This paper aims to review the challenges, toxicity, and routes of synthesis and usage of silver nanoparticles in different applications but also highlighting their sustainability from both medical and environmental issues. Regarding their toxicity, it is known that silver nanoparticles can destroy over 650 microorganisms comparing with antibiotics. Supplementary, will be presented in a comparative manner some conventional synthesis routes (physical and chemical methods) and green synthesis routes using plant extracts. The approach using plant extracts have various advantages comparing with physical, chemical and microbial synthesis methods because there is no need to use chemicals, wasteful purifications and high energy requirements. The paper presents an overview on “green nanotechnology” focused on using either biological micro-organisms or plant extracts as an alternative to the classical chemical and physical methods. An important issue discussed in the paper is an overview of the synthesis routes of silver nanoparticles, some expected applications of silver based active agents and their toxicity and challenges that must be overcome. Also, it needs to focus our attention on the dismissal of silver nanoparticles into the environment and especially in water systems, fact which suggests that this issue must be fully understood and applied the law.

In the wake of COVID-19, there is an urgent need for a diverse public health work force to address problems presented or exacerbated by the global pandemic. Educational programs that create our work force both train and shape the makeup of access through graduate applications. The Graduate Record Exam has a number of standing issues, with additional barriers created by the pandemic. We trace the GRE waiver movement over several years, focusing on the gradual adoption in CEPH accredited programs and the rapid expansion of temporary waivers as a response to testing access. Going forward, we need to consider gaps in waivers during the pandemic and how this data can be used to shape our future use of the GRE.

The utilization of sustainable biomaterials in pharmaceutical and medical applications has garnered significant attention in recent years due to their potential to address environmental concerns while meeting the demands of modern healthcare. This review provides a comprehensive overview of the latest advancements and applications of sustainable biomaterials in the pharmaceutical and medical fields. Beginning with an exploration of sustainable sourcing methods and production techniques, the review highlights the importance of eco-friendly practices in biomaterial development. Subsequently, it delves into the diverse range of sustainable biomaterials, including biopolymers, natural extracts, and bio-based nanoparticles, elucidating their properties and biomedical applications. Moreover, the chapter discusses the role of sustainable biomaterials in drug delivery systems, tissue engineering, wound healing, and medical device fabrication, emphasizing their biocompatibility, biodegradability, and therapeutic efficacy. Along with discussions on regulatory issues and commercialization prospects, the potential and challenges related to the integration of sustainable biomaterials into pharmaceutical and medical practices are also critically reviewed. By elucidating the advancements and potential of sustainable biomaterials, this article aims to inspire further research and innovation in the pursuit of environmentally friendly and effective solutions for healthcare applications.

Metal nanoframes have gained tremendous attention in the domain of modern research and development due to their distinctive 3D spatial structure, efficient physiochemical properties, and comparatively good activity. Different strategies have been implicated by the researchers to design nano frame of varying chemical nature and shapes. Most of the synthetic protocols being adopted to design a nano framee, mainly consist of two main steps, nucleation and growth of solid particles and secondly excavation of the interiors. In this context, many synthetic methods are overviewed. To show their unprecedented performance or activity, a few applications in catalytic, biomedical, theranostic, SERS, sensing of different materials, reduction of CO2, etc are also discussed.

Soybean yield prediction is a challenging problem in plant breeding that is often affected by many different factors simultaneously. Hyperspectral reflectance data from plants and soil data provide breeders with useful information about soybean plant health and using these different types of data to predict yield is an active area of research. Furthermore, breeding programs encounter challenges such as data imbalance and external factors like genotype variability across different environments, which present significant hurdles in the development of yield prediction models for large-scale breeding programs. In this work, we perform a comprehensive study of predicting yield using both hyperspectral reflectance and soil data to understand what scenario's offer the best chances of predicting yield with high accuracy. We demonstrate a cluster based ensemble approach for yield prediction using hyperspectral reflectance data that can perform well for large scale breeding programs by efficiently harnessing useful information from data through an unsupervised approach.

The medical & the dental field is a never ending field of innovations & developments and each time the reasearchers come up with something new. One such new dimension in the fields of medicine being the incorporation of Artificial intelligence assisted technologies improving diagnosis, treatmemt plan and treatment stategies. This review focusses on the application of different technologies of AI in different fields of medicine.

Sustainability, particularly in construction materials, has been a subject of growing interest. Civil construction is one of the industries where more materials are consumed, which leads to high energy consumption and CO2 emissions. The production of cement, especially clinker is largely responsible for these problems. As a solution, new materials emerge, which do not require much energy for their production, which are the alkaline cements, specifically the geopolymers. Geopolymers are inorganic polymers obtained by the alkaline activation of aluminosilicate precursors. In the present study geopolymers were developed with low grade kaolin (as a precursor) from a Portuguese company. The development of these geopolymers will be, due to their properties, a good solution for rehabilitation of earth buildings, especially in adobe. The development of these geopolymers is also a contribution to the sustainability of kaolin exploitations as it opens new markets for the low grade kaolins, presently not easily commercialized. As mechanical strength of adobe materials ranges in literature from 0.6 to 8.3 MPa, the values obtained for the developed geopolymers (between ~2 to 10 MPa) can be considered as totally adequate.

Spherical or hexagonal NaYF4:Yb,Er nanoparticles (UCNPs) with sizes of 25 nm (S-UCNPs) and 120 nm (L-UCNPs) were synthesized by high-temperature coprecipitation and subsequently modified with three kinds of polymers. These included poly(ethylene glycol) (PEG) and poly(N,N-dimethylacrylamide-co-2-aminoethylacrylamide) [P(DMA-AEA)] terminated with an alendronate anchoring group, and poly(methyl vinyl ether-co-maleic acid) (PMVEMA). The internalization of nanoparticles by rat mesenchymal stem cells (rMSCs) and C6 cancer cells was visualized by electron microscopy and the cytotoxicity of the UCNPs and their leaches was measured by the real time proliferation assay. Comet assay was used to determine oxidative damage of UCNPs. In vivo study on mice determined the elimination route and potential accumulation of UCNPs in the body. The results showed that the L- and S-UCNPs were internalized into cells in the lumen of endosomes. Proliferation assay revealed that L-UCNPs were less toxic than S-UCNPs. The viability of rat mesenchymal stem cells (rMSCs) incubated with particles decreased in the order S-UCNP@Ale-(PDMA-AEA) > S-UCNP@Ale-PEG > S-UCNPs > S-UCNP@PMVEMA. Similar results were obtained in C6 cells. Oxidative damage measured by the Comet assay showed that neat L-UCNPs caused oxidative damage to rMSCs more than all coated UCNPs while no difference was observed in C6 cells. An in vivo study indicated that L-UCNPs were eliminated from the body via the hepatobiliary route; L-UCNP@Ale-PEG particles were almost eliminated from the liver 96 h after intravenous application. Pilot fluorescence imaging confirmed the limited in vivo detection capabilities of the nanoparticles.

UHPC is a type of cement-based composite used in new construction and/or rehabilitation of existing buildings to extend service life. It is a novel composite material that can serve as an alternative to concrete construction in hostile climates. Following decades of study and production, a diverse variety of commercial UHPC compositions are now available globally to meet the rising number of applicants and demand for high-quality building materials. Although UHPC offers major benefits over normal concrete, its utilization is restricted due to restrictive design rules and exorbitant costs. As a result, a thorough examination of the durability properties of UHPC is required to give important information for material testing requirements and processes, as well as to broaden its practical uses. This report is aimed at increasing basic understanding of UHPC and supporting more UHPC research and applications.

Actuated carbon (AC) is utilized in various conditions of uses after its disclosure as a solid and dependable adsorbent. A review on AC is introduced along with returning to the wellsprings of AC age; strategies used to produce AC including pyrolysis enactment; actual actuation; synthetic initiation and steam pyrolysis. The significant variables influencing the AC creation, the potential uses of AC and their future possibilities are likewise examined. AC is applied in water, wastewater and leachate medicines in numerous nations, particularly to clean the shading, eliminate the smell and some substantial metals. Taking into account this, an exhaustive rundown of research on compound, physical and organic change strategies of initiated carbon relating to prevent of foreign substance expulsion from watery arrangements was aggregated and investigated. Additionally, the examination of the actual blending strategy and the impregnation technique in enactment with antacid metals shows that the actuated carbon got through actual blending had a higher porosity than the initiated carbon created by the impregnation technique. The uses of initiated carbon items were quickly surveyed.

Recent technical advances in the stem cell field have enabled the in vitro generation of complex structures resembling whole organs termed organoids. Most of these approaches employ culture systems that allow stem cell-derived or tissue progenitor cells to self-organize into three-dimensional (3D)-structures. Since organoids can be grown from various species, organs and from patient-derived induced pluripotent stem cells, they create significant prospects for modelling development and diseases, for toxicology and drug discovery studies, and in the field of regenerative medicine. Here, we report on intestinal stem cells, organoid culture, organoid disease modeling, transplantation, current and future uses of this exciting new insight model to veterinary medicine field.

As the world population grows, fulfilling the need for biomaterials in the healthcare system becomes increasingly important. Biomaterials, which include metals, polymers, ceramics, and composites, play an important role in the creation of medical devices that improve people's lives. The European Society for Biomaterials described biomaterials as non-biological materials used in medical devices to interact with biological systems. This definition has developed to encompass materials that treat, assess, or replace tissues or bodily functions. Biocompatibility, as defined by the FDA, is a key feature of biomaterials, as is the requirement to fulfil design requirements such as mechanical performance, geometry, and electrical control. Over the last decade, advances in regenerative medicine and tissue engineering have boosted biomaterials' usage in medical applications dramatically. Despite its promise, scientists, engineers, and physicians continue to face significant challenges due to the complexity of biomaterials. Biomaterials have various benefits, including biodegradability, non-toxicity, and biocompatibility, making them extremely useful for replacing, sustaining, or improving biological activities in damaged tissues. They are used in a wide range of applications, including tissue engineering implants, human tissue healing and regeneration, medication delivery systems, and biosensors. This study gives insights into the fundamental characteristics, case studies, and ethical issues associated with the usage and use of biomaterials, further examining their potential and significance in healthcare and medicine.

This study investigates the cytotoxicity profile of superparamagnetic Fe3O4-Ag decorated nanoparticles against human fibroblasts (HFF-1) and breast cancer cells (MCF-7). The nanoparticles underwent comprehensive characterization employing scanning electron microscopy (SEM), X-ray diffraction (XRD) analysis, X-ray photoelectron spectroscopy (XPS), and magnetic assays including hysteresis curves and zero-field-cooled (ZFC) plots. Notably, the nanoparticles exhibited superparamagnetic behavior as evidenced by magnetic studies. Remarkably, cytotoxicity assays demonstrated that both HFF-1 and MCF-7 cells maintained nearly 100% viability upon nanoparticle exposure. These results underscore the outstanding biocompatibility of Fe3O4/Ag decorated nanoparticles, suggesting their potential utility in biomedical applications such as drug delivery and magnetic targeting without inducing significant cytotoxic effects. This investigation advances our understanding of the behavior of nanostructured materials in biological settings and highlights their promising prospects in biomedicine.

The growing demand in industrial and biotechnological settings for more efficient enzymes with enhanced biochemical features, particularly thermostability and thermotolerance, necessitates a timely response. Renowned for their versatility, thermostable enzymes, offer significant promise across a range of applications, including agricultural, medicinal, and biotechnological domains. This comprehensive review summarizes the structural attributes, catalytic mechanisms, and the relationships between structure and function of two major classes of thermostable enzymes: α-amylases and laccases. These enzymes serve as valuable models for understanding the structural basis of protein thermostability. Commercial significance of these enzymes and researchers' interest in further optimization and innovation, this article can greatly contribute to ongoing research on thermostable enzymes and aiding industries in optimizing production processes. It also give insights to the exploration of suitable strategies and factors for enhancing thermostability, resulting in heightened multipronged stability and notable enhancements in the enzymes’ industrial applicability.

Due to its distinctive chemical and biological properties, pectin has recently drawn much attention in biomedical and tissue engineering applications. Polymers like pectin with cell-instructive properties are attractive natural biomaterials for tissue repair and regeneration. Besides, bioactive pectin and pectin-based composites exhibit improved characteristics to deliver active molecules. Pectin and pectin-based composites serve as interactive matrices or scaffolds by stimulating cell adhesion and cell proliferation and enhancing tissue remodeling by forming an extracellular matrix in vivo. Several bioactive properties, such as immunoregulatory, antibacterial, anti-inflammatory, anti-tumor, and antioxidant activities, contribute to the pectin's and pectin-based composite's enhanced applications in tissue engineering and drug delivery systems.This paper reviews the promising characteristics of pectin or pectic polysaccharides and highlights its potential applications in drug delivery and tissue engineering. Tissue engineering scaffolds containing pectin and pectin-based conjugates or composites demonstrate essential features such as non-toxicity, tunable mechanical properties, biodegradability, and suitable surface properties. The design and fabrication of pectic composites are versatile for tissue engineering and drug delivery applications.

The invention of nanoscience not only brings a revolutionary change in the field of science but also changed the direction of research. Today the whole world is under the trigger of nano and nanoparticles have multidimensional applications in every aspect of life including environmental point of view. Till today a plethora of nanoparticles have been synthesized and have also been applied for multiple purposes and hence grabbed the attention of researchers all over the world. Among the bunch of NPs discovered to date, we have a particular interest in silver nanoparticles (AgNPs) because of their cost-effectiveness and huge abundance in the earth’s crust. With respect to every passing day, due to various kinds of anthropological activities, the quality of the environment such as air, water and soil is depleting which ultimately hampers the human civilization. To encompass the growing environmental issues, many techniques have been adopted. Among the many strategies, tackling the current issues regarding environment through nanoscience is highly worthy as because of its cost-effectiveness, less time consuming and easy handling process. This article reviews the potential of nanoparticles, particularly silver nanoparticles, for a wide range of environmental applications, such as soil, air, and water remediation.

Background: Somatic tinnitus (ST) is a type of tinnitus that is influenced by changes in somatosensory input from the cervical spine or temporomandibular area. Although traditional physiotherapy has been shown to reduce ST symptoms, in-clinic treatment is not always available, and adherence to home exercise programs is often low. This study aims to investigate the effectiveness of an app-based physiotherapy program to enhance compliance and availability of treatment for ST patients. Methods: This pilot randomized controlled trial included 38 adult patients with chronic somatic tinnitus. Participants were randomly assigned to a treatment group receiving a 9-week app-based cervical spine exercise program or a control group. The primary outcome was tinnitus distress measured using the Tinnitus Handicap Inventory (THI). As a secondary outcome we the Mini-Tinnitus Questionnaire (Mini-TQ). Results: Participants in the treatment group showed significant reductions in tinnitus distress, with a large effect size for both the THI and Mini-TQ (Cohen’s d = 1.71 and 1.02, respectively). The control group showed no significant changes. Conclusion: This study provides evidence that an app-based physiotherapy intervention is a feasible and effective treatment for ST. Further research with larger sample sizes, comparisons to traditional in-clinic treatments is needed to confirm these findings. Additional studies on personalized treatment might further improve the treatment.

Resveratrol (RSV), a naturally occurring phytoalexin, is the most important stilbenoid synthesized by plants, as a defense mechanism, in response to microbial aggression, toxins or ultraviolet radiation. RSV came to the attention of researchers both as a potential chemopreventive agent and a possible explanation for the low incidence of cardiovascular disease (CVD) in French people with a high-fat diet. RSV is mainly administered as a food supplement, its properties being evaluated in vitro or in vivo, on various experimental models. RSV modulates signaling pathways that limit the spread of tumor cells, protects nerve cells from damage, is useful in the prevention of diabetes, and generally acts as an anti-aging natural compound. It was highlighted that RSV could ameliorate the consequences of an unhealthy lifestyle caused by an exaggerated caloric intake. This paper reviews the evidence supporting the beneficial effect of RSV for various pathological conditions, e.g., neoplastic diseases, neurodegeneration, metabolic syndrome, diabetes, obesity, CVDs, immune diseases, bacterial, viral, and fungal infections. The study also focused on the chromatographic analysis of trans-RSV (tRSV) in Romanian wine samples, providing a comprehensive overview of tRSV content across different types of wine.

Graphene hybrid-filler polymer composites have emerged as prominent materials that revolutionize heavy industries. This study encapsulates the in-depth analysis of different influential factors such as filler/graphene type, aspect ratios, dispersion methods, filler-matrix compatibility, fiber orientation, synergistic effects, different processing techniques, and post-curing conditions that affect the processing, properties and determine the resultant applications of graphene hybrid polymer composites. Additionally, it discusses the substantial role of graphene collaboration with other fillers such as carbon nanotubes, silica, nano-clays and metal oxides to produce functionalized hybrid polymer composites with synergistically enhanced tailored properties to offer solutions for heavy industries like aerospace, automotive, electronics, and energy harvesting. The review concludes with some suggestions and outlook on the future of these composite materials by emphasizing the need for continued research to fully optimize their potential.

Ensuring the security of vertical applications in fifth-generation (5G) mobile communication systems and previous generations is crucial. These systems must prioritize maintaining the confidentiality, integrity, and availability of services and data. Examples of vertical applications include smart cities, smart transportation, public services, Industry 4.0, smart grids, smart health, and smart agriculture. Each vertical application has specific security requirements and faces unique threats within the mobile network environment. Thus, it is essential to implement comprehensive and robust security measures. This approach helps minimize the attack surface and effectively manage risks. This survey thoroughly examines mobile networks and their security challenges in vertical applications, shedding light on associated threats and potential solutions. Our study considers the interplay between security considerations in 5G, legacy networks, and vertical applications. We emphasize the challenges, opportunities, and promising directions for future research in this field and the importance of securing vertical applications in the evolving landscape of mobile technology.

The indole nucleus is an important element of many natural and synthetic molecules with significant biological activity. Nonetheless, the co-presence of transitional metals in organic scaffold may represent an important factor in the development of effective medicinal agents. This review covers some of the relevant and recent achievements in the biological, chemical and pharmacological activity of important indole-based metal complexes in the area of drug discovery.

Antimicrobial peptides (AMPs) have emerged as a promising class of bioactive molecules with the potential to combat infections associated with medical implants and biomaterials. This review article aims to provide a comprehensive analysis of the role of antimicrobial peptides in medical implants and biomaterials, along with their diverse clinical applications. The incorporation of AMPs into various medical implants and biomaterials has shown immense potential in mitigating biofilm formation and preventing implant-related infections. We review the latest advancements in biomedical sciences and discuss the AMPs that were immobilized successfully to enhance their efficacy and stability within the implant environment. We also highlight successful examples of AMP coatings for the treatment of surgical site infections (SSIs), Contact lenses, Dental Applications, AMP-Incorporated Bone Grafts, Urinary tract infections (UTIs), Medical Implants, etc. Additionally, we discuss the potential challenges and prospects of AMPs in medical implants, such as effectiveness, instability and implant-related complications. We also discuss strategies that can be employed to overcome the limitations of AMP-coated biomaterials for prolonged longevity in clinical settings.

Polymer hydrogels are 3D networks consisting of hydrophilic crosslinked macromolecular chains, able to swell and retain water. Since their invention in the 1960s, they have become an outstanding pillar in the design, development, and application of engineered polymer systems suitable for biomedical and pharmaceutical applications (such as drug or cell delivery, regeneration of hard and soft tissues, wound healing, and bleeding prevention, among others). Despite several well-established synthetic routes of polymer hydrogels based on batch polymerization techniques, about fifteen years ago academia started to look for alternative methods involving simpler reaction paths, shorter reaction times, and lower energy consumption. In this context, frontal polymerization (FP) undoubtedly has become an alternative and efficient reaction model that allows for converting monomers into polymers via a localized and propagating reaction, by exploiting the formation and propagation of a “hot” polymerization front, able to self-sustain and propagate throughout the monomeric mixture. Therefore, the present work aims to summarize the main research outcomes, achieved during the last years, concerning the design, preparation, and application of FP-derived polymeric hydrogels, demonstrating the feasibility of this technique for the obtainment of functional 3D networks, and providing the reader with some perspectives for the forthcoming years.

In this paper we exploit concepts from Information Theory to improve the classical Chvatal’s greedy algorithm for the Set Covering Problem. In particular, we develop a new greedy procedure, called Surprisal-Based Greedy Heuristic (SBH), incorporating the computation of a “surprisal” measure when selecting the solution columns. Computational experiments, performed on instances from the OR-Library, show that SBH yields a 2.5% improvement in terms of the objective function value over the Chvatal’s algorithm while retaining similar execution times, making it suitable for real-time applications. The new heuristic was also compared with Kordalewski’s greedy algorithm, obtaining similar solutions with much lower times on large instances, and Grossmann and Wool’s algorithm for unicost instances, where SBH obtained better solutions.

This review paper provides a comprehensive overview of machine vision pose measurement algorithms. The paper focuses on the state-of-the-art algorithms and their applications. The paper is structured as follows: The introduction in Section 1 provides a brief overview of the field of machine vision pose measurement. Section 2 describes the commonly used algorithms for machine vision pose measurement. Section 3 discusses the factors that affect the accuracy and reliability of machine vision pose measurement algorithms. Section 4 presents the applications of machine vision pose measurement in various fields. The paper provides specific examples of how machine vision pose measurement is used in each of these fields. Finally, Section 5 summarizes the paper and provides future research directions. The paper highlights the need for more robust and accurate algorithms that can handle varying lighting conditions and occlusion. It also suggests that the integration of machine learning techniques may improve the performance of machine vision pose measurement algorithms. Overall, this review paper provides a comprehensive overview of machine vision pose measurement algorithms, their applications, and the factors that affect their accuracy and reliability. It provides a valuable resource for researchers and practitioners working in the field of computer vision.

ChatGPT, a groundbreaking natural language processing technology released just three months ago, has attracted significant attention due to its remarkable capabilities. This AI milestone has urged researchers, industry, decision-makers, and governments to examine this technology, including its implications, threats, and benefits. Despite the short period since its release, several researchers have examined ChatGPT from different perspectives. This paper presents a comprehensive review of ChatGPT, highlighting its technical novelties compared to previous models and analyzing existing research from various perspectives. We followed a rigorous methodology to conduct a critical review of existing research on ChatGPT and developed a taxonomy for the different areas of study. Additionally, we identify future challenges and research trends associated with ChatGPT. Our paper is the first critical review of ChatGPT literature, providing valuable insights for practitioners and policymakers. This paper serves as a reference for researchers seeking to advance research on ChatGPT, including its applications and development.

Blockchain innovation stands out enough to be noticed and adopted in various countries and organizations around the world. Many businesses, including finance, medical services, inventory networks, security, libraries, and the internet of things, are currently under attack. For the benefit of the blockchain, many businesses incorporate blockchain technology into their frameworks. Despite its solidarity, blockchain faces a few challenges in security, protection, adaptability, and other areas. This paper examines the forward leap in blockchain innovation, as well as its applications and challenges. While many blockchain papers focus on digital currencies, IoT, and security, this paper focuses on the overall best in a class of blockchain innovation, its new twists and turns, and choices, particularly in areas other than cryptographic forms of money. The investigators' goal is to provide a thorough audit of the cryptography underlying blockchain to better understand the innovation. The examiners also conduct general research on people and venture blockchains, as well as future exploration opportunities and their implications for blockchain innovation.

Background: Process mining (PM) exploits event logs to obtain meaningful information about the processes that produced them. As the number of applications developed on cloud infrastructures is increasing, it becomes important to study and discover their underlying processes. However, many current PM technologies face challenges in dealing with complex and large event logs from cloud applications, especially when they have little structure (e.g., clickstreams). Methods: Using Design Science Research, this paper introduces a new method, called Cloud Pattern API – Process Mining (CPA-PM), that enables discovering and analyzing cloud-based application processes using PM in a way that addresses many of these challenges. CPA-PM exploits a new application programming interface (API), with an R implementation, for creating repeatable scripts that preprocess event logs collected from such applications. Results: Applying CPA-PM to a case with real and evolving event logs related to the trial process of a Software-as-a-Service cloud application led to useful analyses and insights, with reusable scripts. Conclusion: CPA-PM helps producing executable scripts for filtering event logs from clickstream and cloud-based applications, where the scripts can be used in pipelines while minimizing the need for error-prone and time-consuming manual filtering.

In this paper, a prototype of an automatic lab on PCB for agarose preparation and electrophoresis is developed. The dimensions of the device are 38×34 mm2 and includes a conductivity sensor for detecting the TAE buffer (Tris-Acetate-EDTA buffer), a microheater for mixing, a NTC thermistor for controlling the temperature, a LDR sensor for measuring the transparency of the mixture, and two electrodes for performing the electrophoresis. The agarose preparation functions are governed by a microcontroller. The device requires a PMMA structure to define the wells of the agarose gel, and to release the electrodes from the agarose. The maximum voltage and current that the system requires are 40 V to perform the electrophoresis, and 1 A for activating the microheater. The chosen temperature for mixing is 80ºC, with a mixing time of 10 min. In addition, the curing time is about 30 min. This device is intended to be integrated as a part of a larger lab on PCB system for DNA amplification and detection. However, it can be used to migrate DNA amplified in conventional thermocyclers. Moreover, the device can be modified for preparing larger agarose gels and performing electrophoresis in an automatic manner.

An image consisting of (m, n) pixels can be seen as a matrix of m × n size. Based on the formula of angles between two subspaces, a pair of angles can be defined between two matrices, by utilizing column spaces and row spaces of the two matrices. The singular values of each matrix can be used to calculate distances. Thus, a distance representation is obtained, and a pair of angles between 2 matrices of the same size.

Many physiotherapy treatments begin with a diagnosis process. The patient describes symptoms, upon which the physiotherapist decides which tests to perform until a final diagnosis is reached. The relationships between the anatomical components are too complex to keep in mind and the possible actions are abundant. A trainee physiotherapist with little experience naively applies multiple tests to reach the root cause of the symptoms, which is a highly inefficient process. This work proposes to assist students in this challenge by presenting three main contributions: (1) A compilation of the neuromuscular system as components of a system in a Model-Based Diagnosis problem; (2) The PhysIt is an AI-based tool that enables an interactive visualization and diagnosis to assist trainee physiotherapists; and (3) An empirical evaluation that comprehends performance analysis and a user study. The performance analysis is based on evaluation of simulated cases and common scenarios taken from anatomy exams. The user study evaluates the efficacy of the system to assist students in the beginning of the clinical studies. The results show that our system significantly decreases the number of candidate diagnoses, without discarding the correct diagnosis, and that students in their clinical studies find PhysIt helpful in the diagnosis process.

ABSTRACT Surfactants are a broad category of tensio-active biomolecules with multifunctional properties applications in diverse industrial sectors and processes. Surfactants are produced synthetically and biologically. The biologically derived surfactants (biosurfactants) are produced from microorganisms with Pseudomonas aeruginosa, Bacillus subtilis Candida albicans and Acinetobacter calcoaceticus as dominant species. Rhamnolipids, sophorolipids, mannosylerithritol lipids, surfactin, and emulsan are well known in terms of their biotechnological applications. Biosurfactants can compete with the synthetic surfactants in terms of performance with established advantages over the synthetic ones including eco-friendliness, biodegradability, low toxicity, and stability over a wide variability of environmental factors. However, at present, the synthetic surfactants are a preferred option in different industrial applications, because of their availability in commercial quantities, unlike the biosurfactants. Usage of synthetic surfactants introduce new species of recalcitrant pollutants to the environment and lead to undesired results where a wrong selection of surfactants is made. Substituting synthetic surfactants with biosurfactants resolves these drawbacks, thus, interest has been intensified in biosurfactant applications in a wide range of industries hitherto considered as experimental fields. This review, therefore, intends to offer an overview of diverse applications where biosurfactants have found useful, with emphases in petroleum biotechnology, environmental remediation and in the agriculture sector. Application of biosurfactant in these settings would lead to industrial growth and environmental sustainability.

The new IoT/IoE (internet of things/everythings) paradigm and architecture permits to rethink about the way the Smart City infrastructures are designed and managed, on the other hand a number of problems have to be solved. In terms of mobility the cities that embrace the sensoring era can take advantage of this disruptive technology to improve the quality of life of their citizen, also thanks the rationalization in the use of their resources. In Sii-Mobility, a national smart city project on mobility and transportation, a flexible platform has been designed and here, in this paper, is presented. It permits to setup heterogeneous and complex scenarios that integrate sensors/actuators as IoT/IoE in an overall scenario of Big Data, Machine Learning and Data Analytics. A detailed and complex case-study has been presented to validate the solution in the context of a system that dynamically reverse the traveling direction of a road segment, with all the safety conditions in place. This case study composes several building blocks of the IoT platform, which demonstrate that a flexible and dynamic set-up is possible, supporting off-grid, security, safety, cloud and mixed solutions.

Background/Objectives: This study aimed to evaluate compliance and accuracy in comparison with traditional PD diaries for tracking motor symptoms using a new smartphone application (My Parkinson’s) and paper diary strategies. Parkinson's disease (PD) is a neurodegenerative disorder with progressive motor symptoms. Treatment becomes more challenging as PD progresses, motor complications in the form of wearing-off phenomenon and levodopa-induced dyskinesia develop. Traditional paper diaries and clinical scales used to evaluate patients may be inadequate in assessing whether the patient is “on” or “off,” resulting in less-than-ideal treatment changes. Methods: A randomized crossover design was utilized to examine 22 advanced PD patients who underwent symptomatic assessment with both diaries during two separate 24-hour periods seven days apart. The compliance and accuracy of data were assessed by comparing diary entries with the clinical examination notes and WhatsApp queries. Latterly, patients' diary preferences were also evaluated.; Results: The digital diary had significantly better compliance and accuracy than the paper diaries, with substantial/almost perfect levels of agreement (κ = 0·615 to 0·818) between logged symptoms and clinical examination notes. 65% of patients preferred the digital diary for follow-ups, and there was no significant difference in ease of use compared to paper diaries. Conclusions: Digital diaries are helpful in the clinical management of PD patients as they minimize recall bias and reduce data errors in appropriately selected patients. Our study suggests a broader adoption of digital health technologies in PD management. Still, additional research is necessary to improve the tools and assess long-term patient outcomes.

Mobile applications, websites and social media networks are nowadays widely used communication tools. With the emergence of communication-related technologies in our lives and, consequently, the rise of social networks and mobile applications, health-related applications, generically encompassed under the term digital health, have become popular among the population. Smartphones and artificial intelligence have become very useful tools for health-related interventions. Because they are very accessible and cost-effective. They are also able to serve a larger number of communities than traditional interventions. Nutrition is not a field that has remained on the sidelines, and numerous mobile applications and technological tools have emerged that are intended to help and support diets or in the process of recovering from disease. However, many of these applications have limitations that are important to consider. For this reason, the aim of this review was to analyze the most widely used tools currently in use, discuss their advantages and disadvantages, and propose hypotheses for improvement.

Despite the recognized potential of nanoparticles, only a few formulations have progressed to clinical trials, and an even smaller number have been approved by the regulatory authorities and marketed. Virus-like particles (VLPs) have emerged as promising alternatives to most explored nanoparticles because of the absence of viral genetic material, their incapacity to replicate, mimicry of viral structure, and tropism conservation. Furthermore, VLPs can be surface functionalized with small molecules to improve circulation half-life and target specificity. Through the functionalization and coating of VLPs, it is possible to optimize the response properties to a given stimulus, such as heat, pH, an alternating magnetic field, or even enzymes. Surface functionalization can also modulate other properties, such as biocompatibility, stability, and specificity, deeming VLPs as potential vaccine candidates or delivery systems. In this review, we address the different types of functionalization of VLPs, their importance, and their consequent biomedical applications.

Respiratory allergic diseases, affect over 500 million people globally and pose a substantial burden in terms of morbidity, mortality, and healthcare costs. Restrictive factors such as geographical disparities, infectious pandemics, limitations in resource, and shortage of allergy specialists in underserved areas, impede effective management. Moreover, the well-established suboptimal adherence in patients with respiratory allergies, contribute further to the accelerated loss of disease control. Telemedicine encompasses real-time visits, store-and-forward options triage, and computer-based technologies for establishing and efficient doctor-patient communication. Recent advances in digital technology, including designated applications, informative materials, digital examination devices, wearables, digital inhalers, and integrated platforms, facilitate personalized and evidence-based care delivery. The integration of telemonitoring in respiratory allergy care has shown beneficial effects in disease control, adherence, and quality of life. While the COVID-19 pandemic accelerated the adoption of telemedicine, certain concerns regarding technical requirements, platform quality, safety, reimbursement, and regulatory considerations remain unresolved. Challenges in telemonitoring implementation further include ensuring effective doctor-patient collaboration, addressing privacy concerns, and establishing cost-effective models. The integration of artificial intelligence (AI) in telemonitoring applications holds promise for data analysis, pattern recognition, and personalized treatment plans. Striking the balance between AI-enabled insights and human expertise is crucial for optimizing the benefits of telemonitoring. This overview reports the up- to- date telemedicine approaches in managing patients with respiratory allergies. While telemonitoring exhibits potential in enhancing patient care and healthcare delivery, critical considerations have to be addressed in order to ensure the successful integration of telemonitoring into the healthcare landscape.

This study investigated the potential of electrospun collagen/Hyaluronic Acid nanofibers in biomedicine and skincare. Needleless electrospinning technology employed in the production process resulted in nanofibers with consistent fiber diameters and improved mechanical properties. The composite nanofibers demonstrated impressive tensile strength and elongation capabilities, indicating a favorable interaction between collagen and HA. Cellular assays revealed that the collagen/HA scaffolds facilitated superior cell adhesion and proliferation compared to the control scaffolds, indicating their biocompatibility and potential for tissue engineering. This biomaterial offers an environment-friendly alternative to conventional skincare products by eliminating the need for preservatives and reducing waste. Although the initial costs are high, the long-term benefits of material durability and active ingredient efficacy provide a compelling case for industrial adoption. Our findings suggest that collagen/HA nanofibers have the potential to be next-generation materials for regenerative medicine and skincare, and further research is needed to optimize their production and assess the economic feasibility of skincare and tissue engineering applications.

Natural polysaccharides, which are described in this study, are some of the most extensively used biopolymers in food, pharmaceutical and medical applications, because they are renewable and have a high level of biocompatibility and biodegradability. The fundamental understanding required to properly exploit polysaccharides potential in the biocomposite, nanoconjugate, and pharmaceutical industries is depended on detailed research of these molecules. Polysaccharides are preferred over other polymers because of their biocompatibility, bioactivity, homogeneity, and bioadhesive. Natural polysaccharides have also been discovered to have excellent rheological and biomucoadhesive properties, which may be used to design and create a variety of useful and cost-effective drug delivery systems. Polysaccharide-based composites derived from natural sources have been widely exploited due to their multifunctional properties, particularly in drug delivery systems and biomedical applications. These materials have achieved global attention and are in great demand because to their biochemical properties, which mimic both human and animal cells. Although synthetic polymers account for a substantial amount of organic chemistry, natural polymers play a vital role in a range of industries, including biomedical, pharmaceutical, and construction. As a consequence, the current study will provide information on natural polymers, their biological uses, food and pharmaceutical applications.Keywords: Polymers Biopolymers, Excipient, Natural source, Food and Pharmaceutical applications.

Micro/Nano Robot is an intelligent and efficient microrobot that can perform specific tasks under the influence of external stimuli. Depending on the application scenarios, the microrobot can adaptively transform into appropriate functional forms under different external stimuli, thus perfectly matching the needs. To date, microbots have been widely used in targeted therapy, drug delivery, tissue engineering, environmental remediation, and other fields. Although the applications of microrobots are promising, there are only a few reviews that can focus on the preparation methods and driving mechanisms. Therefore, it is necessary to outline the current status of the development of these microrobots in order to provide some new insights for the further development of the field. Therefore, this paper reviews the research progress of microrobots in terms of preparation methods, stimulus response mechanisms and applications, and highlights the applicability of different preparation methods and stimulus types. Finally, the current challenges faced by microrobots are highlighted and possible solutions are proposed to facilitate the practical application of microrobots.

Smart livestock farming leverages technology to boost production and meet food demand sustainably. This study delves into smart technologies in animal production, covering opportunities, challenges, and solutions. Smart agriculture employs modern technology to enhance efficiency, sustainability, and animal welfare in livestock farming. It includes remote monitoring, GPS-based animal care, robotic milking, smart health collars, predictive disease control, and other innovations to achieve these goals. While smart animal production holds great promise, it does face challenges related to cost, data management, and connectivity. To address these challenges, potential solutions include remote sensing, technology integration, and farmer education. Smart agriculture offers opportunities for increased efficiency, improved animal welfare, and enhanced environmental conservation. A well-planned approach is crucial to maximize the benefits of smart livestock production while ensuring its long-term sustainability. This study confirms the growing adoption of smart agriculture in livestock production, with the potential to support sustainable development goals and deliver benefits such as increased productivity and resource efficiency. To fully realize these benefits and ensure the sustainability of livestock farming, it is essential to address cost and education challenges. Therefore, this study recommends promoting a positive outlook among livestock stakeholders and embracing smart agriculture to enhance farm performance.

This study presents a methodology to fabricate Ti6Al4V cylindrical compacts with a highly porous core and dense shell with the purpose to mimic the bone microstructure. Compacts with different core diameters were obtained by conventionally press and sintering. Large pores were created with the aid of pore formers. Sintering kinetics was determined by dilatometry, whereas characterization was performed by X-ray computed tomography. Also, permeability was evaluated on the 3D microstructure and the mechanical strength was evaluated by compression tests. Results indicated that sintering is constraint by the different densification rates of the porous and dense layers. Nonetheless, defectless compacts were obtained due to neck bonding between Ti6Al4V particles. Large pores were located in the designed core with similar pore size distribution. Permeability increased following a power law as a function of the pore volume fraction. Stiffness of bilayer components was driven by the porous core, meanwhile, the strength resulted from the combination of both layers. Bilayer materials obtained showed a permeability and mechanical properties, as well as admissible strain (σy /E) similar to those of human bones.

Aquaculture assumes a pivotal role in meeting the escalating global food demand, and shrimp farming, in particular, holds indispensable significance for the global economy and food security, providing a rich source of nutrients for human consumption. Nonetheless, the industry faces formidable challenges, primarily attributed to disease outbreaks, and the diminishing efficacy of conventional disease management approaches, such as antibiotic usage. Consequently, there is an urgent imperative to explore alternative strategies to ensure the sustainability of the industry. In this context, the burgeoning field of epigenetics emerges as a promising avenue for combating infectious diseases in shrimp aquaculture. Epigenetic modulations entail chemical alterations in DNA and proteins, orchestrating gene expression patterns without modifying the underlying DNA sequence by utilizing DNA methylation, histone modifications, and non-coding RNA molecules. Exploiting epigenetic mechanisms presents an opportunity to enhance immune gene expression and bolster disease resistance in shrimp, thus revolutionizing disease management strategies and optimizing shrimp health and productivity. Additionally, the concept of epigenetic inheritability in marine animals holds immense potential for the future of the shrimp farming industry. To this end, this comprehensive review thoroughly explores the dynamics of epigenetic modulations in shrimp aquaculture, with a particular emphasis on its pivotal role in disease management. It accentuates the significance of harnessing advantageous epigenetic changes to ensure the long-term viability of shrimp farming while deliberating on the potential consequences of these interventions. Overall, this appraisal highlights the promising trajectory of epigenetic applications, propelling the field toward strengthening sustainability in shrimp aquaculture.

A common requirement of embedded software in charge of safety tasks is to guarantee the identification of those Random Hardware Failures (RHFs) that can affect digital components. RHFs are unavoidable. For this reason, functional safety standards, like the ISO 26262 devoted to automotive applications, require embedded software designs able to detect and eventually mitigate them. For this purpose, various software-based error detection techniques have been proposed over the years, focusing mainly on detecting Control Flow Errors. Many Control Flow Checking (CFC) algorithms have been proposed to accomplish this task. However, applying these approaches can be difficult because their respective literature gives little guidance on the their practical implementation in high-level programming languages, and they have to be implemented in low-level code, e.g., assembly. Moreover, the current trend in the automotive industry is to adopt the so-called Model-Based Software Design approach, where an executable algorithm model is automatically translated into C or C++ source code. This paper presents two novelties: firstly, the compliance of the experimental data on the capabilities of Control Flow Checking (CFC) algorithms with the ISO 26262 automotive functional safety standard; Secondly, by the implementation of the CFC algorithm in the application behavioral model is automatically translated. There is no need to modify the code generator. The assessment was performed using a novel fault injection environment targeting a RISC-V (RV32I) microcontroller.

The COVID-19 is an exponentially growing disease that has intentioned nations to use technologies to detect the coronavirus infection. Several nations are working greatly to fight against COVID-19. Many nations have been using a range of devices to combat the pandemic, seeking information about growth, monitoring as well as the leaking the confidential information of the residents. This research aims to assist infected people online using the Internet of Things (IoT) and Blockchain technologies through smart devices. IoT-based healthcare devices gather useful information, provide additional insight through symptoms and behaviors, allow remote monitoring, and simply give people better self - determination and healthcare. Blockchain allows the secure transfer of patient health information, regulates the medical distribution network. A four-layer architecture is proposed using IoT and Blockchain to detect and prevent individuals to be COVID 19. This research provides a framework for patients with COVID-19 infectious disease and recognizes health issues and diagnoses online. Smart devices such as smartphones can install any mobile apps such as Aarogya Setu, Tawakkalna, and so on. These applications can track COVID-19 patients properly. The installation of mobile apps on smart devices focuses to reduce the time and cost and increase the performance of the infectious patient's condition. A four-layer architecture is proposed using IoT and Blockchain technologies. Many research works focus on investigating, analyzing, and highlighting the affected individuals through guiding the COVID-19 infection. Eventually, various mobile apps are recognized and addressed in this paper.

Device-to-device (D2D) communication is a promising paradigm for the fifth generation 2 (5G) and beyond 5G (B5G) networks. Although D2D communication provides several benefits, 3 including limited interference, energy efficiency, reduced delay, and network overhead, it faces a lot 4 of technical challenges such as network architecture, and neighbor discovery, etc. The complexity 5 of configuring D2D links and managing their interference, especially when using millimeter-wave 6 (mmWave), inspire researchers to leverage different machine-learning (ML) techniques to address 7 these problems towards boosting the performance of D2D networks. In this paper, a comprehensive 8 survey about recent research activities on D2D networks will be explored with putting more 9 emphasis on utilizing mmWave and ML methods. After exploring existing D2D research directions 10 accompanied with their existing conventional solutions, we will show how different ML techniques 11 can be applied to enhance the D2D networks performance over using conventional ways. Then, still 12 open research directions in ML applications on D2D networks will be investigated including their 13 essential needs. A case study of applying multi-armed bandit (MAB) as an efficient online ML tool 14 to enhance the performance of neighbor discovery and selection (NDS) in mmWave D2D networks 15 will be presented. This case study will put emphasis on the high potency of using ML solutions 16 over using the conventional non-ML based methods for highly improving the average throughput 17 performance of mmWave NDS.

The explosion of data has transformed the world since much more information is available for collection and analysis than ever before. To extract valuable information from the data in different dimensions, various deep learning models have been developed in the past years. Although these models have demonstrated their strong capability on improving products and services in various applications, training them is still a time-consuming and resource-intensive process. Presently, cloud, one of the most powerful computing infrastructures, has been used for the training. However, how to manage cloud computing resources and to perform the training efficiently is still challenging current techniques. For example, general resource scheduling approaches, such as spread priority and balanced resource schedulers, actually do not work well with deep learning workloads. Besides, the resource allocation problem on a cluster can be divide into two subproblems: (1) local resource optimization: improve resource configuration for a single machine; (2) global resource optimization: improve the cluster-wide resource allocation. In this thesis, we propose two novel container schedulers, FlowCon and SpeCon, that are designed to address these two subproblems respectively and specifically to optimize performance of short-lived deep learning applications in the cloud. FlowCon focuses on resource configuration of single-node in a cluster, as show that it efficiently improves deep learning tasks completion time and resource utilization, and reduces the completion time of a specific job by up to 42.06\% without sacrificing the overall system time. SpeCon targets on cluster-wide resource configuration that speculatively migrate slow-growing models to release resources for fast-growing ones. Based on our experiments, SpeCon improves makespan for up to 24.7\%, compared to current approaches.

Nonionizing millimeter-waves (MMW) are reported to interact with cells in a variety of ways. Possible mechanisms of the inhibited cell division effect were investigated using 85-105 GHz MMW irradiation within the ICNIRP (International Commission on Non-Ionizing Radiation Protection) non-thermal 20 mW/cm² safety standards. ~1.0 mW/cm² exposure over 5-6 hours treatment on 50 cells/μl samples of Saccharomyces cerevisiae model organism, resulted in 62% growth rate reduction compared to control (sham). The effect was specific for 85-105 GHz range and energy dose and cell density dependent. Irradiation of wild type and Δrad52 (DNA damage repair gene) deletion cells presented no differences of colony growth profiles indicating non-thermal MMW treatment does not cause genetic alterations. Dose versus response relations studied using a standard horn antenna (~1.0 mW/cm²) and compared to that of a compact waveguide (17.17 mW/cm²) for increased power delivery resulted in complete termination of cell division via non-thermal processes supported by temperature rise measurements. Combinations of MMW mediated Structure Resonant Energy Transfer (SRET), membrane modulations eliciting signaling effects, and energetic resonance with biomolecules were indicated to be responsible for the observations reported. Our results provide novel mechanistic insights enabling innovative applications of nonionizing radiation procedures for eliciting targeted biomedical outcomes.

Nowadays genetic algorithm (GA) is greatly used in engineering pedagogy as an adaptive technique to learn and solve complex problems and issues. It is a meta-heuristic approach that is used to solve hybrid computation challenges. GA utilizes selection, crossover, and mutation operators to effectively manage the searching system strategy. This algorithm is derived from natural selection and genetics concepts. GA is an intelligent use of random search supported with historical data to contribute the search in an area of the improved outcome within a coverage framework. Such algorithms are widely used for maintaining high-quality reactions to optimize issues and problems investigation. These techniques are recognized to be somewhat of a statistical investigation process to search for a suitable solution or prevent an accurate strategy for challenges in optimization or searches. These techniques have been produced from natural selection or genetics principles. For random testing, historical information is provided with intelligent enslavement to continue moving the search out from the area of improved features for processing of the outcomes. It is a category of heuristics of evolutionary history using behavioral science-influenced methods like an annuity, gene, preference, or combination (sometimes refers to as hybridization). This method seemed to be a valuable tool to find solutions for problems optimization. In this paper, the author has explored the GAs, its role in engineering pedagogies, and the emerging areas where it is using, and its implementation.

Chiroptical responses have been an essential tool over the last decades for chemical structural elucidation due to their exceptional sensitivity to geometry and intermolecular interactions. In recent times, there has been an increasing interest for the search of more efficient sensing by the rational design of tailored chiroptical systems. In this Review article, advances on chiroptical systems towards their implementation in sensing applications are summarized. Strategies to generate chiroptical responses are illustrated. Theoretical approaches to assist in the design of these systems are discussed. Development of efficient chiroptical reporters in different states of matter, essential for the implementation in sensing devises, is reviewed. In the last part, remarkable examples of chiroptical sensing applications are highlighted.

With the increase of the Artificial Neural Network (ANN), machine learning has taken a forceful twist in recent times. One of the most spectacular kinds of ANN design is the Convolutional Neural Network (CNN). The Convolutional Neural Network (CNN) is a technology that mixes artificial neural networks and up to date deep learning strategies. In deep learning, Convolutional Neural Network is at the center of spectacular advances. This artificial neural network has been applied to several image recognition tasks for decades and attracted the eye of the researchers of the many countries in recent years as the CNN has shown promising performances in several computer vision and machine learning tasks. This paper describes the underlying architecture and various applications of Convolutional Neural Network.

Today with enhancement in technology, sciences, there is also an increase in global heating rate. There is an urgent need of any alternate efficient source to reduce the wastage of energy and to utilize it efficiently. The advanced preparation of Expanded graphite ,lauric acid, stearic acid as shape stabilized phase change material deals with different energy harvesting applications. The main reason behind the need for synthesis of this matrix is to prepare a material that can be used in low temperature energy storage applications. Mixture of lauric acid , stearic acid impregnated in expanded graphite through vacuum impregnation followed by Vacuum Drying and Microwave acid treatment serves as novel shape stabilized phase change material of its type. The microwave acid treatment was done in order to increase the removal of moisture from the sample thus initiating proper bonding of its constituents. The mixture was produced in 1:1:1 ratio where all expanded graphite, lauric acid , stearic acid has one proportions of each other. The product obtained after microwave acid treatment was subjected to SEM, DSC analysis

Metal nanoparticles are extensively studied due to their unique chemical and physical properties, which differ from the properties of their respective bulk materials. Likewise, the properties of heterogeneous bimetallic structures are far more attractive than those of single-component nanoparticles. The incorporation of a second metal into the nanoparticle structure alters the surface plasmon resonance and magnetic properties of the bimetallic composite. This review focuses on the enhanced optical and magnetic properties offered by bimetallic nanoparticles and their corresponding impact on biological applications. We summarize the predominant structures of bimetallic nanoparticles, outline their synthesis methods, and highlight their use in biological applications, both diagnostic and therapeutic, which are dictated by their various optical and magnetic properties.

Possibilities of utilization of two frequencies synthetic aperture radar (SAR) operating simultaneously in P-band and S or L-band are considered. Advantages of such system are shown. They are caused by improvement of data interpretation and by decrease of ionosphere influence. Tasks of surface and subsurface sensing by the complex SAR were under investigation for vegetation, soil covers are given. As an example, the investigation results of coniferous forest areas backscattering by the spaceborne P-band SAR together with L-band SAR in the September-February period are presented. Studies have shown that the joint use of the results in L and P bands offer unique opportunities to explore the effects of changes in the backscattering properties of forest surfaces.

We propose a quantum version of the well known minimum distance classification model called "Nearest Mean Classifier" (NMC). In this regard, we presented our first results in two previous works. In [34] a quantum counterpart of the NMC for two-dimensional problems was introduced, named "Quantum Nearest Mean Classifier" (QNMC), together with a possible generalization to arbitrary dimensions. In [33] we studied the n-dimensional problem into detail and we showed a new encoding for arbitrary n-feature vectors into density operators. In the present paper, another promising encoding of n-dimensional patterns into density operators is considered, suggested by recent debates on quantum machine learning. Further, we observe a significant property concerning the non-invariance by feature rescaling of our quantum classifier. This fact, which represents a meaningful difference between the NMC and the respective quantum version, allows to introduce a free parameter whose variation provides, in some cases, better classification results for the QNMC. The experimental section is devoted to: i) compare the NMC and QNMC performance on different datasets; ii) study the effects of the non-invariance under uniform rescaling for the QNMC.

Additive Manufacturing (AM), commonly known as 3D printing, has revolutionized various industries, including the medical field. This technology enables the creation of complex structures layer by layer, offering unprecedented customization and precision. In medical applications, AM has proven transformative in producing patient-specific implants, prosthetics, and anatomical models, facilitating better surgical planning and outcomes. Additionally, it has shown promise in bioprinting, where cells and biomaterials are used to create tissue-like structures for regenerative medicine. The ability to rapidly prototype and manufacture medical devices reduces time and costs, and enhances personalized patient care. This abstract explores the diverse applications, benefits, and challenges of AM in the medical sector, highlighting its potential to revolutionize healthcare and improve patient outcomes.

Modern anesthesia continues to be impacted in new and unforeseen ways by digital technology. Combining portability and versatility, mobile applications or “apps” provide a multitude of ways to enhance anesthetic and peri-operative care. Research suggests that uptake of apps into anesthetic practice is becoming increasingly routine, especially amongst younger anesthetists brought up in the digital age. Despite this enthusiasm, there remains no consensus in how apps are safely and efficiently integrated into anesthetic practice. This review summarizes the most popular forms of app usage in anesthesia currently and explores the challenges and opportunities inherent in implementing app use in anesthesia, with an emphasis on a practical approach for the modern anesthetist.

The suspension cross-linking technique has been utilized to create the magnetic chitosan nanoparticles, which were then applied to the magnetic carrier method at 80°C. First the FeCl2 and FeCl3 solution co-precipitated and synthesized Fe3O4 for utilization in the formation of magnetic chitosan nanoparticles, it has been characterized by utilizing DLS (Dynamic light scattering), XRD (X-ray diffraction spectrometer), FTIR (Fourier transform infrared spectroscopy), which identified structure, size, FE-SEM (Field Emission Scanning Electron Microscope) and HR-TEM (High-resolution transmission electron microscopy) which identified structure and particle size whereas magnetic behavior of chitosan nanoparticles was determined by VSM (Vibrating sample magnetometer). According to the results collected, the magnetic chitosan nanoparticles have been spherical in form and size ranging from around 250 to 400 nm.

This paper presents the development of a housing information system application using the Agile development method. The rapid development of information technology has necessitated the use of systems in various fields for efficient administration and data processing. However, traditional soft- ware development approaches are time-consuming and may not meet user requirements. The Agile methodology, with its flexible and iterative approach, addresses these challenges by focusing on delivering a functional system quickly. In this study, credit scoring and background related to housing information systems are examined, along with an overview of related work in Agile software development methodologies. The methods used in developing the housing information system application are explained, including the Agile development process and the stages involved. The results show that the Agile development method effectively produces user-friendly and functional housing information system applications. The system provides a simple and intuitive interface that facilitates easy navigation and access to information. Overall, the Agile development method is a valuable approach for software development to meet evolving user needs.

The current concerns of both society and materials industries about the environmental impact of thermoset composites, as well as new legislation, have led the scientific sector to search for more sustainable alternatives to reduce the environmental impact of thermoset composites. Until now, to a large extent, sustainable reinforcements have been used to manufacture more sustainable composites and thus contribute to the reduction of pollutants. However, in recent years, new alternatives have been developed such as thermosetting resins with bio-based content and/or systems such as recyclable amines and vitrimers that enable recycling/reuse. Throughout this review, some new bio-based thermoset systems as well as new recyclable systems and sustainable reinforcements are described and a brief overview of the biocomposites market and its impact is shown.

Gold nanoparticles (AuNPs) are promising candidates in various biomedical applications such as sensors, imaging, and cancer therapy. Understanding the influence of AuNPs on lipid membranes is important to assure their safety in the biological environment and to improve their scope in nanomedicine. In this regard, the present study aims to analyze the effects of different concentrations (0.5, 1, and 2 w %) of dodecanethiol functionalized hydrophobic AuNPs on the structure and fluidity of zwitterionic 1-stearoyl-2-oleoyl-sn-glycerol-3-phosphocholine (SOPC) lipid bilayer membranes using Fourier transform infrared (FTIR) spectroscopy and fluorescent spectroscopy. The size of AuNPs was found to be 2.2 ± 1.1 nm using transmission electron microscopy. FTIR results have shown that the AuNPs induced a slight shift in methylene stretching bands, while the band positions of carbonyl and phosphate group stretching were unaffected. Temperature-dependent fluorescent anisotropy measurements have shown that the incorporation of AuNPs up to 2 w % did not affect the lipid order in membranes. Overall, these results indicate that the hydrophobic AuNPs in the studied concentration did not cause any significant alterations in the structure and membrane fluidity, which suggests the suitability of these particles to form liposome-AuNP hybrids for diverse biomedical applications including drug delivery and therapy.

Background:Boom in nanotechnology in current era has sketched unforeseen transformations in number of fields, such as medicine, health care, food, space, agriculture, etc. The synthesis of nanoparticles with different chemical compositions, sizes, shapes and controlled disparities is an important area of research in this field. Over the last decade, the biosynthesis of metal nanoparticles has received considerable attention due to their unusual and fascinating properties, with various applications, over their bulk counterparts.Hypothesis: The nanoparticle can have huge application in the field of food, pharmaceutical, and cosmetic industries and thus become a major area of research. Green synthesis of zinc oxide nanoparticles (ZnO NPs) using plant extracts offers an eco-friendly and promising substitute to the conventional methods of chemical synthesis. Conclusion: In the arena of nanoparticle phytosynthesis, novel materials have been produced that are eco-friendly, cost-effective and stable. In the current situation, nanotechnology inspires progress in all spheres of life, and therefore the phytosynthetic path of nanoparticle synthesis has emerged as a safe and best alternative to conventional methods. This review summarizes the recent work in the field of zinc nanoparticle phytosynthesis and critically discusses the mechanism proposed behind it.

Magnesium is a promising material. It has a remarkable mix of mechanical and biomedical properties that made it suitable for a vast range of applications. With alloying, many of these inherent properties can be further improved. Today, it is primarily used in the automotive, aerospace, and medical industry. However, magnesium has its own set of drawbacks which the industry and research community are actively addressing. Magnesium’s rapid corrosion is its most significant drawback, and it dramatically impeded magnesium’s growth and expansion into other applications. This article will review both the engineering and biomedical aspects and applications for magnesium and its alloys. It will also elaborate on the challenges the material faces, how they can be overcome, and its outlook.

Among other applications, radar-rainfall (RR) and QPE (Quantitative Precipitation Estimation) based on radar reflectivity, dual polarization variables, and multi-sensor information, provide important information for land surface hydrology, such as flood forecasting. Therefore, we developed a flood alert system using rainfall-runoff model forced with RR and QPE, and tipping-bucket observations to forecast river water levels (using rating-curves). In this study, we used an hourly dataset from an S-Band dual-polarimetric radar with two tropical R(Z) relations based distrometer data, a polarimetric R(Z,ZDR) algorithm from the literature and a multi-sensor approach using radar, satellite and rain gauge. Two hydrological models were used and calibrated using observed discharge time-series. Although our previous studies indicated accurate RR-based simulations, in some cases floods were not detected when using catchment-lumped rainfall derived from multi-sensor QPE. In this study, we advance further in this subject using improved R(Z,ZDR) relations and QPE for the period of 2016-2017 and flood event-based rainfall-runoff calibration. Thus, we focused on the development (and timing) of floods in the Marrecas River can be complex and strongly related to storms spatiotemporal distribution. To explore this aspect, we also perform a first analysis in using RR in rainfall-runoff model with a nested catchment discretization.

The integration of nanoparticles in biomedical applications requires precise analysis of their photochemical properties. However, computational simulations of these properties are computationally intensive, hindering rapid advancement. This study explores the implementation of Graphics Processing Unit (GPU) acceleration techniques to enhance computational efficiency in analyzing nanoparticle photochemistry within bioinformatics frameworks.By leveraging GPU parallel processing capabilities, we developed optimized algorithms for photochemical property simulations, achieving significant speedup over traditional Central Processing Unit (CPU)-based methods. Our GPU-accelerated framework demonstrated substantial performance gains in computing photochemical properties, such as absorption spectra and energy transfer rates.

This study presents a comprehensive review of the development and progression of autonomous underwater vehicles (AUVs) in polar regions, aiming to synthesize past experiences and provide guidance for future advancements and applications. We extensively explore the history of notable polar AUV deployments worldwide, identifying and addressing the key technological challenges these vehicles face. These include advanced navigation techniques, strategic path planning, efficient obstacle avoidance, robust communication, stable energy supply, reliable launch and recovery, and thorough risk analysis. Furthermore, the study categorizes the typical capabilities and applications of AUVs in polar contexts, such as under-ice mapping and measurement, water sampling, ecological investigation, seafloor mapping, and surveillance networking. We also briefly highlight existing research gaps and potential future challenges in this evolving field.

Text mining is a technological trend often highlighted in the continuous exchange of information through interconnected media. Its applicability goes beyond private organizations, as the public sector also requires it to treat textual information regarding services offered. Within this scenario, public security emerges as a prominent user of text mining that seeks to ensure the construction of data and knowledgebases to support decision-making about law enforcement actions to ensure citizen welfare. The primary objectives of this article are: (i) to develop a survey to identify text mining applications, techniques, opportunities, and challenges in public security, and (ii) to outline research directions concerning these topics and provide insights so that interested researchers can develop new studies. The literature was searched within four databases: Scopus, IEEE Xplore, ACM Digital Library, and Web of Science. A filtering process was applied to extract the works most aligned with the target theme, resulting in the selection of 194 of the most relevant works for a literature review. There were identified nineteen key applications of text mining related to public security and the most recurrent techniques and technologies reported between 2014 to 2021, supporting outlining three axes for future directions: one with possible expansion of objectives for new research; another on changes and adaptations in scopes for the methodological context; and the last one on expansions and changes in application scenarios based on the literature.

Enzymes are biological molecules produced by living entities for carrying out biological processes. The application of enzymes for waste treatment has been gaining pace commercially to solve concerns related to agricultural residues, wastewater, replacement of synthetic processes with natural ones, etc. The application of enzymes for waste management has been an environmentally reliable and sustainable process. Treatment of waste with enzymes such as xylanase, proteases, hydrolases, cellulose, peroxidases, chitinases, laccases, etc. has been studied to be effective. These enzymes act upon the waste products and transform them into biodegradable forms that can be recycled, reused and converted to value-added products. They have wide applications and utility as it has been an effective approach, economically cheaper and sustainable techniques. Application of such enzymes for waste management would be beneficial for reducing the quantity of waste, diminishing the negative effects of waste and pollution on the environment, and would be beneficial in bio-converting the waste products into alternate sources of energy. The current chapter focuses on different types of enzymes, their applications for waste management, and their limitations. This chapter also emphasizes the usage of some prominent microorganisms, their secreted enzymes and their proposed mechanisms of action involved with the degradation of the waste products.

This paper aims to analyze the intellectual structure and research fronts in application information security in smart cities to identify research boundaries, trends, and new opportunities in the area. It applies bibliometric analyses to identify the main authors and their influences on the information security and smart city area. Moreover, this analysis focuses on journals indexed in Scopus databases. The results indicate that there is an opportunity for further advances in the adoption of information security policies in government institutions. Moreover, the production indicators presented herein are useful for the planning and implementation of information security policies, and the knowledge of the scientific community about smart cities. The bibliometric analysis provides support for the visualization of the leading research technical collaboration networks among authors, co-authors, countries, and research areas. The methodology offers a broader view of the application information security in smarty city areas and makes it possible to assist new research that may contribute to further advances. The smart city topic has been receiving much attention in recent years, but to the best of our knowledge, there is no research on reporting new possibilities for advances. Therefore, this article may contribute to an emerging body of literature that explores the nature of application information security and smart city research productivity to assist researchers in better understanding the current emerging of the area.

We studied Ni2FeSi, Co2FeSi and Co2MnSi -based full Heusler alloy glass-coated microwires with the same geometric aspect ratio ρ ≈ 0.50 ± 0.01 prepared using Taylor-Ulitovsky method. The fabrication of X2YZ; (X = Co & Ni, Y = Fe & Mn and Z = Si) based glass coated microwires with fixed aspect ratio is a quite challenging due to the different samples preparation conditions. The XRD analysis shows nanocrystalline microstructure for all the samples. Space group Fm3¯m (FCC) with disordered B2 and A2-types for Ni2FeSi and Co2FeSi and ordered L21-type for Co2MnSi are observed. The change in the positions of Ni, Co and Mn, Fe in X2YSi resulted in a variation in the lattice cell parameters and the average grain size of the sample. Room temperature magnetic behavior shows a dramatic change depending on the chemical composition, where Ni2FeSi-MWs shows the highest coercivity (Hc) compared to Co2FeSi-MWS and Co2MnSi-MWs. The Hc –value of Ni2FeSi-MWs is 16 times higher than that of Co2MnSi-MWs and 3 times higher than that of Co2FeSi-MWS. Meanwhile, the highest reduced remanence is reported for Co2FeSi-MWS (Mr =0.92), , being about 0.33 and 0.22 for Ni2FeSi-MWs and Co2MnSi-MWs, respectively. From the analysis of the temperature dependence of the magnetic properties (Hc and Mr) of X2YZ-MWs we deduced that Hc shows a stable tendency for Co2MnSi-MWs and Co2FeSi-MWs, meanwhile two flipped points are observed for Ni2FeSi-MWs where the behavior of Hc changes with temperature. For Mr monotonic increase by decreasing the temperature is observed for Co2FeSi-MWs and Ni2FeSi-MWs and roughly stable for Co2MnSi-MWs. The thermomagnetic curves at low magnetic field show irreversible magnetic behavior for Co2MnSi-MWs and Co2FeSi-MWs and regular ferromagnetic behavior for Ni2FeSi-MWs. The current result illustrates the ability to tailor the structure and magnetic behavior of X2YZ-MWs at a fixed internal stresses, i.e., with fixed aspect ratio. Additionally, a different behavior was revealed in X2YZ-MWs depending on the degree of ordering and elements distribution. The tunability of magnetic properties of X2YZ-MWs makes them suitable for sensing applications.

Recently, there has been remarkable progress in the development of smart textiles, especially knitted strain sensors, to achieve reliable sensor signals. Stable and reliable electro-mechanical properties of sensors are essential for using textile-based sensors in medical applications. How-ever, challenges associated with significant hysteresis and low gauge factor (GF) values remain for using strain sensors for motion capture. To evaluate these issues, a comprehensive investiga-tion of the cyclic electro-mechanical properties of weft-knitted strain sensors was conducted in the present study to develop a drift-free elastic strain sensor with a robust sensor signal for mo-tion capture for medical devices. Several variables were considered in the study, including the variation of the basic knit pattern, the incorporation of the electrically conductive yarn, and the size of the strain sensor. The effectiveness and feasibility of the developed knitted strain sensors are demonstrated through experimental evaluation, by determining the gauge factor, its non-linearity, hysteresis and drift. The developed knitted piezoresistive strain sensors have a GF of 2.4, a calculated drift of 50 %, 12,5 % hysteresis, and 0.3 % nonlinearity in parts.

An experimental platform for laser-driven ion (sub-MeV) acceleration and potential applications was recently commissioned at the HiLASE laser facility. The auxiliary beam of the Bivoj laser system operating at GW peak power (~10 J in 5-10 ns) and 1-10 Hz repetition rate enabled a sta-ble production of high-current ion beams of multiple species (Al, Ti, Fe, Si, Cu, Sn). The pro-duced laser-plasma ion sources were fully characterized against the laser intensity on target (1013-1015 W/cm2) by varying the laser energy, focal spot size, and pulse duration. This al-lowed to provide accurate scaling laws of the maximum ion energy for the different target ma-terials investigated. Such experimental scaling laws are presented for the first time in the inves-tigated laser intensity range and for ns-class laser pulses, and allow to provide a qualitative in-terpretation of the laser-plasma interaction underpinning physics, thus to tune the main features of the accelerated ion beams (energy, temperature, and current). Such a detailed study was facil-itated by the large amount of data acquired at high repetition rate (1-10 Hz) provided by the Bivoj laser system. The versatility and tuneability of such high-repetition-rate laser-plasma ion sources are of po-tential interest for multidisciplinary user applications.

Surfactants are amphiphilic molecules and one of the most versatile products of the chemical industry. They can be absorbed at the air-water interface and can align themselves so that the hydrophobic part is in the air while the hydrophilic part is in water. This alignment lowers the surface or interfacial tension. Gemini surfactants are a modern variety of surfactants with unique properties and a very wide range of potential applications. Hexamethylene-1,6-bis(N-dodecyl-N,N-dimethylammonium bromide) is one such representative compound that is a better alternative to a single analogue. It shows excellent surface, antimicrobial, and anticorrosion properties. With a highly efficient synthetic method and a good ecological profile, it is a potential candidate for numerous applications, including biomedical applications.

This study aimed at assessing the extent to which the Saudi general population has embraced digital health medical applications to meet their health-related needs so that the Saudi Ministry of Health and government can appropriately be guided on scaling up of digital health across the country. As such, this study was guided by the question of to what extent do the Saudi people use digital health mobile-based applications?. This was a cross-sectional study utilizing snowballing sampling approach. Frequencies, Chi-square, and Spearman rank correlation statistics were used to offer descriptive and inferential analysis of the variables. The majority of the participants were economically able to afford smart devices that have medical apps, had at least an app on such devices, and highly regarded the benefits of the apps. Unfortunately, their understanding of how to use such apps was limited and this posed a barrier to embracing digital health alongside difficulty downloading apps, and medical ethical concerns. Although there is a willingness, extra effort is needed from the Saudi Ministry of Health and government to promote the uptake of digital health in Saudi Arabia.

The advent of graphene opens up the research into two-dimensional (2D) material, which is considered as a revolutionary material in the future. Due to its unique geometric structure, graphene exhibits a series of exotic physical and chemical properties. Besides, single-element-based 2D materials (Xenes) have garnered tremendous interest. At present, 16 kinds of Xenes (silicene, borophene, germanene, phosphorene, tellurene, etc.) have been explored, mainly distributed in the third, fourth, fifth and sixth main groups. The current methods to prepare monolayer or few-layer 2D materials include epitaxy growth, mechanical exfoliation, and liquid phase exfoliation. Although two Xenes (aluminene and indiene) have not been synthesized due to the limitations of synthetic methods or stability of Xenes, other Xenes have been successfully realized by elaborately artificial design and synthesis. Focusing on elemental 2D materials, this review mainly summarizes the recently reported work about tuning the electronic, optical, mechanical, or chemical properties of Xenes via surface modifications achieved by controllable approaches (doping, adsorption, strain, intercalation, phase transition, etc.) to broaden the applications in various fields, including spintronics, electronics, optoelectronics, superconducting, photovoltaics, sensors, catalysis, and biomedicines. These advances in surface modification of Xenes have laid a theoretical and experimental foundation for the development of 2D materials and their practical applications in diverse fields.

Graphene is a remarkable material with numerous applications. Due to its thin and lightweight design, it is ideal for a variety of applications. The synthesis of high-quality graphene in a cost-effective and environmentally friendly manner continues to be a significant challenge. Chemical reduction is considered to be the most advantageous method for preparing reduced graphene oxide (rGO). However, this process necessitates the use of toxic and harmful substances, which can have a detrimental effect on the environment and human health. Thus, to accomplish the objective, the green synthesis principle has prompted researchers worldwide to develop a simple method for green reduction of graphene oxide (GO), which is readily accessible, sustainable, economical, renewable, and environmentally friendly in nature. For example, the use of natural materials such as plants is generally considered safe. Furthermore, plants contain reducing and capping agents. The current review will focus on the discovery and application of rGO synthesis using extracts from a variety of different parts of the plant. The review aims to aid current and future researchers in their search for a novel plant extract that acts as a reductant in the green synthesis of rGO. The review aims to assist current and future researchers in their research for a novel plant extract that acts as a reductant in the green synthesis of rGO as well as their potential applications in a variety of industries.

Today, across the most critical problems faced by hospitals and health centers are those caused by the existence of patients who do not attend their appointments. Among others, this practice generates waste of resources and increases the patients’ waiting list. To handle these problems, hospitals are actively trying to implement methods to reduce the idle time caused by patient no-shows. Many scheduling systems developed require predicting whether a patient will show up for an appointment or not. Although, a challenging problem resides in obtaining these estimates precisely. The goal of this work is to analyze how objective factors influence a patient not to attending their appointment, to identify the main causes that contribute to a patient’s decision, and to be able to predict whether or not the patient will attend the scheduled appointment. As a result, the obtained model is tested on a real dataset collected in a health center linked to the University of Vale do Itajaí (UNIVALI), which includes 25 features and about 5000 samples. The algorithm that produced the best results for the available dataset is the Random Forest classifier. It reveals the best recall rate (0.91), since it measures the ability of a classifier to find all the positive instances and achieves a receiver operating characteristic curve rate of 0.969.

Megacities are complex systems facing the challenges of overpopulation, poor urban design and planning, poor mobility and public transport, poor governance, climate change issues, poor sewerage and water infrastructure, waste and health issues, and unemployment. Smart cities have emerged to address these challenges by making the best use of space and resources for the benefit of citizens. A smart city model views the city as a complex adaptive system consisting of services, resources, and citizens that learn through interaction and change in both the spatial and temporal domains. The characteristics of dynamic development and complexity are key issues for city planners that require a new systematic and modeling approach. Multiscale modeling (MM) is an approach that can be used to better understand complex adaptive systems. The MM aims to solve complex problems at different scales, i.e., micro, meso, and macro, to improve system efficiency and mitigate computational complexity and cost. In this paper, we present an overview of MM in smart cities. First, this study discusses megacities, their current challenges, and their emergence to smart cities. Then, we discuss the need of MM in smart cities and its emerging applications. Finally, the study highlights current challenges and future directions related to MM in smart cities, which provide a roadmap for the optimized operation of smart city systems.

The purpose of this work is to determine the possibility of using an automated SkyWatcher Virtuoso system to analyze distribution of sky luminance. The article presents the procedure for measuring the sky luminance distribution defined according to CIE using a manual luminance meter cooperating with the SkyWatcher system. The use of matrix luminance meter was also analyzed.

The history of Artificial Intelligence (AI) development dates to the 40s. The researchers showed strong expectations until the 70s, when they began to encounter serious difficulties and investments were greatly, reduced. With the introduction of the Industry 4.0, one of the techniques adopted for AI implementation is Machine Learning (ML) that focuses on the machines ability to receive data series and learn on their own. Given the considerable importance of the subject, researchers have completed many studies on ML to ensure that machines are able to replace or relieve human tasks. This research aims to analyze, systematically, the literature on several aspects, including publication year, authors, scientific sector, country, institution, keywords. Analyzing existing literature on AI is a necessary stage to recommend policy on the matter. The analysis has been done using Web of Science and SCOPUS database. Furthermore, UCINET and NVivo 12 software have been used to complete them. Literature review on ML and AI empirical studies published in the last century was carried out to highlight the evolution of the topic before and after Industry 4.0 introduction, from 1999 to now. Eighty-two articles were reviewed and classified. A first interesting result is the greater number of works published by USA and the increasing interest after the birth of Industry 4.0.

Batteries are called to be part of the electricity grid by providing ancillary services. Area regulation services seem to provide substantial revenues and profit. But Li-ion batteries are still too expensive to enter widely into this market. On the other hand, electric vehicle batteries are considered inappropriate for traction purposes when they reached a state of health of 80%. The reuse of these batteries offers affordable batteries for second life stationary applications. This study analyzes how batteries may provide services to a gas turbine cogeneration power plant and how long these batteries may last under different loads.

In biology and medicine, synthetic polymers that mimic intrinsically disordered proteins (IDPs) exhibiting remarkable structural and conformational flexibility due to their lack of stable three-dimensional structures are highly desirable. This conformational flexibility facilitates self-organization, making such polymers highly useful for various biomedical applications. Specifically, intrinsically disordered synthetic polymers show great potential in drug delivery systems, organ transplantation, artificial organ design, and ensuring immune compatibility. There is a pressing need for new synthesis and characterization techniques to develop these polymers which are modeled after intrinsically disordered proteins and can be used for various biomedical purposes. In this paper, we outline our strategies for designing intrinsically disordered synthetic polymers for biomedical applications. These design approaches are inspired by the properties of intrinsically disordered proteins and use of disorder promoting oligopeptides.

Students' ability to exhibit positive behavior towards their environment plays a major role in building a sustainable world in the future. It is important to offer students, who are the subjects of the digital age, a more interactive and customized technology-focused learning experience. This study aims to reveal how environmental education supported by digital games affects middle school students' knowledge, environmentally responsible behaviors, and levels of digital literacy for a sustainable life. The participants of the research consist of 54 students studying at the 5th grade level of secondary school. The research was conducted with a pretest-posttest control group quasi-experimental design, which is one of the quantitative research methods. In the research, environmental education based on digital games created with web 2.0 tools was carried out. Study data; It was obtained through environmental knowledge test, environmentally responsible citizen behavior scale, and digital literacy scale. Study data were analyzed with appropriate statistical analysis using a statistical program. The research results revealed that environmental education supported by digital games is effective. It was determined that students' knowledge level, environmental behavior and digital literacy scores increased. Based on the research results, it is recommended that digital games be used to achieve more qualified learning.

Chronic obstructive pulmonary disease (COPD) is a condition that significantly impacts both patients and healthcare systems. The management of COPD involves various pharmacological intervention strategies, and addressing the issue of low adherence to these strategies has become a subject of significant interest. In response to this concern, there has been a shift towards utilizing telemedicine and mobile applications. The primary objective of this scoping review is to delineate the usage of mobile applications to enhance pharmacological therapeutic adherence in adult patients with COPD. The study involved a search of databases such as Medline, Google Scholar, Cochrane, and ClinicalTrial.gov, focusing on literature published in English and Spanish over the last decade. The selected studies assessed interventions involving mobile applications (mobile apps) designed to improve pharmacological adherence. Four digital aids were identified, available on online platforms, mobile apps, or both: m-PAC, myCOPD, Wellinks mHealth, and Propeller Health. Pro-peller Health, in particular, is an app that directly measures pharmacological adherence through electronic medication monitors attached to participants' inhalers. Opening the app was associated with higher odds of using control medications compared to participants who did not open the app. The findings suggest that these digital interventions serve as valuable tools to enhance patient adherence to treatment. Future research should focus on evaluating the effectiveness of different digital devices, such as digital inhalers and mobile applications, that directly measure pharmacological adherence.

CuAl8Fe5Ni4Zn4Sn1 (OF 2238) is a new lead-free copper alloy for plain bearing applications that was first officially presented in a scientific journal in 2020. Soon after its invention, the use of the alloy for connecting rod bushings in heavy duty internal combustion engines was promoted and validated with customers. The aim of this article is to describe the material properties of the new alloy in more detail than previously and explain how the advantageous properties of CuAl8Fe5Ni4Zn4Sn1 are generated. At the beginning of the article, the general development trends in the field of copper alloys for sliding applications are presented. This is followed by a presentation of the latest research results about CuAl8Fe5Ni4Zn4Sn, before concluding with a brief summary and future outlook, emphasizing on the fact that recent trends in copper-based sliding applications show great additional potential for further material developments.

The escalating demand for multifunctional and advanced materials for growing industries has turned scientific exploration to graphene-based hybrid filler polymer composites. The introduction of graphene with other fillers into polymer matrices results in astonishing properties and thus has gained significant attention in recent years. This study addresses the significance of graphene-based hybrid filler polymer composites in progressive materials science and engineering. The paper encloses some traditional and current preparation techniques employed in the fabrication of composites and a comprehensive review of successfully prepared graphene hybrid polymer composites with enhanced mechanical, thermal, and electrical properties reported in published research. The role of different factors, e.g., filler type, aspect ratios, filler-matrix compatibility, dispersion effects, homogeneity, filler orientation through polymer matrices, and curing conditions, are summarized. Finally, the applications in heavy industries, e.g., aerospace, electronics, energy storage, and automotive sectors were also analyzed. We hope this study will serve as a practical resource for new researchers and practitioners to understand and harness the full potential of these composites in various scientific and industrial fields.

The roots of African grapes (Lannea microcarpa) have long been utilised in traditional medicine for their perceived therapeutic properties. However, their comprehensive proximate and mineral analysis remains largely unexplored. Proximate composition and mineral content of the roots were conducted. The sample was obtained from Katsayal, Katsina State, and analysed using standard procedures. The macro and micro metals were determined using Flame Photometer and Atomic Absorption Spectroscopy, respectively. The results revealed a moisture content of 28.0 ±2.0%, an ash content of 21.3±1.2%, and significant levels of crude fibre (29.0±0.50%), crude lipid (9.60±0.20%), crude protein (6.91±0.01%), and available carbohydrates (33.3±0.10%). The calorific value of the root was determined to be 245 ± 0.01 kcal/g. The micro-mineral analysis revealed the detection of copper (8.60 ± 0.002 mg/kg), magnesium (9.24 ± 0.004 mg/kg), manganese (41.9 ± 0.001 mg/kg), iron (405 ± 0.042 mg/kg), and zinc (9.46 ± 0.001 mg/kg). The macro-minerals, namely calcium (28400 mg/kg), potassium (1530 mg/kg), and sodium (45030 mg/kg), were found to be present in the sample. It is advisable to conduct additional research to examine the identified minerals' bioavailability, clarify the phytochemical profile, and investigate potential bioactive compounds.

Amidst the rapid urbanization of cities worldwide, there is a pressing need for practical tools to manage and preserve the invaluable architectural heritage contained within them. This research introduces a systematic methodology for digitalizing and interpreting architectural heritage through augmented Reality (AR). The primary goal is to enhance the documentation, preservation, and accessibility of cultural assets, addressing both educational and touristic needs. Focusing on the historic district of Pelourinho in Salvador, Bahia, Brazil, the study showcases how 3D digitalization combined with AR can provide users with an interactive experience with historical structures, bridging the temporal divide. By integrating the digital replicas of historic buildings into a real-time environment, users gain enriched insights into these structures’ history, architecture, and cultural significance. Though the preliminary results underscore the potential of this approach, comprehensive user testing remains an area for future exploration. This paper thus highlights the potential of AR in historical and cultural preservation, suggesting its broader implications for education, tourism, and heritage management.

This study investigates the nonlinearities in three-phase inverters for SiC-based systems and compares their performance to IGBT-based systems. An analytical model of inverter voltage distortion is developed, which accounts not only for dead time (td), switching delay time, switching frequency (fs), and voltage drops of power devices, but also for output parasitic capacitance (Cout). Experimental tests validate the model, which provides a more accurate estimate of the inverter’s output phase voltage distortion. The power device characteristics are obtained from datasheets, while Cout is determined through experimentation. Three-phase inverters with varying switching frequencies, fundamental frequencies, and dead-time values are used in simulations and experiments to determine the influence of nonlinearity on phase voltage deviation and current distortion. The results show that, due to SiC devices’ faster switching time, the phase voltage deviation and phase current distortion are lower in SiC-based inverters than in IGBT-based ones for high-frequency applications, as the dead time can be reduced.

The potential of sericin, a protein derived from silkworms, is explored in bone graft applications. Sericin's biocompatibility, hydrophilic nature, and cost-effectiveness make it a promising candidate for enhancing traditional graft materials. Its antioxidant, anti-inflammatory, and UV-resistant properties contribute to a healthier bone-healing environment, and its incorporation into 3D-printed grafts could lead to personalized medical solutions. However, despite these promising attributes, there are still gaps in our understanding. The precise mechanism through which sericin influences bone cell growth and healing is not fully understood, and more comprehensive clinical trials are needed to confirm its long-term biocompatibility in humans. Furthermore, the best methods for incorporating sericin into existing graft materials are still under investigation, and potential allergic reactions or immune responses to sericin need further study.

Marine macroalgae have garnered significant attention in the field of cosmeceutical research due to their rich abundance of bioactive compounds. These compounds offer remarkable skin benefits without inducing any adverse effects on human health, such as cytotoxicity, reproductive toxicity, genotoxicity, mutagenicity, or carcinogenicity. Among the various bioactive compounds found in brown algae, phenolic compounds exhibit diverse chemical structures and are present in high concentrations. In addition to phenolic compounds, brown algae also contain terpenoids, bro-mophenols, mycosporine amino acids (MAAs), and flavonoids, with the well-studied poly-phenol compound, phlorotannin, being particularly prominent. Marine macroalgae further possess an array of pigments derived from their natural pigmentation, including chlorophylls, carotenoids (such as fucoxanthin and β-carotene), phycobiliproteins (such as phycoerythrin and phycocyanin), and melanin. These pigments have been extensively investigated for their potential cos-meceutical applications. The phenolic compounds and pigments derived from marine macroalgae have been thoroughly studied for their beneficial effects on the skin, including skin whitening, moisturizing, photoprotection, anti-aging, anti-wrinkle, anti-melanogenic, and anti-oxidant properties. This comprehensive review aims to explore the extraction, characterization, and skin cosmetic effects of phenolic compounds and pigments derived from marine macroalgae, as documented in the existing literature, thereby enhancing our understanding of their therapeutic potential.

The manuscript presents a unified methodology for the engineering design of resonant inverters used for power sources to power various technologies based on induction heating. The methodology is compiled using the generalized consideration of electromagnetic processes in a series RLC circuit and the quasi-boundary method for the analysis of resonant inverters with and without reverse diodes operating in soft and hard commutation modes. On this basis, the transmission functions of the inverters are derived, and the matching between the parameters of the inverter and the load through complicated output circuits is also considered. The basic ratios for determining the circuit elements and their current and voltage loading are presented. The methodologies are verified using several computational examples, simulations and measurements from induction heating devices. The simplified design approach presented is useful for its application in power electronics training and also in the design of various devices using the principle of induction heating. Another important result is the achievement of formalization and algorithmization of the design process, which is a consequence of the application of a unified approach both in the analysis and in the design of a whole class of power electronic devices.

In current study we have obtained Co2FeSi-glass coated microwires with different geometrical aspect ratio, ρ = d/Dtot (diameter of metallic nucleus, d and total diameter, Dtot). The structure and magnetic properties are investigated at a wide range of temperature. XRD analysis illustrates a notable changing in the microstructure by increasing the aspect ratio of Co2FeSi glass coated microwires. Amorphous structure is detected for the sample with the lowest aspect ratio (ρ = 0.23), whereas a growth of crystalline structure is observed in the other samples (aspect ratio ρ = 0.30 and 0.43). This change at the microstructure properties correlates with dramatic changing in magnetic properties. For the sample with the lowest ρ -ratio, non-perfect square loops are obtained with low normalized remanent magnetization. A notable enhancement in the squareness and coercivity are obtained by increasing ρ -ratio. Changing the internal stresses strongly affects the microstructure, resulting in a complex magnetic reversal process. The thermomagnetic curves show large irreversibility for the Co2FeSi with low ρ -ratio. Meanwhile, if we increase the ρ -ratio, the sample shows perfect ferromagnetic behavior without irreversibility. The current result illustrates the ability to control the microstructure and magnetic properties of Co2FeSi-glass-coated microwires by changing only their geometric properties without performing any addition heat treatment. The modification of geometric parameters of Co2FeSi glass-coated microwires allows to obtain microwires which exhibit an unusual magnetization behavior that offers opportunities to understand the phenomena of various types of magnetic domain structures, which is essentially helpful for designing sensing devices based on thermal magnetization switching.

We report an agent-based model to compare the effectiveness of simple and complex mobile dating application interfaces in generating matches for virtual users. We define the relative complexity of dating applications as the number of available features and dub this variable, the multiplicity. We replicate some of the most popular mobile dating applications through the generation of a synthetic population endowed with attributes, preferences, and behaviors drawn from literature. We treat our data as a network dataset and use a robust statistical procedure (MRQAP) to issue a valid and reliable comparison between simulated applications. We show how the quadratic assignment procedure can be used to compare network simulations rigorously. As a result, we observe a direct relationship between multiplicity and agent-level experiences and expectations in match generation. We also observe the emergence of divergent matching systems with minor rule changes as well as several expected properties of online dating systems. This work serves as a proof-of-concept in the integration of classical social network analysis methods with agent-based modeling to compare virtual designs and to enhance the policy-generation process of online social networks.

Diagnosis and prognosis of failures for aircrafts’ integrity are some of the most important regular functionalities in complex and safety-critical aircraft structures. Further, development of failure diagnostic tools such as Non-Destructive Testing (NDT) techniques, in particular, for aircraft composite materials, has been seen as a subject of intensive research over the last decades. The need for diagnostic and prognostic tools for composite materials in aircraft applications rises and draws increasing attention. Yet, there is still an ongoing need for developing new failure diagnostic tools to respond to the rapid industrial development and complex machine design. Such tools will ease the early detection and isolation of developing defects and the prediction of damages propagation; thus allowing for early implementation of preventive maintenance and serve as a countermeasure to the potential of catastrophic failure. In this paper, following a short introductory summary and definitions, this paper provides a brief literature review of recent research on failure diagnosis of composite materials with an emphasis on the use of NDT techniques in aerospace industry. In addition to this, within a some of significant NDT application extents, prognosis of composites is also briefly discussed.

The use of high strength to weight ratio laminated composites is emerging in marine industry and applications as a very efficient solution for improving productivity. Nevertheless, delamination between the layers is a limiting factor for the wider application of laminated composites, as it reduces the stiffness and strengths of the structure. Interleaving nanofibrous mats between layers of composite laminates has been proved to be an effective method for improving composites delamination resistance. This paper aims to characterize the effect of interleaved nanofiber on mode I interlaminar properties and failure mechanisms when subjected to static and fatigue loadings. For this purpose, virgin and nanomodified woven laminates were subjected to Double Cantilever Beam (DCB) specimens. Static and fatigue tests were performed and the tests were monitored by acoustic emission technique. The mechanical results showed a 130% increase of delamination toughness for nanomodified specimens in the static loadings and more crack growth resistance in the fatigue loading. The AE results revealed that different type of failure mechanisms was the cause of these improvements for the modified specimens compared with the virgin ones.

Placing the needle inside the epidural space for locoregional anesthesia is a challenging procedure, which even today is left to the expertise of the operator. Recently, we have demonstrated that the use of optically sensorized needles significantly improve the effectiveness of such procedure. Here we propose an optimized configuration, where the optical fiber strain sensor is directly integrated inside the epidural catheter. The new design allows to solve the biocompatibility issues and increases the versatility of the former configuration. Through an in vivo study carried out on a porcine model we confirm the reliability of our approach, which also opens the way to catheter monitoring during its insertion inside biological spaces.

Building small populations mortality tables has great practical importance in actuarial applications. In recent years, several works in literature explored different methodologies to quantify and assess longevity and mortality risk, especially within the context of small populations: many models dealing with this problem usually use a two-population approach, modelling a mortality spread between a larger reference population and the population of interest, via likelihood-based techniques. To broaden the tools at actuaries’ disposal to build small population mortality tables, a general structure for a two-step two-populations model is proposed, its main element of novelty residing in a machine-learning based approach to mortality spread estimation. In order to obtain this, Contrast Trees and related Estimation Contrast Boosting techniques have been applied. A quite general machine learning-based model has then been adapted in order to generalize Italian actuarial practice in company tables estimation and implemented using data from Human Mortality Database. Finally, results from the ML-based model have been compared to those obtained from the traditional model.

Recent developments in flexible printed heaters (FPH) for wearable thermal applications, driven by the advancement of printed electronics, show great promise in revolutionizing patient care through the development of wearable flexible heaters for medical applications. Wearable heaters with high thermal stability, heat uniformity, safety, flexibility, comfort, biocompatibility, and power efficiency are desirable for stand-alone medical thermotherapy applications. This paper reviews recent advancements in the design of FPHs for wearable thermal applications. Materials for FPHs, fabrication methods, designs, temperature control mechanisms, medical applications, and performance analysis of specific FPHs are all thoroughly discussed. Materials for FPHs, such as conductive and substrate materials, have received special attention, along with the heater design parameters. Finally, the challenges and future directions for developing FPHs for wearable medical applications are addressed.

The application of three-dimensional (3D) printing/bioprinting technologies and cell therapies has garnered significant attention due to their potential in the field of regenerative medicine. This paper aims to provide a comprehensive overview of 3D printing/bioprinting technology and cell therapies, highlighting their results in diverse medical applications, while also discussing the capabilities and limitations of their combined use. The synergistic combination of 3D printing and cellular therapies has been recognised as a promising and innovative approach, and it is expected that these technologies will progressively assume a crucial role in the treatment of various diseases and conditions in the foreseeable future. This review concludes with a forward-looking perspective on the future impact of these technologies, highlighting their potential to revolutionize regenerative medicine through enhanced tissue repair and organ replacement strategies.