Effective prediction of wastewater treatment is beneficial for precise control of wastewater treatment processes. The nonlinearity of pollutant indicators such as COD and TP makes the model difficult to fit and has low prediction accuracy. The classical deep learning methods have been shown to perform nonlinear modeling. However, there are enormous numerical differences between multi-dimensional data in the prediction problem of wastewater treatment, such as COD above 3000 mg/L and TP around 30 mg/L. It will make current normalization methods challenging to handle effectively, leading to the training failing to converge and the gradient disappears or exploding. This paper proposes a multi-factor prediction model based on deep learning. The model consists of a combined normalization layer and a codec. The combined normalization layer combines the advantages of three normalization calculation methods: z-score, Interval, and Max, which can realize the adaptive processing of multi-factor data, fully retain the characteristics of the data, and finally cooperate with the codec to learn the data characteristics and output the prediction results. Experiments show that the proposed model can overcome data differences and complex nonlinearity in predicting industrial wastewater pollutant indicators and achieve better prediction accuracy than classical models.

Growing interest surrounds the assessment of perivascular spaces (PVS) on magnetic resonance imaging (MRI) and their validation as a clinical biomarker of adverse brain health. Nonetheless, the limits of validity of current state-of-the-art segmentation methods are still unclear. Here, we propose an open-source three-dimensional computational framework comprising 3D digital reference objects and evaluate the performance of three PVS filtering methods under various spatiotemporal imaging considerations (including sampling, motion artefacts, and Rician noise). Specifically, we study the performance of the Frangi, Jerman and RORPO filters in enhancing PVS-like structures to facilitate segmentation. Our findings were three-fold. First, as long as voxels are isotropic, RORPO outperforms the other two filters, regardless of imaging quality. Unlike the Frangi and Jerman filters, RORPO’s performance does not deteriorate as PVS volume increases. Second, the performance of all “vesselness” filters is heavily influenced by imaging quality, with sampling and motion artefacts being the most damaging for these types of analyses. Third, none of the filters can distinguish PVS from other hyperintense structures (e.g. white matter hyperintensities, stroke lesions, or lacunes) effectively, the area under precision-recall curve dropped substantially (Frangi: from 94.21 [IQR 91.60, 96.16] to 43.76 [IQR 25.19, 63.38]; Jerman: from 94.51 [IQR 91.90, 95.37] to 58.00 [IQR 35.68, 64.87]; RORPO: from 98.72 [IQR 95.37, 98.96] to 71.87 [IQR 57.21, 76.63] without and with other hyperintense structures, respectively). The use of our computational model enables comparing segmentation methods and identifying their advantages and disadvantages, thereby providing means for testing and optimising pipelines for ongoing and future studies.

In this note, we provide some effective treatments of a general linear model with adding-up restrictions via algebraic operations of given vectors and matrices in the model, including analytic expressions of the well-known ordinary least-squares estimator (OLSE) and the best linear unbiased estimator (BLUE) of the unknown parameters in the model.

The process of water treatment by nanoparticles is one of the most considerable subjects in the cross-field of hydrodynamics, chemistry and mathematics. This paper is dedicated to the case of the flows that appear when squeezing and stretching a spongy with a mix of water with nanoparticles and contaminants. It is assumed that fluid is homogeneous at the starting moment, the parameters of the nanoparticles and contaminants are known, and there is a constant non-homogeneous magnetic field applied to the system. The flow starts moving when the walls of the channel shift to each other. Exact and numerical solutions of the system of ordinary differential equations are used to receive the results. The article gives an answer to the question about stability of the flow and proposes the technique to evaluate the essential characteristics of the system to achieve the treatment process efficiency. The main result is that the considered system shows excellent treatment properties during some part of squeezing stage. This effect does not appear without magnetic field.

Image segmentation is a new challenge prob- lem in medical application. The use of medical imaging has become an integral part of research, as it allows us to see inside the human body without surgical intervention. Many researcher have studied brain segmentation. One stage method is used to segment the brain tissues. In this paper, we proposed the multi-stage generative ad- versarial network to solve the problem of information loss in the one-stage. We utilize the coarse-to-fine to improve brain segmentation using multi-stage generative adversar- ial networks (GAN). In the first stage, our model generated a coarse outline for (i) background and (ii) brain tissues. Then, in the second stage, the model generated outline for (i) white matter (WM), (ii) gray matter (GM) and (iii) cerebrospinal fluid (CSF). A good result can be achieved by fusing the coarse outline and refine outline. We conclude that our model is more efficient and accu- rate in practice for both infant and adult brain segmenta- tion. Moreover, we observe that multi-stage model is faster than prior models. To be more specific, the main goal of multi-stage model is to see the performance of the model in a few shot learning case where a few labeled data are available. For medical image, this proposed model can work in a wide range of image segmentation where the convolution neural networks and one-stage methods have failed.

Distortion of financial statements is recognized as one of the most important issues in the field of accounting and auditing, which is also one of the most common issues today. In this regard, the present research was conducted, in which stock exchange information was used to investigate, predict, and model accounting distortions. For this purpose, financial performance, non-financial metrics, market-based metrics and commitment, or selection items were reviewed over a 6-year period. For collecting data of distorting companies, database of the Society of Certified Public Accountants in Iran was used and the information was analyzed using data mining methods (decision tree, neural networks, and Bayesian method). The results showed that analysis of financial statements҆ information has a high accuracy in determining and identifying the distorted financial statements. Using this information, it is possible to get better acquainted with the methods of document distortion and to take necessary measures in order to control and prevent administrative violations at national and international levels. Given frequent occurrence of these violations, artificial intelligence models can be used to identify these papers.

Violence is a complex and multifaceted problem requiring a holistic and individualized response. The Good Lives Model (GLM) suggests violence occurs when an individual experiences internal and external obstacles in the pursuit of universal human needs (termed primary goods). With a twin focus, GLM-consistent interventions aim to promote attainment of primary goods, whilst simultaneously reducing risk of reoffending. This is achieved by improving an individuals’ internal (i.e., skills and abilities) and external capacities (i.e., opportunities, environments, and resources). This paper proposes that collaborations between different agencies (e.g., psychological services, criminal justice systems, social services, education, community organizations and healthcare) can support the attainment of primary goods through the provision of specialized skills and resources. Recommendations for ensuring interagency collaborations are effective are outlined, including embedding a project lead, regular interagency meetings and training, establishing information sharing procedures, and defining the role each agency plays in client care.

We propose a chemical language processing model to predict polymers’ glass transition temperature (Tg) through a polymer language (SMILES, Simplified Molecular Input Line Entry System) embedding and recurrent neural network. This model only receives the SMILES strings of polymer’s repeat units as inputs and considers the SMILES strings as sequential data at the character level. Using this method, there is no need to calculate any additional molecular descriptors or fingerprints of polymers, and thereby, being very computationally efficient and simple. More importantly, it avoids the difficulties to generate molecular descriptors for repeat units containing polymerization point `*’. Results show that the trained model demonstrates reasonable prediction accuracy on unseen polymer’s Tg. Besides, this model is further applied for high-throughput screening on an unlabeled polymer database to identify high-temperature polymers that are desired for applications in extreme environments. Our work demonstrates that the SMILES strings of polymer’s repeat units can be used as an effective feature representation to develop a chemical language processing model for predictions of Tg. The framework of this model is general and can be used to construct structure-property relationships for other polymer’s properties.

The concept of this prototype is to create a cold air film layer, in indoor side of the window. The function of this extra cold air layer is to increase the amount of window resistance and decrease its U-Value. It provide air film layer by using the same concept of desert cooler or wind catcher. They both bring the hot air from outside then let it go throw straw miost with cold water. After hot air move through straw, it would become cold, thus increase the window resistance and decrease the haet gian, therefore, decrease the cooling load. So, this window model works by heating the air in air room by sun radiation, then after a while, air pressure will rise and its volume will increase then it will impulse to wet hay, and become cold. Then there is fins to guide air flow to its correct direction, thus inner side of window will become cold, not like other type of window which when temperature rise, the inner side of window become hot. So, this window prevents heat transition and creare regenereted trubouland air flim in the inner side of the window. Using calculation to find air film characteristics and WINDOW7.7 software to calculate heat gain through window, shows that it is expected from this prototype to reduce heat gain by 6.85 % for double glazed clear window.

In Model-Driven Development (MDD), the models, their generation, and imposing changes on them (model transformation) are used for the development of software. Models provide a framework to start from the imagination and abstraction to create and accomplish the final system. Models create a slow and steady transition from whatness to howness, i.e. from the natural path of the generation of software. For supporting this path, the Logic and Functionality of software must be changeable during its evolution. Here we provide a brief introduction to the concept of Model Driven Development.

Immunotherapy has gained great momentum with chimeric antigen receptor T cell (CAR-T) therapy, in which patient’s T lymphocytes are genetically manipulated to recognize tumor-specific antigens increasing tumor elimination efficiency. In the last years, CAR-T cell immunotherapy for hematological malignancies achieved a great response rate on patients and is a very promising therapy for several other malignancies. Each new CAR design requires a preclinical proof-of-concept experiment using immunodeficient mouse models. The absence of a functional immune system in these mice makes them simple and suitable to be mathematically modeled. In this work, we developed a three population mathematical model to describe tumor response to CAR-T cell immunotherapy in immunodeficient mouse models, encompassing interactions between a non-solid tumor and CAR-T cells (effector and long-term memory). We account for several phenomena, such as tumor-induced immunosuppression, memory pool formation, and conversion of memory into effector CAR-T cells in the presence of new tumor cells. Individual donor and tumor specificities were considered as uncertainties in the model parameters. Our model is able to reproduce several CAR-T cell immunotherapy scenarios, with different CAR receptors and tumor targets reported in the literature. We found that therapy effectiveness mostly depends on some specific parameters such as the differentiation of effector to memory CAR-T cells, CAR-T cytotoxic capacity, tumor growth rate, and tumor-induced immunosuppression. In summary, our model can contribute to reduce and optimize the number of in vivo experiments with in silico tests to select specific scenarios that could be tested in experimental research. Such in silico laboratory was made available in a Shiny R-based platform called CARTmath. It is an open-source, easy to run simulator, available at github.com/tmglncc/CARTmath or directly on the webpage cartmath.lncc.br, containing this manuscript results as examples and documentation. The developed model, together with the CARTmath platform, provides potential use for assessing different CAR-T cell immunotherapy protocols and associated efficacy, becoming an accessory towards in silico trials.

Karst aquifers show dissolution/precipitation processes of the minerals present in the carbonate rocks. The Ring of Cenotes (RC) extends along the edge of the Chicxulub crater, in the limestone platform of the Yucatan Peninsula (YP), where groundwater shows preferential flow paths toward the coast near Celestun and Dzilam Bravo towns. This study aimed to describe the regional hydrogeochemical evolution of groundwater of the RC, and its association with the dissolution/precipitation of the minerals present along its path to the ocean. To achieve this aim, we: a) characterized groundwater's hydrogeochemistry; b) determined the calcite, dolomite, and gypsum saturation indexes (reaction phases with the groundwater) in the study area; c) proposed a hydrogeochemical model developed through PHREEQC using an inverse modelling approach. The model predictions confirmed that there are two evolution pathways of the groundwater consistent with the preferential flow paths suggested in a previous regionalization of the RC. On the western path, where groundwater flows towards Celestun, an important marine intrusion influences the hydrogeochemical processes and represents a risk for the prevalence of freshwater. On the eastern path, where groundwater flows toward Dzilam Bravo, the hydrogeochemistry in the sinkholes correlates well with rainfall, suggesting a higher vulnerability during droughts than during rainy periods.

The airport ground handling services (AGHS) equipment supplier selection problem involves a safety guarantee on the part of the AGHS company that carries out the daily work. AGHS company can prevent aircraft damage and delays in airlines schedules, and ensure reliable and high-quality ground handling service. In our research, we developed an AGHS equipment supplier selection model based on the analytic hierarchy process and an AHP weighted fuzzy linear programming approach, and we solved the AGHS equipment supplier’s selection problem. The main objective of this article is to create an AHP and AHP-FLP decision model in order to help the AGHS company authorities select the best AGHS equipment supplier. The practical application in AGHS equipment supplier selection decisions can be interpreted as demonstrating that the proposed model provides knowledge and practical value for the AGHS industry.

This paper inquires on the options pricing modeling using Artificial Neural Networks to price Apple(AAPL) European Call Options. Our model is based on the premise that Artificial Neural Networks can be used as functional approximators and can be used as an alternative to the numerical methods to some extent, for a faster and an efficient solution. This paper provides a neural network solution for two financial models, the BlackScholes-Merton model, and the calibrated-Heston Stochastic Volatility Model, we evaluate our predictions using the existing numerical solutions for the same, the analytic solution for the Black-Scholes equation, COS-Model for Heston’s Stochastic Volatility Model and Standard Heston-Quasi analytic formula. The aim of this study is to find a viable time-efficient alternative to existing quantitative models for option pricing.

This paper presents an effective heart disease prediction model through detecting the anomalies, also known as outliers, in healthcare data using the unsupervised K-means clustering algorithm. Most existing approaches for detecting anomalies are based on constructing profiles of normal instances. However, such techniques require an adequate number of normal profiles to justify those models. Our proposed model first evaluates an \textit{optimal} value of K using Silhouette method. Next, it intends to locate anomalies that are far from a certain threshold distance with respect to their clusters. Finally, the five most popular classification techniques such as K-Nearest Neighbor (KNN), Random Forest (RF), Support Vector Machines (SVM), Naive Bayes (NB), and Logistic Regression (LR) are applied to build the resultant prediction model. The effectiveness of the proposed methodology is justified using a benchmark dataset of heart disease.

Climate justice as a commons is conceived as the intertemporal climate equity and equal- ity exchange amongst generations. Sustainability, intended as the interplay amongst the economy, the society, the environment, and the governance, is essential to forge the climate justice theoretical framework. On this base, the study attempts to model intertemporal choice amongst generations in these four domains, making use of an over- lapping generations (OLG) model. The proxies detected are GDP growth (economy), environmental quality (environment), and labor growth, and environmental investment (society) as assumptions. The governance dimension is captured by the di¤erence in wealth between young and old generations. The work aims at replying to the follow- ing research question: Which are the conditions for sustainable development such that climate justice holds? The intra-intergenerational exchange is de…ned in two periods, while the individual provides their preferred economic and environmental choice mix as consumption-saving. This study shows that sustainable growth is achievable only with increased young e¤ort and less leisure and consumption.

The diagnosis of a rolling bearing for monitoring its status is critical for maintaining industrial equipment using rolling bearings. The traditional method of diagnosing faults of the rolling bearing has low identification accuracy, which needs artificial feature extraction to enhance the accuracy. 1D-CNN method not only can diagnose bearing faults accurately but also overcome shortcomings of the traditional methods. Different from machine learning and other deep learning models, the 1D-CNN method does not need pre-processing one-dimensional data of rolling bearing’s vibration. Thus, it enhances the processing speed and improves the network structure to have a reasonable design for small sample data sets. This study proposes and tests a 1D-CNN method for diagnosing rolling bearings. By introducing the dropout operation, the method obtains high accuracy and improves the generalizing ability. The experimental results show 99.52% of the average accuracy under a single load and 98.26% under different loads.

Agile methods promise to achieve high productivity and provide high-quality software. Agile software development is the most important approach that has spread through the world of software development over the past decade. Software team productivity measurement is essential in agile teams to increase the performance of software development. Due to the prevalence of agile methodologies and increasing competition of software development companies, software team productivity has become one of the crucial challenges for agile software companies and teams. Awareness of the level of team productivity can help them to achieve better estimation results on the time and cost of the projects. However, to measure software productivity, there is no definitive solution or approach whether in traditional and agile software development teams that lead to the occurrence of many problems in achieving a reliable definition of software productivity. Hence, this study aims to propose a statistical model to assess the team’s productivity in agile teams. A survey was conducted with forty software companies and measured the impact of six factors of the team on productivity in these companies. The results show that team effectiveness factors including inter-team relationship, quality conformance by the team, team vision, team leader, and requirements handled by the team had a significant impact on the team’s productivity. Moreover, the results also state that inter-team relations affect the most on software teams’ productivity. Finally, the model fit test showed that 80% of productivity depends on team effectiveness factors.

This pandemic of COVID-19 is tedious to control. The only lockdown is the way to stop the spread of this infection. Conventional health care is facing a real challenge to operate. Primarily the challenge is to provide health care support for COVID-19 patients with limited resources and continue the health care services like earlier.Perhaps, this challenge is the same but magnitude is different from different geographical locations around the globe. In this article, we presented a Bayesian algorithm with the Code to predict cancer death due to COVID-19. This code is possible to run at different time points and different geographical locations around the world. This code will help us to get the best strategy and shift the treatment option for cancer treatment. The model would provide physicians with an objective tool for counseling and decision making at different hotspots and small areas to implement.

Since the outbreak of 2019 novel coronavirus (2019-nCoV) at the hardest-hit city of Wuhan, the fast-moving spread has killed over three hundred people and infected more than ten thousands in China¹. There are more than one hundred cases outside of China, affecting a dozen of countries globally². The genome sequence of 2019-nCoV has been reported and fast diagnostic kits, effective treatment as well as preventive vaccines are rapidly being developed³. Initial fast-growing confirmed cases triggered lock-down of Wuhan as well as nearby cities in Hubei Province. Mathematical models have been proposed by scientists around the world to project the numbers of infected cases in the coming days ^4,5. However, major factors such as transportation and cultural customs have not been weighed enough. Our model is not set out for precise prediction of the number of infected cases, rather, it is meant for a glance of the dynamics under a public epidemic emergency situation and of different contributing factors. We hope that our model and simulation would provide more insights and perspective information to public health authorities around the globe for better informed prevention and containment solution.

Water quality forecasting is increasingly significant for agricultural management and environmental protection. Enormous amounts of water quality data are increasingly being collected by advanced sensors, which leads to an interest in using data-driven models for predicting trends in water quality. However, the unpredictable background noises introduced during water quality monitoring seriously degrade the performance of those models. Meanwhile, artificial neural networks (ANN) with feed-forward architecture lack the capability of maintaining and utilizing the accumulated temporal information, which leads to biased predictions in processing time series data. Hence, we propose a water quality predictive model based on a combination of Kernal Principal Component Analysis (kPCA) and Recurrent Neural Network (RNN) to forecast the trend of dissolved oxygen. Water quality variables are reconstructed based on kPCA method, which aims to reduce the noise from the raw sensory data and preserve actionable information. With the RNN's recurrent connections, our model can make use of the previous information in predicting the trend in the future. Data collected from Burnett River, Australia was applied to evaluate our kPCA-RNN model. The kPCA-RNN model achieved R2 scores up to 0.908, 0.823 and 0.671 for predicting the concentration of dissolved oxygen in the upcoming 1, 2 and 3 hours, respectively. Compared to current data-driven methods like ANN and SVR, the predictive accuracy of the kPCA-RNN model was at least 8 %, 17 % and 21 % better than the comparative models in these 3 cases. The study demonstrates the effectiveness of the kPAC-RNN modeling technique in predicting water quality variables with noisy sensory data.

The work is devoted to the investigation of virus quasispecies evolution and diversification due to mutations, competition for host cells, and cross-reactive immune responses. The model consists of a nonlocal reaction-diffusion equation for the virus density depending on the genotype considered as a continuous variable and on time. This equation contains two integral terms corresponding to the nonlocal effects of virus interaction with host cells and with immune cells. In the model, a virus strain is represented by a localized solution concentrated around some given genotype. Emergence of new strains corresponds to a periodic wave propagating in the space of genotypes. The conditions of appearance of such waves and their dynamics are described.

Cardiac hypertrophy is an important and independent risk factor for the development of heart failure. To better understand the mechanisms and regulatory pathways involved in cardiac hypertrophy, there is a need for improved in vitro models. In this study, we investigated how hypertrophic stimulation affected human induced pluripotent stem cell (iPSC)-derived cardiomyocytes (CMs). The cells were stimulated with endothelin-1 (ET-1) for 8, 24, 48, 72, or 96h. Parameters including cell size, ANP-, proBNP-, and lactate concentration were analyzed. Moreover, transcriptional profiling using RNA-sequencing was performed to identify differentially expressed genes following ET-1 stimulation. The results show that the CMs increase in size by approximately 13% when exposed to ET-1 in parallel to increases in ANP and proBNP protein and mRNA levels. Furthermore, the lactate concentration in the media was significantly increased indicating that the CMs consume more glucose, a hallmark of cardiac hypertrophy. Using RNA-seq, a hypertrophic gene expression pattern was also observed in the stimulated CMs. Taken together, these results show that hiPSC-derived CMs stimulated with ET-1 display a hypertrophic response. The results from this study also provide new molecular insights about the underlying mechanisms of cardiac hypertrophy and may help accelerate the development of new drugs against this condition.

Terpenoids, including monoterpenoids (C₁₀), norisoprenoids (C₁₃) and sesquiterpenoids (C₁₅), constitute a large group of plant-derived naturally occurring secondary metabolites which chemical structure is highly diverse. A quantitative structure-activity relationship (QSAR) model to predict the terpenoids toxicity and to evaluate the influences of their chemical structure, was developed in this study, by assessing the toxicity of 27 terpenoid standards using Gram-negative bioluminescent Vibrio fischeri, in real time. Under the test conditions, at concentration of 1 µM, the terpenoids showed a toxicity level lower than five %, with exception of geraniol, citral, (S)-citronellal, geranic acid, (±)-α-terpinyl acetate and geranyl acetone. Moreover, the standards tested displayed a toxicity level higher than 30 % at concentration of 50 to 100 µM, with the exception of (+)-valencene, eucalyptol, (+)-borneol, guaiazulene, β-caryophellene and linalool oxide. Regarding the functional group, the terpenoids toxicity was observed in the following order: alcohol > aldehyde ~ ketone > ester > hydrocarbons. CODESSA software was employed to develop the QSAR models based on the correlation of terpenoids toxicity and a pool of descriptors related to each chemical structure. The QSAR models, based on t-test values, showed that terpenoids toxicity was mainly attributed to geometric (e.g., asphericity) and electronic (e.g., max partial charge for a C atom [Zefirov's PC]) descriptors. Statistically, the most significant overall correlation was the four-parameter equation with training and test coefficient correlation higher than 0.810 and 0.535, respectively, and square coefficient of cross-validation (Q²) higher than 0.689. According to the obtained data, the QSAR models are a suitable and a rapid tool to predict the terpenoids toxicity in a diversity of food products.

Traditionally, studies on freeze–thaw erosion have used the analytic hierarchy process (AHP) to calculate the weight of evaluation factors, however, this method cannot accurately depict the fuzziness and randomness of the problem. To overcome this disadvantage, the present study has proposed an improved AHP method based on the cloud model to evaluate the impact factors in freeze–thaw erosion. To establish an improved evaluation method for freeze–thaw erosion in Tibet, the following six factors were selected: annual temperature range, average annual precipitation, slope, aspect, vegetation coverage, and topographic relief. The traditional AHP and the cloud model were combined to determine the weight of the impact factors, and a consistency check was performed. The comprehensive evaluation index model was used to evaluate the intensity of freeze–thaw erosion in Tibet. The results show that freeze–thaw erosion is extensive, stretching over approximately 66.1% of Tibet. The problem is the most serious in Ngari Prefecture and Nagqu. However, mild erosion and moderate erosion, accounting for 37.1% and 25.0%, respectively, of the total freeze–thaw erosion are the most widely distributed. The evaluation results for the freeze–thaw erosion was confirmed to be consistent with the actual situation. In brief, this study provided a new approach to evaluate the conditions of freeze–thaw erosion quantitively in Tibet.

This paper investigates an inverse problem of option pricing in the extended Black--Scholes model. We identify the model coefficients from the measured data and attempt to find arbitrage opportunities in financial markets using a Bayesian inference approach. The posterior probability density function of the parameters is computed from the measured data. The statistics of the unknown parameters are estimated by Markov Chain Monte Carlo (MCMC), which explores the posterior state space. The efficient sampling strategy of MCMC enables us to solve inverse problems by the Bayesian inference technique. Our numerical results indicate that the Bayesian inference approach can simultaneously estimate the unknown drift and volatility coefficients from the measured data.

Controlling and managing risk is always a critical point for the managers, investors and even consultants. Having clear and better percepts about future can reduce the risk of fail. In addition it is important for a risk analyzer to consider the stakeholders views and senses about the risk. This research aimed at framing a comprehensive risk management model which builds up on stakeholder requirements and forecasting techniques. This conceptual model attempts to use the stakeholder’s points of view to write prefer and probable scenarios to neglect critical risks and increase the chance of success in long-term plannings. Using futures studies methods prepare decision makers for the possible upcoming events and donate them a powerful tool to handle the uncertainties cover the future.

Scour depth around bridge piers plays a vital role in the safety and stability of the bridges. Existing methods to predict scour depth are mainly based on regression models or black box models in which the first one lacks enough accuracy while the later one does not provide a clear mathematical expression to easily employ it for other situations or cases. Therefore, this paper aims to develop new equations using particle swarm optimization as a metaheuristic approach to predict scour depth around bridge piers. To improve the efficiency of the proposed model, individual equations are derived for laboratory and field data. Moreover, sensitivity analysis is conducted to achieve the most effective parameters in the estimation of scour depth for both experimental and filed data sets. Comparing the results of the proposed model with those of existing regression-based equations reveal the superiority of the proposed method in terms of accuracy and uncertainty. Moreover, the ratio of pier width to flow depth and ratio of d50 (mean particle diameter) to flow depth for the laboratory and field data were recognized as the most effective parameters, respectively. The derived equations can be used as a suitable proxy to estimate scour depth in both experimental and prototype scales.

This research was conducted on the Maribaya Timur school community in Lembang Subdistrict, West Bandung Regency, Indonesia, which is an active community in the area that is threatened by the potential for earthquake disasters due to the active Lembang fault. Disaster risk reduction efforts are pursued through increasing school-based preparedness that involves members of the school community, surrounding communities and various institutions that are associated with reducing the risk of school-based earthquake. Increasing preparedness against earthquakes focuses more on aspects of capacity building of school communities in reducing disaster risk, while aspects of vulnerability and threats have not been the focus of disaster risk reduction. The steps taken refer to the element of preparedness by aligning with the conditions, needs and potential that exist in the school community. Theoretically, if the school community has preparedness to face an earthquake disaster, the risk of earthquake disaster in the school community will be reduced so that it can minimize losses, victims and suffering that will be caused by the earthquake disaster.

Regeneration is a fundamental perpetual tissue process much attributed to functions of adult stem cell. In last decades delivery of adult stem cells to restore tissue structure is widely adopted in regenerative medicine. However, delivery of stem cells can not complete the task of human body regeneration effectively as far as cells need a basis to form a tissue. Certain improvement can be achieved when cells are organized to cells sheets (CS) – primitive tissue-engineered structures comprising of viable cells and ECM proteins. This led us to an assumption that after damage an intermediate feeder is structure formed as a ground for future tissue resulting in gradients of stimuli required to attract other cells (vascular, neural, epithelial, parenchyma etc.) in an organized manner. This feeder acquires increasing heterogeneity and becomes a guiding feeder that provides basic level of organization to increase complexity of structure up to normal tissue. We also highlight potential role of adult mesenchymal stromal cells (MSC) as the main source of guiding feeder and support it by our recent knowledge of their biological function. Finally, we assume that CS known to be a feasible delivery tool in therapeutics resemble guiding feeder and show our own data indicating properties of MSC-based CS. Furthermore, this concept shifts our understanding of therapeutic mechanism underlying a widely known delivery method for regenerative medicine.

The Surface Water and Ocean Topography (SWOT) mission, to be launched in 2021, will provide water surface elevations, slopes, and river width measurements for rivers wider than 100 m. In this study, synthetic SWOT data are assimilated in a regional hydrometeorological model in order to improve the dynamics of continental waters over the Garonne catchment, one of the major French catchments. The aim of this paper is to demonstrate that the sequential assimilation of SWOT-like river depths allows the correction of river bed roughness coefficients and thus simulated river depths. An extended Kalman Filter is implemented and the data assimilation strategy was applied to four experiments of gradually increasing complexity regarding observation and model error over the 1995-2000 period. With respect to a “true” river state, assimilating river depths allows the proper retrieval of constant and spatially distributed roughness coefficients with a root mean square error of 1 m1/3 s-1, and the estimation of associated river depths. It was also shown that river depth differences can be assimilated, resulting in a higher root mean square error for roughness coefficients with respect to the true river state. The last study shows how one can take into account more realistic sources of SWOT error measurements, in particular the importance of the estimation of the tropospheric water content in the process.

The Schelling model of segregation allows for a general description of residential movements in an environment modeled by a lattice. The key factor is that occupants change positions until they are surrounded by a designated minimum number of similarly labeled residents. An analogy to the Ising model has been made in previous research, primarily due the assumption of state changes being dependent upon the adjacent cell positions. This allows for concepts produced in statistical mechanics to be applied to the Schelling model. Here is presented a methodology to estimate the entropy of the model for different states of the simulation. A Monte Carlo estimate is obtained for the set of macrostates defined as the different aggregate homogeneity satisfaction values across all residents, which allows for the entropy value to be produced for each state. This produces a trace of the estimated entropy value for the states of the lattice configurations to be displayed with each iteration. The results show that the initial random placements of residents have larger entropy values than the final states of the simulation when the overall homogeneity of the residential locality is increased.

The use of salt rock for underground radioactive waste disposal facilities requires a comprehensive analysis of creep-damage process in salt rock. A computer-controlled creep setup is employed to carry out a creep test of salt rock lasted as long as 359 days under a constant uniaxial stress. The AE space-time evolution and energy releasing characteristics during creep test are studied in the meantime. A new creep-damage model is proposed on the basis of fractional derivative by combining the AE statistical regularity. It indicates that the AE data in non-decay creep process of salt rock can be divided into three stages. Furthermore, the parameters of new creep-damage model are determined by Quasi-Newton method. The fitting analysis suggests that the creep-damage model based on fractional derivative in this paper provides a precise description of full creep regions in salt rock.

Background. A rat model of chronic tympanic membrane perforation was developed to be used in the search of new materials for the sealing of these perforations. Methods. A longitudinal study was carried out in rats subjected to incisional myringotomy followed by the application of mitomycin C alone or with dexamethasone. Rats were checked at days 3, 7, 10, 14 and weekly thereafter until perforation closure, for up to 6 months. Results. The addition of dexamethasone is a key component in order to obtain a chronic opening. Myringotomies treated with saline had a mean healing time of 8.5 days. At 8 weeks, 70.5% of these remained perforated and at 6 months this number fell to 21.4%. Conclusion. This technique is able to maintain more than 70% of tympanic membrane perforations patent for at least 8 weeks. This rat model is adequate for its use in preclinical or translational research.

Sustainability addresses environmental and social issues affecting this and future generations. When family businesses perceive that the community is disrupted, recognize an environmental problem and respond by implementing new environmental policies or regulations, the family business’s socio-emotional values press to transition to a more sustainable production system, such as the ‘Circular Economy.’ Drawing on the Dubin (1978) methodology—a paradigm for building models through deduction—we design a sustainable model, which shows family businesses’ responses to changes in the environment. It explains the reasons why family firms transition to the Circular Economy, based on the theory of Socio-Emotional Wealth (SEW). We check the model through the case study of the food retail leader in the Spanish market—Mercadona—which applies policies about energy, resources and waste to become a Circular Economy business model. Because of the strong family character of Mercadona, this case can be useful for the decision-making of other family businesses.

Based on the fractional order of nonlinear system for love model with a periodic function as an external force, analyzed the characteristics of the chaotic dynamic in this study. The relationship between the chaotic dynamic of the fractional-love model with the external force and the fractional-order system was analyzed when the parameters are fixed. Further, we also studied the relationship between the chaotic systemic dynamic and the parameters when the fractional-order system is fixed. The results show that when the parameters are fixed, the fractional-order system exhibited segmented chaotic states for the different fractional orders of the system. When fixed the fractional-order system, the system exhibited the periodic and chaotic states as parameter changes.

Dynamic measurement error correction is an effective method to improve the sensor precision. Dynamic measurement error prediction is an important part of error correction, support vector machine (SVM) is often used to predicting the dynamic measurement error of sensors. Traditionally, the parameters of SVM were always set by manual, which can not ensure the model’s performance. In this paper, a method of SVM based on an improved particle swarm optimization (NAPSO) is proposed to predict the dynamic measurement error of sensors. Natural selection and Simulated annealing are added in PSO to raise the ability to avoid local optimum. To verify the performance of NAPSO-SVM, three types of algorithms are selected to optimize the SVM’s parameters, they are the particle swarm optimization algorithm (PSO), the improved PSO optimization algorithm (NAPSO), and the glowworm swarm optimization (GSO). The dynamic measurement error data of two sensors are applied as the test data. The root mean squared error and mean absoluter percentage error are employed to evaluate the prediction models’ performances. The experiment results show that the NAPSO-SVM has a better prediction precision and a less prediction errors among the three algorithms, and it is an effective method in predicting dynamic measurement errors of sensors.

The paper presents the findings of research on companies in the early stage of development based on the concept of ensuring their sustainability. The starting point is attributes shaping a sustainable model of start-ups. A solution has been proposed and examined that identifies the factors dynamising a business model, which give the company the ability to make fast changes in the configuration of the business model, and that applies stabilizing factors based on the concept of the Triple Bottom Line. The research problem is a response to the phenomena of the bankruptcy of many start-ups that cannot find a way to survive in the long term. The business models of companies in the early stage of development should be designed taking into account the possibility of their dynamic and iterative changes when the earlier assumptions do not work when implemented in practice. A cognitive gap has been observed as the relevant literature does not propose a systemic solution to the problem of instability of the startups viewed from the perspective of the business model concept. The research problem, therefore, is to identify potential new methods to ensure the sustainability of young companies trying to combine aspects dynamising changes in business models, taking into account economic, environmental and social aspects. The findings of research on companies in the early stage of development show that when the methods of increasing the flexibility of the business model are applied and attention is drawn to environmental and social activities and a strong emphasis is put on financial performance, reflecting the expectations of shareholders, the concept of ensuring the sustainability of young companies from the point of view of sustainable business model attributes emerges.

In this paper, an original approach allowing the determination of the iron losses in the electromagnetic devices is presented. This new approach exploits the Loss Surface (LS) hysteresis model and the magnetic flux density waveforms resulting from a generalized nonlinear adaptive magnetic equivalent circuit (MEC) using a mesh-based formulation in two-dimensional (2-D) or quasi three-dimensional (3-D). The model coupling has been applied to a 18-slots/16-poles radial-flux interior permanent-magnet (PM) synchronous machine (PMSM) dedicated to automotive applications, mainly for electric/hybrid/fuel cell vehicles (EVs/HEVs/FCVs). The obtained results have been compared with those made retrospectively in the 2-D transient finite-element (FE) Flux. The influence of the MEC discretization on the iron loss calculation and the electromagnetic performances has been analyzed. The computation time is divided by 3/2 with an error less than 7 %.

As renewed interest in human space-exploration intensifies, a coherent and modernized strategy for mission-design and planning has become increasingly crucial. Biotechnology has emerged as a promising approach to increase mission resilience, flexibility, and efficiency by virtue of its ability to efficiently utilize in situ resources and reclaim resources from waste streams. Since its infancy during the Apollo years, biotechnology, and specifically biomanufacturing, have witnessed significant expansions of scope and scale. Here we outline four primary mission classes, on Luna and Mars, that drive a staged and accretive biomanufacturing strategy. Each class requires a unique approach to integrate biomanufacturing into the existing mission architecture and so faces unique challenges in technology development.These challenges stem directly from the resources available in a given mission class – the degree to which feedstocks are derived from cargo and in situ resources – and the degree to which loop-closure is necessary. We see that as mission duration and distance from Earth increase, the benefits of specialized sustainable biomanufacturing processes increases. Here we present a strategic approach, guided by technoeconomics, to development, testing, and deployment of these technologies serves to nucleate the larger effort of supporting a sustained human presence in space. The processes needed for each scenario spans the technical breadth of synthetic biology to design engineering, from sophisticated genetic tailoring of chassis-organisms to building scalable, automated, easily operable bioreactors and processing systems. As space-related technology development often does, these advancements are likely to have profound implications for the creation of a stable, resilient bioeconomy on Earth.

Genome sequencing is enabling precision medicine—tailoring treatment to the unique constellation of variants in an individual’s genome. The impact of recurrent pathogenic variants is often understood, leaving a long tail of rare genetic variants that are uncharacterized. The problem of uncharacterized rare variation is especially acute when it occurs in genes of known clinical importance with functionally consequent frequent variants and associated mechanisms. Variants of unknown significance (VUS) in these genes are discovered at a rate that outpaces current ability to classify them using databases of previous cases, experimental evaluation, and computational predictors. Clinicians are thus left without guidance about the significance of variants that may have actionable consequences. Computational prediction of the impact of rare genetic variation is increasingly becoming an important capability. In this paper, we review the technical and ethical challenges of interpreting the function of rare variants in two settings: inborn errors of metabolism in newborns, and pharmacogenomics. We propose a framework for a genomic learning healthcare system with an initial focus on early-onset treatable disease in newborns and actionable pharmacogenomics. We argue that (1) a genomic learning healthcare system must allow for continuous collection and assessment of rare variants, (2) emerging machine learning methods will enable algorithms to predict the clinical impact of rare variants on protein function, and (3) ethical considerations must inform the construction and deployment of all rare-variation triage strategies, particularly with respect to health disparities arising from unbalanced ancestry representation.

The concept of intelligence has many applications, such as coatings and cyber security. Smart coatings have the ability to sense and/or response to external stimuli and generally interact with their environment. Self-healing coatings represent a significant advance in improving material durability and performance using microcapsules and nanocontainers loaded with self-healing agents, catalysts, corrosion inhibitors, and water-repellents. These smart coatings can repair damage on their own and restore mechanical properties without external intervention and are inspired by biological systems. Properties that are affected by either momentary or continuous external stimuli in smart coatings can be related to corrosion, fouling, fungal, self-healing, pie-zoelectric, microbiological, etc. These coating properties can be obtained via combinations of either organic or inorganic polymer phases, additives and pigments. In this article, a review on the advancements of micro/nanocapsules for self-healing coatings is reported from the aspect of ex-trinsic self-healing ability. The concept of extrinsic self-healing coatings is based on the use of capsules or multichannel vascular systems loaded with healing agents/inhibitors. The outcome is that self-healing coatings exhibit improved properties compared to traditional coatings. It is mentioned that self-healing anticorrosive coating not only has enhanced passive barrier function, but also realizes active defense. As a result, there is a significant improvement of the service life and overall performance of the coating. Future research should be devoted to refining self-healing mechanisms and developing cost-effective solutions for a wide range of industrial applications.

This study aims to develop low-cost, eco-friendly and circular economy-compliant composite materials by creating three types of magnetorheological suspensions (MRSs) utilizing lard, carbonyl iron (CI) microparticles, and varying quantities of gelatin particles (GP). These MRSs serve as dielectric materials in cylindrical cells used to fabricate electric capacitors. The equivalent electrical capacitance (C) of these capacitors is measured under different magnetic flux densities (B≤160 mT) superimposed on a medium-frequency electric field (f = 1 kHz) over a period of 120 seconds. The results indicate that at high values of B, by increasing the GP content to 20 vol.% decreases the capacitance C up to about one order of magnitude compared to MRS without GP. From the measured data, the average values of capacitance Cm are derived, enabling the calculation of relative dielectric permittivities (ϵr′) and the dynamic viscosities (η) of the MRSs. It is demonstrated that ϵr′ and η can be adjusted by modifying the MRS composition and fine-tuned through the magnetic flux density B. A theoretical model based on the theory of dipolar approximations is used to show that ϵr′, η and the magnetodielectric effect can be coarsely adjusted through the composition of MRSs and finely adjusted through the values B of the magnetic flux density. The ability to fine-tune these properties highlights the versatility of these materials, making them suitable for applications in various industries, including electronics, automotive and aerospace.

The medicinal herb Centaurium erythraea is well-known for its natural ability to treat digestive issues like dyspepsia and liver insufficiency by promoting the secretions of the liver and esophagus. This study aimed to analyze the effectiveness of Centaurium erythraea extracts in treating diabetes. Decoction and Soxhlet procedures were used to examine the extracts, with water serving as the solvent. The extracts were tested for antioxidant capacity, antimicrobial effects, and inhibitory effects on alpha-amylase activity. In comparison to the aqueous extract, the decocted extract had higher levels of polyphenols, total tannins, and anthocyanins, as well as more effective antioxidant capacities. Due to its reduction of alpha-amylase activity and enteric absorption of glucose, Centaurium erythraea has also shown considerable anti-hyperglycemic action.

Evapotranspiration is a critical component of the hydrological cycle, and it has a decisive impact on the ecosystem balance in arid and semi-arid regions. The Yinchuan Plain, located in the Gobi of Northwest China, has a strong surface ET. In order to assess the ET pattern in this region, we obtained the ETa of Yinchuan Plain between 1987 and 2020 using the GEE platform. We employed the Surface Energy Balance Model (SEBAL), specifically geeSEBAL, to analyze the change pattern of ET over different seasons, years, and land types. The results show that: ① In spring, the daily ETa of the Yinchuan Plain is highest in the central lake wetland area, with a maximum value of 4.32 mm day-1. In summer, it is concentrated around the croplands and water bodies, with a maximum value of 6.90 mm day-1; In Autumn and winter, it is mainly concentrated around the water bodies and impervious areas, with maximum values of 3.93 and 1.56 mm day-1, respectively. ② From 1987 to 2020, the ET of the Yinchuan Plain showed an obvious upward and downward trend in some areas with significant land use changes, but the overall ET of the region remained relatively stable without dramatic fluctuations. ③ The evapotranspiration of different underlying land use types in the Yinchuan Plain region is as follows: water body>cultivated land>impervious>grassland>bare land. Our results showed that geeSEBAL is highly applicable in the Yinchuan Plain area. It allows for the accurate and detailed inversion of ET in the study area, which facilitates the study of regional hydrological cycle and water governance.

This study evaluates the efficiency of biodegradable polyurethane foams as thermal insulation materials compared to conventional materials like expanded polystyrene (EPS). Key parameters analyzed include thermal conductivity, energy savings, CO2 emission reductions, and investment payback periods. Biodegradable polyurethane foams demonstrated a thermal conductivity of 0.022 W/(m·K), significantly lower than EPS's 0.035 W/(m·K), leading to higher energy savings. Calculations reveal an annual energy saving of 443,820 kWh for polyurethane foams, compared to 441,330 kWh for EPS. Financially, polyurethane foams save approximately €53,258.4 annually, while EPS saves €52,959.6. In terms of environmental impact, polyurethane foams reduce CO2 emissions by 88,764 kg annually, versus 88,266 kg for EPS. Despite the higher initial cost of polyurethane foams (€50/m² compared to €30/m² for EPS), the payback period is remarkably short at 0.094 years (1.13 months) compared to EPS's 0.057 years (0.68 months). These findings highlight biodegradable polyurethane foams as a superior option for thermal insulation, offering significant energy, financial, and environmental benefits.

Mulberry leaves, when infected by pathogens, can suffer significant yield loss or even death if early disease detection and timely spraying are not performed. To enhance the detection performance of mulberry leaf diseases in natural environments and to precisely locate early small lesions, we propose a high-precision, high-efficiency disease detection algorithm named YOLOv8-RFMD. Based on improvements to You Only Look Once version 8 (YOLOv8), we first proposed the Multi Dimension Feature Attention (MDFA) module, which integrates important features at the pixel-level, spatial and channel dimensions. Building on this, we designed the RFMD Module, which consists of the Conv-BatchNomalization-SiLU (CBS) module, Receptive-Field Coordinated Attention (RFCA) Conv, and MDFA, replacing the Bottleneck in the model’s Residual block. We then employed Adown down sampling structure to reduce the model size and computational complexity. Finally, to improve the detection precision of small lesion features, we replaced the complete intersection over union (CIOU) loss function with the Normalized Wasserstein Distance (NWD) loss function. Results show that the YOLOv8-RFMD model achieves an mAP50 of 94.3% and an mAP50:95 of 67.8% on experimental data, representing increases of 2.9% and 4.3% respectively compared to the original model. The model size is reduced by 0.53 MB to just 5.45 MB, and the number of floating-point operations is reduced by 0.3 G to only 7.8 G. The improved model meets the deployment requirements of mobile embedded devices and can provide a theoretical reference for the automated spraying operations of mulberry leaves.

The double burden of malnutrition (DBM) is escalating in low- and middle-income countries (LMICs), including in Rwanda, most notably in urbanizing areas. The 2019-2020 Rwanda Demographic Health Survey (DHS) revealed 33% of children under 5 are stunted while 42% of women in urban areas are overweight or obese. This coexistence has contributed to a surge in non-communicable diseases (NCDs), particularly in secondary cities. Using the World Health Organization’s (WHOs) “double-duty action” (DDA) concept, this study aims to identify and evaluate interventions with double-duty potential in Rwanda, focusing on the districts of Rusizi and Rubavu. Guided by Hawkes et al.’s conceptual framework for identifying DDA entry points, a desk review of national policies pinpointed 4 programs with DDA potential: early childhood development (ECD) centers, the school feeding program, farmer field schools (FFS), and the provision of nutrition-sensitive direct support. In-person interviews with key stakeholders assessed their implementation and a Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis informed context-specific recommendations for their improvement. Recommendations include targeting beliefs surrounding nutrition, improving trainings for community educators, enhancing parent – particularly father – involvement, and close follow-up. These findings offer actionable strategies for governments and nutrition stakeholders and may benefit similar interventions in other rapidly urbanizing LMICs.

Most neurodegenerative diseases, including Alzheimer's disease, ischemic stroke, subarachnoid hemorrhage, and intracerebral hemorrhage are associated with inflammation. Tumor necrosis factor (TNF) is a pleiotropic pro-inflammatory cytokine that regulates cerebral infarction in stroke pathology, and its action is influenced by the bioavailability of its membrane-bound receptors, TNFR1 and TNFR2, and microglial activation. During the initial onset of these diseases, the soluble variant of the cytokine presents with prolonged and excessive activation of TNFR1, resulting in cell death and long-term neurological impairments. Therapeutic interventions for neurodegenerative diseases have targeted TNF to limit the onset of neuroinflammation. First-generation therapeutics have been demonstrated to inhibit membrane-bound TNF to TNFR2 receptor binding, resulting in severe side effects such as infections and cancer. As such, second-generation drugs, including XPro1595, have been developed to selectively inhibit soluble TNF and impede the effects of TNFR1 while still allowing for TNFR2 activation. Early results in murine TBI models demonstrate reduced glial reactivity by 50%, reduced dendritic degeneration by 30%, increased plasticity by 15%, and improved functional outcomes by 20% post-TBI. This scoping review of TNF receptors in various neurodegenerative diseases seeks to evaluate current and future therapeutic strategies as well as highlight potential strategies to eliminate confounding variables present in the current literature.

Introduction: Arterial hypertension and obesity are two major risk factors for cardiovascular morbidity and mortality closely linked. The objective of this study was to describe the epidemiological and clinical characteristics of hypertensive patients according to their level of obesity as proposed by the classification of the World Health Organization (WHO). Patients and Methods: Retrospective study (January 2022-April 2023) involving 230 hypertensive and obese adult patients whose mean age was 52.5 ± 12.05 years [Extremes: 24-85 years] followed on an outpatient basis and classified into 3 Classes according to their body mass index (BMI). Epidemiological, clinical parameters and cardiovascular risk stratification were analyzed. The relevance of the risk stratification method was analyzed by area under the curve (AUC). Statistical analysis was performed using SPPS version 26 software (SPSS Inc., Chicago, IL, USA). Results: Classes 1 (n=170; 73.9%), 2 (n=43; 18.7%) and 3 (n=17; 7.4%) were identified. There were significantly more women than men in Class 3 (9.7% versus 2.7%; p=0.001). The risk of being diabetic (OR=0.135;95%CI=0.034–0.541;p=0.005), of having angina pain (OR=0.084;95%CI=0.009–0.790;p=0.030) or developing chronic renal failure (OR=2.416;95%CI=1.471–85.268;p=0.020) was significantly higher in Class 3 than in Class 1. According to cardiovascular risk stratification, 152 patients (66.1%) were at high and very high risk. The level of cardiovascular risk of our patients was not related to the degree of obesity based on body mass index (p=0.32). The risk stratification method was relevant (AUC˃0.70;p˂0.001). Conclusion: Morbid obesity exposes hypertensives more to cardiovascular, renal and metabolic complications.

This study addresses the gap in research on the application of thermochromic polymers (TPs) in food packaging and their potential for real-time temperature monitoring, aiding in the assessment of food quality and shelf-life. TPs exhibit a visible color change in response to temperature varia-tions. A comprehensive systematic review (SR) across multiple engineering peer-review databases using predefined terms was conducted. Additionally, international patent databases were inves-tigated using the same predefined terms. Independent experts reviewed the methodology to identify and address potential biases. A total of 288 eligible articles and 922 patents were identified. After a duplicate selection and extraction process according to the inclusion criteria, four related full-text publications were selected from the initial 288 articles, and five relevant patents were selected from the 922 patents. The qualitative review suggests that TPs hold significant promise as food packaging materials due to their unique physical properties. The study concludes that TPs offer valuable properties for the food packaging industry, meriting further investigation to exploit their benefits fully.

This study explores the relationship between positive and negative habits and their rewards over time. Using a bibliographic and documentary approach, we analyze how the temporality of rewards influences the formation and maintenance of habits. We developed the "habit chart", which shows that positive habits, such as reading, healthy eating, physical exercise, meditation and personal relationships, offer incremental and lasting rewards. In contrast, negative habits such as sugar consumption, excessive use of social media and a sedentary lifestyle provide immediate gratification followed by a sharp decline. The analysis was based on studies such as those by Warburton et al. (2006), Lally et al. (2010), and Hu (2003) for positive habits, and Malik et al. (2010) and Kross et al. (2013) for negative habits. We conclude that practical strategies, such as financial incentives and creating enabling environments, can make long-term rewards more tangible, promoting the adoption of healthy behaviors. This study reinforces the importance of the temporality of rewards in habit formation and suggests evidence-based interventions to improve quality of life and general well-being.

Related studies have shown that mitophagy plays a role in the progression of Atherosclerosis (AS). Utilizing GSE43292, GSE100927, and GSE159677 datasets, WGCNA and differential expression analysis identified mitophagy-related module genes and DEGs1, while single-cell analysis screened DEGs2. MR analysis revealed causal AS-linked genes in DEGs1. LASSO and expression validation identified biomarkers, evaluated via ROC curves. Further analyses included GSEA, immune infiltration, ANN, regulatory networks, and drug prediction. Four candidate genes were identified through WGCNA, differential expression analysis and MR analysis. The LASSO analysis identified CD36, IL1RN, and MME as biomarkers, while the MR analysis showed that IL1RN and MME are protective factors and CD36 is a risk factor in AS. The ROC curves disclosed that the ANN model had greater diagnostic power for AS than any other biomarker. The GSEA revealed that the biomarkers were enriched in lysosomes and hematopoietic cell lineage and were significantly positively correlated with monocytes. The regulatory network construct showed that small nucleolar RNA host gene 16 regulates MME via the micro RNAs. This study identified CD36, IL1RN, and MME as biomarkers of mitophagy in AS and indicated that they could be applied in the diagnosis and treatment of this condition.

The growing interest in environmental sustainability issues and, at the same time, the advantages offered by blockchain technology have strong connections between these two topics. This study explores the application of blockchain technology across various environmental domains, such as air quality, climate change impacts, and resource management. The research utilised a dual approach, combining a bibliometric analysis with VOSviewer and a topic analysis using BERT models to assess the discourse within both scientific literature extracted from Scopus and practical blockchain projects obtained from GitHub. The findings reveal that food security, energy, and sustainable agriculture are predominant topics in academic discussions, with a noticeable increase in focus from 2017 onwards. Practical projects are focused on transparent tracking and decentralised management. The overlap between academic and practical spheres is evident in the shared focus on energy and environmental management, demonstrating blockchain’s growing role in addressing global environmental challenges. The study underscores the importance of integrating theoretical research with practical implementations to harness blockchain’s full potential in promoting sustainable environmental practices.

To enhance the uplift capacity and facilitate the installation of multi-row perfobond connectors at shallow burial depths, this study puts forward a novel notched T-perfobond connector. The design incorporates an integrated flange at the bottom of the connector and a notch at the edge of the hole. Through pull-out model tests on four notched T-perfobond connectors, this research investigates their failure mechanisms and pull-out capacities. Utilizing the explicit dynamics method in Abaqus, a finite element model of the pull-out resistance test for the notched T-perfobond connector is established and verified against experimental data. Furthermore, a detailed parametric analysis involving 54 models is conducted, examining crucial parameters such as rib dimensions, hole geometry, flange size, notch width, bar diameter, and material properties. Based on the combined experimental and numerical results, the paper assesses the suitability of current formulas for calculating the pull-out capacity of perfobond connectors and proposes a refined calculation method specifically for notched T-perfobond connectors. All the findings in this paper can serve as a reference in the design and construction of composite structures.

This project aims to simulate and analyze population dynamics in evolutionary systems, using stochastic differential equations and probabilistic rules for crossbreeding and benefits. The study employs advanced computational techniques to model the evolution of populations over an extended period. Specifically, we explore a scenario with ten distinct groups, each starting with an equal population and evolving under defined genetic and cooperative behaviors. The simulation spans 1000 years, incorporating individual lifespans, stochastic updates, and probabilistic interactions. To ensure robustness and reliability, the study includes detailed statistical analysis, encompassing descriptive statistics, confidence intervals, and ANOVA tests. We further visualize the temporal behavior of populations and subpopulations, providing insights into the evolutionary dynamics and stability of the system. The results demonstrate the effectiveness of balanced cooperation and genetic diversity in maintaining stable population distributions. The project showcases the integration of mathematical modeling, statistical analysis, and computational simulations, contributing valuable insights into evolutionary biology and complex systems. It highlights the importance of interdisciplinary approaches in understanding and predicting population behaviors, with implications for both theoretical research and practical applications in fields such as ecology, genetics, probability and social sciences.

Vertical wind speed extrapolation is a commonly used method that involves estimating wind speeds at various heights based on measurements taken at a specific height. In this study, we compared the short-and long-term extrapolation methods of wind parameters using data collected on site by anemometers at two different heights. We assessed the accuracy of these methods by calculating the statistical indicators for performance evaluation, and then determined the most appropriate model for the two types of vertical extrapolation. Additionally, we evaluated energy potential characteristics, as well as the statistical analysis of wind speed distribution and wind power density based on the Weibull and Rayleigh distribution. Our results indicated that the power law is the most suitable model for the short-term method, while the modified Mikhail model is the most appropriate for long-term extrapolation. We also found that the wind potential of this area is favorable for wind turbine integration.

This study explores the application of the Laban/Bartenieff Movement System (® LBMS), particularly its Effort component, to analyze the biomechanical dynamics of the golf swing. Focusing on the initiation of the downswing, this paper investigates how the instantaneous screw axis (ISA) with rotational and linear motions contributes to subsequent movements, such as the hip turn. We propose that the physical forces generated by the golf club, characterized by mass moment inertia, extend beyond the body's boundaries, such as through the hand grip. A skilled golfer’s ability to 'allow' these forces to influence body dynamics without opposing them is analyzed through the lens of LBMS's Free Flow Effort, suggesting an indulgent and expansive use of movement energy.

This study analyzes the relevance of governance structure for efficiency in revenue collection by Brazilian Water Utilities (WUs). Data was collected from the National Information System on Sanitation (SNIS) for 127 Brazilian WUs, covering a balanced longitudinal panel from 2018 to 2022. Governance structures evaluated included Ownership (public or private) and Corporatization (publicly traded or not). In scientific databases, we did not find studies on the efficiency of specific WUs in collecting customer bills for services rendered and its relationships with this type of governance; thus, this would be the main innovative contribution of the study to the previous literature. In the first stage, the study utilized the Dynamic Slack Based Model (DSBM) to assess revenue collection efficiency. In the second stage, an econometric model with Generalized Estimating Equations (GEE) to explain the efficiency. The findings revealed a global average inefficiency of 50.47% in revenue collection. Corporatization was linked to higher collection efficiencies, while ownership type was significant, but with lower collection efficiency. Factors such as tariff accessibility, urbanization, and the COVID-19 pandemic also influenced efficiency. The study suggests regulatory bodies should consider these insights to implement policies that prevent inefficiencies from affecting the tariff system for services.

There is a growing consensus that the modern food system lies at the centre of the grand challenges facing humanity and requires urgent and profound changes in the way that food is produced, processed, distributed and consumed. This review analyses sustainability transitions within agrifood systems, focusing on the role of firms and industries as defined by the Sustainable Transitions Research Network (STRN). This paper conducts the first systematic literature review using bibliometric analysis to assess the current state of research on this theme. The findings reveal a significant increase in publications related to firms and industries within agrifood sustainability transitions. Furthermore, the current research is geographically concentrated in the European Union. The review identifies four key themes in the literature based on co-occurrence of keywords. These are agriculture, innovation, governance, food systems and agroecology. The review identifies increasing awareness of the role of farmers in driving sustainability transitions at the farm level. Furthermore, there is an increasing awareness of the interrelated characteristics of the agrifood system which acknowledges the need for sustainable innovations to occur at multiple stages of the agrifood system. It also shows that there is growing evidence that innovations can occur through disruptive as well as incremental innovation, and highlights the importance of governance influencing transitions. The literature raises questions about Alternative Food Networks as sustainable innovations, their potential for significant change in the established food system and the validity of their claims to food equity and environmental sustainability. A key theme emerging from the literature is an ecological perspective that identifies the complex biological processes and ecosystems as an integral part of agricultural production. These findings provide a greater understanding of the current literature landscape of agrifood sustainable transitions relating to firms and industries and lay a foundation for future research.

The field of financial literacy is intricate and developing. This systematic review of the literature provides a thorough overview of financial literacy. It examines the sample of 3182 papers of the period 1990 to 2023 from different databases such as Scopus, Web of Science, IEEE, Science Direct, and JSTOR and identifies important publications, knowledge gaps, and rising themes. The four major topics that emerged are the degree of financial literacy among various groups of people, gender disparity in financial literacy, the influence of financial literacy on financial behavior and planning, and the impact of financial education on day-to-day decisions. Groups with lower levels of financial literacy include adults, women, people in the lower-income band, rural people, etc. Financial planning and financial behavior are positively associated with financial literacy. Financial literacy could be enhanced with financial education; however, output depends upon program design and experimental group. The review also discusses financial literacy in the context of gender disparity, including its definition, difficulties encountered, and possible solutions. This systematic study will help researchers, practitioners, and policymakers by providing insightful information on financial literacy and its ramifications.

(1) E-governance is defined as the application of electronic means in the interaction between government and citizens and government and business, as well as in internal government operations to simplify and improve democratic, governmental, and business aspects of governance. Thus, e-governance is built from a paradigmatic dimension such as e-democracy (relationship between government and citizens) and an operational one such as e-government. The objective was to design and validate an instrument to measure e-governance based on three factors: a) e-administration, b) e-services, and c) e-democracy; (2) Methods: Based on the level of importance given to each factor (sample of 2042 Latin American citizens), as well as the relationships between them, an analysis of the importance of each factor is carried out; (3) Results: After the confirmatory analysis, the definitive instrument with which e-governance can be measured by other researchers and future research is obtained, considering the three selection factors, namely: e-administration, e-services and e-democracy; (4) Conclusions: This research contributes to political science through the design and validation of an instrument consisting of 39 items that can be used to measure e-governance according to the dimensions proposed by the United Nations Educational, Scientific and Cultural Organization.

Environmental factors, such as drought-stress, significantly impact maize growth and productivity worldwide. To improve yield and quality, effective strategies for early detection and mitigation of drought-stress in maize are essential. This paper presents a detailed analysis of three imaging trials conducted to detect drought-stress in maize plants using an existing, custom-developed, low cost, high throughput phenotyping platform. We propose a pipeline for early detection of water stress in maize plants using a Vision Transformer classifier and analysis of distributions of near-infrared (NIR) reflectance from the plants. We also explored suitable regions on the plant that are more sensitive to drought-stress and show that the region surrounding the youngest expanding leaf (YEL) and the stem can be used as a more consistent alternative to analysis involving just the YEL. Our results show good separation between well-watered and drought-stressed trials for two out of the three imaging trials both in terms of classification accuracy from data-driven features as well as through analysis of histograms of NIR reflectance.

Evaluation of structural behaviour of a masonry Venetian Church with pointed vault, is presented in this paper through an analysis following the neccessary steps of a monument study. Having a detailed geometric model and the material estimation, the finite element method was used in order the influence of specific structural parts of the structure like masonry butresses and walls connections to the structural behaviour are examined. The comparison of the eigenfrequencies which are estimated by in situ measurements and finite element modal analysis is used to model identification. The response spectrum analysis, the static analysis for subsidence of some parts and the transient analysis of a specific seismic excitation are used to evaluation of the church strength. Comparison of modal analysis results with the structural pathology is helpful to this research about the structural behaviour of the church.

The main purpose of this study was to design a workplace in the conditions of a digital factory. The goal of this study is to compile a proposal for improving the layout solution in the selected enterprise to reduce material flows between individual workplaces. In addition, the workplace research study uses methods of material flow analysis with the application of visTABLE software support, which serves as a tool for designing production processes. The main result of the study was the reduction of the production time of the beam by 10 minutes and the creation of proposals for solutions for the company. The study helped with the visualization of processes within the digital factory to simplify and clarify processes at the workplace.

Stress has emerged as a major concern in modern society, significantly impacting human health and well-being. Statistical evidence underscores the extensive social influence of stress, especially in terms of work-related stress and associated healthcare costs. This work addresses the critical need for accurate stress detection, emphasising its far-reaching effects on health and social dynamics. Focusing on remote stress monitoring, this work proposes an efficient deep learning framework to discern stress from facial videos. In contrast to research on wearable devices, this paper investigates the application of *dl for stress detection based on *rppg. The methodology involves selecting suitable *dl models (*lstm, *gru, *1dcnn), optimising hyperparameters, and investigating augmentation techniques. 1D-CNNv1 model achieved the best performance compared to other approaches, particularly with augmentation techniques such as linear interpolation and white noise, achieving a stress classification accuracy of 95.83% while maintaining excellent computational efficiency. The experimental results demonstrate the use of *dl for stress detection based on *rppg, with the potential to make significant contributions to the international standard in the field.

The ZIPs (ZRT/IRT-like proteins) play critical roles in the absorption, transport, and intracellular balance of metal ions essential for various physiological processes in plants. However, little is known about the pan-genomic characteristics and properties of ZIP genes in cucumber (Cucumis sativus L.). In this study, we identified 10 CsZIP genes from the pan-genome of 13 C. sativus accessions. Among them, only CsZIP10 showed no variation in protein sequence length. We analyzed the gene structure, conserved domains, promoter cis-elements, and phylogenetic relationships of these 10 CsZIP genes derived from "9930". Based on phylogenetic analysis, the CsZIP genes were classified into three branches. Amino acid sequence comparison revealed the presence of conserved histidine residues in the ZIP proteins. Analysis of promoter cis-elements showed that most promoters contained elements responsive to plant hormones. Expression profiling in different tissues showed that most CsZIP genes were expressed at relatively low levels in C. sativus leave, stem, and tendril, and CsZIP7 and CsZIP10 were specifically expressed in root, indicating their potential involvement in the absorption and transport of metal ions. Transcriptomic data indicated that these 10 ZIP genes displayed responses to both downy mildew and powdery mildew, and CsZIP1 were significantly downregulated after both salt and heat treatments. In conclusion, this study deepens our understanding of the ZIP gene family and enhances our knowledge of the biological functions of CsZIP genes in C. sativus.

Basil, renowned for its aromatic properties, exhibits commendable drought tolerance and holds significant value both as an edible and medicinal plant. Recognizing the scarcity of studies addressing basil’s response to drought stress, we undertook a comprehensive analysis of the volatile metabolome and transcriptome of sweet basil across four distinct levels of drought stress. Our metabolic analysis meticulously identified 830 metabolites, with 215 differentially accumulated metabolites discerned. Notably, the primary constituents of sweet basil volatiles exhibited minimal alterations, while the differentially accumulated metabolites were predominantly lipids and terpenes. The transcriptome analysis unveiled 27,181 differentially expressed DEGs, with a predominant focus on metabolic pathways and biosynthesis of secondary metabolites. Integrating insights from both transcriptome and volatile metabolomic analyses highlighted the pivotal roles of Alpha-linolenic acid, flavonoid, and lignin metabolism in fortifying the resistance of sweet basil against drought stress. This holistic approach not only enriches our understanding of the molecular intricacies underpinning basil’s drought resistance but also furnishes valuable insights for the molecular breeding of basil varieties endowed with enhanced drought tolerance.

Dendrobium Sw have multiple photosynthetic pathways but due to the lack of identification, the influence of CAM pathway on the plastid genomic evolution of Dendrobium and the origin of CAM pathway are still poorly understood. In this study, based on the results of δ13C, net photosynthetic rates (Pn) and titratable acid value, we identified Dendrobium species into three photosynthetic pathways. Then the chloroplast whole genome characteristics of 10 Dendrobium species were analyzed with next-generation sequencing technology. The quadripartite-structure plastomes ranged from 151,673 bp to 160,375 bp and included 127 to 132 genes. Five noncoding mutational hotspots (rbcL-accD, atpB-rbcL, rbcL, trnLUAA, accD, Pi > 0.10) were identified. A total of 41–60 SSRs were recognized, the average trinucleotide content was only about 4%, which is probably due to a more conserved SSR. Among multiple photosynthetic pathways, the apparent differences in IR junctions of SSC/IRB junctions (JSBs) were measured within chloroplast genomes, and the chloroplast genome size of constitutive CAM plants is shorter because of their ndh gene loss or pseudogenization, and this phenomenon accounts for a decreasing proportion of constitutive CAM, facultative CAM, and obligate C3 plants, so that the loss of the ndh gene may be speculated to be closely related to the CAM pathway. Phylogenetic analyses showed that there are multiple independent CAM origins in selected Dendrobium species, and the ancestral Dendrobium plastome possessed full ORFs for all ndh genes. The study aims to clarify the chloroplast genomic features of different photosynthetic pathways, enrich the genomic data of CAM plants, and provide genetic information from different perspectives for further systematic study of Dendrobium.

Introduction: Depending on the kind of cancer and neurological disorders, there might be a direct or inverse link between neurodegeneration and cancer. The inverse interaction between cancer and Alzheimer's pathology has garnered substantial interest in medical research. While this inverse interaction between Alzheimer's pathology and cancer presents a fascinating scientific puzzle, there is still limited information and studies on the potential mechanisms involved in this inverse rela-tionship. Aim: This study aims to identify potential genes from Alzheimer's disease that may protect against cancer. Method: The Gene Expression Omnibus (GEO) database provided the Alzheimer's pathology dataset, and GEO 2R was utilized to examine genes that were differentially expressed. The expression of upregulated Alzheimer's pathology genes in breast, colorectal, and prostate cancer was compared using VENN 2.1, colorectal, and prostate cancer was compared using VENN 2.1, and colorectal and prostate cancers were compared using VENN 2.1. Results: Among the upregulated genes associated with Alzheimer's disease, 25 genes are shared among breast cancer, colorectal cancer, and prostate cancer, while 28 of these genes were exclusive to Alzheimer's dis-ease. Gene expression profiling interactive analysis among the shared genes revealed that AQP1, C4A, CSRP2, CRYAB, MACF1, HSPB2, and NEAT1 were downregulated in breast, colorectal, and prostate cancers. These genes have varying roles in cancer progression and suppression. Functional analysis of Alzheimer's disease-related upregulated genes revealed that C1orf56 and HIST1H2AC control cell proliferation, with HIST1H2AC playing a role in necroptosis. Additionally, C1orf56 worked as an oncogenic modifier to support DNMT3B's tumor suppressor properties. Conclusion: Given the role of HIST1H2AC and C1orf56 in the negative control of cell proliferation and their upregulation in Alzheimer's disease, there is a need to further investigate HIST1H2AC and C1orf56 as potential tumor suppressors responsible for Alzheimer's disease and cancer inverse association.

Phenylketonuria is an inherited metabolic disorder that leads to neurobehavioral dysfunction. The main treatment is a low-phenylalanine diet and/or the cofactor tetrahydrobiopterin. Regular outpatient follow-up care and measurement of the phenylalanine level in the blood are required. We aimed to analyze the economic burden of phenylketonuria on families and the state. The pa-tients with phenylketonuria were divided into three groups according to their treatment: a low-phenylalanine diet group (n= 50), a tetrahydrobiopterin group (n= 44) and a group taking tetra-hydrobiopterin together with the diet (n= 25). A comparative cost analysis was carried out. The annual economic burden to the state was calculated to average €18801±15345 and was lowest in the diet group, then in the tetrahydrobiopterin group and highest in the tetrahydrobiopterin+diet group (p < 0.001). Out-of-pocket costs amounted to €1531±1173 per year, and indirect losses aver-aged €2125±1930 per year for all families. The economic loss was significantly lower in the fami-lies taking tetrahydrobiopterin than in the other groups (p=0.001). The combined use of medical nutrition and BH4 treatments has been shown to increase the economic harm to the state. Reim-bursing low-protein products and increasing the rate of patients eligible for financial allowances may reduce the economic harm to families.

In this study, an experiment was conducted on hoop pine (Araucaria cunninghamii), which is a common tree species in Taiwan. Holes with diameters of 4, 5, 7, 10, 12, and 14 cm were drilled in dry hoop pine timber with a diameter of 39 cm and a height of 57 cm, and an 800 MHz Ground Penetrating Radar (GPR) antenna were used to determine the minimum image resolution and the size of the holes in the wood. GPR images of the timber were subjected to a filtering process to obtain characteristic reflection curves of the damage to the timber. Curves with clear waveforms were observed in the GPR profiles of eccentric holes with a diameter of 4 cm. Profiles of the damage were obtained and profile images with the trunk radius at the centre were transformed into polar coordinate images. From these polar coordinate images, the location, size and diameter of the eccentric holes in the wood were identified, and the reflection curves were characterised by waveforms and irregular fluctuations. The risk levels of tree trunks were determined on the basis of their t/R (minimum normal wall thickness of the cross-section of the trunk/radius of the trunk) values and hole area ratios. A comparison between the GPR-based results and the actual diameters of the holes revealed that the filtered GPR profiles could be used for the efficient and accurate assessment of damage to hoop pine timber (average error rate ≤ 4%).

Leucobryum is a moss genus that exhibits various bioactivities. However, the identification of Leucobryum species with morphology alone remains difficult. Nowadays, chemical profile analysis provides additional tools for plant classification, especially in medicinal plants. This method uses chemical similarities to identify the differences among some plants, especially for the different varieties of the same species. The objective of this study was to obtain the chemical profiles and use chemometrics to identify selected Leucobryum species found in Thailand. Lipophilic extracts from 18 samples of five taxa were analyzed with TLC and HPLC. Cluster analysis and PCA were used to investigate the similarities of the chemical profiles. PERMANOVA was performed to determine the difference among taxa. The results showed that the TLC profile could separate only the genus of the sample, while HPLC chromatograms showed separation among the Leucobryum taxa and consistent patterns within the same species. In addition, the chemical profiles of Leucobryum species were separated at the species level in PCA. The PERMANOVA showed significantly different profiles among the species (P = 0.002). Therefore, this work illustrated the potential of chemometrics as supporting evidence for species delimitation in the moss genus Leucobryum.

In this study, the autofrettage method, which is used to increase the durability of cylindrical parts, was applied to a barrel made of steel material according to the AISI 4340 standard. Assuming that the barrel works continuously under the pressure of 400 MPa, optimum autofrettage was performed. The stresses occurring on the barrel before and after autofretage were calculated. Additionally, analytical equivalent stress values (Von-Mises) formed on the part's wall were compared with numerical analysis results obtained with a commercial finite element program ANSYS®, and their compatibility was examined. As a result of the calculations and analysis performed, it was observed that autofretage has positive effect on increasing the pressure carrying capacity of the barrel. After autofrettage the cylindrical part, it was determined that the Von-Mises stress values formed in the part wall changed and these stresses took the lowest value at a point (elastic-plastic interface). It has been evaluated that this point gives the optimal autofretage pressure value for a certain operating pressure. After the autofrettage application, it was determined that the Von-Mises Stress value in the barrel wall decreased by 25.27 % and the pressure carrying capacity of the barrel increased without any design or dimensional change. It has been seen that the analytical and numerical analysis results are consistent with each other, and it is expected that this study will guide and contribute to the studies to be conducted on increasing the strength or lightening the weight of the parts.

The carbonation of industrial calcium-rich byproducts such as steel slag demonstrates significant potential for CO2 sequestration. This technique aids in reducing carbon emissions while also promoting waste recycling. Despite its advantages, gaps remain in understanding how steel slag characteristics and operational parameters influence the carbonation process, as well as the underlying mechanism of direct aqueous carbonation. We evaluated the carbonation performance of three types of steel slag at temperatures below 100°C using a gas–liquid–solid reaction system. The slag with the highest CO2 sequestration capacity was chosen for a systematic evaluation of the effects of operating conditions on carbonation efficiency. Thermodynamic analysis indicated that the reactivity of CaO and Ca(OH)2 with CO2 exceeded that of CaO•SiO2 and 2CaO•SiO2. Under conditions of 85°C, a particle size less than 75 μm, an initial CO2 pressure of 0.5 MPa, a liquid-to-solid ratio of 5 mL/g, and a stirring speed of 200 rpm, the steel slag achieved a sequestration capacity (K) of 283.5 gCO2/kg and a carbonation efficiency (ζCa) of 51.61%. Characterization of the slag before and after carbonation using X-ray diffraction, SEM‒EDS, thermogravimetric analysis, and Fourier transform infrared spectrometry confirmed the formation of new carbonates. Mechanistic analysis revealed that the rate-limiting step initially involved the mass transfer of CO2, transitioning to Ca2+ mass transfer as time progressed. Our research provides a viable technique for CO2 capture and a beneficial approach for reutilizing waste steel slag. Furthermore, solid residues after capturing CO2 have the potential for conversion into carbon-negative building materials, offering a sustainable strategy for steel companies and other enterprises with high carbon emissions.

The new class of mRNA vaccines is studied extensively, leading to many novel prospects based on the pivotal success of vaccines against the SARS-Cov-2 virus. Many novel mechanisms of immune response discovered have led to the possibility of mutation-resistant vaccines by combining multiple conserved epitopes as antigens to manipulate the T-cell and B-cell responses; these antigens can also come from different organisms, providing protection against numerous diseases and of most tremendous significance is the utility of mRNA vaccines in preventing and treating more than 100 autoimmune disorders. This is a significant humanitarian breakthrough given the ease and faster and low cost of mRNA vaccines, being a chemical entity. This paper provides a prospective analysis of mRNA vaccines with much broader applications than anticipated when these vaccines entered treating SARS-Cov-2 infections.

This study focused on the precise detection and concordant analysis of circulating tumor cells (CTCs) to verify the usefulness of CTCs as promising clinical markers. CTCs are traditionally achieved using immune-fluorescence and morphological techniques. By combining two prostate cancer-specific genes (PSA and PSMA) and two epithelial-specific genes (EpCAM and KRT19), the CTC detection efficiency can be enhanced. The detection rates of localized prostate cancer (LPC), locally advanced prostate cancer (LAPC), metastatic hormone-sensitive prostate cancer (mHSPC), and metastatic castration-resistive prostate cancer (mCRPC) were 63.04 % (29/46), 70.59% (34/51), 94.34% (50/53), and 90% (126/140), respectively. Subsequent correlation analysis between the four genes, baseline PSA, and CTC count revealed that only the CTC count correlated with baseline PSA in the localized stages of prostate cancer, whereas all four genes were correlated with baseline PSA and CTC count in the metastatic stages of prostate cancer. The precise detection of CTCs using both numerical and transcriptomic analyses can improve the accuracy of biomarker investigations. This study provides valuable insights into the limitations of serum PSA testing and offers a promising alternative to diagnose and manage prostate cancer via the CTC number and specific genes for prostate cancer.

This study aimed to elucidate the antibacterial efficacy and unique chemical compositions generated from plasma-activated water (PAW) when utilized a novel vaginal cleansing device. We conducted an analysis of antibacterial activity against probiotics and several chemical compositions including ions under various operational parameters of PAW, including electrical power (12 and 24 V), treatment duration (1, 10, and 20 min), and stay duration (immediate, 30 and 60 min). Our findings revealed that as treatment duration increased, hypochlorous acid (HOCl), Ca2+, and Mg2+ concentrations increased and Cl−concentrations decreased. Higher electrical power and longer treatment duration resulted in increased HOCl levels, which acts to prevent the growth of general microorganisms. Notably, PAW exhibited no antibacterial effects against a type of probiotic, Lactobacillus reuteri that produces lactic acid for vaginal health. This result demonstrated the ability of the vaginal cleaning device to generate ions, primarily HOCl and some cations (Ca2+ and Mg2+), thereby providing vaginal mucosa protecting and cleansing effects with the vaginal environment.

This work seeks to approach the comparative analysis of the distribution of POFs (polymeric optical fibers) arranged in a random and sorted manner in the manufacture of translucent concretes. The following characteristics were analyzed: compressive strength, flexural tensile strength, capillary water absorption and light transmittance. Portland cement, water, fine aggregate (sand), silica fume, polycarboxylate ether-based superplasticizer and polymeric optical fiber were used in the production of the composites. Except the fiber content, the mixture of all other inputs was kept as a fixed parameter. Groups with the following levels of POFs were produced: 0%, 2%, 3.5% and 5%. The POF content is the sum of the areas of the fiber sections in the composite with the total area of the translucent surface. The results showed that for the analysis of mechanical behavior (flexural tensile and compressive strength) and durability (capillary water absorption), composites with random and sorted fibers within the 2% POF presented to belong to statistically distinct groups. For the other fiber contents, there was no pattern of behavior, that is, it cannot be said that there is a difference within 3.5% or 5% POF between sorted and random fibers in relation to the mechanical and durability characteristics analyzed. As for the light transmittance, it was attributed that the statistical difference between the fiber arrangements only within the 5% POF content can be a consequence of their accumulation in certain points of the material, thus influencing this characteristic measured by a light meter.

In the process of construction and operation of the pumping station, a large number of axial flow pumps pose a threat to the survival of fish in the natural environment. Through experiments, the survival conditions of many kinds of fish in extreme positive and negative pressure environment are explored, and -40 kPa is established as the low pressure damage threshold, which is used as a reference to analyze the probability of fish damage caused by pumps. The immersion boundary-lattice Boltzmann numerical method (LES-IB-LBM) based on large eddy simulation is used to optimize the parameters of the prototype runner of axial flow pump. A new type of runner with leading edge thickness of 11.4mm and blade cutting angle of 18 ° is optimized. This kind of runner has both hydraulic performance and fish-passing ability. The runner of the fish-friendly axial flow pump effectively guides the fish into the low risk area and reduces the risk of friction, shear and impact damage to the fish. the total impact mortality ratio of the runner of the axial flow pump before and after optimization is close to 7:1. The pressure damage analysis shows that the lowest negative pressure of the fish is -59.2 kPa, which is higher than -64.5 kPa of the prototype pump, and the highest positive pressure of the body surface is 11.8 kPa, which is lower than the 43.8 kPa of the prototype pump. Shear damage analysis shows that the fish will not be damaged by shear in the prototype pump and fish-friendly pump.

(1) Background: The COVID-19 pandemic significantly affected worldwide, with varying responses implemented to control its spread. This study aimed to compare the epidemic data compiled by the World Health (2) Methods: Organization to understand the impact of the measures adopted by each country on the mortality rate. The increase or decrease in the number of confirmed cases was understood in logarithmic terms, for which logarithmic growth rates “K” were used. The mortality rate was calculated as the percentage of deaths from the confirmed cases, which was also used for logarithmic comparison. (3) Results: Countries that effectively detected and isolated patients had a mortality rate 10 times lower than those that did not. Although strict lockdowns were once effective, they could not be implemented on an ongoing basis. In fact, after their cancelation, large outbreaks occurred because of medical breakdowns. The virus variants mutated with increased infectivity, which impeded the measures that were once effective, including vaccinations. Although the designs of mRNA vaccines were renewed, they could not keep up with the virus mutation rate. The only effective defence was steadily identifying and isolating patients. (4) Conclusions: These findings have crucial implications for the complete containment of the pandemic and future pandemic preparedness.

Hospitals are perpetually challenged by the dual requirements of concurrently improving the quality of healthcare and maintaining financial solvency. Both issues are among the top concerns for hospital executives across the United States, yet some have questioned if the efforts to enhance quality are financially sustainable. led us to examine if improving quality in the hospital setting impacts revenue. Using multivariate regression, we assessed if numerous quality measures were associated with our targeted measure of hospital profitability: the net patient revenue per adjusted discharge. The independent variables included the HCAHPS Summary Star Rating, Hospital Compare Overall Rating, All-Cause hospital-wide Readmission Rate, Total Performance Score, Clinical Outcomes Domain Score, Safety Domain Score, Person and Community Engagement Domain Score, and the Efficiency and Cost Reduction Score. Our results indicated that improving quality was significantly associated with improved net patient revenue per adjusted discharge for seven of the eight of these quality measures at the hospital level. It is clear that failing to address quality and patient safety issues is costly for US hospitals, thus we believe our findings support the premise that increased attention to the quality of care delivered as well as patients’ perceptions of care may allow hospitals to accentuate profitability and advance a hospital’s financial position.

The study sheds light on the impact of urbanization on fragile ecosystems such as the western Himalayas. We use Haldwani in Uttarakhand as an example of human encroachment and loss of ecosystem services. Several environmental parameters such as Nighttime light (NTL), Land Surface Temperatures (LSTs), Aerosol Optical Depth (AOD) and forest cover are used based on satellite imagery to allow a bidecadal comparison (between 2000 and 2020) of the status of these parameters for the city based on these parameters shows a decline in ecosystem services. Significant statistical differences for LSTs and AOD (p < 0.001) can be found in the bidecadal comparison. Furthermore, a strong negative correlation was found between LST-NDVI (r = -0.69) and between NTL-NDVI (r = -0.58) in earlier and last decade intervals. In addition, long-term multi-spectral satellite imagery also shows a decline in tree cover in the reserved forest. Therefore, focusing on ecosystem services related to tree cover in reserved forest areas, particularly in the Indian Himalayan Region (IHR) must be part of a broader action plan to address these issues to further protect fragile Himalayan ecosystems.

The determination of a causal relationship between gut microbiota and a range of dyslipidemia remains uncertain. To clarify these associations, we employed a two-sample mendelian randomization (MR) analysis utilizing the inverse-variance weighted (IVW) method. This comprehensive analysis investigated the genetic variants that exhibited a significant association (p&lt;1e-5) with 129 distinct gut microbiota genera, and their potential link to diverse forms of dyslipidemia. The results indicated a potential causal relationship between 22 gut microbiota genera and dyslipidemia in humans. Furthermore, these findings suggested that the impact of gut microbiota on dyslipidemia regulation is dependent on the specific phylum, family, and genus. Bacillota phylum demonstrated the greatest diversity, with 15 distinct genera distributed among 8 families. Notably, gut microbiota derived from the Lachnospiraceae and Lactobacillaceae families exhibit statistically significant associations with lipid levels that contribute to overall health (p&lt;0.05). The sensitivity analysis indicated that our findings possess robustness (p&gt;0.05). The findings of our investigation provide compelling evidence that supports a causal relationship between the gut microbiota and dyslipidemia in the human body. It is noteworthy to highlight the significant influence of the Bacillota phylum as a pivotal regulator of lipid levels, and the families Lachnospiraceae and Lactobacillaceae should be acknowledged as probiotics that make substantial contributions to this metabolic process.

Recent studies suggest that PEBP1/RKIP and YY1, despite having distinct molecular functions, may interact and mutually influence one another's activity. They exhibit reciprocal control over each other's expression through regulatory loops, prompting the hypothesis that their interplay could be pivotal in cancer advancement and resistance to drugs. To delve into this interplay's functional characteristics, we conducted a comprehensive analysis using bioinformatics tools across a range of cancers. Our results confirm the association between elevated YY1 mRNA levels and varying survival outcomes in diverse tumors. Furthermore, we observed differing degrees of inhibitory or stimulatory effects of these two genes in various cancer pathways, along with correlations between their mRNA expression and immune infiltration. Additionally, YY1/PEBP1 expression (or methylation) displayed connections with genomic alterations across cancer types. Notably, we uncovered links between YY1/PEBP1 and different indicators of immunosuppression, such as immune checkpoint blockade response, and T-cell dysfunction/exclusion levels, across different patient groups. Overall, our findings underscore the significant role of the interplay between YY1 and PEBP1 in cancer progression, influencing genomic changes, tumor immunity or the tumor microenvironment. Additionally, these two gene products appear to impact the sensitivity of anticancer drugs, opening new avenues for cancer therapy.

Probabilistic latent semantic analysis is a statistical technique developed for information retrieval and spanned many fields. It yields intuitive and solid results. However, the rigidity of the assumptions and the iterative nature derived from the Expectation-maximization algorithm generate several problems, dividing detractors and enthusiasts. In this manuscript, we first describe the Probabilistic latent semantic analysis. After, we discuss reformulations that attempt to solve these problems. We pay special attention to the works relating Probabilistic latent semantic analysis and the Singular value decomposition Theorem. Also, Probabilistic latent semantic analysis can be the basis for other techniques, such as kernelization or probabilistic transfer learning, and those that extend the descriptive character of the Principal component analysis to an inferential tool and open a window of opportunities.

Thermal comfort is extremely important in architecture, especially in environments with more people spending longer on studies or intellectual activities. This research describes a case study to investigate university buildings' energy and thermal performance as a part of the ANEEL program. Due to this importance, and the need to save energy in Brazilian public buildings, the ANEEL-the Brazilian Energy Electricity Regulatory Agency, launched 2016 a national program focusing on energy efficiency in public universities around the country. University offices and classrooms require high intellectual effort; thus, environmental comfort is critical for maintaining its users' physical and mental health. This study included a pre-diagnosis of the performance of the envelope, lighting, and air conditioning systems and a survey about the quality of the environments from the users' point of view. The Prescriptive Method of the Brazilian Labeling Program (PBE) for Commercial, Service, and Public Buildings (RTQ-C) assessed the building performance. Statistical analysis was applied to correlate the quality and thermal preference of the users from the PMV/PPD. The results showed a high rate of thermal discomfort in both environments of the studies, even when using air conditioning.

In this paper, the main physicochemical characteristics and evolutionary laws of microbial community structure during the fermentation of aged vinegar were discussed, and the correlation between Cuqu and microorganisms and physicochemical characteristics during fermentation was explored. The results showed that there are significant differences in genera at different stages of fermentation, the main dominant bacteria in R1 are Bacillus, Lactobacillus, Aspergillus and Issatchenkia. At R2 fermentation stage, Lactobacillus, Acetobacter and Saccharomyces showed an upward trend and finally became absolute advantage bacteria. The main genus of bacteria at the end of overall fermentation is Aspergillus. Correlation analysis shows that in Cuqu, the bacterial genera that are significantly positively and negatively correlated with reducing sugars and amino acid nitrogen are the same, while the bacterial genera that are significantly positively and negatively correlated with pH and saccharification power are the same. During the fermentation process, pH, reducing sugar and saccharification ability are mainly positively correlated with bacterial genera. And research has found that during the fermentation process, the overall correlation between fungal communities and physicochemical characteristics is weaker compared to bacteria.

Our objective was to evaluate whether pregnancy is prolonged by use of a proteomics-based maternal serum screening test followed by treatment interventions. This is a secondary analysis of the PREVENT-PTB randomized trial comparing screening with the PreTRM test versus no screening. The primary trial analysis found no significant between-group difference in preterm birth rate. Rather than considering a dichotomous outcome (preterm vs term), we treated gestational age at birth as a continuous variable using survival analysis. We also evaluated between-group difference in NICU length of stay and duration of respiratory support. Results indicated that pregnancy was significantly prolonged in subjects screened with PreTRM test compared to controls (adjusted hazard ratio 0.53, 95% confidence interval 0.36-0.78, P &lt;0.01). Newborns of screened subjects had significantly shorter NICU stay but no significant decrease in duration of respiratory support. In the PreTRM screen-positive group, interventions that were associated with pregnancy prolongation included care management and low-dose aspirin but not 17-hydroxyprogesterone caproate. We concluded that screening with the PreTRM test followed by interventions for screen-positive pregnancies can prolong pregnancy and reduce NICU-LOS.

Metabolic-associated fatty liver disease (MAFLD) and its potential impact on the severity of COVID-19 have gained significant attention during the pandemic. This review aimed to explore the genetic determinants associated with MAFLD, previously recognized as non-alcoholic fatty liver disease (NAFLD), and their potential influence on COVID-19 outcomes. Various genetic polymorphisms, including PNPLA3 (rs738409), GCKR (rs780094), TM6SF2 (rs58542926), and LYPLAL1 (rs12137855), have been investigated in relation to MAFLD susceptibility and progression. Genome-wide association studies and meta-analyses have revealed associations between these genetic variants and MAFLD risk, as well as their effects on lipid metabolism, glucose regulation, and liver function. Furthermore, emerging evidence suggests a possible connection between these MAFLD-associated polymorphisms and the severity of COVID-19. Studies exploring the association between indicated genetic variants and COVID-19 outcomes have shown conflicting results. Some studies observed a potential protective effect of certain variants against severe COVID-19, while others reported no significant associations. This review highlights the importance of understanding the genetic determinants of MAFLD and its potential implications for COVID-19 outcomes. Further research is needed to elucidate the precise mechanisms linking these genetic variants to disease severity and to develop gene profiling tools for early prediction of COVID-19 outcomes. If confirmed as determinants of disease severity, these genetic polymorphisms could aid in identifying high-risk individuals and improving the management of COVID-19.

The COVID-19 pandemic has led to the growth of hybrid and online learning environments and the trend to introduce more technology into the classroom. One such change would be the use of smart synchronous hybrid learning environments (SSHLE), which are settings with both in-person and online students concurrently, and in which technology plays a key role in sensing, analyzing, and reacting throughout the teaching and learning process. These changing environments and the incorporation of new technologies can place a greater orchestration load on participants and a reduction in teacher agency. In this context, the aim of this paper is to analyse the orchestration load and teacher agency in different SSHLEs. The NASA-TLX model was used to measure the orchestration load in several scenarios. Questionnaires and interviews were used to measure teacher agency. The results obtained indicate that the orchestration load of the teacher tends to be high (between 60 and 70 points out of 100 of the NASA-TLX workload), especially when they lack experience in synchronous hybrid learning environments, and the orchestration load of the students tends to have average values (between 50 and 60) in the SSHLEs analysed. Meanwhile, the teacher agency does not appear to be altered but shows potential for improvement.

Microwave transversal filters, which are implemented based on the transversal filter structure in digital signal processing, offer a high reconfigurability for achieving a variety of signal processing functions without changing hardware. When implemented using microwave photonic (MWP) technologies, also known as MWP transversal filters, they provide competitive advantages over their electrical counterparts, such as low loss, large operation bandwidth, and strong immunity to electromagnetic interference. Recent advances in high-performance optical microcombs provide compact and powerful multi-wavelength sources for MWP transversal filters that require a larger number of wavelength channels to achieve high performance, allowing for the demonstration of a diverse range of filter functions with improved performance and new features. Here, we present a comprehensive performance analysis for microcomb-based MWP spectral filters based on the transversal filter approach. First, we investigate the theoretical limitations in the filter spectral response induced by finite tap numbers. Next, we analyze the distortions in the filter spectral response resulting from experimental error sources. Finally, we assess the influence of input signal’s bandwidth on the filtering errors. These results provide a valuable guide for the design and optimization of microcomb-based MWP transversal filters for a variety of applications.

Galectins have been shown to have roles in cancer progression via contributions to angiogenesis, metastasis, cell division, and evasion of immune destruction. This study analyses galectin-1, -3, and -9 serum concentrations in breast cancer patients by enzyme-linked immunosorbent assay (ELISA) against the characteristics of the patient and tumor such as stage, molecular subtype, and receptor expression. Galectin-9 was found to be statistically significantly increased in HER2 enriched tumors as well as being reduced in patients with hormone receptor positive tumors and increased in patients with HER2 amplified tissues. Galectin-1 was found to be statistically significantly increased in the serum of patients who had undergone hormonal, immunotherapy, or chemotherapy. These findings provide insight as to the changes in galectin levels during cancer progress, response to treatment and molecular phenotype. These findings are valuable in the further understanding of the relationships of galectin and tumor biology and can inform future research on therapeutic targets for galectin inhibitors and the utility of galectin biomarkers.

Tissue inhibitors of metalloproteinases (TIMPs) are essential players during tumor progression in various types of cancer, including breast cancer. TIMPs regulate the activity of matrix metallo-proteinases (MMPs) at different levels. However, a dual role for TIMPs in breast carcinogenesis, either promoting or inhibiting tumor progression, has been observed. By a comprehensive bioin-formatics analysis using different databases, we report that of the four TIMPs, only TIMP-1 is overexpressed in tumor breast tissue. However, TIMP-2, TIMP-3, and especially TIMP-4 present lower levels in breast tumor tissue than in normal tissue. Interestingly, high levels of TIMP-1 are associated with shorter survival in breast cancer patients. In contrast, low TIMP-2, TIMP-3, and TIMP-4 levels are associated with poor survival. Function enrichment analysis showed that TIMP-3 has the highest interactions with its target proteins and a higher number of processes re-lated to mammary carcinogenesis. We found differential expression of TIMP-1 concerning TIMP-3 and TIMP-4 in breast cancer and normal tissue. TIMP-4 has a significant relationship with cancer staging, including tumor size, lymph node spread, and metastasis. The correlation between immune cell infiltrates and TIMP expression is of great importance as they provide in-formation on breast cancer diagnosis, prognosis, progression, and response to treatment.

Cervical cancer is associated with persistent infections by high-risk HPV types that may have nucleotide polymorphisms and, consequently, different oncogenic potentials. Therefore, the objective of this study was to evaluate the genetic variability and structural effects of E7 oncogene of HPV58 in cervical scraping samples from Brazilian women. The study was carried out with patients from hospitals in metropolitan area of Recife, PE, Brazil. The most frequent HPV type was HPV16, 18 and 58, respectively. Phylogenetic analysis showed that the isolates were classified as sublineages A2, C1 and D2. Two positively selected mutations were found in E7: 63G and 64T. The mutations G41R, G63D and T64A in E7 protein were predicted to reduce the stability of the protein structure. Regarding the interaction of the E7 variant of HPV58 with the signaling of the NF-kB pathway, we observed that the variant HPV58/UFPE-54S decreased the activity of the pathway when compared to the prototype and the other variants behaved similarly to the prototype and the prototype increased the activity of the pathway when compared to pcDNA. In this study, it was possible to identify mutations that may interfere in the molecular interaction between the viral oncoproteins and host proteins.

The purpose of this article is to conduct a bibliometric review of literary production in terms of market interdependence. We investigate the effect of contagion on agricultural commodities and identify the commodities and methods used in the most cited publications over the decades. For this, we used the SCOPUS database, sorting with Rayyan, Excel, and finally, the Bibliometrix/R-project. The results showed that corn, wheat, soybeans, cotton, and sugar are the most studied commodities. There is no uniqueness concerning the methods used to identify interdependence and the contagion effect. Also, it is possible to perceive the growing scientific interest concerning the subject, with a diversity of authors and magazines. However, there is a direction of the and magazines most cited articles for analyzing agricultural commodities and energy, demonstrated a worldwide trend of studies relating natural resources, biofuels, clean energy, and efficiency. In addition, countries like China and the United States stand out in this scientific scenario as the countries that produce the most articles.

This study aimed to assess the physical characteristics of elite international soccer referees, compare them with other referee populations in the literature, and establish reference tolerance ellipses for the bioelectrical impedance vector analysis (BIVA) point graph. Forty-one elite international soccer referees (age 38.8 ± 3.6 years) participated in the study. the participants underwent body composition assessments, including dual-energy X-ray absorptiometry, BIVA, and somatotype. The Somatotype Attitudinal Distance (SAD), the two-sample Hotelling’s T2 test, and the Mahalanobis test were used to determine somatotype and bioelectrical vector differences with the literature. The average somatotype of the referees was a balanced mesomorph (2.8, 6.5, 2.8). Elite international referees significantly differed from other samples in the literature (SAD = 2.1, 2.6, 2.9 with respect to Zimbabwean, Brazilian, and South African referees, respectively). The bioelectrical vector was significantly different from the general population (T2  76.6; F = 38.8; D = 1.44; P < 0.001) and athletes (T2  25.3; F = 12.6; D = 0.8; P < 0.001). Somatotype values and tolerance ellipses from this study may be useful as a reference for developing training programs and improving the selection process of referees in soccer.

Conducting related research is crucial since there are still many problems that need to be resolved in the research on soft robots in the areas of material selection, structure design and manufacture, and drive control. Soft manipulators, a subset of soft robots, are now a popular area of study for many researchers. In comparison to typical manipulators, soft manipulators feature a high degree of gripping flexibility and a basic morphological structure. They are composed of flexible materials. It has a wide range of potential applications in healthcare, rehabilitation, bionics, and detection, and it can compensate for the drawbacks of rigid manipulators in some use scenarios. A modular soft-body torsional gripping system is developed after a torsional and gripping actuator is conceived, constructed, and its performance is examined. The torsion actuator and the grasping actuator can be combined in the system in a modular fashion. With the help of RGB-D vision algorithms, this multi-modular setup makes it possible to combine soft actuators with various twisting degrees and achieve exact gripping. Through pneumatic control, the target object is precisely grasped and rotated at various angles, enabling rotation of the target object in three dimensions.

The oral cavity is environment to a diverse range of organisms that make up an essential element of the human microbiota. There are 50-1000 kinds of microorganisms capable of colonizing the mouth. The entamoeba parasites have at minimum 7 species (E. histolytica, E. dispar, E. moshkovskii, E. coli, E. polecki E. hartmann, E. gingivalis). These parasites are parasitic single-celled organisms and Entamoeba gingivalis was the first amoeba to be described in 1849 that was capable of existing in humans. It was shown that only Entamoeba gingivalis is able to colonize the oral cavity. The aim of this study was to evaluate prevalence of E. Gingivalis in periodontal disease using two electronic database search engines. In order to have a broader view of the subject, a comprehensive manual search was conducted on these content aggregators and the initial search resulted in 277 articles using the keywords „Entamoeba gingivalis”, „periodontitis”, „E. gingivalis”, „periodontal disease”, „prevalence”, and „incidence”, in different combinations. The results showed that 755 cases were infected with Entamoeba gingivalis out of a total number of 1,729 patients diagnosed with periodontal disease indicating a global prevalence of 43%. When comparing different stages of periodontal diseases results revealed that the prevalence in patients with gingivitis was 58% while in periodontitis group the prevalence was 44%. Overall prevalence of Entamoeba Gingivalis based on gender was 43 % in female patients whilst in male patients was 47%. Based on the results it can be concluded that the higher incidence of Entamoeba Gingivalis in people with periodontal disease compared to healthy people is more than just a sign of the disease; it may also be linked to the severity of the condition and its propensity to progress.

We propose a new statistical analysis of the Acoustic Emissions (AE) produced in a series of triaxial deformation experiments leading to fractures and failure of two different rocks, namely, Darley Dale Sandstone (DDS) and AG Granite (AG). By means of q-statistical formalism, we are able to characterize the pre-failure processes in both types of rocks. In particular, we study AE inter-event time and AE inter-event distance distributions. Both of them can be reproduced with q-exponential curves, showing universal features which are observed here for the first time and could be important in order to understand more in detail rock fractures dynamics.