The work focuses on predicting the bearing response in hydrothermal-aged carbon fiber-reinforced epoxy composite (CFREC) joints through the utilization of machine learning techniques. CFREC are extensively employed in aerospace and other high-performance applications, and their long-term structural integrity is of paramount importance. The hydrothermal aging process can significantly affect the mechanical behavior of such composites, particularly in joint configurations. In this research, an innovative support vector regression approach is present that leverages machine learning algorithms to forecast the bearing response of CFREC joints after undergoing hydrothermal aging. The study encompasses the development of predictive models using a comprehensive dataset of experimental observations. The machine learning technique, support vector regression is trained and evaluated to assess their accuracy and reliability in predicting bearing response. The results show that the overall percent reduction in bearing response, after 30 days of pristine composite bolted joints at 0 Nm bolt torque shows reductions of 23.22 % at 65°C, respectively. Conversely, under the same conditions, MWCNTs added composite bolted joints exhibit only a 9.2% reduction. The predictive models find the value of 0.0081 RSME and 0.8 R2 respectively through support vector regression confirming that the predicted values lie in between the upper and lower bond.

Mott transition is one of the most dramatic physical phenomena involving abrupt insulator-to-metal transition (IMT) in some metal oxides. Recently, it has become one of the highlighted research topics as various Mott devices have been investigated for emerging memory and steep switching transistor applications. That has inspired us to create a proximity-oxidation-produced Ti-VOx thin film sputtered on n-Si substrate. The key finding here is the mixed phase of the Ti-VOx film and the interface of Ti-VOx/n-Si enabling a double Mott switching characteristic - in other words, a Ti-VOx device can switch from two HRS regions to two LRS regions. Particularly, the Ti-VOx/n-Si interface plays two roles: supporting the double Mott switching and facilitating a controlled Mott transition behavior up to 120 degrees C. Our research demonstrated by a deep learning-based modeling technique, yielding a staggering recognition accuracy of 97.78 %. The result is within the theoretically ideal recognition rate of 99.37 %. Due to the remarkably endurant synaptic weight behavior up to 120 degrees C, the usability of our Ti-VOx devices for synaptic applications is demonstrated and discussed in detail to pave a way for uses in advanced neuromorphic applications.

3D printing has emerged as a groundbreaking technology with transformative applications in various health care domains. In drug delivery, it enables the precise fabrication of customized dosage forms, offering controlled release patterns and stimulus-triggered release capabilities. In addition, 3D printing plays a pivotal role in tissue engineering, facilitating the creation of complex structures with biomimetic properties. The impact of 3D printing technology extends to personalized medicine, allowing for the production of patient-specific medications tailored to individual needs. In the realm of regenerative medicine, 3D printing contributes to the fabrication of intricate scaffolds and bioprinted tissues, fostering advancements in the regeneration of damaged or diseased tissues. The versatility and precision of 3D printing make it a powerful tool across these domains, promising innovative solutions and personalized approaches in the field of health care. A comprehensive review of scholarly literature spanning from 1980 to the present was conducted across prominent databases such as PubMed, Wiley Online Library, Multidisciplinary Digital Publishing Institute, Kosmet, Science Direct, and Scopus. The present review offers a comprehensive examination of 3D printing in the biomedical and pharmaceutical sectors, shedding light on its historical progression while envisioning a future where regenerative and customized medicines become commonplace.

Modulation of charge transfer pathways by varying halides in bismuth oxyhalides over 1D CuO nanorods derived from 3D metal–organic framework.

The emergence of the Internet of Things (IoT) signifies a transformative wave of innovation, establishing a network of devices designed to enrich everyday experiences. Developing intelligent and secure IoT applications without compromising user privacy and the transparency of model decisions causes a significant challenge. Federated Learning (FL) serves as a innovative solution, encouraging collaborative learning across a wide range of devices and ensures the protection of user data and builds trust in the process. However, challenges remain, including data variability, potential security vulnerabilities within FL, and the necessity for transparency in decentralized models. Moreover, the lack of clarity associated with traditional AI models raises issues regarding transparency, trust and fairness in IoT applications. The survey examines the integration of Explainable AI (XAI) and FL within the Next Generation IoT framework. It provides a thorough analysis of how XAI techniques can elucidate the mechanisms of FL models, addressing challenges such as communication overhead, data heterogeneity and privacy-preserving explanation methods. The survey brings attention to the benefits of FL, including secure data sharing, effective modeling of heterogeneous data and improved communication and interoperability. Additionally, it presents mathematical formulations of the challenges in FL and discusses potential solutions aimed at enhancing the resilience and scalability of IoT implementations. Eventually, convergence of XAI and FL enhances interpretability and promotes the development of trustworthy and transparent AI systems, establishing a strong foundation for impactful applications in the ever evolving Next-Generation IoT landscape.

Pharmaceutical excipients play a crucial role in determining the outcome of delivered therapeutic cargo density. By far, polymers have captured the biggest share in the excipients market. This surge in demand motivated researchers to look for newer and novel polymeric platforms. Interpenetrating polymeric networks (IPN) are a class of polymer in the same polymer blend league, where two different polymer chains penetrate; and align with each other without any sustainable covalent bond. The novel agreement between the polymer chains equips the IPN with the characteristic features of each participating polymer unit, thus making IPN superior to its predecessors. IPN has crossed a long path, especially in the pharmaceutical medicine field, from the mere coinage of the term to widespread usage, especially in drug delivery, where they increased the bioavailability and efficacy of the co-delivered drugs. The current review will highlight the major studies that have led to the current face of the IPN in various pharmaceutical domains. The present review was conducted by comprehensively reviewing published reports within the recent period using multiple keywords related to IPN and its role in drug delivery. Moving forward, continued exploration and innovation in IPN technologies promise to further enhance their applications, offering novel solutions for the challenges in drug delivery and therapeutic cargo density.

Burn injuries worldwide pose significant health risks due to frequent microbial infections, which worsen complications and increase mortality rates. The conventional antimicrobial formulations are available in the form of ointments and creams. These formulations are very greasy and stick to the clothes. The applications of these formulations by finger or applicator produce pain in the affected area and incur the possibility of microbial infection. To overcome these hurdles, authors developed a novel non-propellent foam (NPF) based formulation containing chlorhexidine for effective topical delivery. Initially, NPF containing Labrasol® (26.7%), sodium lauryl sulfate (1.2%), hydroxy propyl methyl cellulose (0.56%), butylated hydroxytoluene (0.1%), ethanol (1%), and distilled water was prepared and assessed for its consistency, and ability to form foam. The NPF was statistically optimized using the Box-Behnken design to determine the effect of polymer and surfactants on the critical foam properties. The optimized formulation showed a collapse time of 45 s with a unique nature of collapsing upon slight touch which is highly beneficial for burn patients with microbial infection. The diffusion study showed that more than 90% of the drug was released within 6 h. The skin permeation study showed that 23% of the total drug permeated through the skin after 6 h with 7.64 µg/cm2/h permeation flux. The developed formulation showed good antibacterial activity. The minimum inhibitory concentration of prepared NPF was found to be 2.5 µg/mL, 2.5 µg/mL, and 5.0 µg/mL against E. coli (MTCC-1687), P. aeruginosa (MTCC-1688), and S aureus (MTCC-737) respectively. The developed NPF formulation showed quick collapse time, excellent spreadability, good anti-bacterial activity, and a non-sticky nature representing a promising avenue for burn wound treatment without using any applicator.

Diabetes is a chronic metabolic disorder characterized by elevated blood sugar levels and encompasses various types like type 1, type 2, gestational, and prediabetes. This review delves into the intricacies of type-2 diabetes mellitus and its ideal management. Presently, a spectrum of herbal and synthetic drugs is employed for type-2 diabetes mellitus management. We gathered information about diabetes mellitus from articles published up to 2024 and listed in PubMed, Web of Science, Elsevier, Google Scholar, and similar databases. The keywords used in our search included "diabetes", "herbal drugs", "nano-carriers", "transdermal drug delivery", etc. By carefully analyzing the research on type-2 diabetes-mellitus, it was found that there is an increase in diabetes-based research, which can be demonstrated by contemplating the PubMed search engine results using transdermal delivery for type-2 diabetes-mellitus as a keyword. The oral consumption of these drugs is associated with numerous side effects, including obesity, pancreatic cancer, and hormonal imbalances. To surmount these challenges, the utilization of nano-carriers and transdermal drug delivery systems emerges as a promising avenue aiming to enhance the therapeutic efficacy of drugs. Nano-carriers represent a revolutionary approach, integrating cutting-edge technologies, inventive strategies, and methodologies to deliver active molecules in concentrations that are both safe and effective, thereby eliciting the desired pharmacological response. This review critically examines the constraints associated with traditional oral administration of anti-diabetic drugs and underscores the manifold initiatives undertaken to revolutionize drug delivery. This review focuses on the limitations associated with the conventional oral administration of anti-diabetic drugs and the many initiatives made so far for the effective and safe delivery of drugs using innovative constituents and techniques.

The physical properties of asiaticoside (AC), such as its high molecular weight, poor water solubility, and low permeability, restrict its therapeutic benefits. AC-loaded nano-carriers overcome AC limitations in wound healing by enhancing delivery efficiency, stability, and safety.

Nanoparticles (NPs) have been extensively investigated for their potential in nanomedicine. There is a significant level of enthusiasm about the potential of NPs to bring out a transformative impact on modern healthcare. NPs can serve as effective wound dressings or delivery vehicles due to their antibacterial and pro-wound-healing properties. Biopolymer-based NPs can be manufactured using various food-grade biopolymers, such as proteins, polysaccharides, and synthetic polymers, each offering distinct properties suitable for different applications which include collagen, polycaprolactone, chitosan, alginate, and polylactic acid, etc. Their biodegradable and biocompatible nature renders them ideal nanomaterials for applications in wound healing. Additionally, the nanofibers containing biopolymer-based NPs have shown excellent anti-bacterial and wound healing activity like silver NPs. These NPs represent a paradigm shift in wound healing therapies, offering targeted and personalized solutions for enhanced tissue regeneration and accelerated wound closure. The current review focuses on biopolymer NPs with their applications in wound healing.

The optimal power flow (OPF) problem is of paramount interest and challenge for researchers in Electrical power systems. The problem becomes more challenging when Renewable Energy Sources (RES) like solar wind, etc. are integrated with the traditional interconnected network The main goal of this paper is the cost reduction of a hybrid system comprising both conventional and RES with the integration of Flexible AC transmission Systems (FACTs), considering system limitations in emission loss, in transmission line, to cater voltage fluctuations such that the overall system stability is improved. The classic problem of OPF itself is extremely complex and nonlin-ear, coupled with convex, intermittent constraints. The issue’s complexity increases when the unpredictable behaviour of solar and wind energy is considered. Using solar and wind power in traditional thermal power generations in the particular test system, this research suggests an analysis of the OPF problem by using a metaheuristic optimization technique Chaotic African Vulture Optimization Algorithm (CAVOA.) The suggested method was tested using a modified IEEE 30-bus system and executed using MATLAB in sixteen cases via multi-objective functions. The proposed problem is mitigated by computational intelligence tools to solve OPF problems like tuning grid voltages, transformer tap setting, allocation and sizing of FACTs devices, capacitor bank rating, etc. Compared to the outcomes of the (insert full form) FDBAGDE methods and those found in the literature, the simulation outcomes obtained through the suggested approach successfully identified the best solution. Additionally, the suggested algorithm’s superiority is assessed using a statistical method known as the one-way analysis of variance (ANOVA) test.

The COVID-19 pandemic has caused a significant strain on the healthcare system worldwide, resulting in an acute shortage of ventilators. Conventional ventilators are costly, and production is difficult to scale up during a rapidly spreading pandemic. Ambu bags offer a low-cost solution for manual ventilation, but their lack of precise control over parameters makes them unsuitable as a replacement for conventional ventilators. To address this issue, we propose the AARMED (Ambu bag Attachment for Rapid Mass Emergency Deployment) system, which is a low-cost and easy-to-assemble mechanical resuscitator that can achieve most of the recommended modes and parameter ranges for managing COVID-19 patients. AARMED can operate in volume-control, pressure-control, and assist-controlled modes continuously over long periods, making it suitable for use in emergency settings. The AARMED system has been tested using an ISO certified Test-lung over various parameter settings and has been found to be an effective alternative to costly conventional ventilators. It has a battery backup of 2.5 h under normal operating conditions, making it an ideal transport ventilator. In conclusion, the AARMED system offers a low-cost and easy-to-assemble solution for mechanical ventilation in emergency settings. Its ability to achieve most of the recommended modes and parameter ranges for managing COVID-19 patients makes it a viable alternative to conventional ventilators in resource-constrained settings.

This article presents a low-cost irrigation system that harnesses the power of IoT technologies to revolutionize water management practices and enhance agricultural productivity. The system uses soil moisture sensors, climate sensors, and temperature sensors that communicate with a central controlling mechanism. The data collected from the sensors is handled with the help of machine learning algorithms to make automated decisions about irrigation. This system is useful for small-scale farmers who lack access to expensive irrigation technology. The system has undergone field trials and has shown encouraging results. The soil moisture sensors have an average error rate of below 5%, saying that the system can precisely recognize soil moisture levels. The crops grown with the smart irrigation system had a 10% greater yield than the control group, and the system was able to limit water usage by up to 30% in comparison to tradition irrigation techniques. The potential effects of the low-cost smart irrigation system on food security and agriculture in developing countries must be taken into consideration. As water resources become more expensive and scarcer, technology can change irrigation practices and enhance the development of sustainable agriculture. To adapt the system to the unique requirements of small farmers in various regions and to examine the practicality of scaling it up for wider application, more research and development are needed. All things could be done with the low-cost smart irrigation system.

Franz diffusion cell was primarily used to evaluate the permeability and stability of formulations such as topical (gels and creams) and transdermal (lipid nanoparticle formulations). Diffusion cell is a straightforward assay that can reliably measure the in-vitro and ex-vivo drug release from topical preparations such as creams, ointments, liposomes formulations, and gels. It offered crucial critical perspectives on skin, drug, and formulation relationships. In addition, it is also employed for toxicity testing and quality control. Currently, many medications are available in the market, administered through transdermal routes. To treat various skin conditions, medical professionals use a wide range of methods aimed at increasing the skin's permeability and drug absorption. The biggest challenge with transdermal drug delivery system is determining the amount of medication penetrating the skin. Today, there is a lot of research into creating new dermal dosage forms for pharmaceuticals. In this review, the authors discuss various evaluation methods to determine the amount of drug penetrating the skin. With their strengths and weaknesses, these models and assessment methods provide a valuable framework for investigating the dermato-pharmacokinetics of different transdermal formulations. The assessment techniques aid in assessing molecules that cause skin irritation by shedding light on the molecular mechanisms by which they penetrate the skin. These assessment tools and models provide a novel strategy for creating topical medicinal formulations for various skin infection disorders. The present review explores Franz diffusion cell application, its implication in pharmaceutical research, and the regulation related to pharmacopeia.[Graphical Abstract]

Transition metal nitrides are valuable materials for many technological applications due to their favorable physical characteristics, including conductivity, high temperature stability, and hardness. Among them, hafnium nitride (HfN) thin films of high attributes are crucial for semiconductor applications. However, achieving performance efficiency of HfN films with desired electrical performance via conventional deposition methods is a challenging task. Herein, we have achieved room temperature growth of highly oriented cubic HfN thin film grown on Si substrate by process optimization of radio frequency (RF) magnetron sputtering. HfN thin films were structurally, morphologically, and electrically characterized. The metallic behavior of HfN films was confirmed through current-voltage measurements; it showcased excellent electrical conductivity, signifying low resistivity (approximate to 0.55 ka(2) sq(-1)), revealing promising evidence for its potential practical application. Moreover, the firstprinciples calculations were also conducted to gain further insights into the electrical performance of the HfN films. These findings lay a solid foundation for further exploration and development of HfN-based junction diodes, as the observed characteristics offer significant potential for enhancing device performance in various electronic domains.

Background: Micronutrients play a vital role in the maintenance and proper functioning of body tissues. Micronutrients broadly consist of minerals and vitamins. These vitamins and minerals are of supreme importance in the treatment of an eclectic variety of diseases and are obligatory for many metabolic processes. Objective: The objective of this review is to give a comprehensive overview on the role of micronutrients in the treatment of broad-spectrum diseases and also give insightful knowledge regarding the numerous food sources for obtaining nutrients, their dietary reference values, and their deficiencies. In this review, the authors have also highlighted the role of micronutrients in COVID- 19. Findings: A properly balanced diet provides an acceptable amount of nutrients in the body. Deficiency and excessive nutrients in an individual’s diet may cause diseases or abnormal conditions. An improper diet may be responsible for the occurrence of deficiencies in iron, calcium, and iodine. Minerals like iron, boron, calcium, cobalt, phosphorous, and vitamins like K, E, A, D, and Riboflavin can cure and treat fatal diseases like Alzheimer’s, bone development conditions, osteoporosis, anemia, inflammatory bowel, and HIV Infections. Conclusion: Micronutrients are essential for metabolism and tissue function. Sufficient consumption is thus required, but providing additional supplements to persons who do not require them may be detrimental. Large-scale studies of varied micronutrient dosages with accurate outcome indicators are needed to optimize intakes in different patient groups and the general population at large. In this review, the authors have highlighted the crucial role of micronutrients in health and disease.

This review offers a comprehensive depiction of g-C 3 N 4 -based materials for PEC water splitting. The fundamentals of PEC water splitting, along with the applications of g-C 3 N 4 -based materials as photoanodic and photocathodic materials are discussed.

The full text of this preprint has been withdrawn by the authors due to author disagreement with the posting of the preprint. Therefore, the authors do not wish this work to be cited as a reference. Questions should be directed to the corresponding author.

Additive manufacturing (AM), also recognized as 3D printing, has gained significant attention in various industries for its potential to revolutionize production processes. One critical aspect of AM is ensuring the quality and performance of printed parts, particularly concerning mechanical properties like tensile stress. In the present work, the effect of process variables on 3D-printed acrylonitrile butadiene styrene (ABS)/Glass fiber composite materials was explored. The machine learning approach, classification and regression trees (CART) algorithm, was used to predict tensile stress in ABS/Glass fiber composite materials based on predictor variables such as layer thickness, nozzle temperature, bed temperature, and infill density. The objective is to develop an accurate and interpretable model that captures the relationships between these variables and tensile stress. The model is evaluated using performance metrics such as R-squared, mean absolute deviation (MAD), and root mean squared error (RMSE) on both training and test datasets. From results the highest tensile stress of 39 MPa was achieved at nozzle temperature of 250 degrees C, bed temperature of 80 degrees C and infill density at 60%. The CART model predicts the most influencing parameter as infill density followed by nozzle temperature and bed temperature.Highlights Novel material is manufactured by sandwiching glass fiber in ABS layers. FDM process for novel material is optimized based upon process parameters. SEM analysis reported good interlayer adhesion with few micro-porosities. Modeling of tensile stress by classification and regression tree algorithm. CART model predicts significant impact of infill density on tensile stress. Performance evaluation of ABS/GF composites through machine learning approach. image