GrP-MMC, and Al-0.7Fe-0.6Si-0.375Cr-0.25Zn/20 wt%SiCP/8 wt%GrP-MMC based hybrid metal-matrix nano-composites (HMMC's). These hybrid aluminum matrix nano-composites were fabricated using the liquid processing stir casting method for automobile, aviation, defense, and chemical handling sectors owing to their superior strength, and higher thermal conductive properties. The machinability of Al-0.7Fe-0.6Si-0.375Cr-0.25Zn/(SiCp + Grp) - MMC has received limited attention to date. Owing to its exceptional characteristics, this hybrid composite could take the role of cast iron or aluminum composites in a wide-variety multitude of engineering applications. The Mechanical properties of fabricated Hybrid-MMCs have been examined along with tribological properties & other factors, affecting the tensile strength of cast composites in this study. Additionally, the characterization has been performed through X-ray Diffraction (XRD), Energy Dispersive Spectrum (EDS), Scanning Electron Microscope (SEM), and Optical Microscopy. The fabricated samples of hybrid metal matrix nano-composites of Al-0.7Fe-0.6Si-0.375Cr-0.25Zn matrix have been tested for microstructure analysis, micro hardness test, density, tensile strength, and grain size. Microstructure study clears the claim of uniform distribution of SiC & Graphite particles in Al matrix alloy. An average tensile strength has been recorded as 258 MPa and a percent-elongation of 4.6 during the tensile test. Tensile-fractography findings have portrayed the intrinsic fractured-rupture microstructure of tensile-tested samples that seemed to have particle deformation breakage and pull-outs. The density of Al/(SiCp + Grp)-MMCs have been found to be 2.76 g/cm3whereas the average hardness has been recorded at 120HV. An increase in graphite content in Al/SiC-MMC's provides a significant improvement in friction coefficient as well as a reduction in wear rate. Findings have observed that the wear rate is increased with increasing the sliding speed, however, it is almost constant between 300 and 600 rpm, and beyond this wear rate increases drastically. Likewise, the coefficient of friction also shows a considerable increase with the increase in sliding speed. As the composition of graphite in the material increases, a considerable decrease in the wear has been noted down because of the presence of tribo-layer formed between mating surfaces. A high value of applied load and high rotating speed imposed high deformation of the surface and increase in frictional force, as a result, an increase in wear rate. Wear rate increases with the increment in load during the wear test, while friction coefficient decreases. Hence, the development of HMMCs with better mechanical, microstructural, and tribological properties that can be utilized as cast iron or aluminum composites in a diverse spectra multitude of engineering applications involving the study's scientific insights and technological developments. Manufacturers in the myriads of industries, including aerospace, ground-transportation, thermal management, industrial, recreational, and infrastructure, could profit inventors/producers from the findings of the current study.

The identification of overlapping communities in complex networks is crucial for network analysis. However, it becomes extremely challenging when dealing with large-scale networks, like social media platforms or news networks, with billions of entities. This task involves identifying patterns within the vast web of connections among vertices and edges, making it complex and demanding. Despite the development of various community detection methods, the problem persists without any significant improvement. Some algorithms may generate communities with low quality, while others may have resolution issues or scalability challenges, especially when analysing large-scale networks. To overcome these research challenges, we require a method that is highly stable, has low time complexity, and efficiently identifies overlapping communities in networks. Therefore, we introduce the pure expansion-based local community detection algorithm (PE-LCD), designed to identify overlapping communities within complex networks through the selection and expansion of seeds, all without the need for pre-existing community knowledge. Initially, we select a seed node based on its centrality within the network, and subsequently expand this seed node into a community through the node and edge interaction coefficient. To assess the efficacy of PE-LCD, we conducted thorough experiments on both real-world and artificial networks. The results indicate that our proposed approach surpassed state-of-the-art techniques, successfully identifying overlapping communities within complex networks.

The lifetime of a UAV-assisted wireless network is determined by the amount of energy consumed by the UAVs during flight, data collection, and transmission to the ground station. Routing protocols are commonly used for data transmission in a communication network. However, because of the mobility of UAVs, using a routing protocol with a single communication technology results in higher delay and more energy consumption in a UAV-assisted wireless network. To overcome this, we propose two reinforcement learning (RL) algorithms, Q-learning and deep Q-network (DQN), for energy-efficient data transmission over a hybrid BLE/LTE/Wi-Fi/LoRa UAV-assisted wireless network. We consider BLE, LTE, Wi-Fi, and LoRa for communication over a UAV-GS link. The RL algorithms take any random network as input and learn the best policy to output the network with less energy consumption. The reward/penalty is chosen in such a way that the network with the highest energy consumption is penalized and the one with the lowest is rewarded, thereby minimizing total network energy consumption. Based on learning, it creates a hybrid BLE/LTE/Wi-Fi/LoRa UAV-assisted wireless network by assigning the best communication technology to a UAV-GS link. Further, we compare the performance of proposed RL algorithms with a rule-based algorithm and random hybrid scheme. In addition, we propose a theoretical framework for constructing hybrid network for both free space and free space multipath path loss models. We demonstrate the performance comparison of the proposed work with the conventional shortest path routing algorithm in terms of network energy consumption and average network delay using extensive results. Finally, the effect of the velocity of the UAV and the number of packets on the performance of the proposed framework is analyzed.

Electrochemical techniques are commonly used to analyze and screen various environmental pathogens. When used in conjunction with other optical recognition methods, it can extend the sensing range, lower the detection limit, and offer mutual validation. Nowadays, electrochemical-optical dual-mode biosensors have ensured the accuracy of test results by integrating two signals into one, indicating their potential use in primary food safety quantitative assays and screening tests. Particularly, visible optical signals from electrochemical/colorimetric dual-mode biosensors could meet the demand for real-time screening of microbial pathogens. While electrochemical-optical dual-mode probes have been receiving increasing attention, there is limited emphasis on the design approaches for sensors intended for microbial pathogens. Here, we review the recent progress in the merging of optical and electrochemical techniques, including fluorescence, colorimetry, surface plasmon resonance (SPR), and surface enhanced Raman spectroscopy (SERS). This study particularly emphasizes the reporting of various sensing performances, including sensing principles, types, cutting-edge design approaches, and applications. Finally, some concerns and upcoming advancements in dual-mode probes are briefly outlined.

A reduction in the size of electronic equipment increases the heat generation rate. Failure of electronic equipment occurs if the heat is not dissipated properly. This paper examines the performance of aluminium two-phase closed thermosyphon for cooling electronic equipment. Acetone charged aluminium two-phase closed thermosyphon was fabricated with an inside diameter of 17.05 mm and 1 mm thickness. A series of experimentations were performed for inclination angles of 10°–90° at selected filling ratios of 30, 60 and 100 %, along with heat inputs of 100, 200 and 300 W. The condenser section flow rate of water was maintained constant. Minimum thermal resistance was obtained at a 30° inclination angle for all filling ratios and heat inputs. The evaporator and condenser sections have a maximum heat transfer coefficient at a 30° inclination angle. Thermosyphon, with a 30 % or 60 % filling ratio, performed better than a 100 % filling ratio for all inclination angles and heat inputs. As the heat input was increased, the heat transfer coefficients of the evaporator and condenser section were increased, whereas total thermal resistance decreased. For 300 W heat input and 30 % filling ratio, the minimum thermal resistance at a 30° inclination angle was 0.158 °C/W. It is found that, the same heat input and filling ratio, the maximum heat transfer coefficient value for the evaporator and condenser section at a 30° inclination angle was 1602 W/m2°C and 5652 W/m2°C, respectively.

Inadequate research regarding how material characteristics affect vital yield parameters in EDM, particularly for aluminium, brass, and Inconel 617. An limited study has examined how EDM process parameters like peak current (Ip), gap voltage, and pulse on time (Ton) affect recast layer thickness, tool wear rate, surface roughness, crack density, globule formation, material removal rate, and crater formation. The interaction between thermal conductivity, microcracks, and recast layers in EDM is uncertain. Material characteristics' implications on XRD patterns and globule formation during EDM machining were not extensively investigated. EDM process parameters and their impacts on response variables such tool wear rate (TWR), material removal rate (MRR), surface roughness, and globule formation for diverse materials have received minimal analysis. In addition, non-ferrous workpiece materials are integral to the electrical discharge machining process. This study delves into how the physical properties of materials influence key yield parameters, including tool wear rate, surface roughness, crack density, globules, material removal rate, and crater formation. The investigation covers Inconel 617, brass, and aluminum, revealing peak current (Ip) as the most influential factor for highly thermally conductive materials when compared to gap voltage and pulse on time (Ton). Notably, Inconel 617 exhibits longer cracks, while aluminum displays smaller ones, and deeper cracks are found on the surface of aluminum, contrasting with the broader craters observed on Inconel 617. Recast layer thickness varies, ranging from 8.3 μm for brass to 13.8 μm for aluminum. The study validates response values extensively against experimental data and highlights brass's superior surface finish. It further uncovers that plasma-generated craters are predominantly semispherical, with the plasma's diameter expanding faster than the crater's size. Sub-surface re-solidification emerges as a source of stress and micro-cracks, which diminish material's fatigue strength. Notably, aluminum exhibits a greater number of elegant microcracks on the machined surface, while brass and Inconel 617 have fewer. Recast layer thickness measurements indicate approximately 13.8 μm for aluminum, 8.3 μm for brass, and 9 μm for Inconel 617. Additionally, the thermal erosion process enhances the microhardness of the machining zone's subsurface due to the formation of oxides and carbides and the re-solidification of particles. Different material characteristics are revealed through X-ray diffraction (XRD) patterns, while globules, small and spherical, weakly adhere to the machining zone's subsurface. In particular, aluminum machined surfaces feature fewer globules, while brass surfaces exhibit a higher quantity, attributed to their varying thermal conductivity.

The microwave cladding technique for depositing Ni and 10 % B4C coatings on 304 stainless steel has yielded significant advancements in material properties and performance. The key findings of this study revealed remarkable improvements, including a 43.33% increase in material hardness, indicating enhanced wear resistance and mechanical properties. This improvement was attributed to the uniform distribution of B4C and Ni on the cladding surface, ensuring a consistent interfacial layer developed between SS 304 and the cladding surface without cracks and porosity. Microstructural analysis at 500× magnification unveiled an impressive 2233.35 grains per square inch, showcasing the refined grain structure achieved during the cladding process. Wear testing demonstrated a low wear rate of 0.00308 mm³/m and a favorable coefficient of friction of 0.1981, confirming the material's suitability for applications with demanding frictional conditions. Furthermore, the corrosion behavior of the coated 304 stainless steel was assessed, revealing a minimal corrosion weight loss of only 0.42 mg for the Ni and 10% B4C coated sample. The presence of various carbide phases, such as Cr2C, Cr23C6, Cr7BC4, Fe5C2, and Fe23B6, within the cladding further contributed to the material's enhanced mechanical and wear properties.

Recently, unmanned aerial vehicle (UAV)-assisted wireless communication systems gained a lot of attention due to the provision of highly probable line-of-sight communication links. However, reliable UAV communication with the base station is crucial for the control and non-payload communication (CNPC) to smoothly assist the application of UAVs. Specific to CNPC requiring less latency and high reliability, in this work, the performance of the UAV-non-orthogonal multiple access (NOMA) system is evaluated with imperfect successive interference cancellation (SIC) and short packet transmission. Further, the bounds on the fractional power required by each UAV of the NOMA pair are derived under imperfect SIC. In addition, we derive a pairing criterion required for each NOMA pair to achieve rates better than orthogonal multiple access (OMA) based rates. We propose a pairing algorithm and three different power allocation strategies that perform better than the state-of-the-art algorithms. Through Monte Carlo simulations, we show that the proposed user pairing approach performs closer to the optimal solution (obtained using scipy) and better than near-far, uniform channel gain pairing algorithms, and OMA baseline.

Recent breakthroughs in component downsizing and miniaturization emphasise the requirement for deep-hole drilling with an increased aspect ratio, especially in the automobile sector for fuel injectors and the medical sector for manufacturing bone screws or surgical equipment. In this research, deep hole drilling on stainless steel AISI 316L has been conducted using an Abrasive Waterjet Machine (AWJM), and the influence of drilling variables on the material removal rate, roundness deviation of drilled holes have been evaluated to assess the machining and hole characteristics. In addition, machining parameters' statistical relevance has been investigated using a multi-parametric analysis of variance. Quadratic mathematical models for material removal rate and roundness deviation have been established by correlating drilling parameters. The Grey Wolf Optimization (GWO) algorithm has been applied in this research to identify the optimal combination of deep hole drilling parameters for maximizing material removal rate and minimizing the roundness deviation. The results have been compared with Derivative-free optimization, Whale optimization, and Harmony search algorithms. The comparison revealed that the GWO algorithm performed better than other algorithms. In addition, a validation test has been carried out to confirm the accuracy of the results produced by the GWO. The images obtained from the scanning electron microscope showed that the surfaces of the deep holes are smooth. Additionally, the ploughing action was shown to be the principal mechanism responsible for removing the material.

Nowadays, Fiber-reinforced Polymer Composites (FRPCs) are extensively utilized due to their remarkable properties such as high stiffness, excellent strength to weight ratio, resistance to wear, corrosion etc. Earlier, the FRPCs are prepared through synthetic fibers in order to attain high strength in conjunction with high elastic modulus. However, with the increasing economic and environmental factors regarding the accumulation of plastic waste, the development of natural and hybrid (combination of any two) fibers were started. The mechanical properties of FRPCs are largely determined by the way loads are transferred between the matrix and fibers, or by the strength of the bond between the fiber-matrix interfaces. Additionally, these factors play a significant role in determining the overall performance of FRPCs. Therefore, this review discusses the recent advancements in enhancing the interaction between fiber and matrix by means of chemical treatment and the inclusion of nanoparticles. The resulting mechanical performance of the end composites and their intended applications are also presented. Few targeted application areas of FRPCs such as aerospace, automobile, mechanical and biomedical implants were discussed in detail.

Indoor human-carried object detection refers to the use of technologies and methods to detect objects that may be carried by individuals in indoor environments. This can include weapons, explosives, drugs, or other contraband that may endanger the safety and security of individuals or facilities. Detecting potential threats carried by individuals inside buildings is thus a critical and ongoing requirement in a variety of settings, including airports, schools, railway stations, and other public places. It is extremely challenging to detect these objects accurately using noncontact methods. Here, we present a noncontact carry object detection method based on mmWave radar and machine learning. We adopted a tree-based feature selection to reduce the complexity and increase the reliability of the detection process. The performance of the proposed approach has been compared to that of various state-of-the-art approaches. Finally, we deployed the models on various edge computing platforms, including Raspberry Pi, Nvidia Jetson Nano, and AGX Xavier.