Sensing Technology (ST) plays a key role in Structural Health Monitoring (SHM) systems. ST focuses on developing sensors, sensory systems or smart materials that monitor a wide variety of materials properties aiming to create smart structures and smart materials, using Embedded Sensors (ESs), and allowing continuous and permanent measurements of the structural integrity. The integration of ESs is limited to the processing technology to embed the sensor due to its high-temperature sensitivity and the possibility of damage during its insertion into the structure. In addition, the technological process selection is dependent on the base material composition, either metallic or composite parts. The selection of smart sensors or the technology underlying them is fundamental to the monitoring mode. This paper presents a critical review of the fundaments and applications of sensing technologies for SHM employing ESs, focusing on the actual developments and innovation of these, as well as analysing the challenges that these technologies present, to build a path that allows a connected world through distributed measurement systems.

Extracellular vesicles (EV) are a very heterogeneous group of cell-derived vesicles released by almost all kind of living cells. EV are involved in intercellular communication, both locally and systemically, since they induce signals and transfer their contents (proteins, lipids, RNAs) to other cells, which subsequently trigger a wide variety of biological responses in the target cells. How-ever, cell surface receptor-induced EV release is limited to cells from the immune system, includ-ing T lymphocytes. T cell receptor activation of T lymphocytes induces secretion of EV containing T cell receptor for antigen and several bioactive molecules, including proapoptotic proteins. These EV are thus specific for antigen-bearing cells, which make them ideal candidates for a cell-free, EV-dependent cancer therapy. In this review we discuss the generation of EV by T lymphocytes and some potential therapeutic approaches of these EV.

Background: Convectional methods for drug delivery often faces setbacks due to systemic distribution, short half-life and degradation of therapeutics and therefore reduce concentrations of drug available to target tissue. Nanotheranostic provide a novel method for treating and diagnosing diseases Methodology: collection and review of relevant literatureResult: while nanotheranostic offer advantage of personalized medicine and often combines diagnosis and therapy using single molecular approach, nuclear medicine relies on radioactive isotopes to diagnosed and destroys cancer cells. In both cases, nanocarriers such as lipid-based, polymer-based, drug-conjugate, inorganic nanoparticles are used to deliver drugs/probes/isotopes to target site, generating images and thereafter chemotherapy/radiotherapy begins.Conclusion: Nanotheranostic plays important role in diseases diagnostic, therapy, imaging, monitoring of disease progression / response through the use of nanocarriers. This is made possible through nanoparticles/nanocarriers that delivers drug to the target tissues/cells.

Water mist fire suppression technology has attracted an increasing interest from the field of fire protection services such as fire safety for buildings, ships, spacecraft, libraries and museums due to its non-toxic and high efficiency in the suppression of a wide variety of fires. To support the technological development of water mist fire suppression system and its application areas, this review introduces the concept of water mist system and discusses its suppression mechanisms in comparison with other fire protection systems. The recent application areas of water mist system are surveyed for class A fires involving combustible solid materials such as wood, paper and textiles; class B fires involving flammable liquids such as petrol, oils, lubricants, paints and waxes; class C fires involving flammable gasses such as natural gas and liquefied petroleum gas; fires involving electrical (class E) equipment such as computers and information technology facilities; and the class F fires involving flammable cooking oils and fats. Finally, the paper concludes the review by identifying the current research trends, and providing the future direction for water mist technology and applications.

RNA-guided genomic transcriptional regulation tools, namely Clustered Regularly Interspaced Short Palindromic Repeats interference (CRISPRi) and CRISPR-mediated gene activation (CRISPRa), are a powerful technology for the field of functional genomics. Deriving from the CRISPR/Cas9 system, both systems comprise a catalytically dead Cas9 (dCas9) and a single guide RNA (sgRNA). This type of dCas9 is incapable of cleaving DNA but retains its ability to specifically bind to DNA. The binding of the dCas9/sgRNA complex to a target gene results in transcriptional interference. The CRISPR/dCas9 system has been explored as a tool for transcriptional modulation and genome imaging. Despite its potential applications and benefits, the challenges and limitations faced by the CRISPR/dCas9 system include the off-target effects, PAM sequence requirement, efficient delivery methods, and the CRISPR/dCas9-interfered crops being labeled as genetically modified organisms in several countries. This review highlights the progression of CRISPR/dCas9 technology as well as its applications and potential challenges in crop improvement.

Second messengers, cyclic adenosine 3'-5'-monophosphate (cAMP) and cyclic guanosine 3'-5'-monophosphate (cGMP) are playing important roles in many animal cells by regulating intracellular signaling pathways and modulating cell physiology. Environmental cues like temperature, light and chemical compounds can stimulate cell surface receptors and trigger the generation of second messengers and the following regulations. Spread of cAMP and cGMP is further shaped by cyclic nucleotide phosphodiesterases (PDEs) for orchestration of intracellular microdomain signaling. However, localized intracellular cAMP and cGMP signaling requires further investigation. Optogenetic manipulation of cAMP and cGMP offers new opportunities of spatio-temporally precise study of their signaling mechanism. Light-gated nucleotide cyclases are well developed and applied for cAMP/cGMP manipulation. Recently discovered rhodopsin phosphodiesterase gene from protists established new and direct biological connection between light and PDEs. Light-regulated PDEs are under development and of demand to complete the toolkit of cAMP/cGMP manipulation. In this review, we summarize the state of the art, pros and cons of artificial and natural light-regulated PDEs and discuss potential new strategies of developing light-gated PDEs for optogenetic manipulation.

This work describes a new transducer prototype for continuous monitoring both in the structural and geotechnical fields. The transducer is synthetically constituted by a wire of optical fiber embedded between two fiber tapes (fiberglass or carbon fiber) and glued by a matrix of polyester resin. The fiber optical wire ends have been connected to a control unit whose detection system is based on Brillouin optical time-domain frequency analysis. Three laboratory tests were carried out to evaluate the sensor's reliability and accuracy. In each experiment, the transducer was applied to a sample of inclinometer casing sets in different configurations and with different constraint conditions. The experimental data collected were compared with theoretical models and with data obtained from the use of different measuring instruments to perform validation and calibration of the transducer at the same time. Several diagrams allow comparing the transducer and highlighting its suitability for monitoring and maintenance of structures. The characteristic of the transducer suggests its use as a mixed system for reinforcing and monitoring, especially in lifetime maintenance of critical infrastructures such as transportation and service networks, and historical heritage.

Rough volatility models are popularized by \cite{gatheral2018volatility}, where they have shown that the empirical volatility in the financial market is extremely consistent with rough volatility. Fractional Riccati equation as a part of computation for the characteristic function of rough Heston model is not known in explicit form as of now and therefore, we must rely on numerical methods to obtain a solution. In this paper, we give a short introduction to option pricing theory and an overview of the current advancements on the rough Heston model.

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.

The recently proposed Holography-inspired approach to quantum gravity is reviewed and expanded. The approach is based on the foliation of the background spacetime and reduction of the offshell states to the physical states. Careful attention is paid to the boundary conditions. It is noted that the outstanding problems such as the cosmological constant problem and black hole information can be tackled from the common thread of the quantized gravity. One-loop renormalization of the coupling constants and the beta function analysis are illustrated. Active galactic nuclei and gravitational waves are discussed as the potential applications of the present quantization scheme to astrophysics.

This work is aiming to motivation on the prospect of evolving new thiazole dyes with respectable application properties, expected pharmacological activities. Curcumin Coupling with diverse diazonium salts of 2-amino thiazole derivatives as 2-aminobenzothiazole, 2- amino-5-phenylthiazole, 2 amino-5-methylthiazole and 2 amino-5-nitrothiazole to produce novel azo dyes. All synthesised dyes were completely confirmed their structures via elemental and spectroscopic techniques. The synthesised thiazole derivatives were examined for their “antimicrobial, anticancer and antioxidant” activities. All of the synthesized dyes were applied on synthetic fabrics as polyester and successively their dyeing properties, “light, washing, perspiration, rubbing and sublimation” fastness were evaluated. Prepared dyestuffs are suitable for dyeing polyester fabrics. It was initiate that all of prepared dyes own extraordinary colour hue, along with respectable fastness properties. Also the synthesised thiazole derivatives display moral pharmacology activity.

The Inverted F Antenna (IFA) with the parasitic element on a finite conducting plane is proposed in the range frequency of 0.1 to 5.5 GHz and its characteristics are analyzed numerically. In this research, the parasitic element and the main IFA are investigated to obtain the resonance frequency for multiband operation purposes. The parasitic element is placed beneath adjusted to the main IFA to derive more frequency bands. The distance between the parasitic element and main horizontal element extremely affects the performance of the IFA. It is found that when the parasitic element is located closer to the conducting plane, this element is coupled by the current on the conducting plane. Consequently, the return loss bandwidth becomes narrower. Therefore, the gain of the proposed IFA becomes a bit higher. The antenna gain is about 8.21 dB at band #3 (λ_1.747), 7.43 dB at band #5 (λ_2.967) and 8.82 dB at band #6 (λ_4.023). This occurs when the calculation condition is antenna height h1 = 23 mm, h2 = 21 mm, horizontal antenna elements L = 173.2 mm, L1 = 140.9 mm and Lp = 152 mm, shorted element Ls = 30.7 mm, the distance between parasitic element and shorted element pyl = 5 mm. While the size of conducting plane is considered pxp+pxm by pyp+pym as 57.5+57.5 mm by 200+50 mm. In the numerical analysis, the electromagnetic simulator WIPL-D based on Method of Moment is used. The results show that the proposed IFA has UHF and SHF channel receiver which are suitable for advanced wireless service (band #3), mobile phones, Bluetooth, maritime service, radiolocation service (band #5) and radars, mobile phones, commercial wireless LAN (band #6).

Zinc oxide (ZnO) nanostructures have been studied extensively in the past years due to the novel electronic, photonic, mechanical and electrochemical properties. Recently, more attention has been paid to assemble nanoscale building blocks into three dimensional (3D) complex hierarchical structures, which not only inherit the excellent properties of the single building blocks but also provide potential applications in the bottom-up fabrication of functional devices. This review article focuses on 3D ZnO hierarchical nanostructures, and summarizes major advances in the solution phase synthesis, applications in environment, and electrical/electrochemical devices. We present the principles and growth mechanisms of ZnO nanostructures via different solution methods, with an emphasis on rational control of the morphology and assembly. We then discuss the applications of 3D ZnO hierarchical nanostructures in photocatalysis, field emission, electrochemical sensor, and lithium ion batteries. Throughout the discussion, the relationship between the device performance and the microstructures of 3D ZnO hierarchical nanostructures will be highlighted. This review concludes with a personal perspective on the current challenges and future researches.

The prolonged static sitting at the workplace is considered as one of the main risks for development of musculoskeletal disorders (MSDs) and adverse health effects. Factors such as poor posture and extended sitting are perceived to be a reason for conditions such as lumbar hyperlordosis and lower back pain (LBP), even though the scientific explanation of this relationship is still unclear and raises disputes in the scientific community. This publication proposes the low back pain assessment (LBPA) dataset collected through experiment with 100 participants and consisting of photogrammetric images with intentionally placed body markers, calculated postural angles and tags correct and incorrect posture assessed by habilitated rehabilitator, as well as questionnaire-based self-reports regarding the occurrence of LBP and similar symptoms among the participants. Machine learning models trained with this data are employed for recognizing incorrect body postures. Two scenarios have been elaborated for modeling purposes: scenario 1, based on natural body posture tagged as correct and incorrect, and scenario 2, based on incorrect body postures, corrected additionally by the rehabilitator. The achieved accuracies of respectively 75.3% and 85% for both scenarios reveal the potential for future research in enhancing awareness and actively managing posture-related issues that elevate the likelihood of developing lower back pain symptoms.

An accurate outbreak prediction of COVID-19 can successfully help to get insight into the spread and consequences of infectious diseases. Recently, machine learning (ML) based prediction models have been successfully employed for the prediction of the disease outbreak. The present study aimed to engage an artificial neural network-integrated by grey wolf optimizer for COVID-19 outbreak predictions by employing the Global dataset. Training and testing processes have been performed by time-series data related to January 22 to September 15, 2020 and validation has been performed by time-series data related to September 16 to October 15, 2020. Results have been evaluated by employing mean absolute percentage error (MAPE) and correlation coefficient (r) values. ANN-GWO provided a MAPE of 6.23, 13.15 and 11.4% for training, testing and validating phases, respectively. According to the results, the developed model could successfully cope with the prediction task.

Infertility” is a condition defined by the failure to establish a clinical pregnancy after 12 months of regular, unprotected sexual intercourse or due to an impairment of a person’s capacity to reproduce either as an individual or with his/her partner’. The authors have set out to succinctly investigate, explore and assess infertility treatments harnessing the potential of stem cells to effectively and safely treat infertility, in addition to the legal and regulatory complexities at the heart of stem cell research, with an overview of the legislative state of affairs in six major European countries. In couples who cannot benefit from assisted reproductive technologies (ART) to treat their infertility, stem cells-based approaches have been shown to be a highly promising approach. Nonetheless, lingering ethical and immunological uncertainties require more conclusive findings and data before such treatment avenues can become mainstream and applied large scale. The isolation of human embryonic stem cells (ESCs) is ethically controversial, since their collection involves the destruction of human embryonic tissue. Overall, stem cell research has resulted in important new breakthroughs in the treatment of infertility. The effort to untangle the complex web of ethical and legal issues associated with such therapeutic approaches will have to rely on evidence-based, broadly shared standards, guidelines and best practices to make sure that the procreative rights of patients can be effectively reconciled with the core values at the heart of medical ethics.

Mee (Madhuca longifolia) is an important economic tree growing throughout the subtropical region of the Indo-Pak subcontinent. Mee seed constituents have distinctive chemical properties and thus provide nutritional and functional characteristics to the mee fat. The mee seed fat is an edible fat which can provide fat requirement of human diet in an economical way. Research on potential utilizations, understanding on the chemical composition, nutritional value and industrial applications of mee fat is important for efficient use mee seed fat. There were some reports available on potential applications of mee fat in food processing industry. But presently mee fat can be considered as an underutilized seed fat. In India mee fat used for edible purposes for a certain extent with compare to Sri Lanka. Therefore, more scientific researches should conduct on Sri Lankan mee varieties on edible purposes and popularized edible uses of Mee fat in Sri Lanka. Scientific knowledge and agricultural tools should effectively apply to make mee fat beneficial for its commercial applications. This chapter summarizes recent research work and studies available on botany, phytochemistry, functional properties, processing as well as food and non-food industrial applications of mee fat and other valuable parts of mee tree.

Due to its similarity with human milk and its low allergenic properties, donkey milk has long been used as an alternative for infants and patients with cow's milk protein allergy. In addition, this milk is attracting growing interest in human nutrition because of presumed health benefits. It has antioxidant, antimicrobial, antitumoral, antiproliferative, and antidiabetic activity. Also, it stimulates the immune system, regulates the gastrointestinal flora, and prevents inflammatory diseases. Although all components of donkey milk can contribute to functional and nutritional effects, it is generally accepted that the whey protein fraction plays a major role. The purpose of this review is to highlight the active proteins and peptides of donkey milk in comparison with other types of milk, emphasizing their properties and their roles in different fields.

Huge amounts of chitin and chitosans can be found in the biosphere as important constituent of the exoskeleton of many organisms, as well as waste by worldwide seafood companies. Nowadays, politicians, environmentalists, and industrialists encouraged the use of these marine polysaccharides as renewable source, particularly when developed by alternative eco-friendly processes, especially in the production of regular cosmetics. The aim of this review is to outline the physicochemical and biological properties and the different bioextraction methods of chitin and chitosans sources, focusing on enzymatic deproteinization, bacteria fermentation, and enzymatic deacetylation methods. Thanks to their biodegradability, non-toxicity, biocompatibility, and bioactivity, the application of these marine polymers is widely used in the contemporary manufacturing of biomedical and pharmaceutical products. In the end, advanced cosmetic products based on chitin and chitosans are presented, analyzing different therapeutic aspects about skin, hair, nail, and oral care. The innovative formulations described can be considered as excellent solutions regarding problems in the various body anatomical sectors.

Confusion, fear and mixed sentiments prevail in the minds of people towards what is arguably one of the most important of dynamics of modern human society: Artificial Intelligence (AI). This study aims to explore the contributions of news media towards this phenomenon - we analyze nearly seventy thousand recent news headlines on AI, using natural language processing (NLP) informatics methods, machine learning (ML) and large language models (LLMs) to draw insights and discover dominant themes. Our theoretical framework was derived from extant literature which posits the power of fear producing articles and news headlines which produce significant impacts on public behavior even when available in small quantities. We applied extensive textual informatics methods using word and phrase frequency analytics, sentiment analysis and human experts based thematic analysis to discover insights on AI phobia inducing news headlines. Our rigorous analysis of nearly seventy thousand headlines using multiple validation methods in NLP (exploratory informatics including BERT, Llama 2 and Mistral based topic identification), ML (supervised informatics) and LLMs (neural nets for sentiment classification, with BERT, Llama 2 and Mistral) demonstrates the presence of an unreasonable level of emotional negativity and fear inducing verbiage in AI news headlines. The framing of AI as being dangerous or as being an existential threat to humanity can have a profound impact on public perception, and the resulting AI phobia and confusion in public perceptions are inherently detrimental to the science of AI. Furthermore, this can also impact AI policy and regulations, and harm society. We conclude with a discussion deducing implications for society and make recommendations for education and policies that could support human identity and dignity.

Background: Intracavitary contrast-enhanced ultrasound is widely accepted as a highly informative, safe and easily reproducible technique for diagnosis, treatment and follow- up of different pathologies of the biliary tree. Methods: This article reports the combined conclusions of personal experience with pictorial examples and literature review of applications for CEUS in intracavitary biliary scenarios. Different methodologies, choices of equipment, indications for the procedure and expected complications were identified and described from the authors’ own experience as well as from a review of published studies with total of 208 patients for the period between 2009 and 2015. Results: Indications were biliary obstruction by various etiologies, including postoperative strictures, evaluation of the biliary tree of liver donors (n=12) and evaluation of localization of a drainage catheter. One of the studies found a statistically significant difference in the diagnostic accuracy of PTC and PUSC. Conclusion: Intracavitary CEUS brings all the positive features of US together with the virtues of contrast- enhanced imaging, proving comparable accuracy to the listed techniques for diagnostics of biliary tree diseases.

Continuous discovery and update of applications or their boundaries running in cloud environments in an automatic way is a highly required function of modern data center ops solutions. Prior attempts to address this problem within various products or projects were/are applying rule-driven approaches or machine learning techniques on specific types of data − network traffic as well as property/configuration data of infrastructure objects, which all have their drawbacks in effectively identifying roles of those resources. The current proposal (ADLog) leverages log data of sources, which contain incomparably richer contextual information, and native constructs of VMware Aria Operations for Logs in terms of Event Types and their distributions to group those entities, potentially automatically enrich with indicative tags, and recommend for further management tasks and policies. Our methods discriminate not only diverse kinds of applications, but also their specific deployments thus providing hierarchical representation of the applications in time and topology. For several applications under Aria Ops management in our experimental test bed, we discover those in terms of similarity behavior of their components with a high accuracy. The validation of the proposal paves the path for an AI-driven solution in the cloud management scenarios.

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.

Population growth and the need for increased agricultural productivity pose a global problem. Therefore, the development of green compounds to ensure agricultural sustainability becomes an urgent necessity. Surfactant compounds hold significant commercial importance due to their diverse industrial uses. However, the synthetic origin of these agents limits their commercial application due to their toxicity. As a result, extensive research has focused on the production of microbial-originated green surfactants, known as biosurfactants, over the past fifteen years. These biomolecules not only offer a green alternative for agriculture but also exhibit reduced toxicity and excellent stability under specific environmental conditions. Biosurfactants can lower surface tension more effectively than synthetic surfactants. With properties such as detergency and foam formation, biosurfactants are suitable for various agricultural applications, particularly in pesticide and agrochemical formulations. They can function as biopesticides to manage pests, pathogens, phytopathogenic fungi, and weeds due to their antimicrobial activity. Moreover, plants can benefit from biosurfactant molecules and the microorganisms as nutrients. They can also aid efficiently in the distribution of micronutrients and metals in the soil. They also stimulate plant immunity and are utilized for soil hydrophilization to ensure proper moisture levels and uniform fertilizer distribution. This review aims to provide valuable insights into the role and properties of biosurfactants as agricultural adjuvants, fostering the development of sustainable formulations to replace chemical surfactants used in pesticides.

The capability of radially polymerized bio-dendrimers and hyperbranched polymers for medical applications is well established. Among them, perhaps the most important are those that involve interactions with the regenerative mechanisms of cells. Dendritic polymers due to their distinctive architecture may play a multitude of roles such as protein biomimicry (collagen, elastin, hydroxy apatite production), gene and drug delivery (cell differentiation, antimicrobial protection), surface chemistry and charge modulation (adhesion to cells and tissues), polymer cross-linking (eye, skin and internal organ wound healing). The review highlights all the different categories of hard and soft tissues that may be remediated with their contribution. The reader will be also exposed to the incorporation methods to established biomaterials such as scaffolds, the functionalization strategies, and the synthetic paths for the assembly from biocompatible building blocks and natural metabolites.

This study analyses some of the leading technologies for the construction and configuration of IT infrastructures to provide services to users. For modern applications, guaranteeing service continuity even in very high computational load or network problems is essential. Our configuration has among the main objectives of being highly available (HA) and horizontally scalable, that is, able to increase the computational resources that can be delivered when needed and reduce them when they are no longer necessary. Various architectural possibilities are analysed, and the central schemes used to tackle problems of this type are also described in terms of disaster recovery. The benefits offered by virtualisation technologies are highlighted and are bought with modern techniques for managing Docker containers that will be used to build the back-end of a sample infrastructure related to a use-case we have developed. In addition to this, an in-depth analysis is reported on the central autoscaling policies that can help manage high loads of requests from users to the services provided by the infrastructure. The results we have presented show an average response time of 21.7 milliseconds with a standard deviation of 76.3 milliseconds showing excellent responsiveness. Some peaks are associated with high-stress events for the infrastructure, but the response time does not exceed 2 seconds even in this case. The results of the considered use case studied for nine months are presented and discussed. In the study period, we improved the back-end configuration and defined the main metrics to deploy the web application efficiently.

Hydrogels are used for various biomedical applications due to their biocompatibility, capacity to mimic the extracellular matrix, and ability to encapsulate and deliver cells and therapeutics. However, traditional hydrogels have a few shortcomings, especially regarding their physical properties, thereby limiting their broad applicability. Recently, researchers have investigated the incorporation of nanoparticles (NPs) into hydrogels to improve and add to the physical and biochemical properties of hydrogels. This brief review focuses on papers that describe the use of nanoparticles to improve more than one property of hydrogels. Such multifunctional hydrogel nanocomposites have enhanced potential for various applications, including tissue engineering, drug delivery, wound healing, bioprinting and biowearable devices.

The regeneration of bone tissue is a main purpose of most therapies in dental medicine. For bone regeneration, calcium phosphate (CaP)-based substitute materials based on natural (allo- and xenografts) and synthetic origins (alloplastic materials) are applied for guiding the regeneration processes. The optimal bone substitute has to act as a substrate for bone ingrowth into a defect, while it should be resorbed even in the time frame needed for complete regeneration up to the condition of restitution ad integrum. In this context, the modes of action of CaP-based substitute materials have been frequently investigated and it has been shown that such materials strongly influence regenerative processes such as osteoblast growth or differentiation and also on osteoclastic resorption due to different physicochemical properties of the materials. However, the material characteristics needed for the required ratio between the formation of new bone tissue and material degradation has not been found until now. The addition of different substances such as collagen or growth factors and also of different cell types have already been tested but did not allow for sufficient or prompt application. Moreover, metals or metal ions are differently used as basis or as supplement for different materials in the field of bone regeneration. Moreover, it has already been shown that different metal ions are integral components of bone tissue playing functional roles in the physiological cellular environment as well as in the course of bone healing. The present review focuses on frequently used metals as integral parts of materials designated for bone regeneration with the aim to give an overview of currently existing knowledge about the effects of metals in the field of bone regeneration.

The objective of this research was to explore the impact of a gradual replacement of Ca2+ with Mn1+ on the crystal structure of CaHPO4·2H2O, which could be beneficial for producing Ca1−xMnxHPO4·nH2O compounds. In order to prepare the starting solutions required for this study, Ca(NO3) 2·4H2O, (NH4)·2HPO4, and MnNO3·4H2O were used at various molar concentrations. The findings reveal that Mn substitutes Ca partly at low Mn/Ca molar ratios, and Mn doping reduces the unit cell of CaHPO4·2H2O. The produced biphasic compound was composed of monoclinic CaHPO4·2H2O and triclinic/monoclinic nanostructured Mn3PO4O11 after a continuous increase in the Mn/Ca molar ratio to 1. When the Ca/Mn molar ratio is 1.5, a triphasic compound of monoclinic CaHPO4·2H2O, triclinic Mn3P4O11, and Orthorhombic MnHPO4·(H2O)3 phases is formed. The formation of the monophasic MnHPO4·(H2O)3 compound was achieved by increasing the Mn/Ca molar ratio above 4. By adjusting the Mn/Ca molar ratio in the initial solutions, it is possible to produce biomaterials with specific and bespoke characteristics in the future. The Mn/Ca ratio of the starting solution can be modified to produce Ca1-xMnxHPO4-nH2O phases with weight percent compositions, as established. For the first time, Ca1−xMnxHPO4·nH2O compounds were synthesized using novel methods in this manuscript.

The dawn of Artificial Intelligence (AI) in healthcare stands as a milestone in medical innovation. A plethora of different medical fields are heavily involved and pediatric emergency medicine is no exception. These new tools do not merely provide more advanced and efficient systems for patients’ diagnosis, management and treatment. They rather concern a strict shift from traditional methods based upon broad categories towards a more personalized healthcare. AI offers many promises in pediatric emergency medicine with a wide range of applications involving clinical decision making, patients’ flows management and prioritization. Main barriers to a widespread diffusion involve technological challenges but also ethical issues and the paucity of extensive datasets in pediatric contexts. We conducted a narrative review structured in two parts. The first part explores the theoretical principles of AI, providing all the necessary background to feel confident with these new state-of-the-art tools. The second part presents an informative analysis of AI models in pediatric emergencies, pointing out the actual applications and challenges until future feasible research perspectives.

Semenov-Tian-Shansky has introduced the modified classical Yang-Baxter equation, which is called the modified $r$-matrix. Relevant studies have been extensive in recent times. This paper is devoted to study cohomology theory of modified Rota-Baxter pre-Lie algebras and its applications. First we introduce the concept and representations of modified Rota-Baxter pre-Lie algebras. We then develop the cohomology of modified Rota-Baxter pre-Lie algebras with coefficients in a suitable representation. As applications, we consider the infinitesimal deformations, abelian extensions and skeletal modified Rota-Baxter pre-Lie 2-algebra in terms of lower degree cohomology groups.

A Infectious diseases account for about 3 million deaths per year. The advent of molecular techniques has led to an enormous improvement in their diagnosis, both in terms of sensitivity and specificity and in terms of the speed with which a clinically useful result can be obtained. Digital PCR, or 3rd generation PCR, is based on a series of technical modifications that result in more sensitive techniques, more resistant to the action of inhibitors and capable of direct quantification, without the need for standard curves.This review presents the main applications that have been developed for the diagnosis of viral, bacterial and parasitic infections, and the potential prospects for the clinical use of this technology.

AbstractCurrently, public health researchers are investigating several complex and multi-faced health problems that require mixed-method approaches (MMA) to clearly understand problems and design proper intervention approaches and policy formulation. Besides, a comprehensive comprehension of the complex operation of society is vital. The MMA helps in having a broader understanding of the functioning and structure of the community at large. MMA deliberately integrates qualitative and quantitative approaches carefully to build on the merits of each other and confirm that the findings of research are closer to the truth. Therefore, public health scholars must have an excellent comprehension of the MMA and its philosophical foundations for discovering the complex conditions that challenge public health nowadays. However, public health researchers do not clearly understand the theoretical underpinnings and practical application of MMA, despite its popularity in developing countries. Therefore, this article aimed to provide comprehensive evidence on the theoretical underpinnings and practical application of MMA.

Probiotics, living microorganisms with demonstrated health benefits when administered in sufficient quantities, have a rich history as dietary supplements to benefit human health. Recently, understanding of their mechanisms in the gastrointestinal tract has prompted exploration of probiotics in treating human diseases. However, the effective and precise delivery of probiotics remains a significant challenge in therapeutic applications. Here, we review the mechanisms of action of probiotics in human health and the most advanced strategies for efficient probiotic delivery. We also discuss the potential applications of engineered probiotics in disease treatment. This review contributes insights into the evolving landscape of probiotic research for therapeutic applications.

Green synthesis offers a superior alternative to traditional methods for producing metal and metal oxide nanoparticles. This approach is not only benign and safe but also cost-effective, scalable, and straightforward, operating under ambient conditions. Notable metals and metal oxide nanoparticles, such as manganese oxides, iron oxides, silver, and gold, have been produced using various bio-reductants derived from plant extracts. These biological agents not only expedite the reduction process but also stabilize the nanoparticles, serving dual roles as reducing and capping agents. This review presents the green synthesis of nanoparticles (NPs) obtained from biogenic wastes and plant extracts. The green-synthesized nanostructured MnO2 are evaluated as a potential photocatalyst for water treatment and as an electrode material in lithium-ion batteries and supercapacitors. The green-derived iron oxide nanoparticles are examined as promising antioxidant, anti-inflammatory, and anti-diabetic agents. Additionally, this review discusses the green synthesis of precious metal nanoparticles, specifically silver (Ag NPs) and gold (Au NPs), highlighting their potential medical applications in areas like antiviral treatments and cancer therapy.

Background: Advances in additive manufacturing and the development of antimicrobial biopolymers facilitate the production of critical medical devices with strong and safe biocidal properties. The purpose of this study was to assess the antimicrobial efficacy, safety, and longevity of a polylactic acid-based biopolymer supplemented with a copper-based composite additive. Methods: An antimicrobial polylactic acid-based biopolymer was tested against several inoculants including Staphylococcus Aureus, MRSA, E. coli, Listeria, HCoV-229E (a SARS-CoV-2 surrogate), and HIV-1. Material safety was evaluated according to international testing standards for in vitro cytotoxicity. Results: The main findings of the present investigation showed a strong and long-lasting biocidal effect of a polylactic acid-based biopolymer embedded with a copper-based composite additive against Staphylococcus Aureus, MRSA, E. coli, Listeria, HCoV-229E (a SARS-CoV-2 surrogate), and HIV-1. Furthermore, the cytotoxicity and safety assessment of the antimicrobial biopolymer was found to be “non-toxic” and safe for human skin contact. Conclusions: The present investigation showed that the antimicrobial biopolymer exhibits strong and long-lasting biocidal properties against an array of viral and bacterial inoculants.

Carbon dots are carbon based nanoparticles, which have the characteristics of simple preparation process, photoluminescence, biocompatibility, adjustable surface function, water solubility, and low toxicity. They are widely used in biological applications such as imaging, biosensing, photocatalysis, and molecular transfer. It has also aroused great interest among researchers in agriculture and has made significant progress in improving crop growth and production. This review presents the physiological functions of carbon dots for crop growth and development, photosynthesis, water and nutrient absorption, and abiotic stress resistance and their applications in improving ecological environment and agriculture as biosensors, and the future application prospects and research directions of carbon dots in agriculture were prospected.

Chat generative pre-trained transformer (ChatGPT) is a developed language model and a subgroup of artificial intelligence (AI) which has demonstrated noticeable innovation in interactions between computer models and human studies. The release of ChatGPT in November 2022 attracted over 100 million users in a short time. It has unlimited applications in different fields of studies such as technology and science. ChatGPT utilizes deep learning and internet text to produce responses that resemble human language, although their accuracy is not always guaranteed. ChatGPT and GPT-4 can make a huge revolution in biology, medical, dental research, and health care. However, it's important to acknowledge the limitations of ChatGPT such as limitation in accessing the latest data. ChatGPT has generated both excitement and concern regarding its potential misuse. Its utilization in scientific publications has sparked debates and prompted the development of policies to govern the use of it. Although ChatGPT has certain limitations, it could impact on many different fields of study. There are challenges associated with using ChatGPT in the field of laboratory medicine and biology, particularly in the interpretation of test results. In this article, we review some of the applications of ChatGPT and GPT-4 in biology, dental and medical studies, and concerns about ChatGPT. Despite ChatGPT's conversational abilities are impressive, there are important considerations regarding its use in different fields of research and academic public.

3D/4D printing technologies are currently among the fastest growing, cutting edge fabrication technologies. The scale of their applications is vast and applicable to nearly all industries. 3D printing technologies are particularly popular in robotics, and especially in advanced design innovative solutions for areas such as manufacturing, space technology and medicine. The development of robotics, and in particular of the precision of manufactured components, such as actuators, pneumatic muscles, power transmission units, etc., means that new prototypes are still being made, where the use of 3D printers reduces the production time severalfold and allows for completing the necessary simulations and tests. In addition, the use of 3D printers allows for the production of thin-walled and cellular structures, which is a great advantage compared to conventional fabrication technologies. In the range of 3D printers available on the market, only a few selected technologies allow for actual use in the construction of advanced robot elements (muscles, vibration dampers, etc.). In the era of rapid growth of the precision of the available 3D printers and modern materials, 3D printing may soon become a major tool in robotics. This article presents an overview of 3D printing technologies and materials in terms of their application in robotics and provides examples of the use of 3D and 4D printing in prototyping and fabrication of robotic elements with particular emphasis on the current state of the art. The study considered the possibilities of using 3D/4D printing in robotics with the use of polymeric materials. The review of the literature and the research work currently being carried out in this area is very promising and it seems that 3D/4D printing in robotics is widely used and is still developing, which allows to conclude that in the near future the number of research works in this field will increase rapidly.

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.

Nanoparticles (NPs) have uniform chemical composition, size, and morphology. Microorganisms are of great interest in Nanoparticle synthesis. The green production of nanomaterials occurs either intracellularly or extracellularly. Gold and silver nanoparticles are mostly synthesised by the enzymatic degradation of metal ions. The produced NPs are characterized by different instruments such as ultraviolet visible, dynamic light scattering, x-ray diffraction, scanning electron microscope, transmission electron microscope, etc. Our review discusses the various biomedical applications of gold and silver nanoparticles synthesized by microbes via intracellular and extracellular mechanisms.

Earth Observation (EO) techniques have significantly evolved over time, covering a wide range of applications in different domains. The scope of this study is to review the research conducted on EO in the Eastern Mediterranean, Middle East, and North Africa (EMMENA) region and to identify the main knowledge gaps. We searched through the Web of Science database for papers published between 2018 and 2022 for EO studies in the EMMENA. We categorized the papers in the following thematic areas: Atmosphere, Water, Agriculture, Land, Disaster Risk Reduction (DRR), Cultural Heritage, Energy, Marine Safety and Security (MSS) and Big Earth Data (BED); 6647 papers were found with the highest number of publications in the thematic areas of BED (27%) and Land (22%). Most of the EMMENA countries are surrounded by sea, yet there was a very small number of studies on MSS (0.9% of total number of papers). This study detected a gap in fundamental research in the BED thematic area. Other future needs identified by this study are the limited availability of very high resolution and near real time remote sensing data, the lack of harmonized methodologies and the need for further development of models, algorithms, early warning systems and services.

Perovskite solar cells are an evolving technology owing to self-assembling and highly tunable bandgap properties of materials. In this context, thin films of perovskites have attracted immense attention and witnessed great advancement because of their low manufacturing cost. The key development in these devices is the conversion efficiency, which rose from 3.8% to 28%. The formulation of innovative materials with the proper replacement of lead in perovskites is imperative to mitigate lead toxicity. Here, we analyzed the challenges like material and structural stability, device stability under high temperature and humidity conditions, lifetime, manufacturing cost, etc. faced in the commercialization of perovskite devices. This review shows that challenges such as device engineering, performance stability against the harsh environment, cost-effectiveness, and environmental concern should be taken into consideration for the widespread acceptance of perovskite-based solar devices. In conclusion, we suggested that an enormous scope exists for exploring high-performance and long-lasting perovskites for energy applications.

With the exponential growth of machine learning and development of Artificial Neural Network (ANNs) in recent years, there is great opportunity to leverage this approach and accelarate bio-logical discoveries through applications on the analysis of bioinformatics data. Various types of datasets including for example protein or gene interaction networks, molecular structures and cellular signalling pathways, have already been used for machine learning by training ANNs for inference and pattern classification. However, unlike regular data structures that are commonly used in the computer science and engineering fields, bioinformatics datasets present challenges that require unique algorithmic approaches. The recent development of the geometric and deep learning approach within the machine learning field, is very promising towards accelerating analysis complex bioinformatics datasets. The principles of ANNs and their importance for bio-informatics machine learning is demonstrated herein, through presentation of the undelying mathematical and statistical foundations from group theory, symmetry, linear algebra. Further-more, the structure and functions of ANN algorithms that form the core principles of artificial intelligence are explained, in relation to the bioinformatics data domain. Overall, the manuscript provides guidance for researchers to understand the principles required for practicing machine learning and artificial intelligence, with the special considerations towards bioinformatics applications.

When discussing the gauge symmetries of any theory, the Henneaux-Teitelboim transformations are often underappreciated or even completely ignored, due to their on-shell triviality. Nevertheless, these gauge transformations play an important role in understanding the structure of the full gauge symmetry group of any theory, especially regarding the subgroup of diffeomorphisms. We give a review of the Henneaux-Teitelboim transformations and the resulting gauge group in the general case, and then discuss its role in the applications to the class of topological theories called nBF models, relevant for the constructions of higher gauge theories and quantum gravity.

Using flexible AC transmission system (FACTS) devices in power systems while adhering to some equality and inequality constraints, researchers around the world sought to address this issue with the objectives of improving the voltage profile, reducing power losses in transmission lines, and increasing system reliability and safety. The recent development of FACTS controllers opens up new perspectives for safer and more efficient operation of electrical power networks by continuous and rapid action on power systems parameters, such as phase angle shifting, voltage injection and line impedance compensation. Thus, an improvement on voltage profile and enhancement of power transfer capability can be obtained. It is for that, the idea behind the FACTS concept is to enable the transmission system to be an active element in increasing the flexibility of power transfer requirements and in securing stability of integrated power system. It may also be effective in transient stability improvement, power oscillations damping and balancing power flow in parallel lines. The primary issue that has significantly piqued the interest of a number of researchers working in this field is the FACTS optimization problem, which involves determining the optimal type, location, and size of FACTS devices in electrical power systems. For solving this mixed integer, nonlinear and non-convex optimization problem, this paper provides an in-depth and comprehensive review of the various optimization techniques covered in published works in the field. In this review, a classification of optimization techniques in five main groups that are widely used, such as classical optimization techniques or conventional optimization approaches, Meta heuristic methods, analytic methods or sensitive index methods and mixed or hybrid methods, is summarized. In addition, a performance descriptions and comparison of these different optimization techniques are discussed in this study. Finally, some advice is offered for future research in this field.

Attempts to use computers to aid in the detection of breast malignancies date back more than 20 years. Despite significant interest and investment, this has historically led to minimal or no significant improvement in performance and outcomes with traditional computer aided detection. However, recent advances in artificial intelligence and machine learning are now starting to deliver on the promise of improved performance. There are at present more than 20 FDA approved AI applications for breast imaging, but adoption and utilization are widely variable and overall low. Breast imaging is unique and has aspects that create both opportunities and challenges for AI development and implementation. Breast cancer screening programs worldwide rely on screening mammography to reduce the morbidity and mortality of breast cancer and many of the most exciting research and available AI applications focus on cancer detection for mammography. There are however multiple additional potential applications for AI in breast imaging including decision support, risk assessment, breast density quantitation, workflow and triage, quality evaluation, response to neoadjuvant chemotherapy assessment, and image enhancement. In this review the current status, availability, and future directions of investigation of these applications are discussed, as well as the opportunities and barriers to more widespread utilization.

Advanced Nano structural hard coatings are thin materials films that range in thickness from a few nanometers to a few micrometers and are applied to different surfaces to increase their hardness and wear resistance. Physical vapor deposition (PVD) or chemical vapor deposition (CVD) methods are frequently used to deposit these coatings. These coating’s distinctive characteristics result from their microstructure, which is made up of material columns or grains with a diameter of only a few nanometers. Increased hardness, higher wear resistance, and improved tribological properties result from these short dimensions. In applications with significant wear and/or high contact pressures, such as cutting tools, bearings, and engine parts, the usage of sophisticated Nano structural hard coatings is crucial. Advanced Nano structural hard coatings include, 1. Amorphous carbon coatings with high hardness, low friction, and exceptional wear resistance are known as diamond-like carbon (DLC) coatings. DLC coatings are frequently used in a variety of industries, including the automotive, aerospace, and biomedical ones. 2. Coatings made of titanium nitride (TiN): These metallic coatings have high hardness and wear resistance. Cutting tool, machining, and aerospace industries frequently use TiN coatings. 3. Chromium nitride (CrN) coatings: These coatings have great corrosion resistance, good wear resistance, and high hardness. Cutting tools, gears, and bearings are just a few of the objects that use CrN coatings. 4. Tungsten carbide (WC) coatings: These coatings, which have a high degree of hardness and wear resistance, are frequently employed in cutting tools and other applications that require wear resistance. Advanced Nano structural hard coatings provide many advantages over conventional coatings, including better hardness, increased wear resistance, and improved tribological qualities. The automotive, aerospace, and biomedical industries are just a few that could use these coatings. In this paper the author will explain about the design synthesis and applications of advanced Nano structural hard coating in aerospace and automotive applications.

Gravity played an important role in the development of life on earth. The effect of gravity on a living organisms can be investigated by controlling the magnitude of gravity. Most reduced gravity experiments are conducted on the lower earth orbit (LEO) in the International Space Station (ISS). However, running experiments in ISS face challenges such as high cost, extreme condition, lack of direct accessibility, and long waiting period. Therefore, researchers have developed various ground-based devices and methods to perform reduced gravity experiments. However, the search for life outside the earth requires more intensive research. Advancements in conventional methods and the development of new tools are required for this purpose. The advantages of Lab-on-a-Chip (LOC) devices make them an attractive option for simulating microgravity. This paper briefly reviews the advancement of LOC technologies for simulating microgravity in a earth-based laboratory.

Developing new ways to estimate probabilities can be valuable for science, statistics, and engineering. By considering the information content of different output patterns, recent work invoking algorithmic information theory has shown that a priori probability predictions based on pattern complexities can be made in a broad class of input-output maps. These algorithmic probability predictions do not depend on a detailed knowledge of how output patterns were produced, or historical statistical data. Although quantitatively fairly accurate, a main weakness of these predictions is that they are given as an upper bound on the probability of a pattern, but many low complexity, low probability patterns occur, for which the upper bound has little predictive value. Here we study this low complexity, low probability phenomenon by looking at example maps, namely a finite state transducer, natural time series data, RNA molecule structures, and polynomial curves. Some mechanisms causing low complexity, low probability behaviour are identified, and we argue this behaviour should be assumed as a default in the real world algorithmic probability studies. Additionally, we examine some applications of algorithmic probability and discuss some implications of low complexity, low probability patterns for several research areas including simplicity in physics and biology, a priori probability predictions, Solomonoff induction and Occam's razor, machine learning, and password guessing.

Two extensions of the generalized double Lomax distribution are introduced - the spliced-scale generalized double Lomax distribution and the exponentiated generalized double Lomax distribution. Their properties are studied. Usefulness of the new distributions is shown by fitting them to stock returns datasets.

Proteases or peptidases are hydrolases that catalyze the breakdown of polypeptide chains into smaller peptide subunits. Proteases exist in all life forms, including archaea, bacteria, protozoa, insects, animals, and plants, due to their vital functions in cellular processing and regulation. There are several classes of proteases in the MEROPS database based on their catalytic mecha-nisms. This review focuses on the post-proline cleaving enzymes (PPCEs), especially the prolyl endoprotease/oligopeptidase (PEP/POP). To date, most PPCEs studied are of microbial and ani-mal origins. Recently, there are reports of new plant PPCEs. The most common PEP/POP are members of the S9 family that comprise two conserved domains. The substrate-limiting β-propeller domain prevents unwanted digestion, while the α/β hydrolase catalyzes reaction at the carboxyl-terminal of proline residues. PPCEs have diverse applications, are widely used in the beer brewing industry, and have potential as therapeutic agents for Alzheimer’s disease and celiac disease by targeting proline-rich substrates. Protein engineering via mutagenesis has been performed to improve heat resistance, pepsin-resistant capability, specificity, and protein turno-ver of PPCEs for pharmacological applications. This is the first comprehensive review to cover the biotechnological applications of PPCEs and discuss the unique prolyl cleaving activity of dif-ferent enzymes based on the recent structure-function studies from diverse taxa.

Starting from a very simple economic scenario, we build on it a game and then we introduce a general strategy able to reduce a regression problem to an equivalent binary classification problem. This reduction scheme (that we call adaptive reduction or also dynamic reduction) can be also used to derive a new boosting algorithm for regression problems named bOOst^d. The bOOst^d algorithm is very simple to implement, and it can use any learning algorithm with no priori assumptions. We present a conjecture for bOOst^d performances, which ensures a little error on training set. More important we can also provide a very good theoretical upper bound for the generalization error. We give a set of preliminary experimental results that seems to confirm our conjecture for bOOst^d performances on training set and the theoretical assumptions for the generalization error. We also provide a possible justification of why boosting often does not overfit. Finally, we leave some open problems and argue that in the future an adaptive single boosting (with an unique code) algorithm for binary, multi class and regression problems can be derived.

Even though hard, low friction coatings such as diamond like carbon (DLC) would be beneficial for the per-formance and longevity of rubber seals, a crucial challenge – as graphically illustrated in Figure 1.a – remains. The elastic mismatch of rubber substrate and DLC coating prevents a fracture free coating application. In this work a nature inspired approach (Figure 1.b) is applied to render the stiff coating flexible and resilient to delamination at the same time by direct patterning. Rubber substrates were laser structured with tile patterns and subsequently DLC-coated. Tensile and tribology tests were performed on structured and unstructured samples. Unstructured DLC-coatings showed a crack pattern induced by the coating process, which was further fragmented by tensile stress. Coatings with tile patterns did not experience a further fragmentation under load. During continuous tribological loading, less heterogenous damage is produced for tile structured samples. The findings are ascribed to the relief of induced coating stress by the tile structure, meaning a more resilient coating.

From new understanding of pectin molecular structure and physiological effects on man up to new production methods and new applications, significant new knowledge of pectin has emerged in the last two decades (2010-2020). These developments open the route to new and unexpected applications of this uniquely complex heteropolysaccharide ubiquitous in plants and fruits well beyond its traditional use as food hydrocolloid. This study provides a unified perspective on the new science and technology of pectin. Furthermore, we offer an insight into forthcoming pectin uses from an expanded perspective taking into account selected technology and economic factors that, we argue in this study, will shortly impact the pectin production and uptake in many countries.

In the design and development of novel materials that have excellent mechanical properties, classification and regression methods have been diversely used across mechanical deformation simulations or experiments. The use of materials informatics methods on large data that originate in experiments or/and multiscale modeling simulations may accelerate materials discovery or develop new understanding of materials’ behavior. In this fast-growing field, we focus on reviewing advances at the intersection of data science with mechanical deformation simulations and experiments, with a particular focus on studies of metals and alloys. We discuss examples of applications, as well as identify challenges and prospects.

Increasing environmental concern and consumer demand for natural, sustainable and eco-friendly products have prompted the replacement of synthetic surfactants with their natural plant-based alternatives. Saponins are the plant based natural surfactants characterized by their foam forming properties in aqueous solution. Their natural origin makes them eco-friendly, bio-degradable and non-toxic. Further, they possess better physicochemical properties than the syn-thetic ones. They are also reported to exhibit a lot of useful biological activities such as anti-cancer, antifungal, anti-inflammatory, antimicrobial, antioxidant and cholesterol-lowering properties. Because of their excellent surface activity, biological activities and wide distribution in nature, saponin rich plants deserve deeper insight as a sustainable source of natural surfactants as they possess the potential to replace toxic synthetic surfactants abundant today. This review article is intended to provide a brief overview on the saponins with a special notion on their surface-active properties. It encourages further studies on development of commercial formulations based on saponins for the complete replacement of the synthetic counter parts, making better use of plants sources thereby contributing to global agenda of green environment.

Over the years, Artificial Intelligence (AI) has seen a steady progress in development and evolution that can aid many sectors. Today, AI plays an essential role in the Fourth Industrial Revolution, and it is making its way into the military sector as well. Many countries are actively looking into AI military technology. Some of these innovations include image recognition, text analysis, Self-driving vehicles (SDV), gaming, robotic process automation (RPA) & Robotic and Autonomous Systems (RAS), and Autonomous Weapons Systems (AWS). In this research, we discuss the advantages of each innovation mentioned and analyze the many different cybersecurity threats that awaken due to military artificial intelligence systems. We then discuss the open research areas to better improve the current state of military artificial intelligence applications in terms of ethics, system security, proper strategy plan, accepted responsibility matrix, and introduction of relevant laws.

(1) In recent ten years, with the fast development of digital and engineering manufacturing technology, additive manufacturing has already been more and more widely used in the field of dentistry, from the first personalized surgical guides to the latest personalized restoration crowns and root implants. (2) Especially, the bioprinting of teeth and tissue is of great potential to realize organ regeneration and finally improve the life quality. (3) In this review paper, we firstly presented the workflow of additive manufacturing technology. Then we summarized main applications and recent research progresses of additive manufacturing in dentistry. (4) Lastly, we sketched out some challenges and future directions of additive manufacturing technology in dentistry.

Nanotechnology is an emerging field of modern science based on the use of nanoparticles (NPs) with a huge potential in many sectors, including nanomedicine. Their small size confers them unique properties because they are subject to physical laws that are in the middle between classical and quantum physics. In this context, NPs project plays a pivotal role because the composition, size, shape and surface proprieties need to be carefully considered for their optimal design and application. As reported in this review, NPs are classified in inorganic (metallic NPs; quantum dots; carbon-based nanostructures; mesoporous silica nanoparticles) and organic (liposomes and micelles, dendrimers and polymer nanoparticles) ones. Here, we report an accurate description of the potential of each NPs type focusing on their multiple areas of application like theranostics drug delivery, imaging, tissue engineering, antimicrobial techniques and nanovaccines, and therefore they represent a promise to revolutionize the new era of nanomedicine, especially in cancer research.

Background: Forecasting nonlinear stochastic systems most often is quite difficult, without giving in to temptations to simply simplify models for the sake of permitting simple computations. Objective: Here, two basic algorithms, Adaptive Simulated Annealing (ASA) and path-integral codes PATHINT/PATHTREE (and their quantum generalizations qPATHINT/qPATHTREE) are offered to detail such systems. Method: ASA and PATHINT/PATHTREE have been effective to forecast properties in three disparate disciplines in neuroscience, financial markets, and combat analysis. Applications are described for COVID-19. Results: Results of detailed calculations have led to new results and insights not previously obtained. Conclusion: These 3 applications give strong support to a quite generic application of these tools to stochastic nonlinear systems.

The present overview of spin-dependent phenomena in nonmagnetic semiconductor microparticles (MPs) and nanoparticles (NPs) with interacting nuclear and electron spins is aimed at covering a gap between the basic properties of spin behavior in solid-state systems and a tremendous growth of the experimental results on biomedical applications of those particles. The first part of the review represents modern achievements of spin-dependent phenomena in the bulk semiconductors from the theory of optical spin orientation under indirect optical injection of carriers and spins in the bulk crystalline silicon (c-Si) – via numerous insightful findings in the realm of characterization and control through the spin polarization – to the design and verification of nuclear spin hyperpolarization in semiconductor MPs and NPs for magnetic resonance imaging (MRI) diagnostics. The second part of the review is focused on the electron spin-dependent phenomena in Si-based nanostructures, including the photosensitized generation of singlet oxygen in porous Si and design of Si NPs with unpaired electron spins as prospective contrast agents in MRI. The experimental results are analyzed by considering both the quantum mechanical approach and several phenomenological models for the spin behavior in semiconductor/molecular systems. Advancements and perspectives of the biomedical applications of spin-dependent properties of Si NPs for diagnostics and therapy of cancer are discussed.

A mathematical model is proposed for quantitative description of the outbreak of novel coronavirus COVID-19 Although the model is relatively simple, the comparison with the public data shows that an exact solution solution of the model (with the correctly-specified parameters) leads to the results, which are in good agreement with the measured data in China and Austria. Prediction of the total number of the COVID-19 cases is discussed and examples are presented using the measured data in Austria, France and Poland.

The popularity of deep reinforcement learning (DRL) methods in economics have been exponentially increased. DRL through a wide range of capabilities from reinforcement learning (RL) and deep learning (DL) for handling sophisticated dynamic business environments offers vast opportunities. DRL is characterized by scalability with the potential to be applied to high-dimensional problems in conjunction with noisy and nonlinear patterns of economic data. In this work, we first consider a brief review of DL, RL, and deep RL methods in diverse applications in economics providing an in-depth insight into the state of the art. Furthermore, the architecture of DRL applied to economic applications is investigated in order to highlight the complexity, robustness, accuracy, performance, computational tasks, risk constraints, and profitability. The survey results indicate that DRL can provide better performance and higher accuracy as compared to the traditional algorithms while facing real economic problems at the presence of risk parameters and the ever-increasing uncertainties.

Thermoelectrics, in particular solid-state conversion of heat to electricity and vice versa, is expected to be a key energy harvesting and temperature management solution in coming years. There has been a resurgence in the search for new materials for advanced thermoelectric energy conversion applications and to enhance the properties of existing materials. In this paper, we review recent efforts on improving figure-of-merit (ZT) through alloying and nano structuring. As heatsink characteristics dictate the performance of thermoelectric modules, various types of heatsink designs has been investigated. Several reported strategies for improving ZT are critically assessed. A notable increase in figure-of-merit of thermoelectric materials (TE) has opened up new areas of applications especially in the medical field. Peltier cooling devices are widely employed for patient core temperature control, skin cooling, medical device and laboratory equipment cooling. Application of these devices in the medical field both in temperature control and power generation has been studied in detail. It is envisioned that this study will provide profound knowledge on the thermoelectric based materials and its role in medical applications.

Deep Learning is a recent and important addition to the computational toolbox available for image reconstruction in fluorescence microscopy. We review state-of-the-art applications such as image restoration, super-resolution, and light-field imaging, and discuss how the latest Deep Learning research can be applied to other image reconstruction tasks such as structured illumination, spectral deconvolution, and sample stabilisation. Despite its successes, Deep Learning also poses significant challenges, has often misunderstood capabilities, and overlooked limits. We will address key questions, such as: What are the challenges in obtaining training data? Can we discover structures not present in the training data? And, what is the danger of inferring unsubstantiated image details?

To extend the calculation power of tensor analysis, we introduce four new definition of tensor calculations. Some useful tensor identities have been proved. We demonstrate the application of the tensor identities in continuum mechanics: momentum conservation law and deformation superposition.

In the first part of this talk we review some prerequisites for and essential arguments involved in the construction of the thermal-ground-state estimate for the deconfining phase in the thermodynamics of SU(2) Quantum Yang-Mills theory and how this structure supports its distinct excitations. The second part applies deconfining SU(2) Yang-Mills thermodynamics to the Cosmic Microwave Background in view of (i) a modified temperature-redshift relation with an interesting link to correlation-length criticality in the 3D Ising model, (ii) the implied minimal changes in the dark sector of the cosmological model, and (iii) best-fit parameter values of this model when confronted with the spectra of the angular two-point functions TT, TE, and EE, excluding the low-$l$ physics. The latter, which is treated in an incomplete way because of the omission of radiative effects, is addressed in passing towards future work.

Background. Nanotechnology is promising field for generating new applications. A green synthesis of nanoparticles through biological methods using plant extract have a reliable and ecofriendly approach to improve our global environment. Methods. Silver nanoparticles (AgNPs) were synthesized using aqueous extract of Anagalis arvensis L and silver nitrate and were physicochemically characterized. Results. The stability of AgNPs toward acidity, alkalinity, salinity and temperature showed that they remained stable at room temperature for more than two months. The SEM and TEM analysis of the AgNPs showed that they have a uniform spherical shape with an average size in the range of 40–78 nm. Further 1-Dibhenyl-2-Picrylhydrazl radical in Anagalis arvensis L.mediated AgNPs showed a maximum activity of 98% at concentration of 200μg/mL. Hydrogen peroxide scavenging assay in Anagalis arvensis L. mediated AgNPs showed a maximum activity of 85% at concentration of 200μg/mL. Reducing power of Anagalis arvensis L.Ag NPs exhibited a higher activity of 330 μg/mL at concentration of 200 μg/mL. These NPs have cytotoxic effects against brine shrimp (Artemia salina) nauplii with a value of 53% LD 178.04μg/mL. Conclusion. The AgNPs synthesized using Anagalis arvensis L. extract demonstrate a broad range of applications.

Soft set theory is a mathematical tool for dealing with uncertainty. This paper investigates limits of interval type of soft sets (for short, it-soft sets). The concept of it-soft sets is first introduced. Then, limits of it-soft sets are proposed and their properties are obtained. Next, point-wise continuity of it-soft sets and continuous it-soft sets are discussed. Finally, an application for rough sets is given.

In this paper we establish some weighted integral inequalities of Ostrowski, Čebyšev and Lupaş type. Applications for continuous probability density functions supported on infinite intervals with two examples are also given.

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.

Mixtures of methacrylic polymers are the most common materials for making composites to be used as resins for dental and cosmetic applications. Some of these mixtures are composed by poly (ethyl methacrylate) (PEMA) and poly (methyl methacrylate) (PMMA), which constitute a solid component to be mixed with a liquid component made out of methacrylate monomers. The reaction between the thermal initiator benzoyl peroxyde (BPO) present in the solid component and the activator of the polymerization process, N,N-dimethyl-p-toluidine (DMT) present in the liquid component, gives rise to thermoset materials. In the present study, different liquid formulations composed by a mixture of two methacrylic monomers, ethyl methacrylate (EMA) and triethylene glycol dimethacrylate (TEGDMA) for cosmetic applications, were prepared and characterized, using a commercial powder (POW) composed by PEMA and PMMA. With the aim of improving workability during final application of the material, it was necessary to slow down the polymerization rate of liquid formulations. Their thermal behaviour was investigated by DSC in order to check the polymerization rate. Thermal stability of final materials was determined by TGA. DMTA, microindentation hardness and impact tests were performed on final materials, to assess their performance with respect to standard formulation. The combination of thermal and mechanical properties allows choosing which formulations could be suitable for use in cosmetics.

In the era when the market segment of Internet of Things (IoT) tops the chart in various business reports, it is apparently envisioned that the field of medicine expects to gain a large benefit from the explosion of wearables and internet-connected sensors that surround us to acquire and communicate unprecedented data on symptoms, medication, food intake, and daily-life activities impacting one’s health and wellness. However, IoT-driven healthcare would have to overcome many barriers, such as: 1) There is an increasing demand for data storage on cloud servers where the analysis of the medical big data becomes increasingly complex; 2) The data, when communicated, are vulnerable to security and privacy issues; 3) The communication of the continuously collected data is not only costly but also energy hungry; 4) Operating and maintaining the sensors directly from the cloud servers are non-trial tasks.This book chapter defined Fog Computing in the context of medical IoT. Conceptually, Fog Computing is a serviceoriented intermediate layer in IoT, providing the interfaces between the sensors and cloud servers for facilitating connectivity, data transfer, and queryable local database. The centerpiece of Fog computing is a low-power, intelligent, wireless, embedded computing node that carries out signal conditioning and data analytics on raw data collected from wearables or other medical sensors and offers efficient means to serve telehealth interventions. We implemented and tested an fog computing system using the Intel Edison and Raspberry Pi that allows acquisition, computing, storage and communication of the various medical data such as pathological speech data of individuals with speech disorders, Phonocardiogram (PCG) signal for heart rate estimation, and Electrocardiogram (ECG)-based Q, R, S detection. The book chapter ends with experiments and results showing how fog computing could lessen the obstacles of existing cloud-driven medical IoT solutions and enhance the overall performance of the system in terms of computing intelligence, transmission, storage, configurable, and security. The case studies on various types of physiological data shows that the proposed Fog architecture could be used for signal enhancement, processing and analysis of various types of bio-signals.

Europium (Eu)-doped fluorapatite (FA) nanorods has a similar biocompatibility with hydroxyapatite (HA) in terms, attracted much attention as cell imaging biomaterials due to their luminescent property. Here, we will discuss the new feature of europium doped fluorapatite (Eu-FA) nanorods as anticancer drug carrier. Eu-FA nanorods was prepared using a hydrothermal method. The morphology, crystal structure, fluorescence and composition are investigated. The specific crystal structure, enabling an effective loading of drug molecules. Doxorubicin (DOX), used as an anticancer model drug, was shown to be effectively loaded onto the surface of the nanorods. The DOX release was fairly pH-dependent, occurring more rapidly at pH 5.5 than pH 7.4 was observed. The intracellular penetration of the DOX-loaded Eu-FA nanorods (Eu-FA/DOX) can be imaged in situ due to the self-fluorescence property. Treatment of melanoma A375 cells with Eu-FA/DOX elicited a more effective apoptosis rate than direct DOX treatment. Overall, Eu-FA show great promise for tracking and treating tumor, may potentially useful as a multifunctional carrier system to effectively load and sustainably deliver drugs.

Oral bone defects occur as a result of trauma, cancer, infections, periodontal diseases, and caries. Autogenic and allogenic grafts are the gold standard used to treat and regenerate damaged or defective bone segments. However, these materials do not possess the antimicrobial properties necessary to inhibit the invasion of the numerous deleterious pathogens present in the oral microbiota. In the present study poly(ε-caprolactone) (PCL), nano-hydroxyapatite (nHAp), and a commercial extract of Humulus lupulus L. (hops) were electrospun into polymeric matrices to assess their po-tential for drug delivery and bone regeneration. The fabricated matrices were analyzed by scanning electron microscopy (SEM), tensile analysis, thermogravimetric analysis (TGA), FT-IR assay, and in vitro hydrolytic degradation. The antimi-crobial properties were evaluated against the oral pathogens Streptococcus mutans, Porphyromonas gingivalis, and Aggregatibacter actinomycetemcomitans. The cytocompatibility was proved by the MTT assay. SEM analysis proved the nanostructured matrices have suitable fiber size. The present research provides new information about the interaction of natural compounds with ceramic and polymeric biomaterials. The hop extract and other natural or synthetic medicinal agents can be effectively loaded into PCL fibers and has the potential to be used in biomedical applications.

The result of the multidisciplinary collaboration of researchers from different areas of knowledge to validate a solar radiation model is presented. The MAPsol is a 3D local-scale adaptive solar radiation model that allows to estimate direct, diffuse and reflected irradiance for clear sky conditions. The model includes the adaptation of the mesh to complex orography and albedo, and considers the shadows cast by the terrain and buildings. The surface mesh generation is based on surface refinement, smoothing and parameterisation techniques and allows the generation of high quality adapted meshes with a reasonable number of elements. Another key aspect of the paper is the generation of a high-resolution digital elevation model (DEM). This high-resolution DEM is constructed from LiDAR data and its resolution if two times more accurate than the publicly available DEMs. The validation process uses direct and global solar irradiance data obtained from pyranometers at the University of Salamanca located in an urban area affected by systematic shading from nearby buildings. This work provides an efficient protocol for studying solar resources, with particular emphasis on areas of complex orography and dense buildings where shadows can potentially make solar energy production facilities less efficient.

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 use of plant-based and microorganism-based biological inputs is a sustainable agricultural practice, promoting a suitable and better utilization of non-renewable resources in the environment. The benefits of using microorganisms are associated with both direct and indirect mechanisms, primarily related to improvements in nutrient absorption and availability, as well as plant growth. The main benefits of using biochemical pesticides are the promotion of sustainability and the management of resistance. Although the use of microorganisms and botanical metabolites is a promising agricultural alternative, it is still primarily concentrated in grain crops. However, due to the wide range of mechanisms of action of plant-based and microorganism-based biological inputs, there is an opportunity to expand their use in agriculture. In the Brazilian biological inputs market, various terminologies have been used to characterize biological-based products, with many of these terms being employed incorrectly and sometimes conflicting with the country’s legislation. Therefore, this review presents the classes of biological inputs and the application and evolution of the plant-based and microbiological-based product market in different crops in Brazil.

Distributions with bounded support show considerable sparsity over those with unbounded support, despite the fact that there are a number of real-world contexts where observations take values from a bounded range (proportions, percentages, and fractions are typical examples). For proportion modelling, a flexible family of two-parameter distribution functions associated with the exponential distribution is proposed here. Mathematical and statistical properties of the novel distribution are examined, including quantiles, mode, moments, hazard rate function, and its characterization. The parameter estimation procedure using the maximum likelihood method was carried out, and applications to environmental and engineering data were also considered. To this end, various statistical tests are used, along with some other information criterion indicators to determine how well the model fits the data. The proposed model is found to be the most efficient plan in most cases for the datasets considered.

Biosensors are analytical devices that utilize biological interactions to detect and quantify clinical biomarkers, contaminants, allergens, and microorganisms. They combine different disciplines including analytical chemistry, molecular biology, and electrical engineering. Biosensors operate by coupling a bioreceptor, such as nucleic acid or proteins, with a transducer that converts the biological interaction into an electrical signal. Electrochemical and optical transduction are commonly used approaches due to their high detection capability and compatibility with miniaturization. Biosensors provide both high specificity and sensitivity and can be integrated into low-cost microfluidic platforms for rapid and point-of-care applications. These attributes make these devices valuable tools in analytical chemistry, particularly for early diagnostic applications. However, conventional biosensors face challenges related to the immobilization of biorecognition elements on the transducer surface, leading to issues like lost of sensitivity and selectivity. To address these problems, the introduction of nanomaterials, particularly magnetic nanoparticles (MNPs) and magnetic beads (MBs), has been implemented. MNPs combine their magnetic properties with other interesting characteristics such as small size, high surface-to-volume ratio, and excellent biocompatibility. They can be tailored for specific applications and have been extensively used in various fields, including biosensing and clinical diagnosis. Furthermore, MNPs simplify sample preparation by isolating target analytes through magnetic separation, thus improving sensitivity, and reducing analysis time and interference phenomena. The synthesis and modification of MNPs play a crucial role in adjusting their properties for different applications. This review presents an overview of the synthesis and surface modifications of magnetic nanoparticles, their role in the development of biosensors and bioassays, and their applications across various scientific areas.

While the field of personality neuroscience has extensively focused on humans and, in a few cases, primates and rodents, a wide range of research on fish personality has emerged in the last decades. This research is focused mainly on the ecological and evolutionary causes of individual differences, but also aimed less extensively at proximal mechanisms (e.g., neurochemistry or genetics). We argue that, if consistent and intentional work is made to solve some of the meta-theoretical issues of personality research both on fish and mammals, fish personality research can lead to important advances in personality neuroscience as a whole. The five dimensions of personality in fish (shyness-boldness, exploration-avoidance, activity, aggressiveness, and sociability) need to be translated into models that explicitly recognize the impacts of personality in psychopathology, synergizing research on fish as model organisms in experimental psychopathology, personality neuroscience, and ecological-ethological approaches to the evolutionary underpinnings of personality to produce a powerful framework to understand individual differences.

This paper first explains the theory of the origin of force from a new perspective, explaining that the origin of all forces is caused by the "missing of energy in the time dimension" (non-conservation in a certain period of time), and constructs a unified theory of force on this basis. Then, through the unified theory of force, the following phenomena are reinterpreted: (1) The common origin of the inverse square law of gravitation and electromagnetic force; (2) The origin of the repulsive force of electrons and protons at small distances; (3) The mystery of the rotation curve of galaxies and the new explanation of "dark matter"; (4) explain the origin of the magnetic field; (5) Predict that moving magnets have the same radiation effect as charged particles (electron magnetic moments also have radiation effects); (6) Reinterpret Maxwell's displacement current; (7) Reinterpret synchrotron radiation and bremsstrahlung; (8) Explain that the strong and weak forces are short-range forces; (9) Predict the true structure of black holes and the origin of gravitational waves;(10) Possible explanation for the origin of the speed of light.

Holographic subsurface radar (HSR) is currently not in widespread usage. This is due to an historical perspective in the ground penetrating radar (GPR) community that the high attenuation of electromagnetic waves in most media of interest, and the inability to apply time-varying gain to the continuous wave (CW) HSR signal precludes sufficient effective penetration depth. While it is true that the fundamental physics of HSR, with its use of a CW signal, does not allow amplification of later (i.e. deeper) arrivals in lossy media (as is possible with impulse subsurface radar — ISR), HSR has distinct some distinctive advantages. The most important of these is the ability to do shallow subsurface imaging with a resolution that is not possible with ISR. In addition, the design of an HSR system is simpler than for ISR due to the relatively low-tech transmitting and receiving antennae. This paper provides a review of the main principles of HSR through an optical analogy and describes possible algorithms for radar hologram reconstruction. We also present a review of the history of development of systems and applications for HSR of the “RASCAN” type which is possibly the only holographic subsurface radar that is produced in lots. Among the subsurface imaging and remote sensing applications considered are humanitarian demining, construction inspection, surveys of historic architecture and artworks, nondestructive testing of dielectric aerospace materials, security applications, paleontology, detection of wood-boring insect damage, and others. Each application is illustrated with relevant data acquired in laboratory and/or field experiments.

Holographic subsurface radar (HSR) is currently not in widespread usage. This is due to an historical perspective in the ground penetrating radar (GPR) community that the high attenuation of electromagnetic waves in most media of interest, and the inability to apply time-varying gain to the continuous wave (CW) HSR signal precludes sufficient effective penetration depth. While it is true that the fundamental physics of HSR, with its use of a CW signal, does not allow amplification of later (i.e. deeper) arrivals in lossy media (as is possible with impulse subsurface radar — ISR), HSR has distinct some distinctive advantages. The most important of these is the ability to do shallow subsurface imaging with a resolution that is not possible with ISR. In addition, the design of an HSR system is simpler than for ISR due to the relatively low-tech transmitting and receiving antennae. This paper provides a review of the main principles of HSR through an optical analogy and describes possible algorithms for radar hologram reconstruction. We also present a review of the history of development of systems and applications for HSR of the “RASCAN” type which is possibly the only holographic subsurface radar that is produced in lots. Among the subsurface imaging and remote sensing applications considered are humanitarian demining, construction inspection, surveys of historic architecture and artworks, nondestructive testing of dielectric aerospace materials, security applications, paleontology, detection of wood-boring insect damage, and others. Each application is illustrated with relevant data acquired in laboratory and/or field experiments.

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.

Polyhydroxyalkanotes (PHA) are bio-based microbial biopolyesters with stiffness, elasticity, crystallinity and degradability tunable by the monomeric composition, bio-production strategy and post-synthetic processing; they display biological alternatives for diverse technomers of petrochemical origin. This, together with the fact that their monomeric and oligomeric in vivo degradation products do not exert any toxic or elsewhere negative effect to living cells or tissue of humans or animals, makes them highly stimulating for various applications in the medical field. The article provides an overview of PHA application in the therapeutic, surgical and tissue engineering area, and reviews strategies to produce PHA at purity levels high enough to be used in vivo. Tested applications of differently composed PHA and advanced follow-up products as carrier materials for controlled in vivo release of anti-cancer drugs or antibiotics, as scaffolds for tissue engineering, as guidance conduits for nerve repair or as enhanced sutures, implants or meshes are discussed from both a biotechnological and a material-scientific perspective. Particular attention is devoted to the adaptation of traditional polymer processing techniques for production of medicine-related devices based on PHA, such as melt-spinning, melt extrusion, or solvent evaporation, and to emerging processing techniques like 3D-printing, computer-aided wet-spinning, laser perforation, or electrospinning.

The paper focuses on the application of a multilevel, integrative and territorial approach to the protection of cultural heritage through planning instruments in Serbia. It points out the problems of the sectoral approach and discusses international recommendations that can be integrated into the planning methodology. The case study is the area of Djerdap National Park, which has an already established international and national importance. The research suggests that the cultural heritage in the area, with the exception of the Roman limes, is still recognized at the level of individual localities. The study emphasizes the importance of aligning all levels of governance to safeguard the whole spatial entity rather than focusing solely on its particular areas. The interna-tional and national importance of the planning area contributes to the strengthening of awareness of heritage and its institutional protection. However, the vitality of the area, which is key to its sustainable development, comes from the local level. Planning documents contribute to the inte-gration of local values and local communities into development programs and projects.

The scientific community has shown considerable interest in Industry 4.0 due to its capacity to revolutionise the manufacturing sector through digitalisation and data-driven decision-making. However, the actual implementation of Industry 4.0 within complex industrial settings presents obstacles that are typically beyond the scope of mainstream research articles. In this paper, a comprehensive case-study detailing our collaborative partnership with a leading medical device manufacturer is presented. The study traces their evolution from a state of limited digitalisation to the development of a digital intelligence platform that leverages data and machine learning models to enhance operations across a wide range of critical machines and assets. The main business objective was to enhance the energy efficiency of the manufacturing process, thereby improving its sustainability measures while also saving costs. The project encompasses energy modelling and analytics, Fault Detection and Diagnostics (FDD), renewable energy integration and advanced visualisation tools. Together, these components enable informed decision making in the context of energy efficiency.

Plasmonic sensors have great potential for widespread usage, but the prohibitive cost of noble metals restrains the wider adoption of this technology. The aim of our study is to develop a cost-effective Al-based alternative to plasmonic detectors commonly based on noble metals. We considered a structure that consists of a Si p-n junction with an overlying grating-relief aluminum layer. The investigation of the infrared plasmonic resonance dependency on the structure geometric parameters and optimization of the fabrication process allowed us to determine optimal parameters for photodetection enhancement. The resonance photoresponse of optimized aluminum-based plasmon-polariton photodetector was significantly improved and became comparable to noble metal-based analogs. Our results can be used for the creation of cost-effective high-sensitivity plasmonic sensors.

Green carbon-based materials (GCM), i.e. carbon materials produced using renewable biomass or recycled wastes, ought to be used in order to processes become sustainable and carbon neutral. Carbon nanomaterials, like for example carbon dots and nanobichar families, and carbon materials, like for example activated carbon and biochar substances, are sustainable materials with great potential to be used in different technology applications. In this review, the following four applications were selected, and the works published in the last two years (since 2022) critically reviewed: agriculture; water treatment; energy management; and, carbon dioxide reduction and sequestration. GCM improved the performance of the technological applications under revision and play an important role in the sustainability of the processes, contributing to the mitigation of the climate change, namely by reducing emission and increase sequestration of CO2eq..

The accumulation of microorganisms, plants, algae, or small animals on wet surfaces that have a mechanical function causes biofouling, which can result in structural or other functional deficiencies. The maritime shipping industry must constantly manage biofouling to optimize operational performance, which is a common and long-lasting problem. It can occur in any metal structure in contact or submerged in ocean water, which represents additional costs in terms of repairs and maintenance. This study is focused on the production of antifouling coatings, made with nanoparticles of copper selenide (CuSe) modified with gum arabic, within a water-base acrylic polymeric matrix. During the curing of the acrylic resin, the CuSe NPs remain embedded in the resin but this does not prevent the release of ions. The coatings presented to release copper and selenium ions for up to 80 days, selenium was the element that was released the most. The adhesion of film coatings to metallic substrates showed good adhesion, scale 5B (ASTM D3359 standard). Antimicrobial activity tests show that the coatings have an inhibitory effect on Escherichia coli and Candida albicans. The effect being more noticeable when the coating is detached from the substrate and placed on a growing medium, than compared to the coating on a substrate. Scanning electron microscopy (SEM) observations show that nanostructured CuSe coatings are made up of rod-shaped and spherical particles whose range ranges from 12 to 25 nm. The Energy Dispersive X-ray spectroscopy (EDS) studies showed that the ratio of selenium nanoparticles is greater than that of copper and that their distribution is homogeneous

Despite significant strides in prevention, diagnosis, and treatment, cardiovascular diseases remain the number one cause of mortality in the United States, with rates climbing at an alarming rate in the developing world. Targeted delivery of therapeutics to the heart has been a lofty goal to achieve with strategies ranging from direct intra-cardiac or intra-pericardial delivery, intra-coronary infusion, to adeno-associated viral vectors which have preference, if not complete cardio-selectivity, for cardiac tissue. Cell penetrating peptides (CPP) are 5-30 amino acid long peptides that are able to breach cell membrane barriers while carrying cargoes up to several times their size, in an intact functional form. Identified nearly three decades ago, the first of these CPPs came from the HIV coat protein transactivator of transcription. Although a highly efficient CPP, its clinical utility is limited by its robust ability to cross any cell membrane barrier, including crossing the blood-brain barrier and transducing neuronal tissue non-specifically. Several strategies have been utilized to identify cell or tissue specific CPPs, one of which is phage display. Using this latter technique, we identified a cardiomyocyte targeting peptide (CTP) more than a decade ago, a finding that has been corroborated by several independent labs across the world that have utilized CTP for myriad different purposes in pre-clinical animal models. The goal of this publication is to provide a comprehensive review of the identification, validation, and application of CTP, and outlining its potential in diagnostic and therapeutic applications especially in the field of targeted RNA interference.

Descriptive geometry has indispensable applications in many engineering activities, some of which are presented in the first chapter of this paper in order to place the development presented here among them. As a result of the continuous variability of the technological environment according to various optimization aspects, the engineering activities must also be continuously adapted to the changes, which an appropriate approach and formulation are required from the practitioners of descriptive geometry, and can even lead to improvement in the field of descriptive geometry. The imaging procedures are always based on the methods and theorems of descriptive geometry. Resolving contradictions in spatial geometry reconstruction research is a constant challenge, to which a possible answer in many cases is the search for the right projection direction. A special method of enumerating the possible infinite viewpoints for the representation of the surface edge curves is presented in another part of the paper. The procedure for determining the correct directions in a mathematically exact way is also presented through examples. The analysis and some of the results of the Monge mapping, which is suitable for the solution of a mechanical engineering task to be solved in a specific technical environment, are also presented.

Lunar seismology is a critical area of research, providing insights into the Moon's internal structure, composition, and thermal history, as well as informing the design of safe and resilient habitats for future human settlements. This paper presents the development of a state-of-the-art, three-axis broadband seismometer with a low frequency range of 0.001-1 Hz and a target sensitivity over an order of magnitude greater than previous Apollo-era instruments. The paper details the design, assembly, methodology and test results. We compare the acceleration noise of our prototype and commercial seismometers across all three axes. Increasing the test mass and reducing its natural frequency may further improve performance. These advancements in seismometer technology hold promise for enhancing our understanding of the Moon's and other celestial bodies' internal structures, and for informing the design of future landed missions to ocean worlds.

A wideband antenna with excellent efficiency is the most important requirement of wireless communication. There are many ways to improve the antenna bandwidth, such as using a low permittivity substrate, increasing the substrate thickness, and using different shapes of radiating patches. However, this cannot achieve wideband. This problem can be solved by using a metamaterial-based microstrip antenna that can achieve wideband. The proposed patch antenna has a metamaterial unit cell loaded on the top patch and on the bottom ground plane. The top unit cell, which consists of a square loop with Complementary Split Ring Resonator (CSRR) and the bottom unit cell, which is a Square Shaped Cross-Slot (SSCS), is loaded on the patch and ground. The objective is to achieve a compact metamaterial-loaded antenna with enhanced radiation characteristics and to design a metamaterial antenna for wideband applications. The wideband and high gain features of the proposed antenna make it suitable for 5G NR FR1 and Wi-Fi 6E applications.

Rapid developing antimicrobial resistance due to broad antibiotic utilisation in healthcare and food industries and the non-availability of novel antibiotics represents one of the most critical public health issues worldwide. The current advances in nanotechnology allow new materials to address drug-resistant bacterial infections in specific, focused and biologically safe ways. The unique physicochemical properties, biocompatibility, and wide range of adaptability of nanomaterials that exhibit photothermal capability can be employed to develop the next generation of photothermally induced controllable hyperthermia as antibacterial nanoplatforms. Here, we review the current state-of-the-art in different functional classes of photothermal antibacterial nanomaterials and the strategies to optimise antimicrobial efficiency. The recent achievements and trends in developing photothermally active nanostructures, including plasmonic metals, semiconductor, carbon-based, and organic photothermal polymers, and antibacterial mechanisms of action, including anti-multidrug resistant bacteria and biofilms removal, will be discussed. Insights into mechanisms of the photothermal effect and various factors influencing photothermal antimicrobial performance, emphasising the structure–performance relationship, are discussed. We will examine the photothermal agents’ functionalisation for specific bacteria, effects of near-infrared light irradiation spectrum, or active photothermal materials for multimodal synergistic-based therapies to minimise side effects and maintain low cost. Most relevant applications are presented, such as anti-biofilm formation, biofilm penetration or ablation, and nanomaterial-based infected wound therapy. Practical antibacterial applications employing the photothermal antimicrobial agents, alone or in synergistic combination with other nanomaterials, are considered. Existing challenges and limitations in photothermal antimicrobial therapy and future perspectives are presented from the structure, function, safety, and clinical potential points of view.

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.

Bioelectrocatalysis has become one of important research fields in electrochemistry and provided a firm base for an important technology for application to various bioelectrochemical devices such as biosensors, biofuel cells, and biosupercapacitors. The understanding and technology in bioelectrocatalysis have been greatly improved by introducing nanostructured electrode materials and protein-engineering methods over the last few decades. Recently, the electro-enzymatic production of renewable energy resources and useful organic compounds (bioelectrosynthesis) also attracts worldwide attention. In this review, we summarize recent progress in applications of enzymatic bioelectrocatalysis.

The advent of deep learning has completely reshaped our world. Now, our daily life is fulfilled with many well-known applications that adopt deep learning techniques, such as self-driving cars and face recognition. Furthermore, robotics developed more forms of technology which share the same principle with face recognition, such as hand pose recognition and fingerprint recognition. Image recognition technology requires a huge database and various learning algorithms, such as convolutional neural network and recurrent neural network, that requires lots of computational power, such as CPUs and GPUs. Thus, clients could not be satisfied with the computational resource of the local machine. The cloud resource platform emerged at a historic moment. Docker containers play a significant role of microservices-based applications in the next generation. However, it could not guarantee the quality of service. From clients’ perspective, they have to balance the budget and quality of experiences (e.g. response time). The budget leans on individual business owners and the required Quality of Experience (QoE) depends on usage scenarios of different applications, for instance, an autonomous vehicle requires real-time response, but, unlocking your smartphone can tolerate delays. Plenty of on-going projects developed user-oriented optimization resource allocation to improve the quality of the service. Considering the users’ specifications, including accelerating the training process and specifying the quality of experience, this thesis proposes two differentiate containers scheduling for deep learning applications: TRADL and DQoES .