The key links of face recognition are digital image preprocessing, facial feature extraction and pattern recognition, this article aimed at the current problem of slow speed and low recognition accuracy of face recognition , from the above three key links, on the basic of analyzing the therories of Fractional Differential Masks Operator (FDMO), Principal Component Analysis (PCA) and Support Vector Machine (SVM), design a kind of FDMO+PVA+SVM coupling algorithm that applies to face recognition to improve the speed and accuracy of it. To realize FDMO+PCA+SVM coupling algorithm, first, we should apply FDMO to face image processing binary marginalization, the purpose is getting face contour; Then, we apply PCA to get the feature of face image which is disposed by binary marainalization. At last, we can apply One-Against All of the SVM classifier and LibSVM software package to realize face recognition. Beside, the article with nine different coupling algorithm design four groups of experimental reaults on the ORL face database verified by comparative analysic FDMO+PCA+SVM coupling algorithm in the superiority of face recognition accuracy and speed.

The influence of growth temperature on the crystalline quality and optical properties of AlGaN at 325 nm, grown using plasma-assisted molecular beam epitaxy (MBE), was investigated. The growth temperature exhibited a significant impact on the growth mode and surface morphology. Specifically, at a growth temperature of 750 degrees Celsius, when appropriate Al and Ga flow rates were employed, the AlGaN layer exhibited exceptional surface morphology and favorable optical properties. The optical properties of the AlGaN material were explored, revealing the presence of two distinct non-radiative recombination centers characterized by different activation energies. The minority carrier lifetimes were measured as 560 ps at 10 K and 64 ps at room temperature. The underlying mechanism was analyzed from the perspective of carrier dynamics, shedding light on the observed phenomena.

The pharmacological effects of the fruiting body of Ophiocordyceps sinensis (Os) exerted antioxidant, anti-virus, and immunomodulatory, whereas the anti-inflammatory effects and active components of Os mycelial submerged culture (OMSC) are not known. This study aimed to investigate the active compounds in the fermented liquid (FLOS), hot water (WEOS), and 50-95% (EEOS-50, EEOS-95) ethanol extracts of OMSC and their anti-inflammatory effects and potential mechanisms in LPS-stimulated microglial BV2 cells. The results demonstrated that all of the OMSC extracts could inhibit NO production in BV2 cells. EEOS-95 exhibited the strongest inhibitory effects (41 % inhibitory ability at 500 µg/mL), and its ergosterol, GABA, total phenolic, and total flavonoid contents were significantly higher than those of the other extracts (18.60, 18.60, 2.28, and 2.14 mg/g, p < 0.05, respectively). EEOS-95 also has a strong inhibitory ability against IL-6, IL-1β and TNF-α with IC50 of 617, 277 and 507 µg/mL, respectively, which is higher than that of 1 mM melatonin. The anti-inflammatory mechanism of EEOS-95 seems to be associated with up-regulation of PPAR-γ/Nrf-2/HO-1 antioxidant-related expression and down-regulation of NF-κB/COX-2/iNOS pro-inflammatory expression signaling. Therefore, we hypothesized that EEOS-95 can prevent neurodegenerative diseases, but the effect on the application in vivo should be further investigated.

Hox gene clusters are crucial in Embryogenesis. It was observed that some Hox genes are located in order along the telomeric to centromeric direction of the DNA sequence: Hox1, Hox2, Hox3…. These genes are expressed in the same order in the ontogenetic units of the Drosophila embryo along the Anterior-Posterior axis. The two entities (genome and embryo) differ significantly in linear size and in-between distance. This strange phenomenon was named Spatial Collinearity (SP). Later, it was observed that, particularly in the Vertebrates, a Temporal Collinearity (TC) coexists: first is Hox1 expressed, later Hox2 and even later Hox3,…,. According to a Biophysical Model (BM), pulling forces act at the anterior end of the cluster while a cluster fastening applies at the posterior end. Hox clusters are irreversibly elongated along the force direction. During Evolution, the elongated Hox clusters are broken at variable lengths thus split clusters may be created. An Empirical Rule was formulated distinguishing development due to a complete Hox cluster from development due to split Hox clusters. BM can ‘explain’ this Empirical Rule. In a spontaneous mutation where the cluster fastening is dismantled, a weak pulling force automatically shifts the cluster inside the Hox activation domain. This cluster translocation can probably explain the absence of Temporal Collinearity in Drosophila.

In this paper, we first discuss the research status and application progress of the finite element method and the smoothed particle method. By analyzing the advantages of the smoothed particle method and the finite element method, a new coupling algorithm, namely FEM-SPH algorithm, is proposed. By the method of comparison, it shows that finite element method and SPH method in the simulation of large deformation problems each have advantages and disadvantages, the finite element method smoothed particle coupling algorithm is effective to achieve the performance of high computational efficiency and can naturally simulate large deformation problems across. In the process of calculation, the large deformation unit can be freely into an algorithm to facilitate the calculation accuracy and efficiency of three methods of numerical simulation. Through the study found, FEM-SPH algorithm not only overcome the defect of smooth particle tensile instability, but also overcomes the problem of low efficiency of finite element computation. To further test the FEM-SPH algorithm has advantages in the practical engineering, we have carried out the actual test to the example of the super high speed collision, concluded that, since the target of most of the computational domain is always finite element, smoothed particle focused only in contact with the projectile and target of local area, particle number is not much, the whole calculation process just ten minutes, computational efficiency has been greatly improved, at the same time in the simulation of large deformation, the advantage is very obvious .This provides a criterion for the actual project, depending on the specific material deformation mode and choose a more appropriate conversion algorithm.

The aim of this research work is to study the influence of temperature, solar irradiation and wind dust on the production of solar energy. In addition, it aims to study the assessment of water quality, in particular groundwater used for cooling and cleaning photovoltaic panels (quality analysis). it's an important source, stable and sustainable can be used entirely for cooling and cleaning PV modules from the sales dust, in order to improve their performance. To date, all research studies carried out in this context of cooling and cleaning using water have not taken account of the nature of this water. We have taken a sample of water from a well in a rural area in the Darra Tafilalet region of Morocco. This region has an arid and semi-arid climate, with very high temperatures and dust-laden winds, particularly in summer. These meteorological characteristics influence the efficiency of cells, particularly photovoltaic (PV) panels, reducing their performance. Next, the water sample was analysed in the laboratory. The results obtained show that the groundwater in this area is very sensitive to cooling and cleaning photovoltaic panels. Moreover, these results indicate that the temperature, solar irradiation and wind-blown dust have a significant influence on energy production.

Tellurium exhibits exceptional intrinsic electronic properties. However, investigations into the modulation of tellurium's electronic properties through physical modification are notably scarce. Here, we present a comprehensive study focused on the evolution of the electronic properties of tellurium crystal flakes under plasma irradiation treatment by employing conductive atomic force microscopy and Raman spectroscopy. The plasma-treated tellurium experienced a process of defect generation through lattice broken. Prior to the degradation of electronic transport performance due to plasma irradiation treatment, a remarkable observation emerged: in the low-energy region of hydrogen plasma-treated tellurium, a notable enhancement in conductivity was unexpectedly detected. The mechanism underlying this enhancement in electronic transport performance was thoroughly elucidated by comparing it with the electronic structure induced by argon plasma irradiation. This study not only fundamentally uncovers the effects of plasma irradiation on tellurium crystal flakes, but also unearths an unprecedented trend of enhanced electronic transport performance at low irradiation energies when utilizing hydrogen plasma. This abnormal trend bears significant implications for guiding the prospective application of tellurium-based 2D materials in the realm of electronic devices.

Brain is the most complex organ in the human body and it is divided into two hemispheres - left and right hemispheres. Left hemisphere is responsible for control of right side of our body whereas right hemisphere is responsible for control of left side of our body. Brain image segmentation from different neuroimaging modalities is one of the important parts in clinical diagnostic tools. Neuroimaging based digital imagery generally contain noise, inhomogeneity, aliasing artifacts, and orientational deviations. Therefore, accurate segmentation of brain images is a very difficult task. However, the development of accurate segmentation of brain images is very important and crucial for a correct diagnosis of any brain related diseases. One of the fundamental segmentation tasks is to identify and segment inter-hemispheric fissure/mid-sagittal plane, which separate the two hemispheres of the brain. Moreover, the symmetric/asymmetric analyses of left and right hemispheres of brain structures are important for radiologists to analyze diseases such as Alzheimer's, Autism, Schizophrenia, Lesions and Epilepsy. Therefore, in this paper we have analyzed the existing computational techniques used to find brain symmetric/asymmetric analysis in various neuroimaging techniques (MRI/CT/PET/SPECT), which are utilized for detecting various brain related disorders.

In this work we looked for a stable and accurate procedure to perform feature selection in datasets with a much higher number of predictors than individuals, as in Genome-Wide Association Studies. Due to the instability in feature selection when many potential predictors are measured, a variable selection procedure is proposed that combines several replications of shrinkage regression models. A weighted formulation is used to define the final predictors. The procedure is applied to investigate Single Nucleotide Polymorphisms (SNPs) predictors associated to Alzheimer’s disease in the Alzheimer’s disease Neuroimaging Initiative (ADNI) dataset. Two data scenarios are investigated: one considering only the set of SNPs and another with the covariates age, sex, educational level and APOE4 genotype. The SNP rs2075650 and the APOE4 genotype are given as risk factors for AD, which is in line with the literature, and other four new SNPs are pointed, opening new hypotheses for in vivo analyses. These experiments demonstrate the potential of the new method for stable feature selection.

This paper constituted an extension of two previous studies concerning the mathematical development of the grain boundary grooving in polycrystalline thins films in the cases of the evaporation/condensation and diffusion taken separately. The thermal grooving processes are deeply controlled by the various mass transfer mechanisms of evaporation–condensation, surface diffusion, lattice diffusion, and grain boundary diffusion. This study proposed a new original analytical solution to the mathematical problem governing the grain groove profile in the case of simultaneous effects of evaporation-condensation and diffusion in polycrystalline thin films submitted to thermal and mechanical stress, and fatigue effects; by resolving the corresponding fourth-order partial differential equation ∂y∂t=C∂2y∂x2-B∂4y∂x4 obtained from the approximation y'2≪1. The comparison of the new solution to that of diffusion alone proved an important effect of the coupling of evaporation and diffusion on the geometric characteristics of the groove profile. A second analytical solution based on the series development was also proposed. It was proved that change of the boundary conditions of the grain grooving profile largely affected the different geometric characteristics of the groove profile.

Machine learning (ML) is enabling augmented reality (AR) to gain popularity in various fields, including gaming, entertainment, healthcare, and education. ML enhances AR applications in education by providing accurate visualizations of objects. For AR systems, ML algorithms facilitate the recognition of objects and gestures from kindergarten through university. The purpose of this survey is to provide an overview of various ways in which ML techniques can be applied within the field of AR within education. The first step is to describe the background of AR. In the next step, we will discuss the ML models that are used in AR education applications. Additionally, we discuss how ML is used in AR. Each subgroup’s challenges and solutions can be identified by analyzing these frameworks. In addition, we outline several research gaps and future research directions in ML-based AR frameworks for education.

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

The development of accurate dynamic battery pack models for electric vehicles (EVs) is critical for the ongoing electrification of the global automotive vehicle fleet, since these serve as a key element in the energy performance of an EV powertrain system. The Equivalent Circuit Model (ECM) technique at the cell level is commonly employed for this purpose, combining accuracy and efficiency in the representation of battery operation within the broader powertrain system. In this study, a second-order ECM model of a battery cell is being developed using Modelica, an equation-based modeling language. Parameter lookup tables at multiple levels of state of charge (SoC), extracted from Li-ion battery cells with 4 different commonly used cathode materials, have been utilized. This approach allows for a representation of the battery systems that are used in a wide range of commercial EV applications. To verify the model, an integrated EV model is developed and simulation results of the FTP75 driving cycle have been compared against an equivalent application in MATLAB Simulink. The findings demonstrate a close match between the results obtained from both software across different system points. Specifically, the maximum deviation of SoC is limited to 0.08% and the maximum value of relative voltage deviation is 1.4%. The verified model enables the exploration of multiple possible architecture configurations of EV powertrains with Modelica.

Mycotoxins are deleterious fungal secondary metabolites that contaminate food and feed, thereby creating concerns regarding food safety. Common fungal genera can easily proliferate in Indian tropical and sub-tropical conditions, and scientific attention is warranted to curb its growth. To address this, two nodal governmental agencies, namely the Agricultural and Processed Food Products Export Development Authority (APEDA) and Food Safety and Standards Authority of India (FSSAI), have developed and implemented analytical methods and quality control procedures to monitor mycotoxin levels in a range of food matrices and assess risks to human health over the past two decades. However, comprehensive information on such advancements in mycotoxin testing and issues in implementing these regulations is inadequately covered in recent literature. The aim of this review is thus to uphold a systematic picture of the role played by the FSSAI and APEDA for mycotoxin control at the domestic level and for the promotion of international trade along with certain challenges in dealing with mycotoxin monitoring. Additionally, it unfolds various regulatory concerns regarding mycotoxin mitigation in India. Overall, it provides valuable insights to the Indian farming community, food supply chain stakeholders, and researchers about India’s success story in arresting mycotoxins throughout the food supply chain.

Background: This research examines how market knowledge and artificial intelligence (AI) interact in different market designs such as business-to-business (B2B) settings while taking emerging technologies and the changing digitalization landscape into account. Objective: The main goal is to understand how AI affects market knowledge in different market designs such as business-to-business (B2B) contexts, taking into account language barriers, practical difficulties, and the revolutionary effects on decision-making and customer interactions. Result: They underscore the transformative potential of artificial intelligence (AI) by highlighting how it shapes market knowledge, encourages customized approaches, and improves marketing efficacy in the business-to-business (B2B) space. Conclusion: In order to create a path for responsible AI integration in B2B marketing, the study concludes with recommendations for standardized terminology related to AI, practical insights into implementation challenges, and ethical issues.

This paper presents a compact on-chip antenna architecture for 5G body centric networks (BCNs) applications. A dual meander line (DML) integrated antenna consists of two stacked layers of two turns of meander lines and a ground metal layer. The DML structure decreases the resonant frequency, increases tuning bands and broadens the operating bandwidth to be a suitable choice for high data rate biomedical applications. The antenna’s performance is evaluated in both scenarios inside and outside the human body. The proposed antenna is fabricated using UMC180 nm CMOS technology with a total area of 1150µm×200µm, and operates at bands 22 GHz, 34 GHz, 44 GHz and 58 GHz with an operating bandwidth up to 2 GHz at impedance bandwidth ≤ 7.5 dB (VSWR≤2.5). The proposed antenna is simulated using high frequency structure simulator (HFSS) and shows good agreement between measured and simulated results.

This paper presents the PEnsemble 4 model, a sophisticated machine learning framework that integrates IoT-based environmental data to accurately forecast maize yield. With the projected significant growth in global maize demand over the next decade, the inherent risks posed by the crop’s dependence on weather conditions necessitate improved prediction capabilities. The PEnsemble 4 model, developed with high accuracy, incorporates comprehensive datasets encompassing soil attributes, nutrient composition, weather conditions, and UAV-captured vegetation imagery. By employing a combination of Huber and M estimates, the PEnsemble 4 model effectively analyzes temporal patterns in vegetation indices, specifically CIre and NDRE, which serve as reliable indicators of canopy density and plant height. In addition, this research significantly contributes to precision agriculture by offering an efficient and sustainable alternative to conventional farming practices through precise yield predictions. Notably, the PEnsemble 4 model enables earlier estimation, advancing the timeline for yield prediction from the conventional day 100 in the R6 stage to day 79 in the R2 stage. This improvement enhances decision-making processes in farming operations. The remarkable accuracy rate of 91% underscores the importance of adopting a multifaceted data approach that harnesses IoT-derived environmental insights. Additionally, the PEnsemble 4 model extends its benefits beyond yield prediction, facilitating the detection of water and crop stress, as well as disease monitoring in broader agricultural contexts. Ultimately, the PEnsemble 4 model establishes a new standard in maize yield prediction, revolutionizing crop management and protection through the synergistic utilization of IoT and machine learning technologies.

Mannosylerythritol lipids (MELs), one of the most promising biosurfactants (BS), are glycolipids produced by yeasts or fungi, with great environmental performance and a high compatibility with the human body. MELs besides working as typical surfactants, can form diverse structures when in the critical aggregation concentration (CAC), reducing the surface tension of water and other solutions, and being stable over a wide range of conditions. Among others, MELs present anti-microbial, antitumor, antioxidant and anti-inflammatory activity, skin and hair repair capacity, which opens possibilities for their use in applications from cosmetics and pharmaceutics to bio-remediation and agriculture. However, their market share is still low when compared to other glycolipids, due to their less developed production process and higher production cost. This re-view gathers information on the potential applications of MELs mentioned on the literature since 1993. Furthermore, it is also explored the current strategies being developed to enhance the market presence of MELs, in parallel with the ones developed for rhamnolipids and sophorolipids.

Artificial intelligence (AI) and 3D printing (3DP) play critical roles in what is known as the 4th Industrial Revolution by developing data- and machine-intelligence-based integrated production technologies. This shift has led to increasingly complex design requirements and fabrication methods, posing several challenges for implementing design, fabrication process, and architectural quality standardization. This paper systematically reviews AI techniques applied in diverse stages of 3DP production in architecture. The research goals are to (1) offer a comprehensive critical analysis of the body of literature; (2) identify and categorize approaches to integrating AI in the production of 3D-printed structures; (3) identify and discuss challenges and opportunities of AI integration in architectural production of 3DP structures; and (4) identify gap and provide recommendations for future research. The findings indicate that topics related to AI applications are current and trending, receiving increasing attention from researchers in general and particularly those working in machine and deep learning, artificial neural networks, and pattern recognition. However, the potential of the application of AI in architectural production using additive manufacturing still needs to be explored. Considering this, the paper emphasizes the necessity of redefining traditional field boundaries and opening new opportunities in the sector.

There are several well-described molecular mechanisms that influence cell growth and are relat-ed to the development of cancer. Chemokines constitute a fundamental element which are not only involved in local growth but also affect angiogenesis, tumor spread and metastatic disease. Among them, the C-X-C motif chemokine ligand 12 (CXCL12) and its specific receptor the chemo-kine C-X-C motif receptor 4 (CXCR4) have been widely studied. The overexpression in cell mem-branes of CXCR4 has been shown to be associated with the development of different kinds of his-tological malignancies, such as adenocarcinomas, epidermoid carcinomas, mesenchymal tumors or neuroendocrine neoplasms (NENs). The molecular synapsis between CXCL12 and CXCR4 leads to the interaction of G proteins and activation of different intracellular signaling pathways in both gastroenteropancreatic (GEP) and bronchopulmonary (BP) NENs conferring greater ca-pacity for locoregional aggressiveness, epithelial-mesenchymal transition (EMT) and the ap-pearance of metastases. Therefore, it has been hypothesized how to design tools that target this receptor. The aim of this review is to focus on current knowledge of the relationship between CXCR4 and NENs, with a special emphasis on diagnostic and therapeutic molecular targets.

Many protein expression systems are primary utilised to produce a single, specific recombinant protein. In contrast, most biological processes such as virus assembly rely upon a complex of several interacting proteins rather than the activity of a sole protein. The high complexity of the baculovirus genome, coupled with a multiphase replication cycle incorporating distinct transcriptional steps, made it the ideal system to manipulate for high-level expression of a single or co-expression of multiple foreign proteins within a single cell. We have developed and utilised a series of recombinant baculovirus systems to unravel the sequential assembly process of a complex non-enveloped model virus, Bluetongue virus (BTV). The high protein yields expressed by the baculovirus system not only facilitated structure-function analysis of each viral proteins but were also advantageous to crystallography studies and supported the first atomic level resolution of a recombinant viral protein, the major BTV capsid protein. Further, the formation of recombinant double-shelled virus-like particles (VLPs) provided insights into the structure-function relationships among the four major structural proteins of the BTV whilst also representing a potential candidate for a viral vaccine. The baculovirus multi-gene expression system facilitated the study of structurally complex viruses (both non-enveloped and enveloped viruses) and heralded in a new generation of viral vaccines.

Three-dimensional (3D) printing technology, also known as additive manufacturing (AM), has emerged as an attractive state-of-the-art tool for precisely fabricating functional materials with complex geometries, championing several advancements in tissue engineering, regenerative medicine, and therapeutics. However, this technology has an untapped potential for biotechnological applications, such as sensor and biosensor development. By exploring these avenues, the scope of 3D printing technology can be expanded and pave the way for groundbreaking innovations in the biotechnology field. Indeed, new printing materials and printers would offer new possibilities for seamlessly incorporating biological functionalities within the growing 3D scaffolds. Herein, we review the extrusion-based 3D printing modalities emphasizing biosensor applications. We highlighted the application of natural, synthetic, and composite biomaterials as 3D-printed soft hydrogels. Emphasis was placed on the approach by which the sensing molecules were introduced during the fabrication process. Finally, the future perspective was provided.

Fires constitute a significant threat due to the pollutants emitted and the destruction they cause. People who take part in fire-fighting operations must be equipped with appropriate tools, including special clothing that will allow them to work and guarantee safety. One of the threats are compounds from the PAH group, which are characterized by high toxicity and carcinogenicity. Therefore, it is important that the materials used constitute a barrier to contamination. Various materials from which individual elements of special firefighter's clothing are made were tested. Additionally, the importance of height on the possibility of sorption of PAH compounds on a given type of material was analyzed. Based on the results obtained, it was found that the type of material and the zone in which the clothing items are used are important in the sorption processes of pollutants. Data in this area may be important for taking further actions regarding the modification of materials used in special fire brigade clothing and in their cleaning processes.

Based on DFT (density functional theory) using B3LYP/6-31G method, theoretical investigations applied to demonstrate the structural, electronic properties and stability of (N,Nˊ-(1,4-phenylene)bis(1,8-naphthalimide)) is more stable than the compound (N-(4-amino phenyl)1,8-naphthalimide) by (-1.2762 eV or -29.4299 Kcal.mol-1) depending on the values of HOMO, synthesis reaction of imide is spontaneous and endothermic at temperature 275˚C according to the values of ∆rS, ∆rG and ∆rH.

The proliferation of bacteria in fresh water-based paints is a concerning issue, as these microorganisms can utilize the organic materials in paint as a source of carbon to stimulate their growth, leading to the deterioration of painted surfaces. This study analyzed the bacterial quality and lead concentration of six paint samples collected from a manufacturing industry in Lagos. The total bacterial population monitored in the paint samples ranged from 1×106 CFU/ml to 7.8×109 CFU/ml, and while the population density decreased in all the samples throughout the 3-month study period, it was not uniform. The observed decrease in population density may be attributed to the biocide present in the paint samples and environmental factors such as heavy metal concentration. Interestingly, Pseudomonas oleovorans, P. luteola, Burkholderia cepacia, Sphingomonas paucimobilis, and Stenotrophomonas maltophilia were still able to grow in the paint samples from week 0 to week 10. The study highlights the potential for bacterial contamination of water-based paint, and the need for manufacturers to take measures to prevent this. The findings suggest that even fresh paint can serve as a substrate for bacterial growth, which can lead to issues such as loss of viscosity, discoloration, and malodor. Additionally, the presence of bacteria in paint can lead to degradation of painted surfaces, which can have economic and public health implications. The study emphasizes the importance of incorporating effective biocides in paint formulations, as well as communicating proper storage conditions to retailers, to help increase the shelf-life of paint and prevent bacterial contamination. Overall, the study provides important insights into the potential risks associated with bacterial contamination of paint and the need for proactive measures to prevent it.

Performing optimisation and scale-up studies of crystallisation systems requires accurate and computationally efficient mathematical models. The assumption of the ideal mixing conditions in batch reactors typically produce inaccurate results while the computational expense of CFD models is still prohibitively high. Therefore, in this work, a new intermediary approach is proposed that takes into account the non-ideal mixing conditions in the reactor and requires less computational resources than full CFD simulations. Starting with the Danckwerts concept of the intensity of segregation, an analogy between its application to chemical reactions and the kinetics of the crystallisation phenomena (such as nucleation and growth) has been made. As a result, the modified kinetics expressions have been derived which incorporate the effect of non-idealities present in stirred reactors. This way, based on the experimental measurements of the mixing time using the Laser Induced Fluorescence (LIF) technique, computationally more efficient mathematical models can be developed in two ways: (1) the accurate semi-empirical correlations are available for standard mixing configurations with the most often used types of impellers, (2) CFD simulations can be utilised for estimation of the mixing time; in this case it is necessary to simulate only the mixing process. The benefits offered by the LIF experimental technique have been demonstrated and some frequent problems in its application analysed. The mixing time results for configurations with and without baffles for three types of impellers and four different rotational speeds have been presented. The false shorter mixing times in the non-baffled configurations have been observed and this phenomena explained by the existence of two segregated zones in the reactor and confirmed by additional experiments. The precise measurements in these cases have been shown as difficult using the LIF technique, particularly for higher rpms. The experimental data has been compared to the preliminary simulation results obtained from the Smoothed Particle Hydrodynamics method and the standard k-ε turbulence model with the modest success. The shortcomings of the SPH model have been recognized and the directions for the future work discussed.

Circulating tumor cells (CTCs) are cells released from the primary tumor and intravasate into the blood or lymphatic vessels, where they are transported to distant sites and act as seeds that initiate cancer metastases. Recent advances in CTC research have shown their relevance as prognostic markers for early and metastatic disease detection, predictive biomarkers for relapse, and response to medical intervention or therapy. The rapidly evolving landscape of CTC biology has opened new avenues for understanding cancer progression, metastasis, and treatment response. Additionally, translating these findings into clinical applications holds promise for improving cancer diagnostics, prognosis, and personalized therapeutic strategies. This review discusses the significance of CTCs in cancer research and their associated challenges. We explore recent developments in the detection and characterization of CTCs and their implications in cancer research and clinical practice.

Oilseed plants of the Brassicaceae plant family are cultivated for food, feed and industrial purposes on large-scale in Europe. This review gives an overview of current market-oriented applications of new genomic techniques (NGTs) in relevant Brassicaceae oilseed crops based on a literature survey. In this respect, changes in oil quality, yield, growth and resistance to biotic and abiotic stress are goals in oilseed rape (B. napus) and camelina (C. sativa). Environmental risk assessment scenarios are developed for specific NGT applications in Brassicaceae oilseed crops with either a changed oil composition or with fitness related traits. In case of a changed oil composition an increase or decrease of polyunsaturated fatty acids (PUFA) demonstrates risks for health and survival of pollinators. In case of fitness related traits other risks were identified, i. e. an increased risk of spread and persistence of NGT plants. Furthermore, there are indications for potential disturbance of interactions with the environment, involving signalling pathways and reaction to stress conditions. It is shown that for a comprehensive risk assessment the technological potential of NGTs, the plants’ biology and the scale of releases have to be considered in combination. Therefore, the release of NGT plants into the environment for agricultural purposes will require risk assessment and monitoring of single traits as well as of combinatorial and long-term cumulative effects. In addition, risk management should develop concepts and measures to control and potentially restrict the scale of releases. This is especially relevant for NGT Brassicaceae in Europe which is a centre of diversity of this plant family.

A detailed review of the scientific literature was undertaken to examine the most recent developments in plasma processing in the field of medicine. The first part of the review includes a detailed breakdown of the different types of coatings that can be applied onto medical devices using plasma, with a specific focus on antimicrobial surfaces. The developments during 2023 in plasma deposited biocompatible, drug delivery and adhesive coatings are described and specific applications in additive manufacturing are highlighted. The use of plasma and plasma activated liquids as standalone therapeutics continues to evolve and pertinent advances in this field are described. In addition, the combination of plasma medicine with conventional pharmaceutical interventions was reviewed and key emerging trends were highlighted, including the use of plasma to enhance drug delivery directly into tissue. The potential synergies between plasma medicine and chemotherapeutics for oncology and infection treatment is a growing area and recent advancements are noted. Finally, the use of plasma to control excess antibiotics and to intentionally degrade such materials in waste streams is described.

In the context of developing machine learning models, until and unless we have the required data engineering and machine learning development competencies as well as the time to train and test different machine learning models and tune their hyperparameters, it is worth trying out the automatic machine learning features provided by several cloud-based and cloud-agnostic platforms. This paper explores the possibility of generating automatic machine learning models with low-code experience. We have developed criteria to compare different machine learning platforms for generating automatic machine learning models and presenting their results. Thereafter, lessons learned by developing automatic machine learning models from a sample dataset across four different machine learning platforms were elucidated. We have also interviewed machine learning experts to conceptualize their domain-specific problems that automatic machine learning platforms could address. Results showed that automatic machine learning platforms could provide a fast track for organizations seeking digitalization of their businesses. Automatic machine learning platforms help produce results, especially for time-constrained projects where resources are lacking. The contribution of this paper is in the form of a lab experiment in which we demonstrate how low-code platforms could provide a viable option to many business cases and henceforth provide a lane that is faster than the usual hiring and training of already scarce data scientists and to analytics projects that suffer from overruns.

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

Splines and quasi-interpolation operators are important both in approximation theory and applications. In this paper, we construct a family of quasi-interpolation operators for the bivariate quintic spline spaces S₅³ (∆_mn⁽²⁾). Moreover, the properties of the proposed quasi-interpolation operators are studied, as well as its applications for solving two-dimensional Burgers’ equation and image reconstruction. Some numerical examples show that these methods, which are easy to implement, provide accurate results.

Polyhydroxyalcanoates (PHAs) are biodegradable polymers synthesized in cytoplasmic granules in bacteria, such as Cupriavidus necator (Ralstonia eutropha), Alcaligenes latus, Pseudomonas spp., Comamonas spp. and other species. PHAs accumulation occurs in response to stress conditions, i.e. under high carbon and low nitrogen (24:1 ratio). PHA can be synthesized using recombinant microorganisms (provided with the operon phbA/phbB/phbC), escaping the constrains of nutrient request, except addition of high amount of sugar (glucose, lactose, fructose). In this study; E. coli was genetically modified for PHB production in biofermentors. The production of PHA at industrial scale requires a continuous supplementation of fermentable sugars to support the availability of nutrients and to assess the level of the exponential growth phase; since sugars are required either for bacterial growth either for PHA synthesis and energy storage. The biofermentors need to be run in automated system. Sensors are used at many points in fermentators; in the evaluation of parameters: consumption of sugars; cell density; quantification of PHB synthesis. The need of operational control during the fermentation has prompted us to application of three measurements; one unit linked to a Nanodrop to evaluate OD; one linked to a reaction chamber to measure sugars consumed by enzyme based fluorescence detection; and one for bacteria Nile Blue staining and fluorescence intensity reads. The growth of bacteria on three different plant by-products was monitored and PHB production in four days using a banana by-product feed was optimised. These detectors will make possible to exploit the full potential of bioreactors optimizing the time of use and maximizing the number of bacteria synthesizing PHA.

AI is leveraging all aspects of life. Medical services are not untouched. Especially in the field of medical image processing and diagnosis. Big IT and Biotechnology companies are investing millions of dollars in medical and AI research. The recent outbreak of SARS COV-2 gave us a unique opportunity to study for a non interventional and sustainable AI solution. Lung disease remains a major healthcare challenge with high morbidity and mortality worldwide. The predominant lung disease was lung cancer. Until recently, the world has witnessed the global pandemic of COVID19, the Novel coronavirus outbreak. We have experienced how viral infection of lung and heart claimed thousands of lives worldwide. With the unprecedented advancement of Artificial Intelligence in recent years, Machine learning can be used to easily detect and classify medical imagery. It is much faster and most of the time more accurate than human radiologists. Once implemented, it is more cost-effective and time-saving. In our study, we evaluated the efficacy of Microsoft Cognitive Service to detect and classify COVID19 induced pneumonia from other Viral/Bacterial pneumonia based on X-Ray and CT images. We wanted to assess the implication and accuracy of the Automated ML-based Rapid Application Development (RAD) environment in the field of Medical Image diagnosis. This study will better equip us to respond with an ML-based diagnostic Decision Support System(DSS) for a Pandemic situation like COVID19. After optimization, the trained network achieved 96.8% Average Precision which was implemented as a Web Application for consumption. However, the same trained network did not perform like Web Application when ported to Smartphone for Real-time inference, which was our main interest of study. The authors believe, there is scope for further study on this issue. One of the main goals of this study was to develop and evaluate the performance of AI-powered Smartphone-based Real-time Applications. Facilitating primary diagnostic services in less equipped and understaffed rural healthcare centers of the world with unreliable internet service.

Nowadays, the growing concern about improving thermal comfort in textiles, asphalt pavements and buildings has stimulated research into phase change materials (PCMs). However, the incorporation of PCMs directly causes mechanical impacts. Therefore, this study herein reported focuses on designing coaxial phase change fibres using commercial cellulose acetate (CA) or recycled CA obtained from cotton fabrics (CAt) as a coating and polyethylene glycol (PEG) 2000 as a core. The phase change fibres (PCF) were produced by wet spinning, varying the parameters: (i) CA concentration, molecular weight and source (virgin versus recycled); and (ii) PEG concentration and ejection. For phase change recycled fibres (PCFt), PEG concentration and ejection were varied. The fibres were assessed for their optical, chemical, thermal and mechanical properties. Bright-field microscopy images and Scanning Electron Microscopy (SEM) micrographs proved the coaxial structure. Fourier Transform Infrared spectroscopy (FTIR) confirmed the presence of PEG in the core. Thermogravimetric Analysis (TGA) proved the ability of PCFs to tolerate high temperatures. Differential Scanning Calorimetry (DSC) attested to the presence of PEG2000 peaks, since their melting points were close to those of virgin PEG2000, with a slight change in the PCFs and PCFts caused by the protective sheath of CA and CAt.

Merkel cell carcinoma (MCC) is a rare, highly aggressive skin cancer characterized by two etiologies: the first one, observed in most cases, is attributed to Merkel cell polyomavirus (MCPyV), a DNA tumor virus, while the second one is due to ultraviolet (UV) radiation exposure. Currently, there are limited diagnostic, prognostic, and therapeutic biomarkers available for MCC. MicroRNAs (miRNAs) are small RNA molecules that do not code for proteins but regulate various cellular processes, including cell cycle progression, proliferation, differentiation, and apoptosis. The dysregulation of miRNAs has been associated with a wide range of cancers since they can act as either tumor suppressors or oncogenes. This paper aims to collect, summarize and re-organize the recent discoveries concerning miRNAs, whose dysregulation in the pathogenesis and progression of MCC is slowly emerging. Furthermore, the potential clinical applications of miRNAs as diagnostic biomarkers, possible differentiation between MCPyV-related and unrelated MCC, predictors of response to therapy and prognostics are also discussed. From this review, it emerges that miRNAs may have, in the near future, the potential to become clinically significant in improving the diagnostic accuracy, prognostic evaluation, and therapeutic options of MCCs. Finally, the new perspectives on the poorly explored field of artificial intelligence in MCC are provided.

The pervasive presence of atmospheric pollution, particularly polycyclic aromatic hydrocarbons such as 7,12-dimethylbenz(a)anthracene, instigates aberrant cellular and tissue conditions, precipitating oxidative damage and inflammatory cascades conducive to the onset of cancer, notably mammary cancer. Given the hepatic metabolism of 7,12-dimethylbenz(a)anthracene, the imperative of employing natural antioxidants becomes apparent. In this study, we delve into the antioxidant and hepato-protective properties of Pistacia lentiscus against 7,12-dimethylbenz(a)anthracene-induced toxicity, alongside exploring its potential anticancer attributes. Our investigations unveil Pistacia lentiscus's anti-proliferative efficacy against human breast cancer cell lines. Notably, 7,12-dimethylbenz(a)anthracene-intoxication escalates body weight, disrupts lipid profiles, and incites serum oxidative damage. Concurrently, hepatic and renal oxidative stress ensue, accompanied by heightened antioxidant enzyme activity in the 7,12-dimethylbenz(a)anthracene-exposed-group compared to controls (p<0.05). Nonetheless, Pistacia lentiscus co-administration rectifies biochemical imbalances, significantly attenuates oxidative stress aberrations, and augments antioxidant enzyme responses (p<0.05). Importantly, histological analysis evinces Pistacia lentiscus's shielding effect against 7,12-dimethylbenz(a)anthracene induced hepatocyte injury and steatosis. Our findings underscore Pistacia lentiscus's robust anti-proliferative, antioxidant, and hepato-protective capacities, mitigating metabolic disturbances, oxidative stress propagation in liver and kidney functions, and potential histological alterations, thereby impeding 7,12-dimethylbenz(a)anthracene-induced mammary cancer initiation. We posit that Pistacia lentiscus may serve as a prophylactic agent against breast cancer instigated by this carcinogen.

Cell-culture meat is a novel concept to replace traditional meat produced from animals, and to satisfy rising food demands due to the world population growth. It is still a young field and requires to solve technical issues including cell sources and types, efficient culture for large scale production, taste and texture for customer satisfaction. The current review summarized traditional beef meat production and evaluation methods for customer satisfaction. In particular, serum biomarkers for beef meat evaluation are discussed, which can be applicated for cell-culture meat production.

Halide perovskite materials gain enormous attention for their semiconducting properties, higher power conversion efficiency and potential applications in a wide range field of studies along with their two key limitations: stability and toxicity. In spite of great progress made on the halide perovskites and many promising research developments, overcoming these limitations is not realized yet. Therefore, coordination engineering of new framework for obtaining alternative new halide perovskite materials and fundamental understanding of the coordination chemistry and electronic interactions forming the structure of these newly engineered halide perovskite materials might be one way in order to overcome the issues related to both stability and toxicity. In this review, the current development of halide perovskite families, both lead halide perovskites and lead free halide perovskites followed by coordination engineering of the new frameworks to engineer new halide perovskite materials will be reviewed comprehensively. Moreover, all the concerns of the fundamental ideas of coordination chemistry and electronic interactions that are keys in forming the halide perovskite structures are discussed in detail and form the main aim of this review. Interestingly, we also discuss recent potential energy applications beyond photovoltaic and this review has completed with an essential and open question: ‘what could happen in the future of halide perovskites?’ in order to excite commercial enterprises and research institutions again as well as to get motivating new predictions on the future continuity of this field.

In polymer-based dental composites, wear is a three-body wear system mainly abrasive, because of the food particles and wear products suspended in the oral cavity, which are transferred to the microcavities of the surface of the replacements. Due to this fact, the incorporation of nanodiamond as reinforcement in these polymer matrix composites, which promotes the creation of a solid lubricant tribofilm surface could be advantageous. When nanodiamond is added in contents of 0.8, 1.6 and 3.2 wt %, an increase in microhardness from 95 up to 420 %, compared to the one of neat BisGMA/TEGDMA, is observed. Additionally, the incorporation of a content of 1.6 wt % is enough to cause a diminution of ~78 % in the friction coefficient and a reduction of the specific wear rate and Archard’s coefficient of ~50 %. Nevertheless, the addition of relatively high contents reduces the effectiveness of photoinitiation and UV-curing, which is related to the scattering and absorption of UV radiation by ND.

The dataset reports a collection of earnings call transcripts, the related stock prices, and the related sector index. It contains a total of 188 transcripts, 11970 stock prices, and 1196 sector index values. Furthermore, all of these data originated in the period 2016-2020 and are related to the NASDAQ stock market. The data have been collected using Yahoo Finance and Thomson Reuters Eikon. Specifically, Yahoo Finance offered daily stock prices and traded volume. At the same time, Thomson Reuters Eikon has been used as source for the earnings call transcripts. The dataset can be used as a benchmark for the evaluation of several NLP techniques as well as machine learning algorithms for understanding their potential for financial applications. Moreover, it is also possible to expand the dataset by extending the period in which the data originated following a similar procedure.

Although, Artificial intelligence (AI) helps experts with crime analysis, research, and understanding, it has a favourable influence on cyber security. It strengthens the tools that businesses use to safeguard their networks, clients, and workers against dangerous online behaviour. However, artificial intelligence is infamous for requiring a lot of resources. It may not, however, always be relevant. Additionally, it can provide hackers a new tool and advance their abilities. Actually, the VPN industry benefits from AI in the same way. The threat posed by machine learning in AI to user data privacy may be lessened by using a VPN on all of your devices. Because they use machine learning algorithms, VPNs are better equipped to shield their users from internet-based threats. Artificial intelligence (AI) has reportedly being investigated as a means of enhancing internet security for a considerable amount of time, according to Smart Data Collective. We anticipated that AI and machine learning will have a substantial impact on the future of cyber security around two years ago.

Motivated by a common Mathematical Finance topic, this paper surveys several results concerning windings of 2-dimensional processes, including planar Brownian motion, complex-valued Ornstein-Uhlenbeck processes and planar stable processes. In particular, we present Spitzer's asymptotic Theorem for each case. We also relate this study to the pricing of Asian options.

Short-range Internet of Things (IoT) sensor nodes operating at 2.4 GHz need to provide ubiquitous wireless sensor networks (WSNs) with energy-efficient, wide-range output power (POUT) and fully integrated on a single chip for wireless body area networks (WBANs) and wireless personal area networks (WPANs) using low-power Bluetooth (BLE) and Zigbee standards. The proposed fully integrated transmitter (TX) utilizes a digitally controllable current-mode class-D (CMCD) power amplifier (PA) with a second harmonic distortion (HD2) suppression to reduce VCO pulling in an integrated system while meeting harmonic limit regulations. The CMCD PA is divided into 7-bit slices that can be reconfigured between differential and single-ended topologies. Duty cycle distortion compensation is performed for HD2 suppression, and an HD2 rejection filter and a modified C-L-C low-pass filter (LPF) reduce HD2 further. Implemented in a 28 nm CMOS process, the TX achieves a wide POUT range of from 12.1 to -31 dBm and provides a maximum efficiency of 39.8% while consuming 41.1 mW at 12.1 dBm POUT. The calibrated HD2 level is -82.2 dBc at 9.93 dBm POUT, resulting in a transmitter figure of merit (TX_FoM) of -97.52 dB. Higher order harmonic levels remain below -41.2 dBm even at 12.1 dBm POUT, meeting regulatory requirements.

In this study, we present comprehensive climatologies of effective ultraviolet (UV) quantities and photosynthetically active radiation (PAR) over Cyprus for the period 2004 to 2023, leveraging the synergy of earth observation (EO) data and radiative transfer model simulations. The EO dataset, encompassing satellite and re-analysis data for aerosols, total ozone column, and water vapor, alongside cloud modification factors, to capture the nuanced dynamics of Cyprus's atmospheric conditions. With a temporal resolution of 15 minutes and a spatial of 0.05°x0.05°, these climatologies undergo rigorous validation against established satellite datasets and are further evaluated through comparisons with ground-based global horizontal irradiance measurements provided by the Meteorological Office of Cyprus. This dual-method validation approach not only underscores the models' accuracy but also highlights their proficiency in capturing intra-daily cloud coverage variations. Our analysis extends to investigating the long-term trends of these solar radiation quantities, examining their interplay with changes in cloud attenuation, aerosol optical depth (AOD), and total ozone column (TOC). Significant decreasing trends in the noon ultraviolet index (UVI), of -2 to -4% per decade have been found in Autumn, especially marked on the island’s Northeastern part, mainly originating from the (significant) positive trends in TOC. The significant decreasing trends in TOC, of -2 to -3% per decade that were found in Spring do not result in correspondingly significant positive trends in the noon UVI since variations in cloudiness and aerosols also have a strong impact on the UVI in this season. The seasonal trends in the Daylight Integral (DLI) were generally not significant. These insights provide a valuable foundation for further studies aimed at developing public health strategies and enhancing agricultural productivity, highlighting the critical importance of accurate and high-resolution climatological data.

Recent research emphasises the importance of Artificial Intelligence applications as supporting tools for students in higher education. Simultaneously, an intensive exchange of views has started in the public debate in the international educational community. However, for a more proper use of these applications, it is necessary to investigate the factors that explain their intention and actual use in the future. With the Unified Theory of Acceptance and Use of Technology (UTAUT2) model, this work analyses the factors influencing students' use and intention to use artificial intelligence technology. For this purpose, a sample of 199 Greek students at the School of Humanities and Social Sciences from the University of Patras participated in a survey. The findings highlight that expected performance, habit, and enjoyment of these Artificial Intelligence applications are key determinants influencing teachers' intentions to use them. Moreover, behavioural intention, habit, and facilitating conditions explain the usage of these Artificial Intelligence applications. The study did not reveal any moderating effects. The limitations, practical implications, and proposed directions for future research based on these results are discussed.

Biochar is a carbonaceous material derived from the thermochemical conversion of biomass under oxygen-lean conditions. Its properties render it many applications in agriculture, environmental remediation, and catalysis. Commonly produced by pyrolysis under a nitrogen atmosphere, the biochar composition is influenced mainly by residence time, heating rate, and process temperature. The existing literature primarily assesses biochar properties, such as ultimate, proximate, calorific, porosity, and combustibility, generated under various thermo-conversion conditions. However, there is a notable lack of sufficient investigation and attention given to the practical application of biochar. This article seeks to bridge that gap by enhancing the comprehension of the necessary experimental assessment for specific biochar applications and proposing process optimization methods using Multi-Criteria Decision Analysis (MCDA). By delving into these aspects, the study offers a concise overview of biochar production and presents a comprehensive panorama based on articles and patents. Additionally, the research explores the physicochemical characterization techniques associated with biochar and its various applications in conjunction with MCDA. This comprehensive approach aims to uncover valuable insights that could further advance the practical and sustainable utilization of biochar in a wide range of fields.

This study investigates the long-term stability of the Suomi National Polar-orbiting Partnership (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) moderate-resolution Thermal Emissive Bands (M TEBs; M12 – M16) covering a period from February 2012 to August 2020. It also assesses inter-sensor consistency of the VIIRS M TEBs among three satellites-S-NPP, NOAA-20, and NOAA-21 over eight months spanning from March 18 to November 30, 2023. The field of interesting is limited to the ocean surface between 60°S and 60°N, specifically under clear-sky conditions. Taking radiative transfer modeling (RTM) as the transfer reference, we employed the Community Radiative Transfer Model (CRTM) to simulate VIIRS TEB brightness temperature (BTs), incorporating European Centre for Medium-range Weather Forecasts (ECMWF) reanalysis data as inputs. Our results reveal two key findings. Firstly, the reprocessed S-NPP VIIRS TEBs exhibit a robust long-term stability, as demonstrated through analyses of the observation minus background BT differences (O-B ∆BTs) between VIIRS measurements (O) and CRTM simulations (B). The drifts of the O-B BT differences are consistently less than 0.105 K/Decade across all S-NPP VIIRS M TEB bands. Notably, observations from VIIRS M14 and M16 stand out with drifts well within 0.04 K/Decade, reinforcing their exceptional reliability for climate change studies. Secondly, excellent inter-sensor consistency among these three VIIRS instruments is confirmed through the double-difference analysis method (O-O). This method relies on the O-B BT differences obtained from daily data. The mean inter-VIIRS O-O BT differences remain within 0.08 K for all M TEBs, except for M13. Even in the case of M13, the O-O BT differences between NOAA-21 and NOAA-20/S-NPP have values of 0.312 K and 0.234 K, respectively, which are comparable to the 0.2 K difference between some TEBs among VIIRS and MODIS. These disparities are primarily attributed to the significant differences in the Spectral Response Function (SRF) of NOAA-21 compared to NOAA-20 and S-NPP. Our study confirms the RTM-based TEB quality evaluation method’s versatility and effectiveness in assessing long-term sensor stability and inter-sensor consistency. The double-difference approach effectively mitigates uncertainties and biases inherent to CRTM simulations, establishing itself a robust mechanism for assessing inter-sensor consistency. In addition, we’ve observed that, except for M13, M12 always exhibits larger spatial variations of O-O BT differences with greater uncertainties compared to the other M TEBs. The influence of solar contribution through sea surface reflection on the Top of Atmosphere (TOA) radiance measurements during daytime in M12 should not be underestimated.

The world is facing new challenges every day; however, with the spread of the pandemic around the world, this new challenge is different. The pandemic is increasing and concentrating various challenges simultaneously. Although different sectors are facing consequences, the most important sectors, that is, health and economy are the most affected. When the pandemic began, it was not known how long it would last, which complicated health and economic planning. Therefore, it is important for decision makers and the public to know the predictions and expectations of the future of these challenges. In this work, the current situation is analyzed. Then, an expectation model is developed based on the statistics of the pandemic using a growth rate model based on an exponential and logarithmic rate of increase. Based on the available open data about the pandemic spread, the model can successfully predict future expectations, including the duration and maximum number of cases of the pandemic. The model uses the equilibrium point as the day the cases decrease. The model can be used for planning and the development of strategies to overcome these challenges.

We describe a multi-resolution approach for audio classification and illustrate its application to the open data set for environmental sound classification. The proposed approach utilizes a multi-resolution based ensemble consisting of targeted feature extraction of approximation (coarse scale) and detail (fine scale) portions of the signal under the action of multiple transforms. This is paired with an automatic machine learning engine for algorithm and parameter selection and the LSTM algorithm, capable of mapping several sequences of features to a predicted class membership probability distribution. Initial results show an improvement in multi-class classification accuracy.

The fitting and modeling of skewed, complex, symmetric, and asymmetric datasets is an exciting research topic in many fields of applied sciences, notably lifetime, medical, and financial sciences. The Heavy tailed Nadarajah Haghighi model is introduced by compounding the heavy tailed family and Nadarajah Haghighi distribution in this paper. The so obtained model has three parameters accounting for the scale and shape of the distribution. The proposed distribution's fundamental characteristics such as probability density, cumulative distribution, hazard rate, and survival functions are provided, and several key statistical properties are established, and several entropy information measures are proposed. The estimation of model parameter is performed via the ML procedure. Further, different simulation experiments are conducted to demonstrate the performance of proposed estimator’s using some measure, like the average estimate, the average bias, and associated mean square error. Finally, we are applied our proposed model to analyze three different real datasets. In our illustration, we are compared the practicality of the recommended model with several well-known competing models.

We investigate and discuss in detail the structure of the restricted singular value decomposition for a tensor triplet under t-product (T-RSVD). The algorithm is provided with a numerical example illustrating the main result. For applications, we consider color image watermarking processing with T-RSVD.

Background: Long QT syndrome (LQTS) can lead to ventricular arrhythmia and sudden cardiac death. The most common congenital cause of LQTS is mutations in the channel subunits generating the cardiac potassium current IKs. Zebrafish (Danio rerio) have been proposed as a powerful system to model human cardiac diseases due to the similar electrical properties of the zebrafish heart and the human heart. Methods: We used high resolution all-optical electrophysiology on ex-vivo zebrafish hearts to assess the effects of IKs analogues on the cardiac action potential. Findings: We found that chromanol 293B (an IKs inhibitor) prolonged the action potential duration (APD) in the presence of E4031 (an IKr inhibitor applied to drug-induced LQT2) and to a lesser extent in the absence of E4031. Moreover, we show that PUFA analogues slightly shorten the APD of the zebrafish heart. However, PUFA analogues failed to reverse the APD prolongation in drug-induced LQT2. However, a more potent IKs activator, ML-277, partially reversed the APD prolongation in drug-induced LQT2 zebrafish hearts. Interpretation: Our results suggest that IKs plays a limited role in ventricular repolarizations in the zebrafish heart under resting conditions but plays a more important role when IKr is compromised, as if IKs in zebrafish serves as a repolarization reserve as in human hearts. This study shows that potent IKs activators can restore the action potential duration in drug-induced LQT2 in zebrafish heart.

The objective of this study was to detail the monomer composition of resin-based dental materials sold in the market in 2023 and to evaluate the proportion of BPA derivatives in relation with their applications. A search on manufacturers’ websites was performed to reference resin-based dental materials currently on the market. Their monomer composition was determined from material safety data sheets and completed with a search on PubMed database. Among the 543 material compositions exploitable, 382 (70.3%) contained BPA derivatives. Among them, 56.2% contained BisGMA, 28% BisEMA, the most frequently reported. A total of 59 monomers of which 6 BPA-derivatives were found. 309 materials (56.9%) contained UDMA and 292 (53.8%) TEGDMA. Less than one third of materials identified contained no BPA derivatives. These proportions vary a lot depending on their applications with materials dedicated to dental care of young populations containing the highest proportions of BPA-derivatives monomers. The long-term effects on human health of the different monomers identified including BPA-derivatives monomers is a source of concern. For children and pregnant or lactating women arises the question to take a precautionary principle and avoid the use of resin-based dental materials likely to release BPA by opting for alternative materials.

The objective of this study was to carry out the extraction and characterization of chitin and chitosan from the exoskeletons of the Farfantepenaeus californiensis species of shrimp, as well as its application in the formulation of polymeric films plasticized with glycerol used in the process of food packaging. The characterization of the extracted polymer was made by Fourier transform infrared spectroscopy (FTIR), Scanning electron microscopy (SEM), and physicochemical analysis of chitin and chitosan, resulting in a theoretical performance of 29.33% and 16.46%, respectively. In terms of quality, it was determined that the extracted chitosan had a molecular weight of 1.10 x105 g/mol and a deacetylation degree of 80.23%, which classifies it as a low-molecular weight chitosan, suitable for food packaging. It was shown that the extracted chitosan is fit for the production of the films by using the casting method, presenting a tension resistance of 1 MPa. The Antibacterial analysis was carried out using Staphylococcus aureus (S. aureus) and Escherichia coli (E. coli), bacterial models commonly found in food. The results showed an effective inhibition capability of the polymeric materials. Overall, the results for both chitosan and the films indicate a high potential for application in the food packaging sector.

The paper presents the design and the implementation of different advanced control strategies that are applied to a nonlinear model of a thermal unit. A data–driven grey–box identification approach provided the physically meaningful nonlinear continuous–time model, which represents the benchmark exploited in this work. The control problem of this thermal unit is important since it constitutes the key element of passive air conditioning systems. The advanced control schemes analysed in this paper are used to regulate the outflow air temperature of the thermal unit by exploiting the inflow air speed, whilst the inflow air temperature is considered as an external disturbance. The reliability and robustness issues of the suggested control methodologies are verified with a Monte–Carlo analysis for simulating modelling uncertainty, disturbance and measurement errors. The achieved results serve to demonstrate the effectiveness and the viable application the suggested control solutions to air conditioning systems. The benchmark model represents one of the key issues of this study, which is exploited for benchmarking different model–based and data–driven advanced control methodologies through extensive simulations. Moreover, this work highlights the main features of the proposed control schemes, while providing practitioners and heating, ventilating and air conditioning engineers with tools to design robust control strategies for air conditioning systems.

Fall detection systems play a crucial role in addressing the significant health concern of elderly falls, a leading cause of health deterioration and mortality. As the aging population grows and life expectancy increases, the development of accessible tools becomes vital for predicting and preventing falls, offering a practical and widely applicable solution in contrast to costly and expertise-dependent assessment tools. In contrast, due to the formidable challenges encountered, a comprehensive investigation into the comparative performance of standard ML models within this field still needs to be explored. This paper proposes a standard pipeline for pre-processing, training, and evaluating ML models for fall detection on the SisFall dataset. We conducted extensive experiments to evaluate the performance of various ML models for fall detection. The results validate the efficiency of the deep model in identifying the time windows in which a fall occurred. Among the deep models, the architecture, including a combination of convolutional neural networks and fully connected layers, outperforms the others by macro-averaged Precision, macro-averaged Recall, and macro-averaged F1-Score of 87.03\%, 86.83\%, and 86.93\%, respectively.

The current study characterizes the transient M/M/1 queue manifold info-geometrically, through devising Fisher Information matrix(FIM) and its inverse(IFIM). Additionally, the impact of stability on the existence of IFIM and explore the geodesic equations of motion has been revealed. More potentially, the paper discusses some info-geometric applications to Machine Learning are highlighted. Closing remarks with some challenging open problems combined with the next phase of research are given.

Machine Learning (ML) has brought about a transformative era in reactor operations, reshaping monitoring, control, and optimization strategies. This survey comprehensively explores the diverse spectrum of ML applications in industrial reactors. From real-time sensor analysis ensuring reactor functionality to adaptive control algorithms ensuring stability, ML's impact is profound and multifaceted. The benefits of ML are equally evident in optimization, encompassing performance trend prediction, proactive maintenance, and fine-tuning of operations for enhanced efficiency. This review identifies dominant ML techniques, operations stages receptive to ML integration, core data sources, and critical input-output parameters. Aimed at both academics and practitioners, this exploration enriches reactor technologies, unlocking their full potential through insights driven by ML.

Lower Back Pain (LBP) is a disease that needs immediate attention. Person with back pain shall go immediately to doctor for treatment. Injury, excessive works and some medical conditions are result of back pain. Back pain is common to any age of human for different reasons. Due to factors such as previous occupation and degenerative disk disease the chance of developing lower back pain increases for older people. It hampers the working condition of people common reason for seeking medical treatment. The result is absence from work and is unable to normal due to pain. It creates uncomfortable and debilitating situations. Hence, detecting this disease at an early stage will assist the medical field experts to suggest counter measures to the patients. Detection of lower back pain is implemented in this paper by applying ensemble machine learning technique. This paper proposes Stacking ensemble classifier as an automated tool that will predict lower back pain tendency of a patient. Experimental result implies that the proposed method reaches an accuracy of 76.34%, f1-score of 0.76 and MSE of 0.34.

Hyperspectral imaging data holds great potential for the different stages of the mining life cycle in active and post-mining environments. However, the technology has yet to reach the stage of large-scale industrial implementation and acceptance. While hyperspectral satellite imagery can achieve high spectral resolution, signal-to-noise ratio (SNR) and global availability with break-through satellite systems like EnMAP, EMIT and PRISMA, limited spatial resolution poses chal-lenges for sectors like mining, which require decimetre to centimetre scale resolution for applica-tions such as reconciliation, ore/waste estimates, geotechnical assessments and environmental monitoring. Hyperspectral imaging from drones (referred to herein as Uncrewed Aerial Systems; UASs) offers high spatial resolution data relevant to the camp/ mine scale, with the capability for frequent, user-defined re-visit times. This has been made possible by the miniaturization of hy-perspectral imaging systems. Collection of data in the visible to near and shortwave infrared (VNIR-SWIR) wavelength regions enables the detection of different minerals and surface altera-tion patterns potentially revealing crucial information for exploration, extraction, re-mining, waste remediation, and rehabilitation. In this paper, we provide a review of relevant studies de-ploying hyperspectral imaging in or applicable to the mining sector, especially for the use of hy-perspectral VNIR-SWIR Uncrewed Aerial Systems. Where required, we draw on previous in-sights derived from satellite or ground-based systems. We also discuss UAS survey planning, and sampling considerations for validation and interpretation.

Accurately distinguishing between indolent and aggressive tumors is a crucial unmet need in the clinical management of prostate cancer (PCa). The traditional Gleason grading system has been utilized for this purpose; however, there is often ambiguity in classifying tumors with a Gleason grade of 7. Clinicians commonly resort to using secondary Gleason grades, such as 3+4 or 4+3, to classify these tumors as indolent or aggressive, respectively. Unfortunately, such classifications are prone to misinterpretation, leading to erroneous diagnoses and prognoses. To address this challenge, we investigated the application of Machine Learning (ML) techniques to classify PCa patients based on gene expression data sourced from The Cancer Genome Atlas. By comparing gene expression levels between indolent and aggressive tumors, we sought to identify distinctive features for developing and validating a range of ML algorithms and stacking techniques. The stacking based model achieved an impressive accuracy of 96% for all samples encompassing primary Gleason grades 6 to 10. Notably, when excluding Gleason grade 7 from the analysis, the accuracy further improved to 97%. This study underscores the effectiveness of the stacked ML algorithm for accurately classifying indolent versus aggressive PCa. Leveraging gene expression data and employing a combination of classifiers, this approach offers a powerful solution to address the unmet need in robustly distinguishing between different types of PCa tumors. Future implementation of this methodology may significantly impact clinical decision-making and patient outcomes in the management of prostate cancer.

This paper explores the application of machine learning (ML) algorithms in predicting trends in the fixed bond market, where traditional analytical methods have proven inadequate. Focusing on various ML techniques such as supervised and unsupervised learning, neural networks, and deep learning, the study evaluates their effectiveness in forecasting market movements. It details a series of experiments in which different ML models are rigorously trained and tested against historical bond market data. The findings reveal that models employing time series analysis and advanced deep learning show marked potential in accurately predicting bond market trends. Additionally, the paper delves into the challenges and limitations inherent in these ML approaches, including data requirements and the risk of model overfitting. Finally, it proposes directions for future research, emphasizing the integration of ML into broader financial market analysis.

Machine Learning (ML) was used to assess and predict urban air temperature (Tair) considering the complexity of the terrain features in Yerevan (Armenia). The estimation was performed based on the PLSR model with a high number (30) of input variables. The relevant parameters include a newly purposed modification of spectral index IBI-SAVI, which turned out to be strongly impacting on Tair prediction together with land surface temperature (LST). Cross-validation analysis on temperature predictions across a station-centered 1000m circular area revealed quite a high correlation (R2Val = 0.77, RMSEVal = 1.58) between predicted and measured Tair from the test set. It was concluded the remote sensing is an effective tool to estimate Tair distribution where a dense network of weather stations is not available. However, further developments will include incorporation of additional weather parameters from the weather stations such as precipitation and wind speed, and the use of non-parametric ML techniques.

Transportation systems through the ages have seen drastic evolutions in terms of transportation methods, speed of transport, infrastructure, technology, connectivity, influence on the environment, and accessibility. The massive transformation seen in the transportation sector has been fueled by the industrial revolutions which have continued expansion and progress into the fourth industrial revolution. However, the methodologies of data collection and processing used by the many drivers of this progress differ. The significance of standardized data and its mode of collection cannot be overstated as it has a major impact on the process outcomes. In order to achieve a better understanding of the impact of this, this study methodically reviewed the literature on the subject of the data collection and processing mechanism of 4IR technologies in the context of the transport industry with an emphasis on the areas of accomplishment of several studies and drawing attention to their limitations. Although the industry has advanced significantly as a result of the adoption and application of various research studies in this field, considerable focus needs to be placed in the areas of standardized data collection and computational approaches, which will ultimately assist in achieving various benefits in the transportation industry.

Cobalt oxide nanoparticles are promising candidates for various kinds of practical application. The interest of researchers and scientists in these nanoparticles is due to their unique physical and chemical properties such as density, melting point, magnetic characteristics and band gap. The need to develop high performance and low-cost technologies for obtaining Co3O4 nanoparticles is a major reason in research in this area. The widespread commercial use of Co3O4 nanoparticles will open doors for numerous novel cost effective technologies, which are mostly dependent on the type of synthesis method and raw materials. A possible solution to this problem is to use Cobalt nitrates as a cost-effective precursor using the solution combustion method. Solution combustion synthesis method is one of the best candidate methods that have the potential for high performance and is also cost-effective. This review examines recent progress in the synthesis of Co3O4 nanoparticles by solution combustion method and underscores their significance across various fields, highlighting the need for further research and exploration of their multifaceted applications. In addition, the most recent advancements in the applications of Co3O4 nanoparticles in sensitive sensing, catalysis, supercapacitors/batteries and water treatment/pollutants removal are reviewed and described, focusing on current trends, challenges, and future outlooks.

Our study essentially concerns the dynamic behavior of an SIRS epidemic model in discrete time. Two equilibrium points are obtained; one is disease-free while the other is endemic. We are interested in the endemic fixed point as well as its asymptotic stability. Depending on the parameters which are estimated using the data from US Department of health and SIRS modelling with optimization, two Flip and Transcritical bifurcations appear. We illustrate their diagrams, as well as their bifurcation curves using the method of Carcasses \cite{carcasses1993determination,carcasses1995singularities} for the Flip bifurcation and by an implicit function deduced from such an equation for the Transcritical bifurcation. We use the scanning of the parametric plane to have a global view of the behavior of the model and to highlight the zones of stability of the existing singularities. A superposition of the bifurcation curves with the parametric plane can show the overlap of the curves with the boundaries of the stability domains, which confirms the smooth running of the simulation and its correspondence with the theory, we finish this article with constrained optimal control applied to infection rate and recruitment rate for an SIRS discrete epidemic model. Pontryagin's maximum principle is used to determine these optimal controls. Finally using COVID-19 data in the USA, we obtain results that demonstrate the effectiveness of the proposed control strategy to mitigate the spread of the pandemic.

Esterquats constitute a unique group of quaternary ammonium salts (QASs) that contain an ester bond in the structure of the cation. Despite the numerous advantages of this class of compounds, only two mini-reviews discuss the subject of esterquats: the first one (2007) briefly summarizes their types, synthesis, and structural elements required for a beneficial environmental profile and only briefly covers their applications whereas the second one only reviews the stability of selected betaine-type esterquats in aqueous solutions. The rationale for writing this review is to critically reevaluate the relevant literature and provide others with a “state-of-the-art” snapshot of choline-type esterquats and betaine-type esterquats. Hence, the first part of this survey thoroughly summarizes the most important scientific reports demonstrating effective synthesis routes leading to the formation of both types of esterquats. In the second section, the susceptibility of esterquats to hydrolysis is explained, and the influence of various factors, such as the pH, the degree of salinity or the temperature of the solution, was subjected to thorough analysis that includes quantitative components. The next two sections refer to various aspects associated with the ecotoxicity of esterquats. Consequently, their biodegradation and toxic effects on microorganisms are extensively analyzed as crucial factors that can affect their commercialization. Then, the reported applications of esterquats are briefly discussed, including functionalization of macromolecules, such as cotton fabric as well as their successful utilization on a commercial scale. The last section demonstrates the most essential conclusions and reported drawbacks that allow us to elucidate future recommendations regarding the development of these promising chemicals.

This paper focuses on the crucial role that remote sensing plays in divining land features. Data that is collected distantly provides information in spectral, spatial, temporal and radiometric domains, with each domain having the specific resolution to information collected. Diverse sectors such as hydrology, geology, agriculture, land cover mapping, forestry, urban development and planning, oceanography and others are known to use and rely on information that is gathered remotely from different sensors. In the present study, IRS LISS IV Multi-spectral data is used for land cover mapping. It is known, however, that the task of classifying high-resolution imagery of land cover through manual digitizing consumes time and is way too costly. Therefore, this paper proposes accomplishing classifications by way of enforcing algorithms in computers. These classifications fall in three classes: supervised, unsupervised, and object-based classification. In the case of supervised classification, two approaches are relied upon for land cover classification of high-resolution LISS-IV multispectral image. These approaches are Maximum Likelihood and Support Vector Machine (SVM). Finally, the paper proposes a step-by-step procedure for optical image classification methodology. This paper concludes that in optical data classification, SVM classification gives a better result than the ML classification technique.

The article delves into the intricate phase transitions of 1-Octadecanol and n-Nonadecane within a binary system, unveiling dynamic structural changes under varying conditions. Through Fouri-er-transform infrared (FTIR) analysis, specific molecular vibrations were identified, shedding light on the molecular composition and interactions. The study highlights the challenges in detecting subtle phase transitions and emphasizes the individuality of molecular behaviours in closely related compounds. The findings underscore the complexity of phase transitions in binary systems and advocate for a nuanced approach to studying molecular structures and behaviours. Addi-tionally, the research contributes valuable insights into the molecular dynamics of Organic Polymer Functional Adsorption Materials, offering implications for synthetic waxy layers in plant epidermis.

Skin disease is a very common disease for humans. In the medical industry detecting skin disease and recognizing its type is a very challenging task. Due to the complexity of human skin texture and the visual closeness effect of the diseases, sometimes it is really difficult to detect the exact type. Therefore, it is necessary to detect and recognize the skin disease at its very first observation. In today's era, artificial intelligence (AI) is rapidly growing in medical fields. Different machine learning (ML) and deep learning(DL) algorithms are used for diagnostic purposes. These methods drastically improve the diagnosis process and also speed up the process. In this paper, a brief comparison between the machine learning process and the deep learning process was discussed. In both processes, three different and popular algorithms are used. For the machine Learning process Bagged Tree Ensemble, K-Nearest Neighbor (KNN), and Support Vector Machine(SVM) algorithms were used. For the deep learning process three pre-trained deep neural network models

This study introduces a novel nanocomposite coating composed of PANI/CeO2 nanocomposite films, aimed at addressing corrosion protection needs. Analysis through FTIR spectra and XRD patterns confirms the successful formation of the nanocomposite films. Notably, the PANI/CeO2 nanocomposite films exhibit a hydrophilic nature. The bandgap energy of the PANI composite film measures at 3.74 eV, while the introduction of CeO2 NPs into the PANI matrix reduces the bandgap energy to 3.67 eV. Furthermore, the electrical conductivity of the PANI composite film is observed to be 0.40 S.cm-1, with the incorporation of CeO2 NPs leading to an increase in electrical conductivity to 1.07 S.cm-1. To evaluate its efficacy, electrochemical measurements were conducted to assess the corrosion protection performance. Results indicate a high protection efficiency of 92.25% for the PANI/CeO2 nanocomposite film.

This paper explores the transformative impact of computational pharmaceutics, integrating AI, multi-scale modeling, quantum computing, AI-driven drug discovery, and blockchain frameworks. Molecular dynamics simulations provide insights into atomic behaviors crucial for drug design. Quantum computing reduces computational costs for simulating protein-drug interactions. AI and Graph Neural Networks accelerate drug discovery and personalized medicine. Blockchain frameworks facilitate secure pharmacogenetic data sharing. These advancements revolutionize drug development, offering unprecedented efficiency, innovation, and personalized treatments.

Wide bandgap semiconductors-based photocatalysts are usually limited by their low solar energy conversion efficiency due to their limited absorption solar wavelength, surface’s fast recombination of photoelectron–hole pairs and low charge-carrier mobility. Here we report a new stepwise solution synthesis for making a new photocatalytic core/shell of titanate nanowire/reduced graphene oxide shell (or titanate/rGO) 1D-nanocomposite. The new core/shell nanocomposite maximized the specific surface area, largely reduced the charge transfer resistance and reaction energy barrier, and significantly improved the absorption of the visible light. The core/shell nanocomposites’ large on/off current ratio and rapid photo-responses boosted the photocurrent by 30.0%, the photocatalysis rate by 50.0%, and the specific surface area by 16.4%, when comparing with the pure titanate nanowire core. Our numerical simulations support the effective charge separation on the new core-shell nanostructure, which can help further advance the new photocatalysis.

In Functional Analysis as well as Topology, we frequently encounter sets, say $X$, that contain elements close to each other. These sets display a defining "finite-ness" property: for all open cover $\mathcal{O}$ of $X$, there exists a finite subcollection $\mathcal{U}\subseteq\mathcal{O}$ such that $\mathcal{U}$ covers $X$. Such spaces $X$ are called $\textbf{compact}$, and the above "finite-ness" property afford us great convenience, because we can always investigate $X$ by investigating its finite open cover $\mathcal{U}$Arzela-

We consider several unification problems in mathematics. We refer to transcendental numbers. Also, we present some ways to unify the main non-associative algebras (Lie algebras and Jordan algebras) and associative algebras.

An exposition of entropy applications to highlight some potential entropic applications to artificial intelligence and knowledge transfer. Entropy is a basic idea in artificial intelligence that is applied to many different tasks, such as reinforcement learning, data compression, and decision-making. By quantifying uncertainty and information content, it helps artificial intelligence models provide predictions and decisions that are well-informed. Therefore, this paper shines on spotlighting the significance of entropy to attract the attention of artificial intelligence research community to entropy as a powerful tool to advance artificial intelligence.The significance of knowledge transfer (KT), particularly intergenerational knowledge transfer (IGT), in knowledge management is also covered in this paper. To quantify the complexity of knowledge distribution inside an organisation and assess the efficacy of knowledge transfer (KT) initiatives, the notion of knowledge entropy (KE) is presented. Additionally, the KT model—which is based on the conceptions of tacitness and information content—is introduced. It combines personalisation and codification methodologies. Future research directions are offered alongside a few difficult open problems.

Modern language models are designed to generate text so realistic that it could easily be mistaken for human-authored content. Additionally, they can engage in conversations with humans in a manner that appears coherent and reasonable. The epitome of this technological advancement is ChatGPT, a model based on OpenAI's Generative Pretrained Transformer (GPT) language model. With its ability to produce high-quality content in mere seconds, ChatGPT surpasses other chatbots in terms of capabilities, drawing significant interest and excitement from both the business community and academic researchers. This study offers a comprehensive review of current research on ChatGPT, delving into its technological foundation, supportive mechanisms, and findings regarding its benefits across various fields and application areas. We evaluate and discuss the strengths and weaknesses of ChatGPT based on this review and suggest potential avenues for future research.

In this study we propose a semi-parametric, parsimonious Value at Risk forecasting model, based on quantile regression and machine learning methods, combined with readily available market prices of option contracts from the over-the-counter foreign exchange rate interbank market. We aim at improving existing methods for VaR prediction of currency investments using machine learning. We employ two different methods - ensemble methods and neural networks. Explanatory variables are implied volatilities with plausible economic interpretation. The forward-looking nature of the model, achieved by the application of implied volatilities as risk factors, ensures that new information is rapidly reflected in Value at Risk estimates. To the best of our knowledge, this paper is the first to utilize information in the volatility surface, combined with machine learning and quantile regression, for VaR prediction of currency investments. The proposed ensemble models achieve good estimates across all quantiles. The light gra-dient-boosting machine model and the categorical boosting model both yield estimates which are better than, or equal to, those of the benchmark model. The neural network models are in general quite unstable.

The delivery of therapeutical molecules through the skin, particularly to its deeper layers is impaired due to the stratum corneum layer, which acts as barrier to foreign substances. Thus, for the past years, scientists have focused on the development of more efficient methods to deliver molecules to skin distinct layers. Microneedles, as a new class of biomedical devices, consists on an array of microscale needles. This particular biomedical device had been drawing attention due to their ability to breach the stratum corneum, forming micro-conduits to facilitate the passage of therapeutical molecules. The microneedle device has several advantages over conventional methods, such as better medication adherence, easiness and painless self-administration. Moreover, it is possible to deliver the molecules swiftly or over time. Microneedles can vary in shape, size and composition. The design process of a microneedle device must take in account several factors, like the location delivery, the material and manufacturing process. Microneedles have been used in a large number of fields from drug and vaccine application, cosmetics, therapy, diagnosis, tissue engineering, sample extraction, cancer research, wound healing, among others.

this paper we present approaches for generating random numbers along with potential applications. Rather than trying to provide extensive coverage of several techniques or algorithms that have appeared in the scientific literature, we focus on some representative approaches presenting their workings and properties in detail. Our goal is to delineate their strengths and weaknesses as well as their potential application domains so as the reader can judge what would be the best approach for the application in hand, possibly a combination of the available approaches. For instance, a physical source of randomness can be used for the initial seed, then suitable preprocessing can enhance its randomness and then the output of the preprocessing can feed different types of generators, e.g. a linear congruential generator, a cryptographically secure one and one based on the combination of one way hash functions and shared key cryptoalgorithms in various modes of operation. Then, if desired, the outputs of the different generators can be combined giving the final random sequence. Moreover, we present a set of practical randomness tests which can be applied on the outputs of random number generators in order to assess their randomness characteristics. In order to demonstrate the importance of unpredictable random sequences, we present an application of cryptographically secure generators in domains where unpredictability is one of the major requirements, i.e. eLotteries and cryptographic key generation.

In this note we study the computation of the minimum polynomial of a matrix $A$ and how we can use it for the computation of the matrix $A^n$. We also describe the form of the elements of the matrix $A^{-n}$ and we will see that it is closely related with the computation of the Drazin generalized inverse of $A$. Next we study the computation of the exponential matrix and finally we give a simple proof of the Leverrier - Faddeev algorithm for the computation of the characteristic polynomial.

Moore’s law will cease to be relevant once transistors are the size of electrons. Once this occurs, we will no longer be able to use silicon for technological advancement. However, breakthroughs will continue in the form of a sixth paradigm: a 3D molecular computing model such as quantum comput- ing. Some of the promising areas of quantum computing researched in this study are (i) algorithmic advancements - such as Shor’s, Grover’s, error correction, fault tolerance and other new algorithms; (ii) hardware - such as the quantum system architecture, circuit models, entanglement, superposition and no-cloning qubits; (iii) market segments - such as finance, healthcare, and chemistry; (iv) the supremacy established over classical computing; and (v) areas of applicability. Significant progress has been made in these areas in recent decades, and they have garnered immense interest, attention and the funding required to make good progress.

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

Orthogonal operators can successfully be used to calculate eigenvalues and eigenvector compositions in complex spectra. Orthogonality ensures least correlation between the operators and thereby more stability in the fit, even for small interactions. The resulting eigenvectors are used to transform the pure transition matrix into realistic intermediate coupling transition probabilities. Calculated transition probabilities for close lying levels illustrate the power of the complete orthogonal operator approach.

This study focuses on a new class of functions called sub-b-s-preinvex, that is a generalization of sub-b-s-convex and preinvex functions, and discusses some of their properties. A new sub-b-s-preinvex programming is introduced and the sufficient conditions of optimality under this type of function is established.

Entanglement is at the heart of quantum technologies such as quantum information and quantum metrology. Providing larger quantum Fisher information (QFI), entangled systems can be better resources than separable systems in quantum metrology. QFI topic is a very active research area and it has many possible usage areas in quantum information domain. In this study, we review quantum Fisher information research with both from theoritical and application perspective.

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

Indole is a versatile pharmacophore widely distributed in bioactive natural products. This privileged scaffold has been found in a variety of molecules isolated from marine organisms such as algae and sponges. Among these, indole alkaloids represent one of the biggest, most promising family of compounds, having shown a wide range of pharmacological properties, including anti-inflammatory, antiviral and anticancer activities. The aim of this review is to show the current scenario of marine indole alkaloid derivatives, covering not only the most common chemical structures, but also new synthetic routes developed during the last years as well as promising therapeutic applications.

Machine learning is a fundamental aspect of artificial intelligence that involves the development of algorithms and models that allow computers to learn and make predictions or decisions without explicit programming. With the development of neural networks, back-propagation algorithms and deep learning, machine learning has made breakthroughs in the fields of image recognition, natural language processing and handwriting recognition using machine learning techniques. The advent of deep learning has revolutionized the field of handwriting recognition, using convolutional neural networks, recurrent neural networks, and sequence-to-sequence models to provide solutions that go beyond machine learning methods and significantly improve the accuracy and robustness of handwriting recognition systems. But challenges remain, including the need for large labelled datasets, computational resources and addressing potential biases. As research in deep learning techniques continues to drive handwriting recognition closer towards realisability, machine learning approaches remain at the forefront.

Background: Obesity and its attendant conditions have become major health problems worldwide and is currently ranked as the fifth most common leading cause of death globally. Complex environmental and genetic factors are the cause of the current obesity epidemic. Diet, lifestyle, chemical exposures, and other confounding factors are difficult to manage in humans. The mice model is helpful in researching genetics BW gain because genetic and environmental risk factors can be controlled in mice. Studies in mouse strains with various genetic backgrounds and established genetic structures provide unparalleled opportunities to find and analyze trait-related genomic loci. In this study, we used the Collaborative Cross (CC), a large panel of recombinant inbred mouse strains, to present a predictive study using CC mice SMAD4 knockout profiles to understand and effectively identify predisposition to body weight gain. Materials and Methods: Male C57Bl / 6J Smad4+/- mice were mated with female mice from 10 different CC lines to create F1 mice (Smad4+/- x CC). Body weight (BW) was measured weekly until week 16, then monthly until the end (week 48). The heritability H2 of the assessed traits was estimated and presented. Comparative analysis of various machine learning algorithms for predicting the BW changes and genotype of mice was conducted. Results and conclusions: Our data showed that the body weight records of F1 mice with different CC lines differed between wildtype and mutant Smad4 mice during the experiment. Genetic background affects weight gain and some lines gained more weight in the presence of Smad4 knockout, while others gained less, but in general, the mutation caused overweight mice, except for a few lines. In both control and mutant groups, female %BW had a higher heritability H2 value than males. Additionally, both sexes with wildtype genotypes showed higher heritability values than the mutant group. Logistic Regression provides the most accurate mouse genotype predictions using machine learning. We plan to validate the proposed method on more CC lines and mice per line to expand machine learning for BW prediction.

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.

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.

Superantigens are unconventional antigens which recognise immune receptors outside the usual binding sites e.g. complementary determining regions (CDRs), to elicit a response within the target cell. T-cell superantigens crosslink T-cell receptors and MHC Class II molecules on antigen-presenting cells, leading to lymphocyte recruitment, induction of cytokine storms and T-cell anergy or apoptosis among many other effects. B-cell superantigens, on the other hand, bind immunoglobulin receptors on B-cells affecting opsonisation, IgG-mediated phagocytosis, and drive B-cells into apoptosis. Here, through a review of the structural basis for recognition of immune receptors by superantigens, we show that their binding interfaces share specific physicochemical characteristics when compared with other protein-protein interaction complexes. Given that antibody-binding superantigens have been exploited extensively in industrial antibody purification, these observations could facilitate further protein engineering to optimize the use of superantigens in this and other areas of biotechnology.

Saponins are a diverse group of naturally occurring plant secondary metabolites present in a wide range of foods ranging from grains, pulses, green leaves to sea creatures. They consist of a hydrophilic sugar moiety linked to a lipophilic aglycone, resulting in an amphiphilic nature and unique functional properties. The amphiphilic structures enable saponins to exhibit surface-active properties, forming stable foams and complexes with various molecules. In the context of food applications, saponins are utilized as natural emulsifiers, foaming agents, and stabilizers. They contribute to texture and stability in food products and have potential health benefits, including cholesterol-lowering and anticancer effects. Saponins possess additional bioactivities that make them valuable in the pharmaceutical industry as anti-inflammatory, antimicrobial, antiviral, and antiparasitic agents to name a few. Saponins can demonstrate cytotoxic activity against cancer cell lines and can also act as adjuvants, enhancing the immune response to vaccines. Their ability to form stable complexes with drugs further expands their potential in drug delivery systems. However, challenges such as bitterness, cytotoxicity, and instability under certain conditions need to be addressed for effective utilization of saponins in foods and related applications. In this paper, we have reviewed chemistry, functionality, and application aspects of saponins from various plant sources and have summarised the regulatory aspects of food application of Quillaja saponins. Further research to explore the full potential of saponins in improving food quality and human health has been suggested. It is expected that this article will be a useful resource for researchers in food, feed, pharmaceutical and material science.

Two-dimensional (2D) transition metal dichalcogenides (TMDCs) nanosheets have shown extensive applications due to their excellent physical and chemical properties. However, the low light absorption efficiency limits their application in optoelectronics. By rolling up 2D TMDCs nanosheets, the one-dimensional (1D) TMDCs nanoscrolls are formed with spiral tubular structure, tunable interlayer spacing and opening ends. Due to their increased thickness of scroll structure, the light absorption is enhanced. Meanwhile, the rapid electron transportation is confined along the 1D structure. Therefore, the TMDCs nanoscrolls show improved optoelectronic performance compared to 2D nanosheets. In addition, the high specific surface area and active edge site from bending strain of basal plane make them promising materials for catalytic reaction. Thus, the TMDCs nanoscrolls have attracted intensive attention in recent years. In this review, the structure of TMDCs nanoscrolls is firstly demonstrated and followed by various preparation methods of the TMDCs nanoscrolls. Afterwards, the applications of TMDCs nanoscrolls in the fields of photodetection, hydrogen evolution reaction, and gas sensing are discussed.

Biocontainment techniques for genetically modified yeasts (GMYs) are pivotal due to the importance of these organisms for biotechnological processes and also due to the design of new yeast strains by using synthetic biology tools and technologies. Due to the large genetic modifications that many yeast strains display, it is highly desirable to avoid the leakage of GMY cells into natural environments and, consequently, the spread of synthetic genes and circuits by horizontal or vertical gene transfer mechanisms within the microorganisms. Moreover, it is also desirable to avoid that patented yeast gene technologies spread outside the production facility. In this review, it was evaluated the different biocontainment technologies currently available for GMYs. Interestingly, uniplex-type biocontainment approaches (UTBAs), which relies on nutrient auxotrophies induced by gene mutation or deletion, or the expression of simple kill switches apparatus, are still the major biocontainment approaches still in use with GMY. While bacteria like Escherichia coli account for advanced biocontainment technologies based on synthetic biology and multiplex-type biocontainment approaches (MTBAs), GMYs are distant from this scenario due to many reasons. Thus, a comparison of different UTBAs and MTBAs applied for GMY and genetically engineered microorganisms (GEMs) was made, indicating the major advances of biocontainment techniques for GMYs.

At present, nanomaterials (NMs) show extreme potential for a wide range of applications ranging from aerospace to energy storage applications owing to a number of properties which, in a number of contexts have been reported to be superior as compared to those of bulk counterparts. However, the high surface area to volume ratio which yields extraordinary properties to NMs also turns out to be the major limitation in a number of different contexts. For instance, the high surface area to volume ratio in these materials leads to a high reactivity, especially in electrochemical environments which are prevalent during energy storage applications. In this context, it is worthwhile mentioning that processing of NMs play a crucial role in influencing their mechanical properties. On the other hand, surface engineering has emerged as a powerful tool to deconvolute bulk properties from surface characteristics and has been reported to exhibit tremendous potential for overcoming the aforementioned limitation of NMs. The present review therefore, is aimed at highlighting the present status of research in the field of processing and applications of surface engineered NMs. Moreover, challenges and future perspectives in the aforementioned avenue have been discussed.

The steep rise in reinforcement learning (RL) in various applications in energy as well as the penetration of home automation in recent years are the motivation for this article. It surveys the use of RL in various home energy management system (HEMS) applications. There is a focus on deep neural network (DNN) models in RL. The article provides an overview of reinforcement learning. This is followed with discussions on state-of-the-art methods for value, policy, and actor–critic methods in deep reinforcement learning (DRL). In order to make the published literature in reinforcement learning more accessible to the HEMS community, verbal descriptions are accompanied with explanatory figures as well as mathematical expressions using standard machine learning terminology. Next, a detailed survey of how reinforcement learning is used in different HEMS domains is described. The survey also considers what kind of reinforcement learning algorithms are used in each HEMS application. It suggests that research in this direction is still in its infancy. Lastly, the article proposes four performance metrics to evaluate RL methods.

Thrombosis has threatened human health in past decades. Bacillus nattokinase is a potential thrombolytic drug without side-effect and low cost and has been introduced into the consumer market as a functional food or dietary supplement. This review firstly summarizes the biodiversity sources and fermentation process of nattokinase, and systematically expounds the structure, cata-lytic mechanism and enzymatic properties of nattokinase; In view of the problems of low fermen-tation yield, insufficient activity and stability of nattokinase, this review discusses the heterologous expression of nattokinase in different microbial hosts, and summarizes the protein and genetic en-gineering progress of nattokinase-producing strains; Finally, this review summarizes the clinical application of nattokinase.