Omics technologies, viz., genomics, transcriptomics, proteomics, metabolomics, and phenomics, are becoming an integral part of virtually every commercial cereal breeding program because they provide substantial dividends per unit time in both pre-breeding and breeding phases. Continuous advances in cereal-omics promise—in combination with time efficiency—the cost benefits. In this review, we provide a comprehensive overview of the established cereal-omics methods in five major cereals, viz., rice, sorghum, maize, barley, and bread wheat. We cover the evolution of technologies in each omics section independently and concentrate on their use to improve economically important agronomic as well as biotic and abiotic stress-related traits. Advancements in the (1) identification, mapping, and sequencing of molecular/structural variants, (2) high-density transcriptomics data to study gene expression patterns, (3) global and targeted proteome profiling to study protein structure and interaction, (4) metabolomic profiling to quantify organ level small-density metabolites and their composition, and (5) high-resolution high-throughput image-based phenomics approaches are surveyed in this review.

The aim of this review is to provide a survey of the recent advances and the main remaining challenges related to the ultrananocrystalline diamond (UNCD) nanowires and other nanostructures which exhibit excellent capability as the core components for many diverse novel sensing devices, due to the unique material properties and geometry advantages. The doping introduced in the gas phase during deposition promotes p-type or n-type conductivity. With the establishment of the UNCD nanofabrication techniques, more and more nanostructure based devices are being explored in measuring basic physical and chemical parameters via classic and quantum methods, as exemplified by gas sensors, ultraviolet photodetectors, piezoresistance effect based devices, biological applications, and nitrogen-vacancy color center based magnetic field quantum sensors. Highlighted finally are some of the remaining challenges and future outlook in this area.

Applications in security and electronic surveillance require combination of excellent magnetic softness with good mechanical and anti-corrosive properties and low dimensionality. We overviewed the properties of soft magnetic glass-coated microwires and different post-processing making them quite attractive for electronic article surveillance and security applications. We studied the magnetostatic interaction between the microwires and routes to tune the non-linear hysteresis loops by using the arrays of different types of amorphous microwires. The presence of neighbouring microwire (either Fe or Co-based) significantly affects the hysteresis loop of the whole microwires array. In a microwires array containing magnetically bistable microwires we observed splitting of the initially rectangular hysteresis loop with a number of Barkhausen jumps correlated with the number of magnetically bistable microwires. Essentially, non-linear and irregular hysteresis loops have been observed in mixed arrays containing Fe and Co-rich microwires. The observed non-linearity in hysteresis loops allowed to increase the harmonics and tune their magnetic field dependencies. Non-linear hysteresis loops have been also observed upon devitrification of amorphous microwires. On the other hand, several routes allowing to tune the switching field by either post-processing or modifying the magnetoelastic anisotropy have been reviewed. Observed unique combination of magnetic properties together with thin dimensions and excellent mechanical and anti-corrosive properties provide excellent perspectives for the use of glass-coated microwires for security and electronic surveillance applications.

Membrane remodeling and phospholipid biosynthesis are normally tightly regulated to maintain the shape and function of cells. Indeed, different physiological mechanisms ensure a precise coordination between de novo phospholipid biosynthesis and modulation of membrane morphology. Interestingly, the overproduction of certain membrane proteins hijack these regulation networks, leading to the formation of impressive intracellular membrane structures in both prokaryotic and eukaryotic cells. The proteins triggering an abnormal accumulation of membrane structures inside the cells (or membrane proliferation) share two major common features: 1) they promote the formation of highly curved membrane domains and 2) they lead to an enrichment in anionic, cone-shaped phospholipids (cardiolipin or phosphatidic acid) in the newly formed membranes. Taking into account the available examples of membrane proliferation upon protein overproduction, together with the latest biochemical, biophysical and structural data, we explore the relationship between protein synthesis and membrane biogenesis. We propose a mechanism for the formation of these non-physiological intracellular membranes that shares similarities with natural inner membrane structures found in α-proteobacteria, mitochondria and some viruses-infected cells, pointing towards a conserved feature through evolution. We hope that the information discussed in this review will give a better grasp of the biophysical mechanisms behind physiological and induced intracellular membrane proliferation, and inspire new applications, either for academia (high-yield membrane protein production and nanovesicle production) or industry (biofuel production and vaccine preparation).

Metallic nanoparticles (NPs), among polymeric NPs, liposomes, micelles, quantum dots, dendrimers, or fullerenes, are becoming more and more important due to their potential use in the novel medical therapies. Titanium dioxide (titanium(IV) oxide, titania, TiO2) is an inorganic compound that owes its recent rise in scientific interest to photoactivity. After the illumination in aqueous media with UV light, TiO2 produces an array of reactive oxygen species (ROS). The capability to produce ROS and thus induce cell death has found application in the photodynamic therapy (PDT) for the treatment of a wide range of maladies, from psoriasis to cancer. Titanium dioxide NPs were studied as photosensitizing agents in the treatment of malignant tumors as well as in photodynamic inactivation of antibiotic-resistant bacteria. Both TiO2 NPs themselves, as well as their composites with other molecules, can be successfully used as photosensitizers in PDT. Moreover, various organic compounds can be grafted on TiO2 NPs, leading to hybrid materials. These nanostructures can reveal increased light absorption allowing their further use in targeted therapy in medicine. In order to improve efficient anticancer therapy, many approaches utilizing titanium dioxide were tested. The most significant studies are discussed in this review.

This article reports new measurements of laser-induced plasma hypersonic expansion measurements of diatomic molecular cyanide (CN). Focused, high-peak power 1064-nm Q-switched radiation of the order of 1 TW/cm² generates optical breakdown plasma in a cell containing a 1:1 molar gas mixture of N₂ and CO₂ at a fixed pressure of 1.1 × 10⁵ Pascal and in a 100 ml/min flow of the mixture. Line-of-sight (LOS) analysis of recorded molecular spectra indicate the outgoing shockwave at expansion speeds well in excess of Mach number 5. Spectra of atomic carbon confirm an increased electron density near the shock wave, and equally, molecular CN spectra reveal higher excitation temperature near the shockwave. The results are consistent with corresponding high-speed shadow graphs obtained by visualization with an effective shutter speed of five n anosecond. In addition, LOS analysis and application of integral inversion techniques allow inferences about the spatio-temporal distribution of the plasma.

To understand the mystery of final unification, in our earlier publications, we proposed two bold concepts: 1) There exist three atomic gravitational constants associated with electroweak, strong and electromagnetic interactions. 2) There exists a strong elementary charge in such a way that its squared ratio with normal elementary charge is close to reciprocal of the strong coupling constant. In this paper we propose that, can be considered as a compound physical constant associated with proton mass, electron mass and the three atomic gravitational constants. With these ideas, an attempt is made to understand nuclear stability and binding energy. In this new approach, nuclear binding energy can be fitted with four simple terms having one unique energy coefficient with a formula, where is an estimated mean stable mass number. With this new approach, Newtonian gravitational constant can be estimated in a verifiable approach with a model relation of the form, where is the Fine structure constant. Estimated and is 62 ppm higher than the CODATA recommended It needs further investigation. Proceeding further, an attempt is made to fit the recommended quark masses.

Plasma being the fourth and most abundant form of matter extensively exists in the universe in the inter-galactic regions. It provides an electrically neutral medium of unbound negative and positive charged particles, which has been produced by subjecting air and various other gaseous mixtures to strengthen the electromagnetic field and by heating compressed air or inert gasses for creating negative and positive charged particles known as ions. Nowadays, many researchers are paying attention to the formation of artificial Plasma and its potential benefits for mankind. The literature is sparsely populated with the applications of Plasma. This paper presents specific methods of generation and applications of Plasma, which benefits humankind in various fields, such as in electrical, mechanical, chemical and medical fields. These applications include hydrogen production from alcohol, copper bonding, semiconductor processing, surface treatment, Plasma polymerization, coating, Plasma display panels, antenna beam forming, nanotechnology, Plasma Torch, Plasma pencils, low-current non-thermal Plasmatron, treatment of prostate cancer, Plasma source ion implantation, cutting by Plasma, Plasma etching, pollution control, neutralization of liquid radioactive waste, etc. Resultantly, worth of Plasma technology in the medical industry is increasing exponentially that is closing the gap between its benefits and cost of equipment used for generating and controlling it.

Binary star DataBase (BDB) is the database of binary/multiple systems of various observational types. BDB contains data on physical and positional parameters of 260,000 components of 120,000 stellar systems of multiplicity 2 to more than 20, taken from a large variety of published catalogues and databases. We describe the new features in organization of the database, integration of new catalogues and implementation of new possibilities available to users. The development of the BDB index-catalogue, Identification List of Binaries, is discussed. This star catalogue provides cross-referencing between most popular catalogues of binary stars. We describe ideas and methods for reliable cross-identification of different entities (systems, pairs, components) in binary and multiple stellar systems.

In the paper, the authors survey integral representations of the Catalan numbers and the Catalan–Qi function, discuss equivalent relations between these integral representations, supply alternative and new proofs of several integral representations, collect applications of some integral representations, and present sums of several power series whose coefficients involve the Catalan numbers.

Web-Based GIS is a platform that deals with the representation, management and storage of locational information for real-time online retrieval, viewing and manipulations. Web-based GIS incorporate all the functions of information systems: gathering, storing, retrieving, visualizing and dissemination of spatial information on the Internet. In modern-day GIS serve as vital means to not only geo-referenced locational information that are been retrieved but also the development in web-based technologies as well as there involvements in decision making processes. In recent time the increased used of GIS on the Internet have totally transformed and made three distinct changes across our GIS environment. These are: (i) how are GIS data being accessed (ii) what are modes of its transmission and (iii) what available GIS functionalities are within the environment for analysis. This paper examines the applications and technologies associated with web-based GIS architectures with an emphasis on the internet GIS based structure and case studies of GIS Web-based applications in MapInfo, Microsoft Silverlight Technology and the ArcGIS Server using ArcGIS JavaScript API.

Over the past 10 years, developments in precision oncology have led to a rise in the demand for predictive tools that assist the screening and stratification of patients for treatment. The possibility digitizing tissue slide images has led to the creation of artificial intelligence (AI) and machine learning algorithms in digital pathology, which could ultimately improve patient care. In this context, we assessed a variety of AI-based computational models for digital pathology. In this review, we described the latest AI applications in oncology, current challenges and future prospects.

For quantitative determination of honokiol (HNK) and metformin hydrochloride (M-HCl) by HPLC from formulations and from dissolution studies, a QbD approach using three factors - three levels design was implemented to optimize the chromatographic conditions. To study the impact of each variable on the peak area of both compounds and retention time of last compound eluted, the 3D response surface plots were developed. HNK and M-HCl were separated on a Thermoscientific ODS Hypersyl TM (250 x 4.6 mm, 5 μm) column mantained at 30 C, using 0.02 M acetate buffer (pH = 3) / methanol (15 / 85, v / v) as mobile phase with a flow rate of 1 mL/min. The injected volume was 20 L, and the detection wavelengths were 256 nm for HNK and 236 nm for M-HCl. The validation of proposed method showed a linearity range of 4 – 1200 μg/mL for M-HCl (r2 = 0.9992) and 1 – 300 μg/mL for HNK (r2 = 0.9998), good precision (RSD < 2% for both HNK and M-HCl, in intra- and inter-day studies) and accuracy (mean recovery = 100.09% in the range 99.80 – 100.43% for M-HCl and mean recovery = 100.12% in the range 99.70 – 100.38% for HNK). The environmentally friendly nature of the method was confirmed by the evaluation of the method’s greenness. The proposed method was applied for the determination of HNK and M-HCl from oral dosage forms and in dissolution studies.

Affinity reagents, or target-binding molecules, are quite versatile and are major workhorses in molecular biology and medicine. Antibodies are the most famous and frequently used type and they have been used for a wide range of applications, including laboratory techniques, diagnostics, and therapeutics. However, antibodies are not the only available affinity reagents and they do have significant drawbacks, including laborious and costly production. Aptamers are one potential alternative that have a variety of unique advantages. They are single stranded DNA or RNA molecules that can be selected for binding to many targets including proteins, carbohydrates, and small molecules – for which antibodies typically have low affinity. There are also a variety of cost-effective methods for producing and modifying nucleic acids in vitro without cells, whereas antibodies typically require cells or even whole animals. While there are also significant drawbacks to using aptamers in therapeutic applications, including low in vivo stability, aptamers have had success in clinical trials for treating a variety of diseases and two aptamer-based drugs have gained FDA approval. Aptamer development is still ongoing, which could lead to additional applications of aptamer therapeutics, including antitoxins, and combinatorial approaches with nanoparticles and other nucleic acid therapeutics that could improve efficacy.

This review contains a popular presentation of the history of carbon nanostructure discovery, a short description of the main approaches to production and investigation of such objects and consideration of the most perspective ways of their applied usage.

Being the predominant cause of disability, neurological diseases have received much attention from the global health community. Over a billion people suffer from one of the following neurological disorders: dementia, epilepsy, stroke, migraine, meningitis, Alzheimer's disease, Parkinson’s disease, multiple sclerosis, amyotrophic lateral sclerosis, Huntington’s disease, prion dis-ease, or brain tumors. Diagnosis and treatment options are limited for many of these diseases. Aptamers, being small and non-immunogenic nucleic acid molecules that are easy to chemically modify, offer potential diagnostic and theranostic applications to meet these needs. This review covers pioneer studies to apply aptamers, which show promise for future diagnostics and treatments of neurological disorders that pose increasingly dire worldwide health challenges.

NiV2O6 thin films were grown on glass slides with varying thicknesses using the nebulizer spray pyrolysis. The impact of thickness on the thin films' optical, structural, morphological, and electrical characteristics was systematically investigated. The X-ray diffraction and micro-Raman analysis confirmed the formation of the triclinic NiV2O6 system. Surface morphology and roughness variations in the as-deposited NiV2O6 films were studied using scanning electron microscopy (SEM) and a profilometer. Optical properties, including optical band gap (Eg), extinction coefficient (k), absorption coefficient (α), and refractive index (n), were determined through optical reflectance and transmittance measurements. The optical energy gap of the as-deposited NiV2O6 films decreased from 2.3 eV to 1.9 eV with increased layer thickness. Furthermore, the photo-detectivity of the films demonstrated an enhancement corresponding to the prolonged spray time. The sensitivity values obtained for visible irradiation are 328, 511, and 433 for samples S1, S2, and S3, respectively. The obtained results are imputed to the specific porous microstructure.

Reinventing approved therapeutic proteins for a new dose, a new formulation, a new route of administration, an improved safety profile, a new indication, or a new conjugate with a drug or a radioactive source is a creative approach to benefit from the billions spent on developing new therapeutic proteins. These new opportunities were created only recently with the arrival of AI/ML tools and high throughput screening technologies. Furthermore, the complex nature of proteins offers mining opportunities that are not possible with chemical drugs; bringing in newer therapies without spending billions makes this path highly lucrative financially while serving the dire needs of humanity. This paper analyses several practical reinventing approaches and suggests regulatory strategies to reduce development costs significantly. This should enable the entry of hundreds of new therapies at affordable costs.

Existing implants used with Total Knee Arthroplasty (TKA), Total Hip Arthroplasty (THA), and other joint reconstruction treatments, have displayed premature failures and frequent needs for revision surgery in recent years, particularly with young active patients who represent more than 55% of all joint reconstruction patients. Bone cement and stress shielding have been identified as the major reasons for premature joint failures. A breakdown of the cement may happen, and revision surgery may be needed because of the aseptic loosening. The significant mismatch of stiffness properties of patient trabecular bones and metallic implant materials in joint reconstruction surgery results in the stress shielding phenomenon. This could lead to significant bone resorption and increased risk of bone fracture and the aseptic loosening of implants. The present project introduces an approach for development of customized cellular structures to match the mechanical properties and architecture of human trabecular bone. It consists of four modules:1- characterization of geometry, microarchitecture, and stiffness properties of patient’s bones; 2- developing customized lattice structures tailored to mimic properties established in module 1; 3 - integration of developed lattice structure into full scale implant design and manufacturing; 4- implementation of the developed technology for use in implants that would overcome most of the issues related to aseptic loosening phenomenon. The present work aims at fulfilling the objectives of module 2 by exploring new designs of customized lattice structures and texture tailored to mimic closely patients’ bone anisotropic properties and that can incorporate an engineered biological press-fit fixation technique. The effects of various lattice design variables on mechanical performance of the structure are examined through systematic experimental plan using statistical design of experiments technique and analysis of variance method. All tested lattice designs were explored under realistic geometrical, biological, and manufacturing constraints. Of the four design factors examined in this study, strut thickness was found to have the highest percent contribution (41%) regarding the structure stiffness, followed by unit cell type, and cell size. Srut shape was found to have the lowest effect with only 11% contribution. The introduced solution offers lattice structure designs that can be adjusted to match bone stiffness distribution and promote bone ingrowth and hence eliminating the phenomenon of stress shielding while incorporating biological press-fit fixation technique.

Hydrogels have gained significant attention as vehicles for transdermal delivery systems. These polymeric-structured substances have been implemented in pharmaceutical and cosmetic applications. Many factors are employed in their preparation. Light, pH, temperature and specific molecules ligands are commonly used in the production of hydrogels. The Transdermal delivery system (TDD) is a widely used route, used to deliver actives/drugs via the skin. Hydrogels as a TDD have been shown to be efficient in enhancing the efficacy in delivering the actives and drugs. The aim of this review is to discuss the hydrogels application in both dermatological therapeutic and cosmetic applications, covering the types, and properties of hydrogel polymer networks. Furthermore, this review presents one of the most extensively used polymers (Hyaluronic acid HA) in this field and discusses the properties, preparation methods and applications of HA hydrogel patches as a delivery system, including methods of loading the actives and the releasing of these actives from them.

In recent years, there has been a growing interest in utilizing natural antioxidants as alternatives to synthetic additives in food products. Apples and apple by-products have gained attention as a potential source of natural antioxidants due to their rich phenolic content. This review aims to provide a comprehensive overview of the antioxidant properties of apples and apple by-products, their extraction techniques as well as potential applications in food products because of their an-tioxidant or nutritional properties. The findings here reported highlights the proper utilization of apples and their by-products in food to reduce the detrimental effect on the environment and provide a positive impact on the economy.

Wetlands are recognized as one of the world’s most valuable natural resources. With the increasing world population, human demands on wetland resources for agricultural expansion and urban development continue to increase. In addition, global climate change has pronounced impacts on wetland ecosystems through alterations in hydrological regimes. To better manage and conserve wetland resources, we need to know the distribution and extent of wetlands and monitor their dynamic changes. Wetland maps and inventories can provide crucial information for wetland conservation, restoration, and management. Geographic Information System (GIS) and remote sensing technologies have proven to be useful for mapping and monitoring wetland resources. Recent advances in geospatial technologies have greatly increased the availability of remotely sensed imagery with better and finer spatial, temporal, and spectral resolution. This chapter presents an introduction to the uses of GIS and remote sensing technologies for wetland mapping and monitoring. A case study is presented to demonstrate the use of high-resolution light detection and ranging (LiDAR) data and aerial photographs for mapping prairie potholes and surface hydrologic flow pathways.

Analytics have become increasingly popular among sports in recent years, providing valuable information regarding physical and mental health. Many athletes have found that analytics during training can help to improve their performance, reduce the risk of injury, and enhance their overall well-being. This paper aims to improve the results of handball players by applying a method for measuring the influence of different trials on aggregate performance calculated per each athlete in order to qualify for the Olympic Games. By separating the isolated action of each trial, the result is an additional influence caused by the interaction of factors or the simultaneous action. That might explain why the neuromuscular feedback loop is utterly necessary to perform handball motor actions. Further on, the ML analysis can help identify areas for improvement, optimize training programs, and enhance overall team performance.

Solid asperity interactions are common and inevitable under severe loading conditions for any lubricated contact. Heavy-duty machine components (gear, bearings etc.) generally operate under Mixed Lubrication, where uneven surface features contact each other when generated fluid pressure is not enough to support the external load. The Reynolds equation is commonly used to simulate smooth lubricated contacts numerically. In rough lubricated interfaces where opposite surface asperities make contact, the Reynolds equation alone cannot accurately predict pressure using the traditional numerical simulation method. In this paper, Lubrication-Contact interface conditions (LCICs) have been implemented and extended to solve the multiple asperity contact problem using the full-multigrid approach. The developed novel algorithm has significantly accelerated the solution process and improved the accuracy and efficiency of pressure calculation for fluid-solid sub-interactions that can occur under ML regions. The Finite Difference Method (FDM) results have been compared with Computational Fluid Dynamics (CFD) simulation to validate the newly developed model. Hence, the proposed optimized solution method will provide valuable insight to researchers and industry engineers interested in simulating the ML problem where the effect of the fluid-solid interface can be captured effectively to improve reliability in the calculation of the life expectancy of the lubricated parts.

The evolution towards Industry 4.0 is driving the need for innovative solutions in the area of network management, considering the complex, dynamic and heterogeneous nature of ICT supply chains. To this end, Intent-Based networking (IBN) which is already proven to evolve how network management is driven today, can be implemented as a solution to facilitate the management of large ICT supply chains. In this paper, we first present a comparison of the main architectural components of typical IBN systems and, then, we study the key engineering requirements when integrating IBN with ICT supply chain network systems while considering AI methods. We also propose a general architecture design that enables intent translation of ICT supply chain specifications into lower level policies, to finally show an example of how the access control is performed in a modelled ICT supply chain system.

Diffusion Models (DMs) are probabilistic models that create realistic samples by simulating the diffusion process, gradually adding and removing noise from data. These models have gained popularity in domains such as image processing, speech synthesis, and natural language processing due to their ability to produce high-quality samples. As DMs are being adopted in various domains, existing literature reviews that often focus on specific areas like computer vision or medical imaging may not serve a broader audience across multiple fields. Therefore, this review presents a comprehensive overview of DMs, covering their theoretical foundations and algorithmic innovations. We highlight their applications in diverse areas such as media quality, authenticity, synthesis, image transformation, healthcare, and more. By consolidating current knowledge and identifying emerging trends, this review aims to facilitate a deeper understanding and broader adoption of DMs and provide guidelines for future researchers and practitioners across diverse disciplines.

Alena Tensor is a recently discovered class of energy-momentum tensors that provides mathematical framework in which, as demonstrated in previous publications, the description of a physical system in curved spacetime and its description in flat spacetime with fields are equivalent. The description of a system with electromagnetic field based on Alena Tensor can be used to reconcile physical descriptions. 1) In curvilinear description, Einstein Field Equations were obtained with Cosmological Constant related to the invariant of the electromagnetic field tensor, which can be interpreted as negative pressure of vacuum, filled with electromagnetic field. 2) In classical description for flat spacetime, three densities of four-forces were obtained: electromagnetic, gravity, and the force responsible for the Abraham-Lorentz effect (radiation reaction force). Obtained connection of Einstein tensor with gravity and radiation reaction force, after transition to curvilinear description, excludes black hole singularities. There was obtained Lagrangian density and generalized canonical four-momentum, containing electromagnetic four-potential and a term responsible for other two forces. In this description charged particles cannot remain at complete rest and should have spin, their energy results from the existence of the energy of the magnetic moment and the density of this energy is part of the Poyting four-vector. The distribution of charged matter was expressed as polarization-magnetization stress-energy tensor, what may explain why gravity is invisible in QED. 3) In quantum picture, QED Lagrangian density simplification was obtained, and the Dirac, Schrödinger and Klein-Gordon equations may be considered as approximations of the obtained quantum solution. Farther use of Alena Tensor in unification applications was also discussed.

ATTO 565, a Rhodamine-type dye, has garnered significant attention due to its remarkable optical properties, such as a high fluorescence quantum yield, it is a relatively stable structure, and has low biotoxicity. ATTO 565 has found extensive applications in combination with microscopy technology. In this review, the chemical and optical properties of ATTO 565 are introduced, along with the principles behind them. Then, the functionality of ATTO 565 in confocal microscopy, stimulated emission depletion (STED) microscopy, single-molecule tracking (SMT) techniques, and fluorescence correlation spectroscopy (FCS) is discussed. These studies demonstrate that ATTO 565 plays a crucial role in areas such as biological imaging and single-molecule localization, thus warranting further in-depth investigation. Finally, we present some prospects and concepts for the future applications of ATTO 565 in the fields of biocompatibility and metal ion detection.

As a crucial part of head protection equipment, research on the material and structural application of helmet liners has always been a hot topic in the field of helmet manufacturing. This paper aims to introduce the most common helmet liner materials, including traditional polystyrene, polyethylene, polypropylene, and others, as well as emerging materials such as anisotropic materials, and polymer nanocomposites. In addition, the design principles of helmet liner structure are discussed, including the evolution of multi-layer structure, integration of geometric irregular bubbles, etc., to enhance energy absorption capacity. Advanced manufacturing processes such as additive manufacturing technology are introduced to fabricate complex structures. Finally, the application of biomimetic structures in helmet liner design is explored. For example, the structure of biological tissue can be used to design a helmet liner structure with superior energy absorption performance. On this basis, the idea of bionic structures is extended to the combination of plant stalks and animal skeletal structures, and additive manufacturing technology is used to significantly reduce energy loss during elastic yield energy absorption, thus designing a reusable helmet. This provides a promising research direction for future helmet liner materials and structural applications.

Imaging flow cytometry (ImFC) represents a significant technological advancement in the field of cytometry, effectively merging the high-throughput capabilities of flow analysis with the image-based features of microscopy. In this comprehensive review article, we take a historical perspective and delve into the evolution of this methodology, presenting its past, exploring current state-of-the-art and forecasting potential future advancements. The inception of ImFC arose from the innovative combination of a flow cytometer's hydraulics with the technology of an advanced camera. This synergistic coupling facilitated the morphological analysis of cell populations at a high-throughput scale, effectively evolving the landscape of cytometry. However, the application of ImFC faced several challenges. Among the most prominent ones, is the development and implementation of software systems that can reliably and efficiently handle the robust data acquisition and analysis inherent in ImFC. The scale and complexity of the data generated by ImFC necessitates the creation of novel analytical tools that can effectively manage and interpret this data, thus allowing us to unlock the full potential of ImFC. Interestingly, artificial intelligence (AI) algorithms have begun to be implemented on ImFC, offering a promise for enhancing analytical capabilities. The adaptability and learning capacity of AI may prove to be essential in knowledge mining from high-dimensional data produced by ImFC, potentially enabling more accurate analyses. Looking forward, we project that ImFC may become an indispensable tool, not only in research laboratories but also in clinical settings. Given the unique combination of high-throughput cytometry and detailed imaging offered by ImFC, we foresee a critical role for this technology in the next generation of scientific research and diagnostics. As such, we encourage both current and future scientists to consider the integration of ImFC as an addition to their research toolkit and clinical diagnostic routine.

Rapid haemostasis during surgery is essential when one wants to reduce the duration of opera-tions, reduce the need for transfusions, and above all when one wants to achieve better patient management. The use of haemostatic agents, sealants and adhesives improves the haemostatic process by offering several advantages, especially in vascular surgery. These agents vary widely in their mechanism of action, composition, ease of application, adhesion to wet or dry tissue, im-munogenicity and cost. The most used are cyanoacrylate-based glues (Glubran 2) or polysaccharide hydrogel-microsphere powder (AristaTMAH). This work is based on a retrospective study carried out on a sample of patients with different vascular diseases (FAV, pseudoaneurysm and PICC application) in which two different haemostatic sealants were used. The aim was to assess the safety, the advantages and the ability of both sealants to activate the haemostatic process at the affected site, also in relation to their chemical-physical characteristics. The obtained results showed that the application of Glubran 2 and AristaTMAH as surgical wound closure systems is effective and safe as the success achieved was ≥94% on anastomoses of FAV; 100% on stabilization of Picc catheters; ≤95% on pseudoaneurysms.

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

To estimate the degree of quantum entanglement, it is important to understand the statistical behavior of functions of spectrum of density matrices such as von Neumann entropy, quantum purity, and entanglement capacity. These entangle- ment metrics over different generic state ensembles have been studied intensively in the literature. As an alternative metric, in this work we study sum of square root spectrum of density matrices, which is relevant to negativity and fidelity in quantum information processing. In particular, we derive the exact mean and vari- ance of sum of square root spectrum over the Bures-Hall generic state ensemble extending known results obtained recently over the Hilbert-Schmidt ensemble.

The climate and its changes are major risk factors for agricultural activities because agricultural systems are highly affected by climatic factors such as temperature, rain, humidity, wind, and solar radiation. While some agricultural areas of the world are irrigated, the vast majority rely on natural rainfall patterns. Farmers will consume more water as a result of climate change and experience less water availability, resulting in a decrease in production capacity. As one of the most important natural phenomena affecting agricultural productivity, drought is a limiting environment for plant growth. The response of plants to water stress is complex and involves coordination between gene expression and its integration with hormones. It has been shown that bacteria have mechanisms to reduce water stress in these plant species and promote greater growth. The underlined mechanism involves root-to-shoot phenotypic changes in growth rate, architecture, hydraulic conductivity, water conservation, plant cell protection, and damage restoration through integrating phytohormones modulation, stress-induced enzymatic apparatus, and metabolites. This review demonstrates how plant growth-promoting bacteria can mitigate negative effects associated with water stress in plants and provide examples of technology conversion in agroecosystems.

Let G be a connected Lie group with Lie algebra g. This review is devoted to studying the fundamental dynamic properties of elements in the normalizer NG of G. Through an algebraic characterization of NG we analyze the different dynamics inside the normalizer. NG contains the well-known left-invariant vector fields and the linear and affine vector fields on G. In any case, we show the shape of the solutions of these ordinary differential equations on G. We give examples in low-dimensional Lie groups. It is worth saying that these dynamics generate the linear and bilinear control systems on Euclidean spaces and the invariant and linear control systems on Lie groups. Moreover, the Jouan Equivalence Theorem shows how to extend this theory to control systems on manifolds.

Materials known as Nd:YAG are crystalline materials of the cubic system made from neodymium-doped yttrium aluminum garnet, which among others, have excellent optical properties. Nd:YAG four-level laser devices are highly used in both the health and industrial sectors. In this study, a simple and inexpensive alternative to manufacturing Nd:YAG materials through solid state reactions following powder processing routes was proposed. For this, an intense mixture of the precursor materials (Al2O3 and Y2O3) was carried out, followed by the addition of neodymium atoms to improve the optical properties of the resulting material. High energy mechanical mixing of the precursor powders resulted in submicron particles, with good size distributions of the powders. The advance of YAG formation was monitored by intermediate phase formation during heat treatment through interrupted tests at different temperatures and analysis by X-ray diffraction. From this analysis, it was found that reaction for the formation of the desired YAG is completed at 1500°C. Fourier-transform infrared spectroscopy analyses determined the presence of functional groups corresponding to the YAG. Finally, the study by optical emission spectroscopy showed wavelengths in agreement with the electronic structure of the elements of the synthesized Nd:YAG.

Diffuse optical tomography (DOT) is a medical imaging procedure using light to measure the geometric and working properties of cells, for instance, oxygen consumption, water content, and fat percentage in the tissue by performing three-dimensional visualization of the tissue. This paper aims to explain the theory behind diffuse optical tomography imaging and how the technology works. The paper explains how photon migration techniques based on diffusion theory can be used to image the optical properties of tissue. There are several reasons why near-infrared (NIR) imaging is the most effective method in terms of recovering optical parameters quantitatively in the near-infrared region. The author discusses the methods in detail. This research also presents various advantages, practical uses, and potential problems that have been related to DOT in this work. There is also a brief discussion of current research developments in medical imaging using near-infrared wavelengths, and what the future holds for that area.

It is expected that scholars will continue to strengthen the depth and breadth of theoretical research on RIS, so as to provide a higher theoretical upper bound for the engineering application of RIS. While making breakthroughs in academic research, great progress has been made in engineering application research and industrialization promotion. This article will provide an overview of RIS engineering applications, mainly including the typical features, typical classifications, and typical deployment scenarios of RIS. Then the challenges and candidate solutions of the RIS are systematically and deeply analyzed, involving the cascade channel decoupling for solving the RIS beamforming, the influences and solutions of RIS regulation constraints, RIS system architecture of network controlled mode, the integrated channel regulation and information modulation, TDD mechanism used for RIS. Future trends and challenges are also provided.

There has been a surge in the interest for (semi)transparent photovoltaics (sTPVs) in recent years, since the more traditional, opaque, devices are not ideally suited for a variety of innovative appli-cations spanning from smart and self-powered windows for buildings to those for vehicle inte-gration. Additional requirements for these photovoltaic applications are a high conversion effi-ciency (despite the necessary compromise to achieve a degree of transparency) and an aesthetical-ly pleasing design. One potential realm to explore in the attempt to meet such challenges is the bio-logical world, where evolution has led to highly efficient and fascinating light-management structures. In this mini-review, we explore some of the biomimetic approaches that can be used to improve both transparent and semitransparent photovoltaic cells, such as moth-eye inspired structures for improved performance and stability or tuneable, coloured, and semi-transparent devices inspired by beetles’ cuticles. Lastly, we will briefly discuss possible future developments for bio-inspired and potentially bio-compatible sTPVs.

SGLT2 inhibitors act to induce glucosuria and have been shown to be efficacious in the treatment of type II diabetes mellitus but have also been shown to positively affect cardiovascular conditions and some benefitting kidney function. SGLT2i have shown to be effective in more advanced conditions but may eventually be a treatment in more general metabolic dysfunction. Metabolic syndrome is a clinical diagnosis for establishing risk for ischemic heart disease and diabetes. The prevalence of type II diabetes mellitus, heart disease, and severe vascular events steadily increased over recent decades. In addition to the established benefits of diet and exercise, SGLT2 inhibitor therapy may be able to aid in the treatment of metabolic syndrome from its proven cardiovascular and anti-hyperglycemic benefits. Pharmacologic modeling in canines is also considered, as they present with similar diagnoses and offer a model that shares many of the characteristics of humans in metabolic dysfunction.

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

Since their identification over twenty-five years ago, the plethora of cell penetrating peptides (CPP) and their applications has skyrocketed. These 5 to 30 amino acid long peptides have the unique property of breaching the cell membrane barrier while carrying cargoes larger then themselves into cells in an intact, functional form. CPPs can be conjugated to fluorophores, activatable probes, radioisotopes or contrast agents for imaging tissues, such as tumors. There is no singular mechanism for translocation of CPPs into a cell, and therefore, many CPPs are taken up by a multitude of cell types, creating the challenge of tumor specific translocation and hindering clinical effectiveness. Varying strategies have been developed to combat this issue and enhance their diagnostic potential by derivatizing CPPs for better targeting by constructing specific cell activated forms. These methods are currently being used to image integrin expressing tumors, breast cancer cells, human histiocytic lymphoma and protease secreting fibrosarcoma cells, to name a few. Additionally, identifying safe, effective therapeutics for malignant tumors has long been an active area of research. CPPs can circumvent many of the complications found in treating cancer with conventional therapeutics by targeted delivery of drugs into tumors, thereby decreasing off-target side effects, a feat not achievable by currently employed conventional chemotherapeutics. Myriad types of chemotherapeutics such as tyrosine kinase inhibitors, anti-tumor antibodies and nanoparticles can be functionally attached to these peptides leading to the possibility of delivering established and novel cancer therapeutics directly to tumor tissue. While much research is needed to overcome potential issues with these peptides, they offer a significant advancement over current mechanisms to treat cancer. In this review, we present a brief overview of the research leading to identification of CPPs with a comprehensive state of the art review on the role of these novel peptides in both cancer diagnostics as well as therapeutics.

Medicinal mushrooms have got much attention from biologists in the last decades. Leucocalocybe mongolica Imai remains one of the famous clinical and edible mushrooms used in traditional Chinese medicine and Mongolian medicine to treat various diseases. It also commonly consumes in Chinese and Mongolian daily dishes. Numerous biochemical components exist in L. mongolica, especially in the fruiting bodies include; polysaccharides, sterol, lectins, laccase, amino acids, terpenes, volatile compounds, and so on. These biomedical components possess a lot of medicinal properties and provide diverse therapeutical effects for human health, such as anti-tumor activity, antiproliferative activity, anti-diabetes properties, hypotensive effect, hepatoprotective effect, antioxidant activity, cardioprotective effect, and so on. However, this review's main objectives are to illustrate some basics about the bioactive components in L. mongolica and its pharmacological effects on the human body. In addition to giving forward some suggestions for future research on this medicinal mushroom.

In the current age of the Fourth Industrial Revolution ($4IR$ or Industry $4.0$), the digital world has a wealth of data, such as Internet of Things (IoT) data, cybersecurity data, mobile data, business data, social media data, health data, etc. To intelligently analyze these data and develop the corresponding real-world applications, the knowledge of artificial intelligence (AI), particularly, machine learning (ML) is the key. Various types of machine learning algorithms such as supervised, unsupervised, semi-supervised, and reinforcement learning exist in the area. Besides, the deep learning, which is part of a broader family of machine learning methods, can intelligently analyze the data on a large scale. In this paper, we present a comprehensive view on these machine learning algorithms that can be applied to enhance the intelligence and the capabilities of an application. Thus, this study's key contribution is explaining the principles of different machine learning techniques and their applicability in various real-world applications areas, such as cybersecurity, smart cities, healthcare, business, agriculture, and many more. We also highlight the challenges and potential research directions based on our study. Overall, this paper aims to serve as a reference point for not only the application developers but also the decision-makers and researchers in various real-world application areas, particularly from the technical point of view.

In this article, the Legendre-Gould Hopper polynomials are combined with Sheffer sequences to introduce certain mixed type special polynomials. Generating functions, differential equations and certain other properties of Legendre-Gould Hopper based Sheffer polynomials are derived. Further, operational and integral representations providing connections between these polynomials and known special polynomials are established. Certain identities and results for some members of these new mixed polynomials are also obtained. Finally, the determinantal definitions of Legendre-Gould Hopper based Sheffer polynomials are also given.

Currently, the COVID‐19 has directly affected the millions of humans lives. The symptoms of the disease involving fever, malaise, chest infection, and breathing difficulties, were identified, and its existence is continuously becoming restructured. The World Health Organization (WHO) had mentioned the wide diagnostics test besides COVID-19 that would also assist medical facilities to recognize infectious diseases as well as currently focusing efficiently on preventing and afterward defeating this viral disease. The infection is usually transmitted among human beings in direct contact, greatest through the liquid bubbles generated through cough, sneeze, or speaking. This paper reviews the COVID 19 pandemic, its history, current updates, contact tracing applications, and use of emerging technologies like the Internet of Things (IoT) and Blockchain for stopping the spreading and provide service online to the patient from a distance.

The development of the Internet of Things (IoT) has triggered a virtual wave of interconnection and intercommunication among an enormous number of universal things. This has caused an exceptional surge of colossal heterogeneous information, known as an information explosion. Until now, cloud computing has filled in as a proficient method to process and store these data. Still, it came to light that by utilizing just cloud computing, pesky issues like, the expanding requests of actual-time or speed-sensitive applications and the restrictions on system transfer speed could not be solved. Consequently, another computing platform, called fog computing has been advanced as a supplement to the cloud arrangement. Fog computing spreads the cloud administrations and services to the edge of the system, and brings processing, communications and reserving and storage capacity closer to edge gadgets and end-clients and, in the process, aims at enhancing versatility, low latency, transfer speed and safety and protection. This paper takes an extensive and wide-ranging view of fog computing, covering several aspects. At the outset is outlined the many-layered structural design of fog computing and its attributes. After that, chief advances like communication and inter-exchange, computing, reserving and storage, asset administration, naming, safety and safeguarding of privacy are delineated while showing how this backup and facilitate the installations and various applications. Then, numerous applications like augmented reality (AR), healthcare, gaming and brain-machine interface, vehicular computing, smart scenarios etc. are highlighted to explain the fog computing application milieu. Following that, it is shown that how, despite fog computing being a features-rich platform, it is dogged by its susceptibility to several security, privacy and safety concerns, which stem from the nature of its widely distributed and open architecture. Finally, some suggestions are advanced to address some of the safety challenges discussed so as to propel the further growth of fog computing.

In past few years, unmanned aerial vehicles (UAV) or drones has been a hot topic encompassing technology, security issues, rules and regulations globally due to its remarkable advancements and uses in remote sensing and photogrammetry applications. This review paper highlights the evolution and development of UAV, classification and comparison of UAVs along with Hardware and software design challenges with diverse capabilities in civil and military applications. Further, safety and security issues with drones, existing regulations and guidelines to fly the drone, limitations and possible solutions have also been discussed.

With reference to our earlier published views on large nuclear gravitational constant , nuclear elementary charge and strong coupling constant , in this paper, we present simple relations for nuclear stability range, binding energy of isotopes and magic proton numbers.

In this paper, we study the third order semilocal convergence of the Newton-like method for finding the approximate solution of nonlinear operator equations in the setting of Banach spaces. First, we discuss the convergence analysis under ω-continuity condition, which is weaker than the Lipschitz and Hölder continuity conditions. Second, we apply our approach to solve Fredholm integral equations, where the first derivative of involved operator not necessarily satisfy the Hölder and Lipschitz continuity conditions. Finally, we also prove that the R-order of the method is 2p + 1 for any p $\in$ (0,1].

In the paper, the authors survey integral representations (including the Lévy--Khintchine representations) and applications of some bivariate means (including the logarithmic mean, the identric mean, Stolarsky's mean, the harmonic mean, the (weighted) geometric means and their reciprocals, and the Toader--Qi mean) and the multivariate (weighted) geometric means and their reciprocals, derive integral representations of bivariate complex geometric mean and its reciprocal, and apply these newly-derived integral representations to establish integral representations of Heronian mean of power 2 and its reciprocal.

Within the polyurethanes market, only 0.1% are from renewable sources. With the continuing concern about climate change and greenhouse gases in the atmosphere, the substitution of monomers and processes derived from petrochemical sources is crucial for the development of biomaterials. Therefore, polyhydroxyurethanes have propose due to their synthesis route with the incorporation of monomers from renewable sources such as vegetable oils and the substitution of isocyanates known for their high toxicity, carcinogenicity and petrochemical origin, and the use of carbon dioxide. There are few reports on the use of soybean oil and the possible application of these materials in wound healing applications. Based on this, polyhydroxyurethanes were obtained from carbonated soybean oil and two diamines, one aliphatic: 1,4-butadiamine (putrescine), and one cycloaliphatic: 1,3-cyclohexanobis(methylamine), and they were physiochemically, mechanically, and thermally characterized. Four polyhydroxyurethanes were obtained, with stability to hydrolytic and oxidative media, thermal stability above 200 °C, tensile strength between 0.9-1.1 MPa, elongation at break between 81 and 222%. And water absorption up 102% and contact angle between 63,70 and 101,39. Achieving new formulations of biobased NIPHUs that represent a more sustainable process which allow to achieve the physicochemical, mechanical, and thermal properties required for the preparation of wound dressings.

Foot pain related to the plantar fascia is common in adults regardless of athletic background. Many pathologies can lead to the diagnosis of plantar enthesopathy, but the most relevant to this case study is related to structural defects within the plantar fascia. Due to the variety and sometimes unclear etiology of plantar fasciosis, no single standard of care treatment protocol is recognized. Often, patients receive NSAIDs, night splints, taping, physical therapy, foot orthosis, and extracorporeal shock wave therapies. This study presents Wharton Jelly (WJ) tissue allografts as an additional intervention for patients who fail standard-care treatments. WJ allografts are applied to supplement connective tissue defects directly, unlike other therapies, which aim to reduce swelling and symptomatic pain. This retrospective study includes seven patients who have previously failed standard-of-care treatment. Each received extracorporeal pulse-activated therapy (EPAT), WJ, laser therapy, and an orthotic. Patient progress was tracked using a visual analog scale, 0-10 scoring, reported by the patient at the initial visit and approximately thirteen weeks following, reporting an average improvement of 50%. The study's limitations include a small cohort size and a non-blinded retrospective design. These promising results provide evidence for a more extensive, randomized study to define dosage protocols further and confirm safety and efficacy.

Leukemia is a form of blood cancer that results in an increase in the number of white blood cells in the body. The correct identification of Leukemia at any stage is essential. The current traditional approaches rely mainly on field expertise' knowledge, which is time consuming. The poor understanding and a long period of examination might damage the human body. In this situation an automated Leukemia identification delivers more reliable and accurate diagnostic information. To effectively diagnose acute lymphoblastic Leukemia from blood smears pictures, a new strategy based on traditional image analysis techniques with machine learning techniques, and a composite learning approach was constructed in this experiment. To identify the type of acute Leukemia first, four well-known machine learning models were utilized. It was discovered that Support Vector Machine (SVM) Provides the highest accuracy in this scenario. To boost the performance, a deep learning model Resnet50 was hybridized with SVM model. Finally, it was revealed that this composite approach achieved 99.9% accuracy.

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

Microbial pigments have many structures and functions with excellent characteristics, such as being biodegradable, non-toxic, and ecologically friendly, constituting an important source of pigments. Industrial production presents a bottleneck in production cost that restricts large-scale commercialization. However, microbial pigments are progressively gaining popularity because of their health advantages. The development of metabolic engineering and cost reduction of the bioprocess using industry by-products opened possibilities for cost and quality improvements in all production phases. We are thus addressing several points related to microbial pigments, including the major classes and structures found, the advantages of use, the biotechnological applications in different industrial sectors, their characteristics, and their impacts on the environment and society.

Optical microcombs represent a new paradigm for generating laser frequency combs based on compact chip-scale devices, which have underpinned many modern technological advances for both fundamental science and industrial applications. Along with the surge in activity related to optical micro-combs in the past decade, their applications have also experienced rapid progress ‒ not only in traditional fields such as frequency synthesis, signal processing, and optical communications, but also in new interdisciplinary fields spanning the frontiers of light detection and ranging (LiDAR), astronomical detection, neuromorphic computing, and quantum optics. This paper reviews the applications of optical microcombs. First, an overview of the devices and methods for generating optical microcombs is provided, which are categorized into material platforms, device architectures, soliton classes, and driving mechanisms. Second, the broad applications of optical microcombs are systematically reviewed, which are categorized into microwave photonics, optical communications, precision measurements, neuromorphic computing, and quantum optics. Finally, the current challenges and future perspectives are discussed.

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

The wide metabolic diversity of microalgae, their fast growth rates, and cost-effective production make these organisms highly promising resources for a variety of biotechnological applications, addressing critical needs in industry, agriculture, and medicine. The utilization of microalgae in consortia with bacteria is proving to be valuable in different biotechnological fields, including treating various types of wastewaters, producing biofertilizers, and extracting various products from their biomass. Monoculture of the microalgae Chlamydomonas has been a prominent research model for many years, extensively utilized in studying photosynthesis, sulfur and phosphorus metabolism, nitrogen metabolism, respiration, and flagella synthesis, among others. Recent re-search has increasingly recognized the potential of Chlamydomonas-bacteria consortia as a bio-technological tool for various applications. Bioremediation of wastewater using Chlamydomonas, and its bacterial consortia presents significant potential for the sustainable reduction of contam-inants, while also facilitating resource recovery and valorization of microalgal biomass. Using Chlamydomonas and its bacterial consortia as biofertilizers can offers several benefits, such as en-hancing crop yield, protecting crops, maintaining soil fertility and stability, aiding in CO2 miti-gation, and contributing to sustainable agriculture practices. Chlamydomonas-bacterial consortia play a significant role in the production of high-value products, particularly in biofuel and en-hancing H2 production. This review aims to achieve a comprehensive understanding of the po-tential of Chlamydomonas monoculture and its bacterial consortia, identifying current applications, and proposing new research and development directions to maximize its potential.

Nanocelluloses have gained significant attention in recent years due to singular properties (good biocompatibility, high optical transparency and mechanical strength, large specific surface area and good film-forming ability) and wide-ranging applications (paper, food packaging, textiles, electronics and biomedical). This article is a comprehensive review of nanocelluloses from the preparation methods to their applications in conservation and restoration of historical paper documents. The novelty lies in the information collected about nanocelluloses as renewable, environmentally friendly and sustainable materials in the field of preservation of cultural heritage as an alternative to conventional methods. Several studies have demonstrated that nanocelluloses, with or without other particles, impart to the paper documents excellent optical and mechanical properties, very good stability against temperature and humidity aging, stronger antibacterial and antifungal activity, high protection to UV light and do not require additional adhesive.

In the present study. At first, some properties of r-fuzzy soft β-closed sets are introduced on fuzzy soft topological spaces based on the paper Aygunoglu et al. (Hacet. J. Math. Stat. 2014, 43, 193-208). Also, we define the closure and interior operators with respect to the classes of r-fuzzy soft β-closed and r-fuzzy soft β-open sets, and investigate some of their properties. In addition, the concept of r-fuzzy soft β-connected sets is introduced and characterized with help of fuzzy soft β-closure operators. Thereafter, some properties of a fuzzy soft β-continuity are studied. Furthermore, we introduce and study the concepts of fuzzy soft almost (weakly) β-continuous functions, which are weaker forms of a fuzzy soft β-continuity. Also, the relationships between these classes of functions are specified with the help of some illustrative examples. Finally, we explore new types of fuzzy soft functions called, fuzzy soft β-irresolute (strongly β-irresolute, β-irresolute open, β-irresolute closed and β-irresolute homeomorphism) functions, and discuss some properties of them. Also, we show that fuzzy soft strongly β-irresolute → fuzzy soft β-irresolute → fuzzy soft β-continuity, but the converse may not be true.

In the rapidly evolving landscape of wireless communication, each successive generation of networks has achieved significant technological leaps, profoundly transforming the way we connect and interact. From the analog simplicity of 1G to the digital prowess of 5G, the journey of mobile networks has been marked by constant innovation and escalating demands for faster, more reliable, and more efficient communication systems. As 5G becomes a global reality, laying the foundation for an interconnected world, the quest for even more advanced networks leads us to the threshold of the sixth-generation (6G) era. This paper presents a hierarchical exploration of 6G networks, poised at the forefront of the next revolution in mobile wireless technology. This study delves into the technological advancements that underpin the need for 6G, examining its key features, benefits, and the key enabling technologies. We dissect the intricacies of cutting-edge innovations like terahertz communication, ultra-massive MIMO, artificial intelligence (AI), machine learning, quantum communication, and re-configurable intelligent surfaces. Through a meticulous analysis, we evaluate the strengths, weaknesses, and state-of-the-art research in these areas, offering a wider view of the current progress and potential applications of 6G networks. Central to our discussion is the transformative role of AI in shaping the future of 6G networks. By integrating AI and machine learning, 6G networks are expected to offer unprecedented capabilities, from enhanced mobile broadband to groundbreaking applications in areas like smart cities and autonomous systems. This integration heralds a new era of intelligent, self-optimizing networks that promise to redefine the parameters of connectivity and digital interaction. This paper also addresses critical challenges in the deployment of 6G, from technological hurdles to regulatory concerns, providing a holistic assessment of potential barriers. By highlighting the interplay between 6G and AI technologies, this study not only maps out the current landscape but also lights the path forward in this rapidly evolving domain. This paper aims to be a cornerstone resource, providing essential insights, addressing unresolved research questions, and stimulating further investigation into the multifaceted realm of 6G networks. By highlighting the synergy between 6G and AI(Artificial Intelligence) technologies, we aim to illuminate the path forward in this rapidly evolving field.

: For a long time, atherosclerosis has been regarded as a mere lipid deposit in the blood vessels. However, in the recent years a growing body of experimental and clinical evidence have highlighted the role of inflammation and immunity as a central mechanism of disease. Moreover, in the last decade the coming of next-generation sequencing and its application to large human population has broken the barrier between inflammation and cancer. Indeed, acquired mutation in key genes related to the control of hematopoiesis and myeloproliferation have paved the way to establish the new concept of clonal hematopoiesis of indeterminate potential. This phenomenon is being considered not such “indeterminate”, but as an emerging cardiovascular risk factor. Thus, this may explain the mechanisms of myeloproliferation and inflammation in atherosclerosis. And in a bidirectional journey, it has helped to explain the extremely high cardiovascular risk in cancer survivors, in particular in myeloprolfierative neoplasm patients. A deeper understanding of these mechanisms may pave the way for the future early diagnosis and potential preemptive treatments of the leading worldwide cause of death.

The notion of luck clustering has gained traction in recent years due to its potential influence on performance and decision-making across a range of domains. This study concentrates on the application of luck clustering in sports, with an emphasis on its consequences for performance metrics and strategic decision-making. We employ time series analysis to investigate the presence of luck clustering in sports data, such as win-loss records, scoring, and player rankings, while considering the role of the Principle of Luck Conservation in the observed clustering patterns. Our findings provide evidence of luck clustering in sports, implying that periods of high luck tend to be followed by more high luck events, and vice versa for low luck events. These insights carry significant implications for coaches, players, and teams, who can utilize the understanding of luck clustering to develop more effective strategies, manage resources efficiently, and ultimately enhance their performance. By enriching our comprehension of luck's nature and its effects on sports outcomes, this study contributes valuable knowledge for practitioners and researchers in sports analytics and performance management.

All living organisms require some metal ions for their energy production as well as metabolic and biosynthetic processes. Within cells, metal ions are involved in the formation of adducts interact with metabolites and macromolecules (proteins and nucleic acids). The proteins that require binding to one or more metal ions to be able to carry out their physiological function are called metalloproteins. About one third of all protein structures in the Protein Data Bank involve metalloproteins. Over the past few years there has been a tremendous progress in the number of computational tools and techniques making use of 3D structural information to support the investigation of metalloproteins. This trend has been boosted also by the successful applications of neural networks and deep learning approaches in molecular and structural biology at large. In this review, we discuss recent advances in the development and availability of resources dealing with metalloproteins from a structure-based perspective. We start by addressing tools for the prediction of metal-binding sites (MBSs) using structural information on apo-proteins. Then, we provide an overview of methods for and lessons learned from the structural comparison of MBSs in a fold-independent manner. We then move to describing databases of metalloprotein/MBS structures. Finally, we summarize recent DL applications enhancing the functional interpretation of metalloprotein structures.

Droplet-based microfluidics has revolutionized numerous fields such as biomedical research, pharmaceuticals, drug discovery, food engineering, flow chemistry, and cosmetics. This paper presents a comprehensive study focusing on the detection and characterization of droplets with volumes in the nanoliter range. Leveraging the precise control of minute liquid volumes, we introduced a novel spectroscopic On-Chip microsensor equipped with integrated microfluidic channels for droplet generation, characterization, and sensing, simultaneously. The microsensor, designed with Interdigitated-Ring-Shaped Electrodes (IRSE) and seamlessly integrated with microfluidic channels, offers enhanced capacitance and impedance signal amplitudes, reproducibility, and reliability in droplet analysis. We were able to make analyses of droplets length in the range 1.0-6.0 mm, velocity 0.66-2.51 mm/s, droplet volume 1.07nL-113.46nL. Experimental results demonstrated that the microsensor's has a great performance in terms of droplet size, velocity, and length, with a significant signal amplitude of capacitance and impedance, and real-time detection capabilities, thereby highlighting its potential for facilitating microcapsule reactions and enabling on-site real-time detection for chemical and biosensor analyses on-chip.

A new data processing method based on Machine Learning (ML) algorithms has been developed and tested in clinical environments during COVID-19 trials at the Medical University of South Carolina (MUSC). Breath samples have been collected in 2020–2021 from 100 participants using gas chromatography combined with ion mobility spectrometry (GC-IMS, G.A.S. mbH). Polymerase chain reaction (PCR) tests combined with clinical diagnostics were used as a reference method. Once the ML training-testing cycle is completed, the diagnostics of a single data set may be completed in as little as a few seconds. A convincing difference in the Volatile Organic Compounds (VOS) breath biomarker signature with COVID-19 positive and negative participants has been found. This approach is a generic method that can be applied to data sets collected with other types of hardware, e.g., different settings of GC, GC-MS, GC-GC, or other methods providing high information density in 2-dimensional data sets.

Background: ChatGPT is becoming a new reality. Where do we go from here? Objective: to show how we can distinguish ChatGPT-generated publications from counterparts produced by scientist. Methods:By means of a new algorithm, called xFakeBibs, we show the significant difference between ChatGPT-generated fake publications and real publications. Specifically, we triggered ChatGPT to generate 100 publications that were related to Alzheimer’s disease and comorbidity. Using TF-IDF, using the real publications, we constructed a training network of bigrams comprised of 100 publications. Using 10-folds of 100 publications each, we also 10 calibrating networks to derive lower/upper bounds for classifying articles as real or fake. The final step was to test xFakeBibs against each of the ChatGPT-generated articles and predict its class. The algorithm successfully assigned the POSITIVE label for real ones and NEGATIVE for fake ones. Results: When comparing the bigrams of the training set against all the other 10 calibrating folds, we found that the similarities fluctuated between (19%-21%). On the other hand, the mere bigram similarity from the ChatGPT was only (8%). Additionally, when testing how the various bigrams generated from the calibrating 10-folds against ChatGPT we found that all 10 calibrating folds contributed (51%-70%) of new bigrams, while ChatGPT contributed only 23%, which is less than 50% of any of the other 10 calibrating folds. The final classification results using the xFakeBibs set a lower/upper bound of (21.96-24.93) number of new edges to the training mode without contributing new nodes. Using this calibration range, the algorithm predicted 98 of the 100 publications as fake, while 2 articles failed the test and were classified as real publications. Conclusions: This work provided clear evidence of how to distinguish, in bulk ChatGPT-generated fake publications from real publications. Also, we also introduced an algorithmic approach that detected fake articles with a high degree of accuracy. However, it remains challenging to detect all fake records. ChatGPT may seem to be a useful tool, but it certainly presents a threat to our authentic knowledge and real science. This work is indeed a step in the right direction to counter fake science and misinformation.

In the realm of modern drug discovery, the integration of chemoinformatics and quantitative structure-activity relationship (QSAR) modeling has emerged as a formidable alliance, enabling researchers to harness the vast potential of machine learning (ML) techniques for predictive molecular design and analysis. This review delves into the fundamental aspects of chemoinformatics, elucidating the intricate nature of chemical data and the crucial role of molecular descriptors in unveiling the underlying molecular properties. Molecular descriptors, including 2D fingerprints and topological indices, in conjunction with the structural-activity relationships (SARs), play a pivotal role in unlocking the pathway to small-molecule drug discovery. Technical intricacies of developing robust ML-QSAR models, including feature selection, model validation, and performance evaluation, are discussed herewith. Various ML algorithms, such as regression analysis and support vector machines, are showcased in the text for their ability to predict and comprehend the relationships between molecular structures and biological activities. This review serves as a comprehensive guide for researchers, providing an understanding of the synergy between chemoinformatics, QSAR, and ML. By embracing these cutting-edge technologies, predictive molecular analysis holds promise for expediting the discovery of novel therapeutic agents in the pharmaceutical sciences.

This paper focuses on the concepts used in Quantum Cryptography and recent progress made. We look at the different categories of key distribution protocols that have been proposed and issues related to those. We then take an overview of two main approaches of communication using quantum cryptography concepts, i.e., ``Quantum Secure Direct Communication'' and ``Deterministic Secure Quantum Communication'', and limitations related to the usage of these schemes in a real-world system. At last, we list down the problems identified in the research and conclude our discussion by proposing areas of focus for future research.

Millimeter wave antennas have significance for high-speed, high-capacity wireless communication, delivering substantial advantages and an array of applications. This literature review analyzes the historical development, assessment of performance, difficulties with design, and contemporary applications in telecommunications, radar systems, and imaging. It covers the development of technology, especially within the context of 5G and beyond, and focuses at several antenna designs that deal with technical issues including reduction and impedance matching. The review also studies the effect of design on efficiency and bandwidth, identifying research gaps and potential futures, notably in terms of 5G technology. The commitment to develop millimeter wave antenna technology for next-generation wireless networks is evident, underlining the need of continued research and development.

Visible light communication (VLC) has attracted much attention in recent years among industrial and academic researchers. The large scale use of energy efficient light emitting diodes (LEDs) in lighting applications offers a unique opportunity to use license free visible light as the carrier in wireless communications. It has many potentials to complement radio frequency (RF) based communication systems. Researchers working on VLC have mainly concentrated on improving the communication speeds to match with Wi-Fi though the latest generation Wi-Fi standards offer significantly higher capacity at longer communication distances. Thus, market penetration is challenging for VLC as Wi-Fi is already quite mature and ubiquitous. Commercialization of VLC can only be successful if VLC can offer applications where VLC is more advantageous over the existing RF-based solutions. Once VLC enters the market through specific applications and becomes more mature in the process, the ecosystem may gradually adopt VLC in broader applications. In this article, the state of the art of the three promising VLC applications, their issues, and possible solutions have been discussed.

Linear polyamides, known as nylons, are a class of synthetic polymers with a wide range of applications due to their outstanding properties, such as chemical and thermal resistance or mechanical strength. These polymers have been used in various fields: from common and domestic applications, such as socks and fishing nets, to industrial gears or water purification membranes. By their durability, flexibility and wear resistance nylons have been started to be used in addictive manufacturing technology as a good material choice to get sophisticated devices with precise and complex geometric shapes. Furthermore, the emergence of triboelectric nanogenerators and the development of biomaterials have highlighted the versatility and utility of these materials. In order to enhance the triboelectric performance and the range of applications, nylons show a potential use as tribo-positive materials. Because of the easy control of their shape they can be subsequently integrated into nanogenerators. The use of nylons has also extended into the field of biomaterials, where their biocompatibility, mechanical strength and versatility have paved the way for groundbreaking advances in medical devices as dental implants, catheters and non-absorbable surgical sutures. By means 3D bioprinting nylons have been used to develop scaffolds, joint implants and drug carriers with tailored properties for various biomedical applications. The present paper aims to collect these recently specific applications of nylons by reviewing the literature produced in the last decades, with a special focus on the newer technologies in the field of energy harvesting and biomedicine.

Human urinary bladder cancer has similar development that invasive urothelial cancer that happens spontaneously in dogs. Then, research of these malignant tumors associated with the advances of new treatment drive to the specific fight of the urothelial tumor cell, is highly valuably related to efficiency and reduction of side effects of treatments, even extrapolating to humans. Thus, a review of the most important aspects on new advances on canine bladder cancer was achieved. Many forms of therapies are being reported; one of them, of highly promising feature is the treatment with immunomodulators. The resemblance between spontaneous urothelial cancer in canines and urinary bladder cancer in humans makes this canine tumor relevant animal model to research and advances of treatment that may be more efficient in management of tumor with less unfavorable effects. Immunomodulators exhibit important outcomes when used in the therapy of bladder cancer in dogs. Nanomaterials that are possible carriers of some kinds of drugs to the target tumor significantly raise the efficiently of the therapy. Therefore, the advancement of immunomodulator combined with nanoparticles can disrupt the therapy of bladder cancer in dogs and thereafter in humans.

Aquaculture is a fast-emerging food-producing sector in which fishery production plays an im-perative socio-economic role, providing ample resources and tremendous potential worldwide. However, aquatic animals are exposed to the deterioration of the ecological environment and in-fection outbreaks, which represent significant issues nowadays. One of the reasons for these threats is the excessive use of antibiotics and synthetic drugs that have harmful impacts on the aquatic atmosphere. It is not surprising that functional biotic feeds such as probiotics, prebiotics, postbiotics, and synbiotics have been developed as natural alternatives to sustain a healthy microbial envi-ronment in aquaculture. These functional feed additives possess several beneficial characteristics, including gut microbiota modulation, immune response reinforcement, resistance to pathogenic organisms, improved growth performance, and enhanced feed utilization in aquatic animals. Nevertheless, their mechanisms in modulating the immune system and gut microbiota in aquatic animals are largely unclear. This review discusses current research advancements to fill research gaps and promote effective and healthy aquaculture production.

With the rapid development of artificial intelligence, machine learning is gradually becoming popular in predictions in all walks of life. In meteorology, It is gradually competing with traditional climate predictions dominated by physical models. This survey aims to consolidate the current understanding of Machine Learning (ML) applications in weather and climate prediction—a field of growing importance across multiple sectors including agriculture and disaster management. Building upon an exhaustive review of more than 20 methods highlighted in existing literature, this survey pinpointed eight techniques that show particular promise for improving the accuracy of both short-term weather and medium-to-long-term climate forecasts. According to the survey, while ML demonstrates significant capabilities in short-term weather prediction, its application in medium-to-long-term climate forecasting remains limited, constrained by factors such as intricate climate variables and data limitations. Current literature tends to focus narrowly on either short-term weather or medium-to-long-term climate forecasting, often neglecting the relationship between the two, as well as general neglect of modelling structure and recent advances. By providing an integrated analysis of models spanning different time scales, this survey aims to bridge these gaps, thereby serving as a meaningful guide for future interdisciplinary research in this rapidly evolving field.

We review the main features of the non-local gauge, named the contour gauge. The contour gauge belongs to the axial type of gauges and extends the local gauge used in the most of approaches. The geometry of gluon fields and the path-dependent formalism are the essential tools for the description of non-local gauges. The principle feature of the contour gauge is that there are no the residual gauges which are left in the finite domain of space. In the review, we present the useful correspondence between the contour gauge conception and the Hamiltonian (Lagrangian) formalism. The Hamiltonian formalism is turned out to be a very convenient framework for the understanding of contour gauges. The comprehensive comparison analysis of the local and non-local gauges advocates the advantage of the contour gauge use. As an example of practical worth, we consider the Drell-Yan process and discuss the gauge invariance of the corresponding hadron tensor. We show that the appropriate use the contour gauge leads to the existence of extra diagram contributions. These additional contributions, first, restore the gauge invariance of the hadron tensor and, second, give the important terms for the observable quantities. We also demonstrate the significant role of the additional diagrams to form the relevant contour in the Wilson path-ordered exponential. Ultimately, it leads to the spurious singularity fixing. Moreover, in the present review, we discuss in detail the problem of spin and orbital angular momentum separation. We show that in SU(3) gauge theories the gluon decomposition on the physical and pure gauge components has a strong mathematical evidence provided the contour gauge conception has been used. In addition, we prove that the contour gauge possesses the special kind of residual gauge that manifests at the boundary of space. Besides, the boundary field configurations can be associated with the pure gauge fields.

Sensor fusion algorithms and models have been widely used in recent times. Although research evidence has informed the use of sensor fusion models in diverse applications, there is room for improvement, especially in home-based health monitoring applications which require less supervision and technical knowledge of users. The present work compares data mining-based fusion software packages such as RapidMiner Studio, Anaconda, Weka, and Orange, and proposes a data fusion framework suitable for in-home applications. 574 privacy-friendly (binary) images and 1,722 datasets gleaned from thermal and Radar sensing solutions respectively, were fused using the software packages on instances of homogeneous and heterogeneous data aggregation. Experimental results indicated that the proposed fusion framework achieved an average Classification Accuracy of 84.7% and 95.7% on homogeneous and heterogeneous datasets respectively, with the help of data mining and machine learning models such as Naïve Bayes, Decision Tree, Neural Network, Random Forest, Stochastic Gradient Descent, Support Vector Machine, K-Nearest Neighbours and CN2 induction. Further evaluation of the sensor data fusion framework based on cross validation of features indicated average values of 94.4% for Classification Accuracy, 95.7% Precision and 96.4% for Recall.

Graphene is a two-dimensional carbon atom with a hexagonal honeycomb-like structure in which the sp2 hybridization occurs. GO sheets are highly oxidized and have oxygen-containing groups. Properties of electronics, mechanicals, magnetics, and optics, Oxidized Methods for Brodies, Staudenmaier, Hofmann, Hummers, Improved Hummers, and Tour's Methods Reduction of Graphene oxide methods Chemical Reduction using sodium borohydride, 2,4-dinitrophenylhydrazine, Hydrazine hydrate, Thermal Reduction, Hydrothermal Reduction, Exfoliation Reduction, Catalytic Reduction, and the green synthesis route for reduction, The reduction of graphene oxide has a wide range of applications, including its use in energy storage devices for Solar cells, Supercapacitors, LIG-based sensors, River Water sensors, Battery, hydrovoltaic generators, OLED Screen, and Graphene Shielding. This review will determine the future of a few applications.

Biodegradable poly(ɛ-caprolactone) (PCL) and its composites or blends have gotten a lot of attention in the last decade because of their potential applications in human life and environmental remediation. As a result, there is a growing interest in the synthesis of PCL-composites/blends and their applications. Greater efforts have been made to develop biodegradable chemical materials as adsorbents that do not pollute the environment in order to replace traditional materials. Among the numerous types of degradable materials, PCL is currently the most promising, the most popular, and the best material to be developed, and it is referred to as the "green" eco-friendly material. Membranes and adsorbents for water treatment, packaging and compost bags, controlled drug carriers, biomaterials for tissues such as bone, cartilage, ligament, skeletal muscle, skin, cardiovascular and nerve tissues are just some of the applications of this biodegradable polymer (PCL). The goal of this review is to present a brief overview of PCL, its properties, syntheses of PCL, PCL composites, and PCL blends, but to provide a detailed investigation into the utility of PCL/PCL-based adsorbing agents in the removal of dyes/heavy metal ions.

Biosurfactants (BSs) are microbial compounds that have emerged as potential alternatives to chemical surfactants due to their multifunctional properties, sustainability, and biodegradability. Owing to their amphipathic nature and distinctive structural arrangement, biosurfactants exhibit a range of physicochemical properties, including excellent surface activity, efficient critical micelle concentration, humectant properties, foaming and cleaning abilities and the capacity to form microemulsions. Furthermore, numerous biosurfactants display additional biological characteristics, such as antibacterial, antifungal, and antiviral effects, antioxidant, anticancer, and immunomodulatory activities. Over the past two decades, numerous studies have explored their potential applications, including pharmaceuticals, cosmetics, antimicrobial and antibiofilm agents, wound healing, anticancer treatments, immune system modulators and drug/gene carriers. These applications are particularly important in addressing challenges such as antimicrobial resistance and biofilm formation in clinical, hygiene, and therapeutic settings. They can also serve as coating agents for surfaces, enabling antiadhesive, suppression, or eradication strategies. Not least importantly, biosurfactants have shown compatibility with various drug formulations, including nanoparticles, liposomes, micro and nanoemulsions, and hydrogels, improving drug solubility, stability, and bioavailability and enabling targeted and controlled drug release. These qualities make biosurfactants promising candidates for the development of next-generation antimicrobial, antibiofilm, anticancer, wound healing, immunomodulating, drug or gene delivery agents, as well as adjuvants to other antibiotics. Analyzing recent literature, this review aims to provide insights into the present understanding, highlight emerging trends, identify promising directions and advancements in the utilization of biosurfactants within the pharmaceutical and biomedical fields.

A well-known result of Posner’s second theorem states that if the commutator of each element in a prime ring and its image under a nonzero derivation is central, then the ring is commutative. In the present paper, we extend this bluestocking theorem to an arbitrary ring with involution involving prime ideals. Further, apart from proving several other interesting and exciting results, we establish *-version of Vukman’s theorem [48, Theorem 1]. Precisely, we describe the structure of quotient ring A/L, where A is an arbitrary ring and L is a prime ideal of A. Further, by taking advantage of the *-version of Vukman’s theorem, we show that if a 2-trosion free semiprime ring A with involution admits a nonzero *-centralizing derivation, then A contains a nonzero central ideal. This result is in a spirit of the classical result due to Bell and Martindale [19, Theorem 3]. As the applications, we extends and unify several classical theorems proved in [6],[25,[42], and [48] . Finally, we conclude our paper with a direction for further research.

Recently, a paper was written to reformulate and strengthen the theory of semi-structured complex numbers H (a new number set invented to enable division by zero). Whilst the paper had profound results, the application of this number set in many areas of engineering and science has not been fully explored. Consequently, the aim of this paper was to establish the number set H as a useful mathematical tool by providing an amalgam of results arising from its application in several areas of engineering and science. In the process, this paper makes four major contributions: these are (1) determining the product of the gradients of a horizontal and vertical line; (2) developing new Euler formulas relating exponential, trigonometric and hyperbolic functions in the 3D semi-structured complex number Euclidean space; (3) establishing semi-structured complex numbers as a better extension of the complex number set than quaternions; (4) resolving singularities that may arise in engineering and science equations (because of division by zero) to develop reasonable conclusions in the absence of experimental data. These results and their applications provide a firm foundation to advance the number set H as a useful mathematical tool.

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 radio spectrum is a scarce and extremely valuable resource that demands careful real-time monitoring and dynamic resource allocation. Dynamic spectrum access (DSA) is a new paradigm for managing the radio spectrum, which requires AI/ML driven algorithms for optimum performance under rapidly changing channel conditions and possible cyber-attacks in the electromagnetic domain. Fast sensing across multiple directions using array processors, with subsequent AI/ML based algorithms for sensing and perception of the waveforms that are measured from the environment is critical for providing decision support in DSA. As part of directional and wideband spectrum perception, the ability to finely channelize wideband inputs using efficient Fourier analysis is much needed. However, a fine-grain fast Fourier transform (FFT) across a large number of directions is computationally intensive and leads to high chip area and power consumption. We address this issue by exploiting recently proposed approximate discrete Fourier transforms (ADFT) which has its own sparse factorization for real-time implementation at low complexity and power consumption. The ADFT is used to create a wideband multi-beam RF digital beamformer and temporal spectrum-based attention unit which monitors 32~discrete directions across 32~sub-bands in real-time using a multiplierless algorithm having low computational complexity. The output of this spectral attention unit is applied as a decision variable to an intelligent receiver that adapts its center frequency and frequency resolution via FFT channelizers that are custom-built for real-time monitoring at high resolution. This two-step process allows the fine-gain FFT to be applied only to directions and bands of interest as determined by the ADFT based low-complexity 2D spacetime attention unit. The fine-grain FFT provides a spectral signature that can find future use cases in neural network engines for achieving modulation recognition, IoT device identification, and RFI identification. Beamforming and spectral channelization algorithms, digital computer architecture, and early prototypes using a 32-element fully digital multichannel receiver and field programmable gate array (FPGA)-based high-speed software defined radio (SDR) is provided.

Air passenger traffic prediction is crucial for the effective operation of civil aviation airports. Despite some progress in this field, the prediction accuracy and methods need further improvement. This paper proposes an integrated approach to the prediction of air passenger index as follows. Firstly, the air passenger index is defined and classified by the K-means clustering method. Based on the mutual information (MI) principle, the information entropy is used to analyze and select the key influencing factors of air passenger travel. By incorporating the MI principle into the support vector regression (SVR) framework, this paper presents an innovative MI-SVR machine learning model used to predict the air passenger index. Finally, the proposed model is validated by passenger throughput data of the Shanghai Pudong International Airport, China. The experimental results prove the model feasibility and effectiveness by comparing them with conventional methods, such as ARIMA, LSTM, and other machine learning models, outperformed by the MI-SVR model. Besides, it is shown that the prediction effect of each model could be improved by introducing influencing factors based on mutual information. The main findings are considered instrumental to the airport operation and air traffic optimization.

Air passenger traffic prediction is crucial for the effective operation of civil aviation airports. Despite some progress in this field, the prediction accuracy and methods need further improvement. This paper proposes an integrated approach to the prediction of air passenger index as follows. Firstly, the air passenger index is defined and classified by the K-means clustering method. Based on the mutual information (MI) principle, the information entropy is used to analyze and select the key influencing factors of air passenger travel. By incorporating the MI principle into the support vector regression (SVR) framework, this paper presents an innovative MI-SVR machine learning model used to predict the air passenger index. Finally, the proposed model is validated by passenger throughput data of the Shanghai Pudong International Airport, China. The experimental results prove the model feasibility and effectiveness by comparing them with conventional methods, such as ARIMA, LSTM, and other machine learning models, outperformed by the MI-SVR model. Besides, it is shown that the prediction effect of each model could be improved by introducing influencing factors based on mutual information. The main findings are considered instrumental to the airport operation and air traffic optimization.

Artificial Intelligence (AI) is revolutionizing head and neck cancer (HNC) care by providing innovative tools that enhance diagnostic accuracy and personalize treatment strategies. This review highlights the advancements in AI technologies, including deep learning and natural language processing, and their applications in HNC. The integration of AI with imaging techniques, genomics, and electronic health records is explored, emphasizing its role in early detection, biomarker discovery, and treatment planning. Despite noticeable progress, challenges such as data quality, algorithmic bias, and the need for interdisciplinary collaboration remain. Emerging innovations like explainable AI, AI-powered robotics, and real-time monitoring systems are poised to further advance the field. Addressing these challenges and fostering collaboration among AI experts, clinicians, and researchers is crucial for developing equitable and effective AI applications. The future of AI in HNC holds significant promise, offering potential breakthroughs in diagnostics, personalized therapies, and improved patient outcomes.