In healthy humans, taste sensitivity varies widely, influencing food selection and nutritional status. Chemosensory reductions have been associated with numerous pathological disorders or pharmacological interventions. Reliable psychophysical methods are crucial resources to analyze the taste function during routine clinical assessment. However, in the daily clinical routine, they are often considered to be too time-consuming. We used the Supervised Learning (SL) regression method to analyze with high precision the overall taste status of healthy controls (HC) and patients with chemosensory loss and to characterize the combination of responses that best can predict the overall taste status of two groups. Random Forest regressor allowed us to achieve our objective. The analysis of the order of importance and impact of each parameter on the prediction of overall taste status in the two groups showed that salty (low concentration) and sour (high concentration) stimuli specifically characterized healthy subjects, while bitter (high concentration) and astringent (high concentration) stimuli identified patients with chemosensory loss. The identification of these distinctions appears to be of interest to the health system since they may allow the use of specific stimuli during routine clinical assessments of taste function reducing the commitment in terms of time and costs.

Type 1 Diabetes mellitus (T1DM) occurs when insulin is not produced in the Pancreas due to the autoimmune destruction of the beta cells. The patients may be diagnosed after 80-90% of the beta cells are destroyed. Insulin remains the essential medicine in T1DM management, under continuous monitoring of glucose levels. T1DM management consists of a complex and precise set of self-care measures for the entire life; during this continuous process, the patients could become overwhelmed, angry, anxious, frustrated, and/or discouraged. Therefore, to be widely used and provide general benefits, new technologies for DM management must provide clinical advantages without compromising safety. The compliance of T1DM patients is also essential, thus improving their quality of life (QoL). Moreover, the accessibility of the new system for patients through healthcare coverage is critical; they must be able to buy the new devices and accessories and pay for their maintenance. In this context, our work aims to describe the most recent technologies regarding continuous glucose monitors (CGMs) and continuous subcutaneous insulin infusion (CSII) systems, correlated with psychosocial impact and capacity to improve T1DM patients' life quality.

As a result of the COVID-19 pandemic, as well as other outbreaks such as SARS and Ebola, bats are recognized as a critical species for mediating zoonotic infectious disease spillover events. While there is a growing concern of increased antimicrobial resistance (AMR) globally during this pandemic, knowledge of AMR circulating between bats and humans is limited. In this paper, we have reviewed the evidence of AMR in bats and discussed the planetary health aspect of AMR to elucidate how this is associated with the emergence, spread and persistence of antibiotic resistance at the human-animal interface. The presence of clinically significant resistant bacteria in bats and wild life has reflective and broad impact on zoonotic pandemic surveillance, disease transmission and treatment modalities. We searched MEDLINE through PubMed and Google Scholar to retrieve relevant studies (n=38) that provided data on resistant bacteria in bats till September 30, 2022. There is a substantial variability in the results from studies measuring the prevalence of AMR based on geographic location, bat types and time. We found all major groups of gram positive and gram negative bacteria in bats which are resistant to commonly used antibiotics. The most alarming issue is- recent studies have increasingly identified Methicillin Resistant Staphylococcus aureus (MRSA), ESBL producing and Colistin resistant Enterobacteriaceae in samples from bats. This evidence of superbugs abundance in both humans and wild mammals like bats, could facilitate a greater understanding of which specific pathways of exposure should be targeted. We believe that these data will also facilitate future pandemic prepareness as well as global AMR containment during the pandemic events and beyond.

We performed a systematic search and meta-analysis of available literature to determine the safety profile of Cerebrolysin in acute ischemic stroke, filling existing safety information gaps and inconsistent results. We searched EMBASE, PubMed and Cochrane Databases of Systematic Reviews and Clinical Trials up to the end of February 2021. Data collection and analysis was conducted using methods described in the Cochrane Handbook for Systematic Reviews of Interventions. All safety outcomes were analyzed based on risk ratios (RR) and their 95% confidence intervals. The meta-analysis pooled 2202 patients from twelve randomized clinical trials, registering non-statistically significant (p>0.05) differences between Cerebrolysin and placebo throughout main and subgroup analyses. The lowest rate of Serious Adverse Events (SAE), as compared to placebo, was observed for the highest dose of Cerebrolysin (50 mL), highlighting a moderate reduction (RR = 0.6). We observed a tendency of superiority of Cerebrolysin regarding SAE in high dose treatment courses for moderate-severe ischemic stroke, suggesting some effect of the agent against adverse events. This comprehensive safety meta-analysis confirms the safety profile for patients treated with Cerebrolysin after acute ischemic stroke, as compared to placebo.

The influence of the hot isostatic pressing (HIP) post-processing step on structural and phase changes, porosity healing and mechanical strength in a powder metallurgy Ti35Nb2Sn alloy was studied. Powders were pressed at room temperature at 750 MPa, and then sintered at 1,350°C in a vacuum for 3 h. The standard HIP process at 1,200°C and 150 MPa for 3 h was performed to study its effect on a Ti35Nb2Sn powder metallurgy alloy. The influence of the HIP process and cold rate on density, microstructure, the quantity of interstitial elements, mechanical strength and Young's modulus was investigated. HIP post-processing for 2 h at 1,200°C and 150 MPa led to greater porosity reduction and a marked retention of the β phase at room temperature. The slow cooling rate during the HIP process affected phase stability, with a large amount of α”-phase precipitate, which decreased the titanium alloy’s yield strength.

Background Natural Saccharomyces cerevisiae yeast strains exhibit very large genotypic and phe-notypic diversity. Breeding programs taking advantage of this characteristic, are widely used for yeast selection in the wine industry, especially in the recent years when winemakers need to adapt their production to climate change. The aim of this work was to evaluate a Marker Assisted Se-lection (MAS) program to improve malic acid consumption capacity of Saccharomyces cerevisiae in grape juice. Methods Optimal individuals of two unrelated F1-hybrids were crossed to get a new genetic background carrying many “malic consumer” loci. Then, eleven QTLs already identified were used for implementing the MAS breeding program. Results By this way, extreme individuals able to consume more than 70% of malic acid in grape juice were selected. These individuals were tested in different enological matrixes and compared to their original parental strains. They greatly reduced the malic acid content at the end of alcoholic fermentations, they appeared to be robust to the environment and accelerate the ongoing of malo-lactic fermentations by Oenococcus oeni. Conclusions This study illustrates how MAS can be efficiently used for selecting industrial Saccharomyces cerevisiae strains with outlier properties for winemaking.

Rural households across developing countries rely on diversified sources of income and forest resource play important role in this regard. This study is designed with the objectives of assessing the contribution of forests to annual income of rural households and identifying its determinants with the case of Essera woreda forest in western Ethiopia. It also examined the gender dimensions of forest income and how this income varies with the wealth status of households key informants interview focus group discussion and household based questionnaire survey were used to collect data. On average income from crop production accounted for (40.7%) of the total annual household income. Forest income is second in importance contributing (32.6%), income from livestock off and non-farm activities and woodlots accounted for (13.6%), (11.4%) and (1.7%) of the total household income respectively. Firewood is the most used forest product and constituted the largest proportion (79%) of the total forest income. Forest income is more important for poor households (47.3%) than for medium (30.5%) or rich (20.2%) households. It is also more important for female headed households (58.2%) than for male headed households (29%). The gender dimension of forest income is also important within the household. Female members generated about four times more forest income (77% of the household forest income) than male members (23%). Policy to promote new forest management arrangement such as participatory forest management (PFM) needs to take in to account the major forest users and the types of products they depend on and be accompanied with other poverty reduction measures so that improved forest conservation outcome will not have negative consequences on local livelihoods particularly on poor and women who depend most on the forest.

Single nucleotide polymorphisms (SNP) are associated with diseases and drug response variabilities in humans. Elucidating the damaging and disease-associated SNPs using wet-laboratory approaches can be challenging and resource-demanding due to the large number of SNPs in the human genome. Due to the growth in the field of computational biology and bioinformatics, algorithms have been developed to help screen and filter out the most deleterious SNPs that are worth considering for wet-laboratory studies. Here we review the existing in-silico based methods used to predict and characterize the effects of SNPs on protein structure and function. This cutting-edge approach will facilitate the search for novel therapeutics, help understand the etiology of diseases and fast-track the personalized medicine agenda.

Plant leaf diseases can affect the plants’ leaves to an extent that the plants can collapse and die completely. These diseases may drastically drop the supply of vegetables and fruits to the market, and result in a low agricultural economy. In the literature, different laboratory methods of plant leaf disease detection have been used. These methods were time consuming and could not cover large areas for the detection of leaf diseases. This study infiltrates through the facilitated principles of the Convolutional Neural Networks (CNN) in order to model a network for image recognition and classification of these diseases. Neuroph was used to perform the training of a CNN network that recognized and classified images of the maize leaf diseases that were collected by use of a smart phone camera. A novel way of training and the methodology used, expedite a quick and easy implementation of the system in practice. The developed model was able to recognize 3 different types of maize leaf diseases out of healthy leaves. The Northern Corn Leaf Blight (Exserohilum), Common Rust (Puccinia sorghi) and Gray Leaf Spot (Cerospora) diseases were chosen for this study as they affect most parts of Southern Africa’s maize fields.

This paper presents the automation of the thin-layer chromatography technique whose separation and identification of molecules present in a mixture are currently done manually and laboriously [1]. We have therefore found an interest in automating this technique. In this part, the method implemented comprises 2 steps. First we proceeded to the segmentation of the spots obtained on the chromatographic plate. We then developed a program to identify families of molecules such as coumarins, terpenes, tannins, flavonoids, polyphenols, etc. by calculating segmentation parameters such as standard deviation, entropy, average pixel intensity from an algorithm on the matlab software. Finally our results have been compared to the results obtained by the traditional identification technique in laboratories. Some similarity between the two results obtained shows the reliability and the robustness of our technique.

Genes and gene trees have been extensively used to study the evolutionary relationships among populations, species, families and higher systematic clades of organisms. This brought modern Biology into a sophisticated level of understanding about the evolutionary relationships and diversification patterns that happened along the entire history of organismal evolution in Earth. Genes however have not been placed in the center of questions when one aims to unravel the evolutionary history of genes themselves. Thus, we still ignore whether Insulin share a more recent common ancestor to Hexokinase or DNA polymerase. This brought modern Genetics into a very poor level of understanding about sister group relationships that happened along the entire evolutionary history of genes. Many conceptual challenges must be overcome to allow this broader comprehension about gene evolution. Here we aim to clear the intellectual path in order to provide a fertile research program that will help geneticists to understand the deep ancestry and sister group relationships among different gene families (or orthologs). We aim to propose methods to study gene formation starting from the establishment of the genetic code in pre-cellular organisms like the FUCA (First Universal Common Ancestor) until the formation of the highly complex genome of LUCA (Last UCA), that harbors hundreds of genes families working coordinated into a cellular organism. The deep understanding of ancestral relationships among orthologs will certainly inspire biotechnological and biomedical approaches and allow a deep understanding about how Darwinian molecular evolution operates inside cells and before the appearance of cellular organisms.

In this paper, we study the fractional pseudo-parabolic equations u_t + (−△)^s u + (−△)^s u_t = u log |u|. Firstly, we recall the relationship between the fractional Laplace operator (−△)^s and the fractional Sobolev space H^s and discuss the invariant sets and the vacuum isolating behavior of solutions with the help of a family of potential wells. Then, we derive a threshold result of existence of global weak solution: for the low initial energy J(u₀) < d, the solution is global in time with I(u₀) > 0 or ∥u₀∥X_0(Ω) = 0 and blows up +∞ with I(u₀) < 0; for the critical initial energy J(u₀) = d, the solution is global in time with I(u₀) ≥ 0 and blows up at +∞ with I(u₀) < 0. The decay estimate of the energy functional for the global solution is also given.

At least one ultrasound is recommended to predict fetal growth restriction and low birthweight earlier in pregnancy. However, in low-income countries imaging equipment and trained manpower are scarce. Hence, we developed and validated a model and risk score to predict low birthweight using maternal characteristics during pregnancy, for use in resource limited settings. We conducted a prospective cohort study among 379 pregnant women in South Ethiopia. A step-wise multivariable analysis was done to develop the prediction model. To improve clinical utility, we developed a simplified risk score to classify pregnant women at high- or low-risk of low birthweight. The accuracy of the model was evaluated using the area under the receiver operating characteristics curve (AUC) and calibration plot. We evaluated the clinical impact of the model using a decision curve analysis across various threshold probabilities. Age at pregnancy, underweight, anemia, height, gravidity, and presence of comorbidity remained in the final multivariable prediction model. The area under the receiver operating characteristics curve (AUC) of the model was 0.83 (95% confidence interval: 0.78 to 0.88). The decision curve analysis shows the model provides a higher net benefit across ranges of threshold probabilities. In general, this study showed the possibility of predicting low birthweight using maternal characteristics during pregnancy. The model could help to identify those at higher risk of having a low birthweight baby. This feasible prediction model would offer an opportunity to reduce obstetric-related complications and thus improving the overall maternal and child healthcare in low- and middle-income countries.

Student attrition poses significant societal and economic challenges, leading to unemployment, lower earnings, and other adverse outcomes for individuals and communities. To address this, predictive systems leveraging machine learning and Big Data aim to identify at-risk students early and intervene effectively. This project focuses on extracting key parameters from past dropout data to construct a predictive model and alert authorities to intervene promptly. Two preliminary trials refine machine learning models, establish evaluation standards, and optimize hyperparameters. These trials facilitate systematic exploration of model performance and data quality assessment. Achieving 100% accuracy in dropout prediction, the study identifies academic performance as the primary influencer, with early-year subjects like Mechanics and Materials, Design of Machine Elements, and Instrumentation and Control having significant impact. The longitudinal effect of these subjects on attrition underscores the importance of early intervention. Proposed solutions include early engagement and support or restructuring courses to better accommodate novice learners, aiming to reduce attrition rates.

Mechanisms for somatic chromosomal mosaicism (SCM) and chromosomal instability (CIN) are incompletely understood. During SNP-array molecular karyotyping and bioinformatic analyses of children with neurodevelopmental disorders and congenital malformations (n=612), we observed colocalizaion of regular chromosomal imbalances or copy number variations (CNV) with mosaic ones (n=47 or 7.7%). Analyzing molecular karyotyping data and pathways affected by CNV burdens, we proposed a mechanism for SCM/CIN, which had been designated as “chromohelkosis” (from the Greek chromosome ulceration/open wound). Briefly, structural chromosomal imbalances are likely to cause local instability (“wreckage”) at the breakpoints, which results either to partial/whole chromosome loss (e.g. aneuploidy) or elongation of duplicated regions. Accordingly, a function for classical/alpha satellite DNA (protection from the wreckage towards the centromere) has been hypothesized. Since SCM and CIN are ubiquitously involved in development, homeostasis and disease (e.g. prenatal development, cancer, brain diseases, aging), we have metaphorically (ironically) designate the system explaining chromohelkosis contribution to SCM/CIN as the cytogenomic “theory of everything” like the homonymous theory in physics inasmuch as it might explain numerous phenomena in chromosome biology. Recognizing possible empirical and theoretical weaknesses of this “theory”, we nevertheless believe that studies of chromohelkosis-like processes are required to understand structural variability and flexibility of the genome.

Logistic Regression and Random Forest are used to identify risk factors that influence traffic accident fatalities in the United Kingdom. The mean decrease accuracy was used to measure variable importance. The speed limit, police attendance and quarter had an increasing influence on accident fatalities. They had a mean decrease of 102.1669, 221.5322, and 120.894 respectively. The speed limit, had a parameter estimate of 0.0046902 and a standard deviation of 0.0004875. Light Conditions: Night had a parameter estimate of 1.2657635 and a standard deviation of 0.0118409. Road Type Round About had a parameter estimate of -0.4055796 and a standard deviation of 0.0210848. Police Attendance classified as Yes had a parameter of 0.8546232 and a standard deviation of 0.0151043. The best predictors were speed limit, police attendance and quarter since they had p values that were less than 0.05. The findings of the study indicated that logistic Regression had a higher accuracy rate 79.85% as compared to 64.00% for Random Forest. A split test was used and a standard deviation of 0.0010486 was obtained for the Logistic Regression model.

Private Set Intersection Cardinality(PSI-CA) is a cryptographic method in secure multi-party computation that allows entities to identify cardinality of the intersection without revealing their private data.Traditional approaches assume similar-sized datasets and equal computational power, overlooking practical imbalances.In real-world applications, dataset sizes and computational capacities often vary, particularly in the Internet of Things and mobile scenarios where device limitations restrict computational types. Traditional PSI-CA protocols are inefficient here, as computational and communication complexities correlate with the size of larger datasets. Thus, adapting PSI-CA protocols to these imbalances is crucial.This paper explores unbalanced scenarios where one party (the receiver) has a relatively small dataset and limited computational power, while the other party (the sender) has a large amount of data and strong computational capabilities.This paper, based on the concept of commutative encryption, introduces Cuckoo filter, cloud computing technology, homomorphic encryption, among other technologies, to construct three novel solutions for unbalanced Private Set Intersection Cardinality (PSI-CA): an unbalanced PSI-CA protocol based on Cuckoo filter, an unbalanced PSI-CA protocol based on single cloud assistance, and an unbalanced PSI-CA protocol based on dual cloud assistance. Depending on performance and security requirements, different protocols can be employed for various applications.

This work utilizes the particle swarm optimization (PSO) for the optimal sizing of a solar – wind – battery hybrid renewable energy system (HRES) for a rural community in Rivers State, Nigeria (Okorobo-Ile Town). The objective is to minimize the total economic cost (TEC), the total annual system cost (TAC) and the levelized cost of energy (LCOE). A two – step approach is used. The algorithm first determines the optimal number of solar panels and wind turbines. Based on the results obtained in the first step, the optimal number of batteries and inverters is computed. The overall results obtained is then compared with results from the NGSA-II and hybrid GA – PSO optimization algorithm. An energy management system monitors the energy balance and ensures the load management is adequate using the battery state of charge as a control strategy. Results obtained showed that the optimal configuration consist of solar panels (154), wind turbine (3), inverter (136) and batteries (29). This results in a minimized TEC, TAC and LCOE of 476, 731USD, 301, 947USD and 0.011USD/kWh respectively. The optimal configuration when simulated under various climatic scenarios was able to meet the energy needs of the community irrespective of ambient condition.

Depression is the most common chronic mental illness and is characterized by low mood, insomnia, and affective disorders. However, the pathological mechanisms of depression remain unclear. Numerous studies have suggested that the ghrelin/GHSR system may be involved in the pathophysiologic process of depression. Ghrelin plays a dual role in the experimental animals, increasing depressed behavior and decreasing anxiety. By combining several neuropeptides and traditional neurotransmitter systems to construct neural networks, it modifies signals connected to depression. The present review emphasizes the role of ghrelin in neuritogenesis, astrocyte protection, inflammatory factor production, and endocrine disruption in depression. Furthermore, ghrelin/GHSR can activate multiple signaling pathways to produce antidepressant effects, including cAMP/CREB/BDNF, PI3K/Akt, Jak2/STAT3, and p38-MAPK. Given the antidepressant effects of ghrelin/GHSR1a system, it is expected to become a potential therapeutic target for the treatment of depression.

Importance: p53 is an unequivocal tumor suppressor altered in half cancers. The immune system produces systemic p53 autoantibodies (p53 Abs) in many cancer patients. Objective: The focus of this systemic review and meta-analysis is on the prognostic value of p53 Abs expressed in the serum of patients with solid tumors. Data Sources: All the clinical investigations were searched on PubMed, MBase and Cochrane from 1993 reporting the first study until May 2021. Study Selection: Studies were included that met the following criteria: 1) participants with cancer; 2) outcome results expressed in relation to the presence of a p53 antibody; 3) a primary outcome (disease free survival, overall survival or progression free survival) expressed as hazard ratio (HR). The following exclusion criteria were used: 1) insufficient data available to evaluate outcomes; 2) animal studies; 3) studies with less than 10 participants. 1333 potentially relevant articles; studies as duplicates, non-patients studies or reviews were excluded. After viewing the titles and abstracts of the 52 remaining studies, the full texts of 34 studies were retrieved and 12 studies were included in the analysis. Data Extraction and Synthesis: PRISMA guidelines were used for abstracting and assessing data quality and validity by three independent observers. The summary estimates were generated using a fixed-effect model (Mantel–Haenszel method) or a random-effect model (DerSimonian–Laird-method) depending on the absence or presence of heterogeneity (I2). Main Outcome(s) and Measure(s): The primary study outcome was to determine the prognostic value of p53 Abs from a large population size of patients with solid tumors, as determined before data collection. Results: In total 12 clinical studies and of which 2094 patients were included and it was determined that p53-wt Abs expression in the serum significantly correlated with a worse survival of cancer patients (95% CI 1.48 [1.24, 1.77]; p<0.00001). On the contrary, data from literature indicated that there was a potential association between p53-mut Abs antibodies with better survival. Conclusions and Relevance: This is the first meta-analysis proving the diagnostic utility of p53-Abs for cancer patients, predicting a worse outcome. The serum-p53 value (s-p53-value) could be useful for future theranostics.

Curcumin, a hydrophobic bioactive constituent from Curcuma longa, has several health promoting properties including anti-atherosclerotic properties. Further, curcumin has blood glucose- and lipid-lowering properties, prophylactic, and therapeutic effects in other cardiometabolic diseases. Atherosclerosis is an umbrella term for a series of degenerative and hyperplasic lesions such as thickening or sclerosis in large and medium -sized artery walls that decrease vascular-wall elasticity and lumen diameter. The atherosclerotic cerebro-cardiovascular disease has become a major concern to human health in recent years. Considering the notable cardiovascular actions of curcumin, the present review summarizes the current status and future perspective of research on curcumin and its ability to prevent or treat atherosclerosis

This article argues that we need to look at living examples provided by non-state communities in various regions of the world that are, perhaps unwittingly, contributing to the maintenance of the Earth's optimal thermal balance. These fully sustainable communities have been living outside the mainstream for centuries, even millennia, providing examples in the global struggle against the degradation of social–ecological systems. They have all, to varying degrees, embraced simple forms of living that make them ‘exemplary ethical communities’ (EECs) – human communities with a track record of sustainability related to forms of traditional knowledge and the capacity to survive outside the capitalist market and nation-state system. The article proceeds in three steps: First, it condenses a large body of research on the limits of the existing nation-state system and its accompanying ideology, nationalism, identifying this institutional–ideological complex as the major obstacle to tackling climate change. Second, alternative social formations that could offer viable micro-level and micro-scale alternatives are suggested. These are unlikely to identify with existing nation-states as they often form distinct types of social communities. Taking examples from hunter-gatherer societies and simple-living religious groups, it is shown how the protection and maintenance of these EECs could become the keystone in the struggle for survival of humankind and other forms of life. Finally, further investigation is called for, into how researchers can come forward with more examples of actually existing communities that might provide pathways to sustainability and resistance to the looming global environmental catastrophe.

The incidence of gastrointestinal diseases (cancer in particular) has increased progressively with considerable morbidity, mortality, and a high economic impact on the healthcare system. Dietary intake of natural bioactive phytochemicals showed to have cancer-preventing and therapeutic effects. This includes the cruciferous vegetable derivative phenethyl isothiocyanate (PEITC), a bioactive compound from watercress. PEITC antioxidant, anti-inflammatory and anti-cancer properties are of particular importance. This review summarizes the current knowledge on the role of PEITC as a potential natural nutraceutical or an adjuvant against oxidative/inflammatory-related disorders in the gastrointestinal tract. We also discuss the safe and recommended dose of PEITC. Besides, we establish a framework to guide the research and development of sustainable methodologies for obtaining and stabilizing this natural nutraceutical for industrial use. This is a topic that still needs more scientific development, but with the potential to lead to a viable strategy in the prevention of cancer and other associated diseases of the gastrointestinal tract.

The microorganisms able of accumulating lipids in high percentages, known as oleaginous microorganisms, have been widely studied as an alternative for producing oleochemicals and biofuels. Microbial lipid, so called Single Cell Oil (SCO), production depends on several growth parameters, including the nature of the carbon substrate, which must be efficiently taken up and converted into storage lipid. Οn the other hand, substrates considered for large scale applications must be abundant and of low acquisition cost. Among others, lignocellulosic biomass is a promising renewable substrate containing high percentages of assimilable sugars (hexoses and pentoses). However, it is also highly recalcitrant and therefore it requires specific pretreatments in order to release its assimilable components. The main drawback of lignocellulose pretreatment is the generation of several by-products that can inhibit the microbial metabolism. In this review, we discuss the main aspects related to the cultivation of oleaginous microorganisms using lignocellulosic biomass as substrate, hoping to contribute to the development of a sustainable process for SCO production in the near future.

Iron deficiency anemia (IDA) is a world health problem for humans and other mammals; affecting critical stages of development. Pigs have been used as an experimental model for the study and prevention of anemia because of physiological and metabolic similarities with humans. Iron dextran particles (IDP) are used for intramuscular (IM) IDA treatments in pigs, but are insufficient for prevention of anemia due to quick metabolization. Therefore, the objective of this study was to develop chitosan thermosensitive hydrogels (CTH) formulations and to study their potential as a mammalian parenteral iron dextran supplementation strategy. CTH were loaded with IDP at increasing iron concentrations (0.1, 0.2 and 0.4 g of theorical iron/g of chitosan) and characterized as an IM iron supplement. All the CTH-IDP formulations were thermosensitive and can be injected IM at ~4 °C, with a significant rise in viscosity between 25-37 °C. IDP content was physically trapped in the chitosan hydrophobic gel at 37 °C, without evidence of chemical bonding. We conclude that CTHs are a promising strategy for IM delivery strategy of IDP, and these results may be useful for future iron supplementation studies in pigs, humans and other mammals.

The following paper describes an economical, multiple model predictive control (EMMPC) for an air conditioning system of a confectionery manufacturer in Germany. The application consists of a packaging hall for chocolate bars, in which a new local conveyor belt air conditioning system is used and thus the temperature and humidity limits in the hall can be significantly extended. The EMMPC calculates the optimum energy or cost humidity and temperature set points in the hall. For this purpose, time-discrete state space models and an economic objective function with which it is possible to react to flexible electricity prices in a cost-optimised manner are created. A possible future electricity price model for Germany with a flexible EEG levy was used as a flexible electricity price. The flexibility potential is determined by variable temperature and humidity limits in the hall, which are oriented towards the comfort field for easily working persons, and the building mass. The building mass of the created room model is used as a thermal energy store. Considering electricity price and weather forecasts as well as internal, production plan-dependent load forecasts, the model predictive controller directly controls the heating and cooling register and the humidifier of the air conditioning system.

In order to achieve its carbon peak and neutrality targets, a high proportion of distributed power sources are connected to the distribution network in China, which greatly increases the risk of distribution network operation. Aiming at the above problems, this paper proposes an active distribution network risk planning model based on multi-source data from carbon capture and power internet of things, and conducts exceeding testing of distribution network based on the stochastic load flow algorithm with semi-invariant and level expansion, calculates the semi-invariant of each order of node state vectors and branch current vectors, and then utilizes the Gram-Charlier level expansion to obtain the exceeding probability density function and probability distribution function of the node voltages and line powers of the distribution network. The probability density function and probability distribution function of the exceeding are obtained using the Gram-Charlier series expansion. Combined with multi-source data, the active distribution network with integrated energy system considering carbon capture is modeled. According to the risk scenario of the distribution network, the nonconvex constraints in the model are simplified by the second-order cone relaxation, and the optimal planning scheme of the distribution network is solved by combining the gurobi solver with the risk index as the first-level objective and the economic benefit as the second-level objective. The simulation results of a coupled network consisting of a 39-node distribution network and an 11-node transportation network verify the effectiveness of the proposed model.

We introduce poly-symplectic extension of Souriau Lie groups Thermodynamics based on higher-order model of statistical physics introduced by R.S. Ingarden. This extended model could be used for small data analytics and Machine Learning on Lie groups. Souriau Geometric Theory of Heat is well adapted to describe density of probability (Maximum Entropy Gibbs density) of data living on groups or on homogeneous manifolds. For Small Data Analytics (Rarified Gases , sparse statistical survey,…), density of maximum entropy should consider Higher Order Moments constraints (Gibbs density is not only defined by first moment but fluctuations request 2nd order and higher moments) as introduced by R.S. Ingarden. We use Poly-sympletic model introduced by Christian Günther, replacing the symplectic form by a vector-valued form. The poly-symplectic approach generalizes the Noether theorem, the existence of momentum mappings, the Lie algebra structure of the space of currents, the (non-)equivariant cohomology and the classification of G-homogeneous systems. The formalism is covariant, i.e. no special coordinates or coordinate systems on the parameter space are used to construct the Hamiltonian equations. We underline the contextures of these models, and the process to build these generic structures.

1,3 Dipolar cycloaddition of imidazolidine derivatives containing exocyclic double bonds is a convenient method of creating spiro-conjugated molecules with the promising anticancer active. In this work, the derivatives of parabanic acid (2-thioxoimidazolidine-4,5-diones and 5-aryliminoimidazolidine-2,4-diones) were firstly investigated as dipolarophiles in the reactions with nitrile imines. The generation of nitrile imines was carried out either by the addition of tertiary amine to hydrazonoyl chlorides «drop by drop» or using recently proposed diffusion mixing technique, which led to ~5-15% increasing of target compounds yields. It was found that the addition of nitrile imines to C=S or C=N exocyclic double bonds led to 1,2,4-thiazolines or triazolines and occurred regioselectively in accordance to the ratio of FMO coefficients of reactants. The yield of the resulting spiro-compound depended on the presence of alkyl substituents in nitrile imine structure and was significantly decreases at the reactions with imines with strong electron donor or electron-withdrawing groups. Some of the obtained compounds showed reasonable in vitro cytotoxicity. IC50 values were calculated for HCT116 (colon cancer) cells using the MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) test.

This study investigates the therapeutic potential of new compound, potassium 2-[2-(2-oxo-4-phenylpyrrolidin-1-yl) acetamido]ethanesulfonate (Compound I), in depression. Willner's chronic unpredictable mild stress model of depression in 48 male Wistar rats was used a depression model. The rats were randomized into four groups, including an intact group, a Compound I group, a Fluoxetine group, and a control group receiving saline. Behavioral tests, such as the Porsolt forced swim test, hole-board test, elevated plus maze test, and light-dark box, were used to assess the animals' conditions. Our results demonstrated that Compound I effectively reduced the immobilization time of rats in the forced swim test, increased orientation and exploratory behavior, and decreased the latency period of going into the dark compartment compared to the control group. Hippocampal and striatal serotonin concentrations were increased in the Compound I group, and the compound also reduced the level of corticosterone in the blood plasma of rats compared to intact animals. These results suggest that Compound I has a reliable antidepressant activity, comparable to that of the reference antidepressant Fluoxetine.

Background. Depression, a widespread mental health issue, is often marked by impaired cognitive control, particularly in managing proactive and reactive processes. The Dual Mechanisms of Control (DMC) framework differentiates between these two modes of cognitive control: proactive control involves sustained goal maintenance, while reactive control is more stimulus-driven and transient. Stress, known to exacerbate cognitive dysfunction in depression, may influence the balance between these control processes, though the specific effects remain poorly understood. This study aimed to investigate how acute stress influences proactive and reactive control in individuals with depressive symptoms. Methods. A total of 88 participants were divided into high-stress and control conditions and further categorized based on their depression levels, measured using the Beck Depression Inventory-II (BDI-II). Cognitive control was assessed using the AX-Continuous Performance Task (AX-CPT), both before and after exposure to a stress-inducing anagram task, which was designed to differentiate between high-stress and low-stress conditions. Results. Participants exposed to the high-stress condition reported significantly greater stress and fatigue levels compared to the control group, validating the stress manipulation. Although the balance between reactive and proactive control, as measured by the Proactive Behavioral Index (PBI), did not show significant changes, depressive individuals in the high-stress condition exhibited a significant decline in their ability to retain contextual information, as indicated by a reduction in the d’-context index. This suggests that depressive individuals are particularly vulnerable to stress-induced impairments in proactive control. Discussion. These findings highlight the selective impact of stress on proactive cognitive control in individuals with depressive symptoms, shedding light on a potential cognitive vulnerability in depression. While the balance between reactive and proactive control remained stable, the impaired retention of contextual information post-stress points to a specific deficit in proactive control. This could have implications for targeted cognitive interventions, such as cognitive control training, aimed at enhancing resilience against stress in depressive populations. Future research should explore the long-term effects of stress on cognitive control, particularly in clinically diagnosed individuals.

The literature shows that both in laboratory and in industrial conditions, photocatalytic oxidation method copes quite well with the degradation of the most organic substances, including environmental toxins and molecular components of pathogenic microorganisms. However, the effective utilization of photocatalytic processes for environmental decontamination and disinfection require significant technological advancement both in the area of semiconductor material synthesis and its application. Here we focused on the presence and ‘photocatalytic capability‘ of photocatalysts among soil minerals, and their potential contribution to the environmental decontamination in vitro and in vivo. (The issue is sketched from the perspective of chemists and environmental scientist.) Reactions caused by sunlight on the soil surface are involved in its normal redox activity. It appears that some of them may take part in the soil decontamination. However, their importance for decontamination in vivo cannot be overstated due to diversity of soils on the Earth caused by the environmental conditions, such as climate, parent material, relief, vegetation etc. The solar-light induced reactions are just a part of extremely complicated processes dependent on a plethora of environmental determinates. Such multiplicity of affecting factors, which we tried to sketch from the perspective of chemists and environmental scientist, makes us rather sceptical about the effectiveness of the photocatalytic processes in vivo. On the other hand, there is a huge potential of the soils as the alternative source of useful photocatalytic materials of unique properties

Organic fertilization is gaining importance as an alternative to chemical fertilization. However, the effects of organic products on crop quality need to be characterized, especially in those new products. Therefore, the effect of an enriched-biochar supplied with an organic fertilizer (Biochar) on yield and nutritional compounds of artichoke (Cynara cardunculus var scolymus) heads was analyzed and compared to inorganic conventional fertilization (Control). For that, number of fruits and their weights were determined together with nutritional properties of artichoke heads, as mineral content, fatty acids, content phenolic compounds and flavonoids. Results showed that biochar increased crop yield as registered by the higher number of marketable or commercial artichokes, as compared to the Control. Besides, biochar-treated plants presented significantly higher contents of Ca, Mg, and S, in the artichoke heads, but a decreased content of Co, Cu, and Zn. The fatty acids content in the Biochar-treated artichokes was also significantly higher, mainly because of the palmitic acid. On the contrary, the contents of phenolic compounds (e.g. flavonoids), were negatively and significantly affected by the Biochar application. In conclusion, the application of Biochar as soil amendment in combination with organic matter, for growing artichokes in the Semiarid Mediterranean area (e.g. Murcia Region) favoured an increasement in yield without comprising its nutritional composition. Further developments to establish protocols for extended use of Biochar in this agri-food production area are guaranteed.

In this research, we investigate the integration of an Intrusion Detection System (IDS) with a Digital Twin (DT) to enhance the cybersecurity of physical devices in cyber-physical systems. Using Eclipse Ditto as the DT platform and Snort as the IDS, we developed a near-realistic test environment that included a Raspberry Pi as the physical device and a Kali Linux virtual machine to perform common cyberattacks such as Hping3 flood attacks and NMAP reconnaissance scans. The results demonstrated that the IDS effectively detected Hping3-based flood attacks but showed limitations in identifying NMAP scans, suggesting areas for IDS configuration improvements. Furthermore, the study uncovered significant system resource impacts, including high CPU usage during SYN and ACK flood attacks and persistent memory usage after NMAP scans, highlighting the need for enhanced recovery mechanisms. This research presents a novel approach by coupling a Digital Twin with an IDS, enabling real-time monitoring and providing a dual perspective on both system performance and security. The integration offers a holistic method for identifying vulnerabilities and understanding resource impacts during cyberattacks. The work contributes new insights into the use of Digital Twins for cybersecurity and paves the way for further research into automated defense mechanisms, real-world validation of the proposed model, and the incorporation of additional attack scenarios. The results suggest that this combined approach holds significant promise for enhancing the security and resilience of IoT devices and other cyber-physical systems.

The quality of education in Bangladeshi universities has long been a topic of concern, yet comprehensive studies on its statistical impact remain limited. This research aims to assess the current state of education quality in Bangladeshi higher education institutions using secondary data from national surveys, academic reports, and global rankings. A variety of statistical methods are employed to identify key factors influencing educational outcomes, including faculty-to-student ratios, infrastructure, research output, and employability of graduates. The findings reveal significant disparities in academic standards across public and private universities, highlighting challenges such as inadequate funding, lack of modern teaching practices, and low research productivity. This study provides data-driven insights and recommendations for policymakers and educators to enhance the quality of university education in Bangladesh, with the ultimate goal of aligning it with global standards.

Physical inactivity is a leading risk factor for non-communicable diseases. Climate change is now regarded as the biggest threat to global public health. Electric micromobility (e-micromobility, including e-bikes e-cargo bikes and e-scooters) has the potential to simultaneously increase people’s overall physical activity while decreasing greenhouse gas emissions where it substitutes for motorised transport. The ELEVATE study aims to understand the impacts of e-micromobility, including identifying the people, places and circumstances where they will be most beneficial in terms of improving people’s health while also reducing mobility-related energy demand and carbon emissions. A complex mixed methods design collected detailed quantitative and qualitative data from multiple UK cities. First, a nationally representative (n=2000), city-wide (n = 1200) and targeted study area surveys have collected data on travel behaviour, levels of physical activity, vehicle ownership and use as well as attitudes towards e-micromobility. Then, to provide insights on an understudied type of e-micromobility, 49 households have been recruited to take part in e-cargo bike one-month trials. Self-reported data from participants are validated with objective data using methods such as GPS trackers and smartwatches recording of routes and activities. CO₂ impacts of e-micromobility use are also calculated. Participant interviews provided detailed information on preferences, expectations, experiences, barriers and enablers of e-micromobility.

Wear phenomena on natural stone of the cultural heritage are induced from various sources. Consequently, the development of multifunctional protective materials which combine two or more useful properties is an effective strategy to address the synergistic effects of various wear mechanisms. A multifunctional coating, consisting of a silane-based precursor and zinc oxide (ZnO) nanoparticles (NPs), is produced and tested for the protection of limestone. The hybrid coating combines the following three properties: superhydrophobicity, including water-repellency, photocatalytic self-cleaning and biocidal activity. The relative concentration of the NPs (0.8% w/w), used for the suggested composite coating, is carefully selected according to wetting studies, colourimetric measurements and durability (tape peeling) tests. The non-wetting state is evidenced on the surface of the composite coating by the large contact angle of water drops (≈153°) and the small contact angle hysteresis (≈5°) which gives rise to the physical self-cleaning scenario (lotus effect). The photocatalytic chemical self-cleaning is shown with the removal of methylene blue, induced by the UV-A radiation. Moreover, it is shown that the suggested coating hinders the incubation of E. coli and S. aureus as the inhibition were 94.8 and 99.9 %, respectively. Finally, preliminary studies revealed the chemical stability of the suggested coating.

This paper presents the results of theoretical and applied research that employs methodological experimentation in a project for the protection and integrated transformation of the downtown area of the city of Sharjah in the United Arab Emirates. The particular nature of this case relates to two factors: a) strategies and policies to protect and enhance the cultural heritage as a driver for tourism and the cultural development of the entire Emirate, from archaeological goods to the urban fabrics in historical centres (Sharjah and Korfakkhan), in contrast to the surrounding emirates; and b) the client’s request to define guidelines to manage the urban cultural heritage during a time of transition while awaiting the results of UNESCO candidacy. This unique fact denotes a sensitivity and long-term policies regarding the cultural heritage which view the recovery of the urban historical heritage, assuming the cultural component as the fourth dimension of sustainability. The methodological approach, the results of the research, and design are organized on the dual urban and building scale to understand the structure and elements of the historical centre. The actions and interventions are differentiated with respect to urban fabric, building, archaeological good, and landscape and translate the strategies of the guidelines into short-/mid-/long-term design actions.

Background: Acute kidney injury (AKI) and contrast-induced nephropathy (CIN) are common complications following percutaneous coronary intervention (PCI) or coronary angiography (CAG), presenting significant clinical challenges. Machine learning (ML) models offer promise for improving patient outcomes through early detection and intervention strategies. Methods: A comprehensive literature search following PRISMA guidelines was conducted in PubMed, Scopus, and Embase from inception to June 11, 2024. Study characteristics, ML Models, performance metrics (AUC, accuracy, sensitivity, specificity, precision), and risk of bias assessment using the PROBAST tool were extracted. Statistical analysis used a random-effects model to pool AUC values, with heterogeneity assessed via the I² statistic. Results: From 431 initial studies, 14 met the inclusion criteria. Gradient Boosting Machine (GBM) and Support Vector Machine (SVM) models showed the highest pooled AUCs of 0.87 (95% CI: 0.82-0.92) and 0.85 (95% CI: 0.80-0.90), respectively, with low heterogeneity (I² < 30%). Random Forest (RF) had a similar AUC of 0.85 (95% CI: 0.78-0.92) but significant heterogeneity (I² > 90%). Multilayer Perceptron (MLP) and XGBoost models had moderate pooled AUCs of 0.79 (95% CI: 0.74-0.84) with high heterogeneity. RF showed strong accuracy (0.83, 95% CI: 0.70-0.96), while SVM had balanced sensitivity (0.69, 95% CI: 0.63-0.75) and specificity (0.73, 95% CI: 0.60-0.86). Age, serum creatinine, left ventricular ejection fraction, and hemoglobin consistently influenced model efficacy. Conclusions: GBM and SVM models, with robust AUCs and low heterogeneity, are effective in predicting AKI and CIN post-PCI/CAG. RF, MLP, and XGBoost, despite competitive AUCs, showed considerable heterogeneity, emphasizing the need for further validation.

Drug hypersensitivity reactions (DHR) to platinum-based compounds (PC) are on the rise, and their personalized and safe management is essential to enable first-line treatment of these cancer patients. This study aimed to evaluate the usefulness of basophil activation test by flow cytometry (BAT-FC) and the newly developed sIgE-microarray and BAT-microarray in diagnosing IgE-mediated hypersensitivity reactions to PC. 24 patients with DHR to PC (20 oxaliplatin and four carboplatin) were evaluated: 13 patients diagnosed as allergic with positive skin tests (STs) or drug provocation tests (DPTs), six patients diagnosed as non-allergic with negative STs and DPTs, and five patients classified as suspected allergic because DPTs could not be performed. In addition, four carboplatin-tolerant patients were included as controls. BAT-FC was positive in 2 of 13 allergic patients, with a sensitivity of 15.4% and specificity of 100%. However, the sIgE- and BAT-microarrays were positive in 11 of 13 DHR patients, giving a sensitivity of over 84.6% and a specificity of 90%. Except for one patient, all samples from the non-allergic and control group were negative for the sIgE- and BAT-microarrays. Our experience indicates that sIgE- and BAT-microarray could be helpful in the endophenotyping of IgE-mediated hypersensitivity reactions to PC and may provide an advance in decision-making for drug provocation testing.

This study investigated the accuracy and precision of a commercially available PNT solution that uses DGNSS-SBAS technology. Time and position data were sampled at a frequency of 20Hz during both short and long trajectory of a simulated controlled dry-land slalom, as well as during a real-world on-water slalom exercise. The primary objective was to assess the positional accuracy, availability, integrity, and service continuity of the PNT solution while evaluating its ability to differentiate between trajectories. Additionally, the simulated results were compared with an on-water real-world slalom test to validate the findings. The results of the controlled dry-land slalom test indicate that the PNT solution provided accurate measurements with an overall mean±SD Hrms of 0.20±0.02m. The integrity measures, HDOD and PDOP were found to be ideal to excellent, with values of 0.68±0.03, and 1.36±0.07, respectively. The PNT solution utilised an average of 20±1 satellites from the constellation, resulting in an accuracy of

The integration of clean energy generation with wastewater treatment holds promise for addressing both environmental and energy concerns. Focusing on photocatalytic hydrogen production and wastewater treatment, the study introduces PdIn/TiO2 catalysts for the simultaneous removal of the pharmaceutical contaminant paracetamol and hydrogen production. Physicochemical characterization showed a high distribution of Pd and In on the support as well as a high interaction with it. The Pd and In deposition enhances the light absorption capability and significantly improved HER in comparison to TiO2 not only in the electrolyte but also in presence of paracetamol. Regarding PTM oxidation, TiO2 and PdIn/TiO2 showed an irreversible behavior toward the PTM oxidation, limited by the adsorption of species on the electrode surface. Thus, PdIn-doped TiO2 stands out as a promising catalyst, showcasing enhanced physicochemical properties and superior catalytic performance. This underscores its potential for both environmental remediation and sustainable hydrogen production.

Upon declaration of poliovirus (PV) type 2 eradication in 2015, the World Health Organization (WHO) published PV containment requirements in the Global Action Plan III (GAPIII) for mitigating risks of a facility-associated release post-eradication. In 2018, the 71st World Health Assembly resolution urged Member States retaining PV to appoint a National Authority for Containment (NAC), reduce the number of PV facilities, and submit applications for containment certification. The United States (US) NAC was established in 2018 for containment oversight, and two paths to WHO GAPIII containment certification were developed. Facilities retaining PV were identified through national poliovirus containment surveys. US NAC conducted 27 site visits at 18 facilities (20 laboratories: A/BSL-2 (65%), A/BSL-3 (20%), and storage-only (15%)) to verify implementation of US NAC’s preliminary containment measures. NAC identified areas for improvement in seven categories: primary containment, decontamination, hand hygiene, security, emergency response, training, and immunization practices. Sixteen facility applications were endorsed to pursue poliovirus-essential facility (PEF) certification whereas four facilities opted to withdraw during the containment certification process. The US made noteworthy progress in PV containment to enhance biosafety and biosecurity practices at US PV facilities to safeguard the polio eradication effort.

The global increase in population, the phenomenon of climate change, the issue of water pollution and contamination, and the inadequate management of water resources all exert heightened strain on freshwater reserves. The potential utilization of the interfacial solar steam generation (ISSG) system, which utilizes photothermal conversion to generate heat on material surfaces for wastewater purification and desalination purposes, has been successfully demonstrated. Textile materials based ISSG devices, including (woven, nonwoven and knitting) fabrics and electrospinning membranes, exhibit distinct properties such as rough surface texture, high porosity, significant surface area, exceptional flexibility, and robust mechanical strength. These characteristics, combined with their affordability, accessibility, and economic viability for widespread implementation, make them extremely attractive for applications in SSG. In this review, a comprehensive analysis of emerging concepts, advancements and applications of textile materials (woven, nonwoven, and knitting) fabrics and electro-spun membranes in the ISSG for wastewater purification and desalination is highlighted. We also emphasize significant obstacles and potential prospects in both theoretical investigations and real-world implementations, aiming to contribute to future advancements in the domain of textile based materials interfacial evaporation in wastewater purification and desalination. Furthermore, the drawbacks and the challenges of ISSG systems are also highlighted.

This study aimed to expand and inform the growing body of research on Postpartum Depression, from a netnographic standpoint, for the social media platform of Pinterest. More specifically, it aimed to assess the type of content present on Pinterest regarding Postpartum Depression shared by women in 2018 and 2021, as well as examine the subjective experience of women who have experienced this mood disorder. Data were collected following a search using the keywords “Postpartum Depression” on Pinterest on two separate timestamps (2018 and 2021); pins were limited to those in English or French. Master themes were identified among all pins and those identified as “self-disclosure” were subsequently further analyzed. Self-disclosure data were separately analysed using thematic analysis, specifically adopting a deductive thematic approach for the 2021 data set stemming from the coding scheme created from analysis of the 2018 data. Major themes were identified, and an analysis of frequency was performed for each sub-theme, along with qualitative examples associated with each theme. A total of 483 Pinterest pins from two different time points of 2018 (169 pins) and 2021 (314 sample pins) were analyzed. Five primary categories were identified in both 2018 and 2021, with five additional categories being identified in the 2021 dataset. Comparative results from 2018 and 2021 self-disclosure pins show individuals’ varying yet similar subjective manifestations of PPD, the potential need to adopt a multidisciplinary treatment approach, and the societal fears, such as mental health stigma, that are playing a part in help-seeking behavior hesitancies. Our findings provide imperative insight into individuals’ subjective experiences with PPD; experiences that involve a sensitive and vulnerable topic. Netnographic research, along with Pinterest, must be taken into consideration for future work delving into women’s health issues, including those surrounding perinatal mental health.

Greenhouse gases (GHGs) trap heat in the atmosphere, causing the Earth's surface temperature to rise. The main GHGs are carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), perfluorocarbons (PFCs), hydrofluorocarbons (HFCs), and sulfur hexafluoride (SF6). Human activities are increasing GHG concentrations rapidly, which is causing global climate change. Global climate change is increasing environmental and public health problems. To reduce greenhouse gas (GHG) emissions, identify where the emissions are coming from, develop a plan to reduce them, and then implement and monitor the plan to ensure that emissions are actually reduced. Anthropogenic global climate change has large and increasingly adverse economic effects. Cities emit the most GHGs due to fossil fuel burning and power usage. The four major GHG emitters are energy, transportation, waste management, and urban land use sectors. Organizations should prepare action plans to lower their GHG emissions and stop the worst consequences of climate change. These action plans require companies and local authorities to submit their GHG emissions reports on a yearly basis. A GHG emissions management system includes several processes and tools created by organizations to understand, measure, monitor, report, and validate their GHG emissions. Two of the most widely adapted frameworks for GHG inventory reporting are ISO 14064 and the GHG Protocol. This review paper aims to identify some of the key points of GHG inventory preparation and mitigation strategies.

Background: Extremametly lean athletes could have difficulty for adaptive eating (intuitively or consciously) during ad libitum refeed. Aims: we verificate if there is a relation between adaptive eating and coping strategies with energy intake in a model of refeed proposed for bodybuilders. Methods: Fourteen male bodybuilders (29.6±3.1yrs; 85.6±6.8kg, ≥6 competitions) completed the 4-week each consisting of a 5 days of energy restriction following 2 days of refeed. Dietary assessment, body composition (ultrasound), Recovery stress questionnaire (REST-Q) and BRUMS (Brunel mood scale) were accessed pre and post; Coping Function Questionnaire (CFQ), Mindful Eating Scale version 2 (MES 2) and the Intuitive Eating Scale-2 (IES-2) was used at 4th week. Results: The refeed day resulted in a daily caloric increment of ~44% compared to the average energy intake on the energy restriction days, culminating in a weekly calorie deficit of ~27% and a drop in body mass of 3.1±1.4% with relation to initial values. The most reduced body fat and preserved or gained lean mass. The energy consumption during refeed maintained an inverse relationship with the perception of satiety (r=-0.9; p<0.01), total scores of IES 2 (r=-0.82; p<0.01), as well as with the confidence in hunger and satiety cues (r=-0.62; p=0.02) and congruence in food-body choice (r=-0.56; p=0.04) dimensions. Coping based in emotions maintained a relationship with the total scores of IES 2 (r=0.54; p<0.05) and an inverse relationship with energy intake during refeed (r=-0.42; p<0.05). Conclusion: Our results suggest that a higher perception of internal hunger and satiety signals and higher scores in intuitive eating may contribute to energy intake adequate, even when high scores of coping based in emotions are present.

Chicken collagen is a promising raw material source to produce gelatins and hydrolysates. Gelatins and hydrolysates can be prepared biotechnologically using proteolytic enzymes produced by submerged fermentation of genetically modified microorganisms. By choosing the appropriate process conditions, such changes can be achieved at the molecular level of collagen, making it possible to prepare products with targeted properties for advanced cosmetic, pharmaceutical, medical or food applications. The research aims to investigate: i) antioxidant activity (DPPH and ABTS) in model samples of chicken collagen products (gelatin and hydrolysates); ii) distribution of molecular weights by the GPC-RID analysis method; iii) functional groups and configuration of polypeptide chains related to molecular level properties using FTIR; iv) the microbiological properties on SDA, PCA, TSA and VRBL microbial populations using MALDI method. Antioxidant activity towards ABTS radicals of more than 80% was found for all samples. The molecular weights of all gelatin samples showed typical α- and β-chains. FTIR analysis confirmed that the samples showed all typical vibrational regions for collagen cleavage products, Amide A and B, Amide I, II, and III at characteristic values. Microbiological analysis of the prepared samples showed no undesirable bacteria that would limit advanced applications of the prepared products. Gelatins and hydrolysates from chicken stomachs represent a promising alternative to products made from standard collagen tissues of terrestrial animals.

The small-molecule iododiflunisal (IDIF) is a transthyretin (TTR) tetramer stabilizer and acts as a chaperone of the TTR-Amyloid beta interaction. Oral administration of IDIF improves Alzheimer’s Disease (AD)-like pathology in mice, although mechanism of action and pharmacokinetics remain unknown. Radiolabeling IDIF with positron or gamma emitters may aid in the in vivo evaluation of IDIF using non-invasive nuclear imaging techniques. In this work, we report isotopic exchange reaction to obtain IDIF radiolabeled with 18F. [19F/ 18F]exchange reaction over IDIF in dimethylsulfoxide at 160 °C resulted in the formation of [18F]IDIF in 7±3% radiochemical yield in a 20 minutes reaction time, with a final radiochemical purity of >99%. Biodistribution studies after intravenous administration of [18F]IDIF in wild-type mice using positron emission tomography (PET) imaging showed capacity to cross the blood-brain barrier (ca. 1% of injected dose per gram of tissue in the brain at t&gt;10 min post administration), rapid accumulation in the liver, long circulation time and progressive elimination via urine. Our results open opportunities for future studies in larger animal species or human subjects.

One of the fastest growing customer service delivery platforms is online banking. However, the increasing number of attacks on online banking platforms is driving the banks to constantly review their security. This study developed an enhanced Multi-factor authentication scheme with a track and trace capability to reduce these threats. Five modalities of authentication were incorporated in the proposed scheme: The traditional username, password, personal identification number (PIN) and one-time PIN (OTP), augmented with fingerprints or facial scans, registered smart devices, and a time locked user's location. One of the scheme's most impressive accomplishments is its capacity to seamlessly detect undesired activities and send alerts in the form of secretly obtained photographs and location triangulation.

In the field of computational fluid dynamics (CFD) for rotorcraft, two significant challenges are resolving the complex vortex structures in rotor wakes, and representing the moving rotor blades in the ambient airflow. This study addresses the first challenge by using a third-order unstructured finite volume solver, which, compared to its second-order counterpart, has substantially lower dissipation. Consequently, even relatively coarse meshes are capable of resolving small vortices. With this background flow field solver, the second challenge is addressed by modeling each rotor as an Actuator Disk Model (ADM), or describing individual rotor blades as actuator lines, designated as the Actuator Line Model (ALM). Both of these models are equipped with an improved correction for aerodynamic losses at blade tips, which is thoroughly presented in the methodology section. The numerical experiments section centers on analyzing errors linked to various sampling approaches. Additionally, the article discusses comparisons between vortex theory and ALM, specifically regarding calculations for fixed-wing aircraft. Furthermore, high-order precision and parallel efficiency are exemplified in scenarios encompassing rotors engaged in both hovering and forward flight rotors. The results in this paper demonstrate that the combination of the ALM/ADM with the improved tip loss correction and the third-order finite volume solver presents a new way of developing efficient tools for the aerodynamic analysis of helicopter rotors.

The concept of supply chain leadership strategy has garnered increasing interest from policymakers and experts, because it is among the most dependable practices to sustainable innovation and competitiveness for large firms across developing economies. Effective implementation of supply chain leadership strategic actions at each level in the company can create sustainable innovative and competitiveness. The study collected statistical data using an online Survey- Monkey questionnaire from 46 of top 100 JSE-listed companies with the intention to discover supply chain leadership strategic actions that drive sustainable innovation and competitiveness in the company. The findings expose a significant empirical insight for companies to understand and leverage supply chain leadership strategic actions to influence sustainable innovative position in a developing economy. These results suggest that firms operating within emerging markets should embrace and acknowledge the importance of sustainable innovation in business practices in the provision of goods and services. This study therefore delivers major contributions to the literature to assist in planning sustainable innovation and supply chain practices in a developing economy to survive in a competitive global environment. The paper also offers a vigorous awareness and knowledge of the common characteristics and strategies for supply chain management that needs to be prioritized by the JSE-listed companies.

Introduction: Prostate cancer (PCa) is one of the deadliest and most common causes of malignancy and death in men worldwide, more specifically with higher prevalence and mortality in developing countries. Factors such as age, family history, race and certain genetic mutations are some of the factors contributing to the occurrence of PCa in men. The recent advances in technology and algorithms gave rise to the computer-aided diagnosis (CAD) of PCa. With the availability of medical image datasets and emerging trends in state-of-the-art machine and deep learning techniques, there is a growth in recent related publications. Materials and Methods: In this study, we present a systematic review of PCa diagnosis with medical images using machine learning and deep learning techniques. We conducted a thorough review through relevant studies indexed in four databases (IEEE, PubMed, Springer and ScienceDirect) using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. With well-defined search terms, a total of 608 articles were identified and 77 met the final inclusion criteria. Key elements in the included papers were presented and conclusions were drawn from them. Results: Findings showed that the United States has the most research in PCa diagnosis with machine learning, Magnetic Resonance Images are the most used datasets and transfer learning is the most used method of diagnosing PCa in recent times. In addition, some available PCa datasets and some key considerations for choice of loss function in the deep learning models were presented. The limitations and lessons learnt were discussed and some key recommendations were made. Conclusion: The discoveries and the conclusions of this work have been organized so as to enable researchers in the same domain to use this work and make crucial implementation decisions.

Rotavirus is a major cause of diarrhea globally in animals and young children under 5 years. Here, molecular detection and genetic characterization of porcine rotavirus in smallholder and commercial pig farms in the Lusaka Province of Zambia were con-ducted. Screening of 148 stool samples by RT-PCR targeting the VP6 gene revealed a prevalence of 22.9 % (34/148). Further testing of VP6-positive samples with VP7-specific primers produced 12 positives, which were then Sanger-sequenced. BLASTn of the VP7 positives showed sequence similarity to porcine and human rota-virus strains with identities ranging from 87.5% to 97.1%. By next-generation se-quencing, the full-length genetic constellation of the representative strains RVA/pig-wt/ZMB/LSK0137 and RVA/pig-wt/ZMB/LSK0147 were determined. Geno-typing of these strains revealed a known Wa-like genetic backbone and their genetic constellations were G4-P[6]-I5-R1-C1-M1-A8-N1-T1-E1-H1 and G9-P[13]-I5-R1-C1-M1-A8-N1-T1-E1-H1, respectively. Phylogenetic analysis revealed that these two viruses might have their ancestral origin from pigs, though some of their gene segments were related to human strains. The study shows evidence of reas-sortment and possible interspecies transmission between pigs and humans in Zambia. Therefore, the “One Health” surveillance approach for rotavirus A in animals and humans is recommended to inform the design of effective control measures.

Cheese whey, a significant byproduct of the dairy industry, poses a substantial environmental challenge due to its organic pollutant load and large size demands for effective and affordable valorization methods. The abundance of lactose and other organic compounds in whey contributes to its elevated Biological Oxygen Demand (BOD) and Chemical Oxygen Demand (COD), further straining natural ecosystems. This study aimed to investigate the nutritional potential of whey and its conversion into value-added products: bioethanol and probiotic drinks. To achieve this, Bacillus megaterium and Bacillus subtilis strains, isolated from soil samples in Nepal, were co-cultured with Lactobacillus and Saccharomyces cerevisiae in the whey broth for fermentation. Ethanol distillation was carried out using a Rota-evaporator to maintain the viability of the fermenting organisms within the broth. Moreover, the probiotic criteria of the fermenting strains were extensively examined. Experimental observations revealed a remarkable concentration of 9.2 g/L of ethanol, resulting in an ethanol yield of 0.23 g/L. Extrapolating this rate, it is theoretically possible to produce an annual global bioethanol output of 5 million tons using whey as a sustainable and abundant resource. Furthermore, the study explores novel advancements in the distillation process, demonstrating the successful extraction of ethanol at room temperature. Additionally, the fermentation organisms employed in this research exhibit robust viability, surviving various in-vitro stress-tolerant tests, including exposure to bile salt, pH fluctuations, and gastric juice. These resilient strains also demonstrated long-term stability in fermented whey broth, making them promising candidates for the development of probiotic beverages. This dual approach not only enables the sustainable management of organic waste but also presents an opportunity to create value-added products with positive implications for both the environment and human health.

The available direct gas dynamic tables provide the area ratio and the Prandtl-Meyer(P-M) angle of isentropic expansion of gas as functions of the Mach number. But finding the value of the Mach number in terms of area ratio or P-M angle requires an inverse function. Currently, the Mach number is determined by either interpolation from the direct gas dynamics tables, or the application of non-linear equation solvers using numerical methods. Both methods are approximate. In this paper inverse functions for Mach number in the form of Taylor’s series expansions are reported to develop the so-called inverse tables. The advantage of this approach is that the Mach number with a greater accuracy, referred to as nearly exact can be obtained by reading from the inverse table. In the presentation, the necessary equations and new tables that can be used by the scientific community will be disseminated. The data and computer programs are made available on GitHub through https://github.com/anillals/Inverse-gas-dynamic-tables- and-codes. A paper describing the complete mathematical steps in the derivation of the equations can be found at https://rdcu.be/dgrlf

Silicon anodes have been considered one of the most promising anode candidates for the next generation of high-energy density lithium-ion batteries due to the high theoretical specific capacity (4200 mAh g-1) of Si. However, high lithiation capacity endows silicon anodes with severe volume expansion effects during the charge/discharge cycling. The repeated volume expansions not only lead to the pulverization of silicon particles and the separation of electrode materials from the current collector, but also brings rupture/formation of solid electrolyte interface (SEI) and also continuous electrolyte consumption, which seriously hinders the commercial application of silicon anodes. Structural design and optimization is the key to improving the electrochemical performances of silicon anodes, which has attracted wide attention and research in recent years. This paper mainly summarizes and compares the latest research progress for the structural design and optimization of silicon anodes.

High-precision inertial sensors or accelerometers can provide us references of free-falling motions in gravitational field in space, and serve as the key payloads for gravity recovery missions. In this work, a systematic method of electrostatic inertial sensor calibrations for gravity recovery satellites is introduced, which is applied to and verified with the Taiji-1 mission. Taiji-1 is the first technology demonstration satellite of the ``Taiji Program in Space'', which, in its final extended phase in 2022, could be viewed as a gravity recovery satellite operating in the high-low satellite-to-satellite tracking mode. Based on the calibration principles, swing maneuvers with time span about 200 s and rolling maneuvers for 19 days were conducted by Taiji-1 in 2022. The inertial sensor's operating parameters including the scale factors, the center of mass offset vector and the intrinsic biased acceleration are precisely re-calibrated and are updated to the Taiji-1 science team. Data from one of the sensitive axis is re-processed with the updated operating parameters, and the performance is found to be slightly improved compared with former results. This approach could be of high reference value for the accelerometer or inertial sensor calibrations of the GFO, the Chinese GRACE-type mission, and the planned Next Generation Gravity Missions. This could also shed some light on the in-orbit calibrations of the ultra-precision inertial sensors for future GW space antennas because of the technological inheritance between these two generations of inertial sensors.

Neurofeedback (NF) shows promise in enhancing memory, but its application to the medial temporal lobe (MTL) still needs to be studied. Therefore, we aimed to develop an NF system for the memory function of the MTL and examine neural activity changes, and memory task score changes through NF training. We created a memory NF system using intracranial electrodes to acquire and visualise the neural activity of the MTL during memory encoding. Twenty trials of a tug-of-war game per session were employed for NF and designed to control neural activity bidirectionally (Up/Down condition). NF training was conducted with three patients with intractable epilepsy, and we observed an increasing difference in NF signal between conditions (Up−Down) as NF training progressed. Similarities and negative correlation tendencies between the transition of neural activity and the transition of memory function were also observed. Our findings demonstrate NF's potential to modulate MTL activity and memory encoding. Future research needs further improvements to the NF system to validate its effects on memory functions. Nonetheless, this study represents a crucial step in understanding NF's application to memory and provides valuable insights for developing more efficient memory enhancement strategies.

Because most of the recognized causes of superior gluteal nerve (SGN) injury are iatrogenic, detailed knowledge of the anatomy of the SGN is crucial to prevent its injury associated with surgical procedures. This study aims to describe the precise location of SGN or its branches at the greater sciatic foramen, measure the distances of these nervous structures to palpable bony landmarks, and evaluate the possible correlation between these parameters and pelvis size. Twenty human cadaveric hemipelvises were studied. After dissection to expose the SGN or its branches at the greater sciatic foramen, the distances from these nervous structures to the greater trochanter (GT), to the anterior superior iliac spine (ASIS), to the posterior superior iliac spine (PSIS), to the ischial tuberosity (IT), and to the greater sciatic notch were measured. We found that at the greater sciatic foramen the SGN emerges as a common trunk in 75% of hemipelvises, and already divided in its superior and inferior branches in 25% of hemipelvises. When the SGN exits the pelvis as a common trunk, it does so, in most cases, in contact with the bone at the apex of the greater sciatic notch or superior to it. The median distance from the SGN at the greater sciatic notch to the PSIS, ASIS, GT and IT is 7.6 cm, 10.9 cm, 7.5 cm and 10.8 cm, respectively. We found a positive correlation between some of the analyzed parameters and the size of the pelvis. The anatomical data of this study may serve as pivotal guides during orthopedic pelvic surgery, contributing to minimize SNG iatrogenic lesions with significant implications in the patient's quality of life.

Introduction. The main task of the study was to measure the workload of nurses in the care of a patient with a developmental defect–congenital diaphragmatic hernia (CDH)–in the neonatal intensive care unit, which was evaluated on the basis of standardized tools Therapeutic Intervention Scoring System (TISS–28), Nine Equivalents of Nursing Manpower use Score (NEMS) and Nursing activities Score (NAS). Methods. Retrospective study. The workload was measured using TISS–28, NEMS and NAS tools. Descriptive statistics were used to analyze the data, and a single-sample test was used to verify the research hypotheses. Results. The sample consisted of medical records of 33 patients; 592 observations were analyzed. Studies prove the need for research on the workload of nurses in neonatal intensive care units. A patient with CDH should receive nursing care as required. Conclusion. It is necessary to implement a model for measuring the workload of nurses in neonatal intensive care units, taking into account the evaluation of work and its optimization.

The average age of in-service bridges has increased in recent years in the United States. To address this issue, structural health monitoring and damage identification approaches can be employed to prioritize maintenance/replacement of aging bridges. Among the damage identification and operational health monitoring approaches, finite element (FE) model updating methods can offer a solution to evaluate the mechanics-based characteristics of bridges. However, in a real-world setting, unidentifiability and mutual dependency between model parameters, modeling errors, especially due to boundary conditions, as well as ill-conditioning of updating algorithms can pose challenges to the application of FE model updating methods. To address these challenges, this study presents a two-step FE model updating approach. In the first step, modal-based model updating is used to estimate linear model parameters mainly related to the stiffness of boundary conditions and material properties. In the second step, in order to refine parameter estimation accounting for nonlinear response behavior of the bridge, a time-domain model updating is carried out. In this step, boundary conditions are fixed at their final estimates using modal-based model updating. To prevent the convergence of updating algorithm to local solutions, the initial estimates for nonlinear material properties are selected based on their corresponding final estimates in the modal-based model updating. To validate the applicability of the two-step FE model updating approach, a series of forced-vibration experiments are designed and carried out on a pair of decommissioned and deteriorated prestressed bridge I-girders. After carrying out the two-step FE model updating, the final estimates of concrete compressive strength are shown to provide reasonable assessment of the damage extent in the girders.

The dentate gyrus (DG) is part of the hippocampal formation and is essential for important cognitive processes such as navigation and memory. The oscillatory activity of the DG network is believed to play a critical role in cognition. DG circuits generate three main rhythms: theta, beta, and gamma, which participate in the specific information processing performed by DG neurons. In the temporal lobe epilepsy (TLE), cognitive abilities are impaired, which may be due to drastic alterations in the DG structure and network activity during epileptogenesis. The theta rhythm and theta coherence are especially vulnerable in dentate circuits; disturbances in DG theta oscillations and their coherence may be responsible for general cognitive impairments observed during epileptogenesis. Some researchers suggested that the vulnerability of DG mossy cells is a key factor in the genesis of TLE, but others did not support this hypothesis. The aim of the review is not only to present the current state of the art in this field of research but to help pave the way for future investigations by highlighting the gaps in our knowledge to completely appreciate the role of DG rhythms in brain functions. Disturbances in oscillatory activity of the DG during TLE development are described in detail that may be a diagnostic marker in the treatment of this disease.

The integration of metal nanoparticles and solid carriers can achieve ideal stability, high load and good conductivity. In this work, copper nanoparticles (Cu NPs) were sequentially deposited on a cobalt metal-organic framework (Co-MOF) by bonding with exposed imino groups, followed by a reduction reaction to prepare a new Cu@Co-MOF composite. Cu@Co-MOF acts as a non-enzymatic electrochemical sensor to detect glucose (Glu) in an alkaline medium. The composite working electrode of Cu@Co-MOF/GCE (GCE = glassy carbon electrode) improves the electrocatalytic activity for Glu oxidation. Cu@Co-MOF/GCE shows excellent electrocatalytic performances in Glu concentration ranging 0.005~1.8 mmol∙L⁻¹ (mM): the sensitivities are 282.89 μA∙mM⁻¹∙cm⁻² in 0.005-0.4 mM Glu and 113.15 μA∙mM⁻¹∙cm⁻² in 0.4-1.8 mM Glu respectively with low detection limit of 1.6 μM (S/N = 3) and high selectivity and stability.

The modern diet, which consists of food produced with high level of industrial processing, is associated with an increased risk of the development of lifestyle diseases. Current nutritional science is, however, focused on chemical composition of food, and not on the type and degree of processing used during the food production. Here, we take a fresh perspective on the relationship between the extent and type of food processing, and evaluate its impact on consumer health. We argue that the preference for consumption of minimally processed foods, and restriction of ultra-processed foods should be an essential part of a healthy lifestyle, disease prevention, and even treatment. We also present a simple, user-friendly consumer guide, which is intended to be used as a practical, ready-to-go identifier of ultra-processed foods.

SCARED-C instrument (the child version, 41 items) is used for screening anxiety in children between 8 to 18 years old and has been first introduced by Birmaher & collab. in 1995, with good psychometric data - internal consistency from α =.74 to .93 - and good discriminative validity indices in the original versions (1997, 1999). Since then, many countries have adopted the scale, for its utility in identifying five subsets of anxiety disorders (subscales): somatic/panic disorder, generalized anxiety, separation anxiety, social phobia, and school avoidance. The present study contains the first Romanian translated and adapted version of the SCARED-C instrument on a community sample of 477 children (8-18 years old) from Mureș county schools. The instrument showed moderate to good internal consistency (α Cronbach from to .63 to .91 for the total scale) and good test-retest reliability (.70) on a subset of 85 children sample. A confirmatory factorial analysis (CFA) was conducted to test the factor structure of the Romanian version of SCARED-C; results showed that SCARED-C has good psychometric properties to be used for screening anxiety in Romanian children and adolescents. The implications for using SCARED-C in dental practice are discussed. Future studies need to be conducted for exploring convergent and discriminative validity of the instrument and the sensitivity to current DSM-V criteria. Application on a dental pediatric sample is also required.

Adenine and thymine homopolymer strings of at least 8 nucleotides (AT 8+mers) were characterized in Salmonella entericasubspecies I. The motif differed between cother taxonomic classes but not between Salmonella enterica serovars. The motif in plasmids was associated with serovar. Approximately 12.3% of the S. enterica motif loci had mutations. Mutability of AT 8+mers suggests that genomes undergo frequent repair to maintain optimal gene content, and that the motif facilitates self-recognition; in addition, serovar diversity is associated with plasmid content. A theory that genome regeneration accounts for both persistence of predominant Salmonella serovars and serovar diversity provides a new framework for investigating root causes of foodborne illness.

Abstract: Intensity of natural and man-made disasters is increasing day by day. Disaster is one of the major threats that affects the sustainable development of tourist attractions. Big data and Internet of Things(IoT) will greatly improve the disaster management. Based on the Big Data and IoT, a tourism attraction disaster management system is designed, divided into several stages namely pre-disaster early warning prevention, disaster mitigation, recovery and reconstruction after disaster and updating disaster planning. Then, the system flow is analysed, as well as the system structure is constructed. Additional, system function and its operation flow are introduced, including disaster warning, disaster relief, disaster assessment, real-time monitoring and supporting disaster planning functions. Finally, an application case is introduced. Research intends to improve tourism area disasters management.

The cyclic regeneration of human endometrium is guaranteed by the proliferative capacity of Endometrial Mesenchymal Stromal Cells (E-MSCs). Due to this, the autologous infusion of E-MSCs has been proposed to support endometrial growth in a wide range of gynecological diseases. We aimed to compare two different endometrial sampling methods, the surgical curettage and the Vacuum Aspiration Biopsy Random Assay, and to validate a novel xeno-free method to culture human E-MSCs. Six E-MSCs cell lines were isolated after a mechanical tissue homogenization and cultured using human platelet lysate. E-MSCs were characterized for the colony formation capacity, proliferative potential and multilineage differentiation. The expression of mesenchymal and stemness markers was tested by FACS analysis and Real-Time PCR, respectively. Chromosomal alterations were evaluated by karyotype analysis, whereas tumorigenic capacity and invasiveness were tested by soft agar assay. Both endometrial sampling techniques allowed to efficiently isolate and expand E-MSCs using a xeno-free method preserving their mesenchymal and stemness phenotype, proliferative potential and multi-lineage differentiation ability during the culture. No chromosomal alterations and invasive/tumorigenic capacity were observed. Herein we report the first evidence of efficient E-MSCs isolation and culture in Good Manufacturing Practice compliance conditions, suggesting Vabra endometrial sampling as alternative to surgical curettage.

In this work, fluorinated graphene nanoscrolls (FGN) were synthesized via facile chemical methods under simple and mild conditions. Interestingly, the formation of the featured FGN was significantly solvent sensitive. Experimental results indicated that in the presence of aprotic solvent, for example, N,N dimethylformamide (DMF), the reaction system inclined to form the interesting FGN nanostructures. The structure and morphology of the prepared FGN were detailed characterized by atomic force microscopy (AFM), transmission electron microscopy (TEM), scanning electron microscopy (SEM) and X-ray diffraction (XRD) etc. The obtained FGN was used as a cathode material for primary lithium ion batteries with superior discharge specific capacity (eg. 979.3 mAhg-1), stable discharge platform and high energy density (eg. 2287.9 Wh kg-1), which fosters it a high density, low cost and durable candidate for cathode material for lithium ion batteries..

In this work, a mixture of Elzaki transform and projected differential transform method is applied to solve a nonlinear wave-like equations with variable coefficients. Nonlinear terms can be easily manipulated by using the projected differential transformation method. The method gives the results show that the proposed method is very efficient, simple and can be applied to other applications.

La integration (at various nominal contents) in SBA-15 prepared under acidic medium was intended from corresponding direct nitrate addition during mesoporous silica formation. Materials were impregnated with Pt (1.5 w%) and studied through several textural (N2 physisorption), structural (XRD, TG-DTG) and surface (FTIR, STEM-HAADF, SEM-EDS, NH3 and CO2 TPD) instrumental techniques. Pt-impregnated solids were tested in phenol hydrodeoxygenation (HDO, T=250 °C, 3.2 MPa, batch reactor, n-decane as solvent). Catalytic activity (in pseudo first order kinetic constant, kHDO basis) was related to Pt dispersion which was, however, not determined by nominal rare earth content. Determining actual composition of modified SBA-15 materials is crucial in reaching sound conclusions regarding their physicochemical properties, especially when La modifier is directly added during mesoporous matrices formation where efficient interaction among constituents could be difficult to get. Otherwise, results from some characterization techniques (N2 physisorption and FTIR, for instance) could be misleading and even contradictory. Indeed, extant modifier precursors when under SBA-15 synthesis conditions could affect the properties of prepared materials even though they were absent in obtained| formulations. Performing a simple compositional analysis could eliminate uncertainties regarding the role of various modifiers on characteristics of final catalysts. However, several groups have failed in doing so.

There are clear signs that the COVID-19 pandemic has had a straining impact on the higher education sector in South Africa and the far reaches of the world. This chapter examines (Science, Technology, Engineering, Mathematics) STEM lecturers’ experiences with digital assessments and perspectives on the continuity of digital transformation in the higher education sector in the post-COVID-19 era at one of South Africa’s rural universities. In 2022, a survey of ten (10) lecturers was conducted and the reports were also consulted by four (4) e-learning practitioners to obtain their views on the use of online learning and contact courses in the post-pandemic period. A qualitative research approach was used to understand the puzzling issues faced by STEM lecturers at a previously disadvantaged institution in Limpopo Province. The results of this study show that there is a puzzling conundrum that hinders the integration of effective online learning. Furthermore, online education as the new normal in the post-COVID-19 era seems unattainable due to extremely complex and difficult-to-understand mathematical symbols. Software and tools proved to be limitations in terms of establishing an interactive learning environment for students and lecturers on online learning platforms. Lecturers’ use of learning and teaching platforms deteriorated due to numerous unknown challenges, but also the tightening of COVID-19 restrictions. Furthermore, the results showed that science and mathematics lecturers believe that contact learning remains irreplaceable across generations and eras as some are not technology enthusiasts. This paper recommends that the lack of technological systems as well as technological resistance and reluctance to adopt transformative pedagogy and innovative practices, should not be viewed as a thorny issue to end the dominance of online education in STEM courses. Keywords: connectivism, Learning Management Systems, affordances, technological resistance, Moodle, transformative pedagogy

Conjunctival melanoma (Co-M) is an aggressive, invasive eye and eyelid cancer. Its global inci-dence of ~1 in a million is increasing at a rate ratio of ~1.4, but this rises sharply in over 65-year-olds. Although rare, Co-M has a devastating impact on the lives of those who develop it. Co-M is often misdiagnosed or overlooked leading to vision loss either from destructive effects of the tumour or side effects of therapy, facial disfigurement from radical surgery, and death from metastases. Due to its rarity, there is limited evidence for diagnosis and management; hence, there is no standardised treatment and not all cases are referred to a specialised ocular oncology centre. Recent progress in cancer immunology and genetics have revolutionised treatment of cutaneous melanoma, which share some similarities to Co-M. A better understanding of Co-M and its precursor lesions is urgently needed to lead the development of novel targeted and immuno-therapies both for local tumour control and disseminated disease. This review aims to provide a comprehensive clinical overview of the current knowledge of Co-M, its epidemiology, pathogenesis, presentation, diagnosis, management, and recent advances in novel biological therapies for personalised treatment of this disease.

In the context of the coordinated development of digital economy and environmental protection, we take the strategy of "Broadband China" as a quasi-natural experiment, adopts the panel data of 284 cities from 2007 to 2020, and evaluates the policy effect of digital economy on total factor energy efficiency through the progressive DID model. The results show that: The construction of "Broadband China" demonstration city has significantly promoted the improvement of the total factor energy efficiency of the demonstration city, but there is also significant urban heterogeneity, which is manifested in the significant improvement of the total factor energy efficiency of the eastern city, the southern city, the big city and the non-resource-based city. It has no significant effect on the total factor energy efficiency of central and western cities, northern cities, small and medium-sized cities and resource-based cities. The construction of "Broadband China" demonstration cities can improve total factor energy efficiency by promoting technological progress and strengthening environmental regulations. Therefore, we should further strengthen the construction of digital infrastructure and give full play to its positive effects. Different cities should seek differentiated development paths based on their own resource endowments and development conditions, so that the digital economy can effectively improve the total factor energy efficiency.

Abstract: In complex revision total hip arthroplasties, careful preoperative planning is crucial for the proper reconstruction of hip biomechanics. With conventional 2D radiographic images and computed tomography (CT) the 3D extension of the defect and the remaining bone stock may not be evaluated properly. 3D printed models can aid the surgeon in planning complex acetabular re-construction. The purpose of this study is to compare the accuracy of bony defect assessment in severe acetabular defects surgery with 3D printing planning as compared to conventional 2D ra-diographic planning and CT. Twenty-five patients with severe acetabular defects (Paprosky IIC and III) undergoing revision surgery were enrolled. Each patient had a 3D printed model of their femur and pelvis created based on CT scans. Clinical and radiographic assessments were conducted before and after surgery, with four key bone landmarks measured and compared between X-ray, CT, 3D models, and surgical reports. The findings revealed discrepancies in defect assessment between different imaging methods. X-rays and CT scans sometimes incorrectly identified affected areas, while the 3D models showed more accurate results. This study suggests that 3D printed models can significantly improve the preoperative evaluation of complex hip arthroplasty revisions, enabling surgeons to better select optimal surgical strategies.

To assess the degree to which it has met its commitments under the Paris Agreement, Morocco is called upon to carry out carbon assessments and transparent evaluations. Within the forestry sector, little is known about the role of Morocco’s forests in contributing to carbon uptake. With this aim, we applied for the first time in literature the 3-PG model to Cedrus atlantica ((Endl.) Manetti ex Carrière, 1855), which represents about 131.800 ha of Morocco’s forest area (i.e. Azrou forest). Through the Differential Evolution - Markov Chains (DE-MC) we tested and assessed the sensitivity and calibrated the 3-PG model. This process-based model provided significant results regarding the carbon sequestration capacity. The results showed the following: i- Parameters related to stand properties, canopy structure, and processes, as well as biomass partitioning, are the most important or sensitive for the performance of the model; ii- The DE-MC method optimized the values of the 3-PG parameters which was confirmed by the means of Gelman-Rubin convergence test; iii- According to the predictions of the calibrated 3-PG, the Net Primary Production in the pure Azrou forest varies between 0.32 and 7.88 tC.ha−1.yr−1, it is equal in average to 4.9 tC.ha−1.yr−1, which given the total area corresponds to 7078 tC.yr−1.

In this study, the thermal behavior of a PCM-based heat sink in the presence of horizontal fins was numerically investigated. These types of heat sink can be effective in electronic cooling applications. Independent variables included aspect ratio (AR), number of horizontal fins (n) and their length (LR), while the objective function was defined to maximize the safe operation time (tmax). The incorporation of horizontal fins has a positive effect (thermal conductivity enhancement) as well as a negative effect (latent heat reduction). Based on the results, the optimal number of horizontal fins was five. As the number of fins rises up to five, the incremental effect of thermal conductivity improvement (positive effect) was superior to the decremental effect of the latent heat reduction (negative effect), hence the objective function (tmax) improved. However, with a further increase in the number of fins upon five, the negative effect prevailed over the positive effect and therefore the safe operation time diminished.

In order to solve the problem of the poor oil displacement effect of high molecular weight alkali/surfactant/polymer (ASP) solution in low permeability reservoirs, Daqing Oilfield uses a partial quality tool to improve the oil displacement effect in low permeability reservoirs. Without changing the oil displacement capability of high molecular weight ASP solution in high permeability oil layers, the ASP solution is actively sheared in low permeability oil layers by using a partial quality tool to increase the injection capability of the solution and improve the overall oil recovery. In order to study the ability of the partial quality tool to improve the oil displacement effect, firstly, the matching degree of high molecular weight ASP solution to low permeability cores is studied, and the ability of quality control tools to change the molecular weight is studied. Then, experimental research on the pressure and oil displacement effect of high molecular weight ASP solution before and after the actions of the partial quality tool is carried out. The results show that ASP solutions with molecular weights of 1900 × 104 and 2500 × 104 have a poor oil displacement effect in low permeability reservoirs. After the action of the partial quality tool, the injection pressure is reduced by 5.22 MPa, and the oil recovery is increased by 7.79%. The injection pressure of the ASP solution after shearing by the partial quality tool is lower than that of the ASP solution with the same molecular weight and concentration without shearing, but the oil recovery is lower. On the whole, the use of the partial quality tool can obviously improve the oil displacement effect in low permeability reservoirs.

Measurement of breast cancer burden and identification of its influencing factors help in the development of public health policy and strategy against the disease. This study aimed to examine the variability of the excess mortality of female breast cancer patients in the North East Region of Peninsular Malaysia. This retrospective cohort study was conducted using breast cancer data from the Kelantan Cancer Registry between 2007 and 2011, and Kelantan general population mortality data. The breast cancer cases were followed up for five years until 2016. Out of 598 cases, 549 cases met the study criteria and were included in the analysis. Modelling of excess mortality was conducted using Poisson regression. Excess mortality of breast cancer varied according to age group (50 years old and below vs above 50 years old, Adj. EHR: 1.47; 95% CI: 1.31, 4.09; P=0.004), ethnicity (Malay vs non-Malay, Adj. EHR: 2.31; 95% CI: 1.11, 1.96; P=0.008), and stage (stage III and IV vs. stage I and II, Adj. EHR: 5.75; 95% CI: 4.24, 7.81; P<0.001). In conclusion, public health policy and strategy aim to improve cancer survival should focus more on patients presented at age below 50 years old, Malay ethnicity, and at a later stage.

The Doubly-Fed Induction Generator (DFIG) has significant features in comparison with Fixed Speed Wind Turbine (FSWT), which has popularized its application in power system. Due to partial rated back-to-back converters in the DFIG, Fault Ride-Through (FRT) capability improvement is one of the great subjects regarding new grid code requirements. To enhance the FRT capability of the DFIG, many studies have been carried out. Fault current limiting devices as one of the techniques are utilized to limit the current level and protect switches of the back-to-back converter from over-current damage. In this paper, a review is done based on fault current limiting characteristic of the proposed fault current limiting devices Therefore, Fault Current Limiters (FCLs) and Series Dynamic Braking Resistors (SDBRs) are mainly taken into account. Operation of all configurations including their advantages and disadvantages is explained. Impedance type and the fault current limiting devices’ location are two important factors, which significantly affect the DFIG behaviour in the fault condition. These two factors are basically studied by the simulation and their effects on the key parameters of the DFIG are investigated. Finally, future works in respect to the FCL application in the FRT improvement of the DFIG have also been discussed.

The once near-extinct Russell’s viper has recently come back to the area beyond its historical range. A couple of decades ago, its distribution was confined to 11 districts of Bangladesh. Hence, the study aims to update the distributional map of Russell’s Viper (Daboia russelii) across the country and find out the surges' causal factors. Both primary and secondary data were utilized in this study. It revealed that the distributional range of this snake was expanded from 22 to 33 districts in 2024. The respondents noted that climatic stressors like high humidity accompanied by hot weather, increased salinity, and increased frequency of flooding facilitated the spreading of the viper across the country. The cropping pattern and crop diversification were another major contributing factor. The shifting of the single-cropped area to a multi-cropped area in the Barind tract increased its main prey rats. Contrarily, the killing of natural predators such as foxes, monitor lizards and mongoose led to the rise in the snake population. The destruction of vegetation coverage in the upper part of the Padma Basin facilitated the movement of Russell's vipers downstream.

During tensile testing of materials, strain measurement is conducted using either contact or non-contact methods. Contact methods offer high accuracy and precision but are limited by the specimen's thickness and dimensions, whereas non-contact methods minimize damage to thin specimens and allow measurements in various environments, though they require longer prep-aration and calculation times. This paper proposes a circular grid marking pattern and a strain prediction algorithm using artificial intelligence (AI), which simplifies the preparation process and allows strain prediction without additional equipment. The circular grid pattern can be ar-ranged in various configurations from 1×5 to 5×7, and a laser marker, which requires minimal time, was used to engrave the pattern on the specimen to shorten the preparation time. The AI model, trained on image-based data, enables strain calculation regardless of the specimen's gauge length and size, and allows measurement of local strain as well as gauge-length strain. The reliability of this concept was verified by applying it to tensile testing.

In genomics analysis, with the development of sequencing technology and the rapid growth of data volume, how to effectively identify and analyze important information in genomic data from massive data has become an important challenge. Identify key patterns and features in genomic data through advanced community testing methods. First, genomic data often contains a lot of noise and redundant information that needs to be processed through steps such as normalization, filtering, and dimensionality reduction. Normalization makes data at different scales comparable, filtering out data points with low quality or missing values, and dimensionality reduction reduces data dimensions through principal component analysis (PCA) and retains the main information. The gene co-expression network establishes the connection relationship between genes by calculating the expression similarity matrix between genes, and applies the Louvain algorithm to maximize the modularity of the network, aggregate nodes layer by layer, form a community structure, and identify the complex relationship and potential structure between genes. Finally, we utilize the cancer dataset to evaluate the proposed model. From our extensive experimental results, we can observe that Louvain's algorithm indicates outperformances and accuracy in the recognition and analysis of genomic data.

In this study, we explored the application of deep learning techniques for credit card fraud detection, aiming to improve the performance and reliability of anomaly detection methods in financial transactions. We first utilized the Isolation Forest algorithm, achieving a detection accuracy of 26% for the top 1000 transactions. Subsequently, we experimented with the Autoencoder algorithm, an unsupervised deep neural network model, which enhanced the detection accuracy to 33.6% in the best case despite some fluctuations. The results demonstrate deep learning models' strong feature extraction capability and adaptability, highlighting their potential to surpass traditional methods. However, the high imbalance in the dataset, with only 0.17% of transactions being fraudulent, poses a significant challenge. This study underscores the necessity for further experimentation and optimization of network structures and hyperparameters to achieve more stable and efficient fraud detection. The findings provide valuable insights and reference points for future research in financial fraud detection using deep learning methodologies.

In the past few decades, the transmission of data over an unsecure channel resulted in an increased rate of hacking. Therefore, the requirement to make multimedia data more secure is increasing day by day. Numerous algorithms are developed to improve efficiency and robustness. In this article, a novel and secure image encryption algorithm is presented. It is based on a modified chaotic logistic map (CLM) that provides the advantage of having less computational time to encrypt an input image. The encryption algorithm is based on Shannon’s idea of using substitution-permutation and one-time-pad network to achieve ideal secrecy. The CLM is used for substitution and permutation to improve randomness and increase dependency on the encryption key. Various statistical tests are conducted, such as Key Space Analysis, Complexity Analysis, Sensitivity Analysis, Strict Avalanche Criteria (SAC), Histogram Analysis, Entropy Analysis, Mean of Absolute Deviation (MAD) Analysis, Correlation Analysis, Contrast Analysis, and Homogeneity to give a comparative analysis of the proposed algorithm and verify its security. As a result of various statistical tests, it is evident that the proposed algorithm is more efficient and robust as compared to previous ones.

FV (finger vein) identification is a biometric identification technology that extracts the features of FV images for identity authentication. To address the limitations of CNN-based FV identification, particularly the challenge of small receptive fields and difficulty in capturing long-range dependencies, an FV identification method Let-Net(Large kernel and attention mechanism Network) was introduced, which combines local and global information. Firstly, Let-Net employs large kernels to broaden the receptive field and incorporates depthwise convolution with residual connections to reduce parameter count. Secondly, Let-Net integrates an attention mechanism to enhance the information flows in channels and spaces for more comprehensive and distinctive FV feature extraction. The experimental results on nine public datasets show that Let-Net has excellent identification performance, and the EER and accuracy rate on the FV_USM dataset can reach 0.04% and 99.77%. The parameter number and FLOPs of Let-Net are only 0.89M and 0.25G, which means that the time cost of training and reasoning of the model is low, and it is easier to deploy and integrate into various applications.

The dynamic factors in the environment violate the static environment assumption of the SLAM algorithm, leading to a decrease in the accuracy of camera positioning. In recent years, many studies have attempted to address dynamic objects by combining semantic constraints and geometric constraints, but issues such as poor real-time performance, the potential for mistakenly treating humans as rigid objects, and subpar performance in high-dynamic scenes still persist. This paper proposes a dynamic scene visual SLAM algorithm called Dynamic SLAM based on target tracking and multi-view geometry (TKG-SLAM), based on object detection, Kalman filters, and multi-view geometry. This algorithm takes into consideration both real-time performance and algorithm accuracy. It combines semantic constraints and multi-view geometry constraints, selectively running the algorithm in different scenarios, filtering and preserving static points for camera pose estimation. Experimental results demonstrate that, compared to current state-of-the-art dynamic SLAM methods, our approach achieves optimal performance in some scenarios and exhibits stronger real-time capabilities.

This paper presents a novel approach for the recognition of multiple sclerosis (MS) using wavelet entropy and a Particle Swarm Optimization (PSO)-based neural network. MS is a complex neurological disorder with diverse clinical manifestations, often challenging to diagnose accurately. In this study, we leverage wavelet entropy (WE) as a feature extraction method to capture intricate patterns within brain imaging data. These extracted features are then employed to train a neural network model optimized through PSO to enhance classification accuracy. Experimental results on a dataset comprising MS and healthy control subjects demonstrate the effectiveness of our proposed approach, viz., WE-PSONN, which achieves a sensitivity of 91.95±1.15, a specificity of 92.36±0.88, a precision of 92.28±0.88, an accuracy of 92.16±0.90. The combination of advanced signal processing techniques and machine learning optimization holds promise for improving the early diagnosis and management of MS, offering potential benefits to both patients and healthcare providers.

Identification of plants is a challenging task which aims to identify the family, genus, and species level according to morphological features. Automated deep learning-based computer vision algorithms are widely used for identifying plants and can help users to narrow down the possibilities. However, numerous morphological similarities between and within species make the classification difficult. In this paper, we tested a custom convolution neural network (CNN) and vision transformer (ViT) based models using the PyTorch framework to classify plants. We used a large dataset of 88K and 16K images for classifying plants at genus and species levels respectively. Our results show that for classifying plants at the genus level, ViT models perform better compared to CNN-based models ResNet50 and ResNet-RS-420, and other state-of-the-art CNN-based models suggested in previous studies on a similar dataset. The ViT model achieved top accuracy of 83.3% for classifying plants at the genus level. ViT models also perform better for classifying plants at the species level compared to CNN-based models ResNet50 and ResNet-RS-420, with a top accuracy of 92.5%. We show that the correct set of augmentation techniques plays an important role in classification success.

Background: There is limited data on gender-based differences in atrial fibrillation (AF) treatment and prognosis. Our aim was to examine gender-related differences in medical attention in emergency department (ED) and follow-up (FU) among patients diagnosed with an AF episode and to determine whether there are gender-related differences in clinical characteristics, therapeutic strategies and long-term adverse events in this population. Methods: We performed a retrospective observational study of patients who presented to a tertiary hospital ER for AF from 2010-2015, with a minimum FU of one year. Data on medical attention received, mortality and other adverse outcomes were collected and analyzed. Results: Among the 2 013 patients selected, 1232 (60%) was female. Women were less likely than men to be evaluated by a cardiologist during the ED visit (11.5% vs. 16.6%, p=0.001) and were less likely to be admitted (5.9% vs. 9.5%, p<0.05). Electrical cardioversion was performed more frequently in men, both during the first episode (3.4% vs. 1.2%, p=0.001) and during FU (15.9% vs. 10.6%, p<0.001), in spite of a lower AF recurrence rate in women (9.9% vs 18.1%). During FU, women had more hospitalizations for heart failure (26.2% vs. 16.1%, p<0.001). Conclusions: In patients with AF, although no gender differences in mortality, there were significant differences in clinical outcomes, medical attention received, and therapeutic strategies. Women underwent fewer attempts at cardioversion, had a lower probability of being evaluated by cardiologists and showed a higher probability of hospitalization for heart failure. Being alert to these iniquities should facilitate to adopt measures to correct them.

Due to the special characteristics of the shooting distance and angle of remote sensing satellites, the pixel area ratio of ship targets is small and the feature expression is insufficient, which leads to unsatisfactory ship detection performance and even situations such as missed detection and false detection. In this study, we propose an improved-YOLOv5 algorithm. The improvement strategies mainly include: (1) Add the Convolutional Block Attention Module (CBAM) into the Backbone to enhance the extraction of target-adaptive optimal features; (2) Introduce cross-layer connection channel and lightweight GSConv structure into the Neck to achieve higher-level multi-scale feature fusion and reduce the number of model parameters; (3) The Wise-IoU loss function is used to cal-culate the localization loss in the Output, and assign reasonable gradient gains to cope with dif-ferences in image quality. In addition, during the preprocessing stage of experimental data, a me-dian and bilateral filter method is used for noise reduction to reduce interference from ripples and waves and highlight the information of ship features. The experimental results show that Im-proved-YOLOv5 has a significant improvement in recognition accuracy compared to various mainstream target detection algorithms; Compared to the original YOLOv5s, the mean Average Precision (mAP) has improved by 3.2% and the Frames Per Second (FPN) has accelerated by 8.7%.

Integrating artificial intelligence (AI) and natural language processing (NLP) technologies with project-based learning experiences in Science,Technology, Reading, Engineering, Arts, and Mathematics (STREAM) education offers the potential to enhance student engagement, critical thinking,problem-solving, and interdisciplinary learning. This paper presents a comprehensive approach to implementing AI and NLP technologies, such as ChatGPT, in STREAM project-based learning experiences, highlighting the potential advantages and challenges of this innovative educational approach. The proposed method encompasses personalized learning pathways, AI-powered research assistance, collaborative AI tools, real-time feedback, virtual mentoring, advanced simulation, and objective assessment. We also discuss the implications of this integration for educators, institutions, and students, along with recommendations for future research and development in this emerging field.

In recent years, intelligent identification and prediction of ore deposits based on deep learning algorithm and image processing technology has gradually become one of the research frontiers in the field of geological and metallogenic prediction. However, this method also has many problems that need to be solved. For example, (1) very few trainable image samples containing mineral point labels; (2) The geological image features are small and irregular, and the image similarity is high; (3) It is difficult to calculate the influence of different geological prospecting factors on ore mineralization. Based on this, this paper constructs a deep learning network model Multiscale Feature Attention Framework (MFAF) based on geo-image data. The results show that MFCA-Net module in MFAF model can solve the problem of scarce mine label images to a certain extent. In addition, the channel attention mechanism SE-Net module can quantify the difference of influence of different source factors on mineralization. The prediction map is obtained by applying the MFAF model in the study of deposit identification and prediction in the research area of the southern section of Qin-hang metallogenic Belt. In the forecast map, many regions have high metallogenic potential. Through observation, it is found that the above predicted area covers 100% of the known ore deposits at present, which has a good prediction effect. Later, we can find out the specific reasons through field verification. The multi-scale feature fusion and attention mechanism MFA Framework in this paper can provide a new way of thinking for geologists in mineral exploration. The research of this paper also provides resource guarantee and technical support for the sustainable exploitation of mineral resources and the sustainable growth of society and economy.

Voice cloning, an emerging field in the speech processing area, aims to generate synthetic utterances that closely resemble the voices of specific individuals. In this study, we investigate the impact of various techniques on improving the quality of voice cloning, specifically focusing on a low-quality dataset. To contrast our findings, we also use two high-quality corpora for comparative analysis. We conduct exhaustive evaluations of the quality of the gathered corpora in order to select the most suitable audios for the training of a Voice Cloning system. Following these measurements, we conduct a series of ablations by removing audios with lower SNR and higher variability in utterance speed from the corpora in order to decrease their heterogeneity. Furthermore, we introduce a novel algorithm that calculates the fraction of aligned input characters by exploiting the attention matrix of the Tacotron 2 Text-to-Speech (TTS) system. This algorithm provides a valuable metric for evaluating the alignment quality during the voice cloning process. We present the results of our experiments, demonstrating that the performed ablations significantly increase the quality of synthesised audios for the challenging low-quality corpus. Notably, our findings indicate that models trained on a 3-hour corpus from a pre-trained model exhibit comparable audio quality to models trained from scratch using significantly larger amounts of data.

A novel scheme that can simultaneously measure the Doppler frequency shift (DFS) and angle of arrival (AOA) of microwave signals is proposed. At the signal receiving unit (SRU), two echo signals and the reference signal are modulated by a Sagnac loop structure and sent to the central station (CS) for processing. At the CS, two low-frequency electrical signals are generated after polarization control and photoelectric conversion. The DFS without direction ambiguity and wide AOA measurement can be real-time acquired by monitoring the frequency and power of the two low-frequency electrical signals. In the simulation, an unambiguous DFS measurement with errors of ±3×10-3 Hz and a -90° to 90° AOA measurement range with errors of less than ±0.5° are realized. The safety and robustness of the system to environmental disturbance are improved, and it is more suitable for the modern electronic warfare system.

By constructing a static map, the library robot can navigate and access documents autonomously, which greatly improves efficiency; however, for libraries where shelves can be moved, map changes do not allow for direct navigation, and also sensors such as radar and camera are relatively expensive, so we propose a low-cost navigation algorithm based on real-time ranging data and map matrices, and designed a mobile archive access robot. In order to obtain real-time distance data, nine near/long range sensors were mounted on the chassis and a composite digital filtering algorithm was designed based on different moving area characteristics; then the access task matrix and map matrix were designed based on the archive access task and the location characteristics of the archive shelf placement, the robot can rely on the range data to update the map matrix during the moving process, complete its own positioning, and use the task matrix The robot can use the task matrix to complete autonomous navigation and access to multiple files. Experiments show that the filtered positioning accuracy can reach 1cm, while the robot can move to the target shelf autonomously, which is more practical and less costly.

The paper investigates new robust code constructions based on bent functions and spline–wavelet transformation. Implementation of bent functions in codes construction increases the probability of error detection in the data channel and cryptographic devices. Meanwhile, use of spline–wavelets theory for constructing the codes gives the possibility to increase system security from the actions of an attacker. Presented constructions combines spline–wavelets functions and bent functions. Developed robust codes, compared to existing ones, have a higher parameter of maximum error masking probability. Illustrated codes are ensuring the security of transmitted information. Some of the granted constructions were implemented on FPGA.

IT auditing quality has been defined by results obtained in the process. Meanwhile, multiple studies have been done to enhance it therefore, it continues to be a topic interest and research so it is worth mentioning that its quality relies on trained competent auditors and experts able to develop a process correctly, adapting to clients and to manage auditing inherent risks According to results from the IT audit, low quality and security levels have been identified in terms of the human, technical and contextual factors, affecting audit quality. The objective of this investigation is to identify metrics and to determine their own corresponding factor applying an exploratory type of research. In order to achieve such aim, a targeted survey was designed and implemented at the Institute of Internal Auditors of Ecuador since they have the knowledge and expertise in the field. A factor analysis statistics technique was applied to data gathered to verify that it relates to the identified factors as dimensions are reduced, thus the most impacting metrics may assess the quality of IT audits. Analysis results yielded a mean score for each one of the assessed metrics, concluding that the technical factor is the most significative since it relates roles and task performance during the auditing process as well as control proce-dures. Finally, most auditing quality-related issues are mainly the outcome of an inferior management auditing process, therefore it is crucial that collegiate groups and professionals in the field validate the auditing process.

Like traditional single label learning, multi-label learning is also faced with the problem of dimensional disaster.Feature selection is an effective technique for dimensionality reduction and learning efficiency improvement of high-dimensional data. In this paper, Logistic regression, manifold learning and sparse regularization were combined to construct a joint framework for multi-label feature selection (LMFS). Firstly, the sparsity of the eigenweight matrix is constrained by the $L_{2,1}$-norm. Secondly, the feature manifold and label manifold can constrain the feature weight matrix to make it fit the data information and label information better. An iterative updating algorithm is designed and the convergence of the algorithm is proved.Finally, the LMFS algorithm is compared with DRMFS, SCLS and other algorithms on eight classical multi-label data sets. The experimental results show the effectiveness of LMFS algorithm.