To date, several independent methods and algorithms exist exploiting constraint-based stoichiometric models to find metabolic engineering strategies that optimize microbial production performance. Optimization procedures based on metaheuristics facilitate a straightforward adaption and expansion of engineering objectives as well as fitness functions, while being particularly suited for solving problems of high complexity. With the increasing interest in multi-scale models and a need for solving advanced engineering problems, we strive to advance genetic algorithms, which stand out due to their intuitive optimization principles and proven usefulness in this field of research. A drawback of genetic algorithms is that premature convergence to sub-optimal solutions easily occurs if the optimization parameters are not adapted to the specific problem. Here, we conducted comprehensive parameter sensitivity analyses to study their impact on finding optimal strain designs. We further demonstrate the capability of genetic algorithms to simultaneously handle (i) multiple, non-linear engineering objectives, (ii) the identification of gene target-sets according to logical gene-protein-reaction associations, (iii) minimization of the number of network perturbations, and (iv) the insertion of non-native reactions, while employing genome-scale metabolic models. This framework adds a level of sophistication in terms of strain design robustness, which is exemplarily tested on succinate overproduction in Escherichia coli.

Background: Few studies have investigated the longitudinal association between cervical spondylosis (CS) and migraine by using a nationwide population-based database. Methods: We conducted a retrospective cohort study from 2000 to 2011 identifying 27,930 cases of cervical spondylosis and 111,720 control subjects (those without cervical spondylosis) from a single database. The subjects were frequency-matched on the basis of sex, age, and diagnosis date. The non- cervical spondylosis cohort was four times the size of the cervical spondylosis cohort. To quantify the effects of cervical spondylosis on the risk of migraine, univariate and multivariate Cox proportional hazard regression analyses were used to calculate the hazard ratio (HR) and 95% confidence interval (CI). Results: After a 10-year follow-up controlling for potential confounding factors, overall migraine incidence was higher in the cervical spondylosis cohort than in the non- cervical spondylosis cohort (5.16 and 2.09 per 1,000 people per year, respectively; crude hazard ratio = 2.48, 95% confidence interval = 2.28–2.69) with an adjusted hazard ratio of 2.03 (95% confidence interval = 1.86–2.22) after accounting for sex, age, comorbidities, and medication. Individuals with myelopathy in the cervical spondylosis cohort had a 2.19 times (95% confidence interval = 1.80–2.66) higher incidence of migraine compared than did those in the non- cervical spondylosis cohort. Conclusion: Individuals with cervical spondylosis exhibited a higher risk of migraine than those without cervical spondylosis. The migraine incidence rate was even higher among individuals with cervical spondylotic myelopathy.

This paper outlines evidence-based practice in the context of professionalism, and highlights the contribution evidence-based practice can make to the professional practice of higher education course managers. Implications of the changing HE landscape for the status of academics as professionals are reviewed, and evidence-based practice is proposed as a solution for both enhanced course management and to remedy perceived deprofessionalisation. Finally, questions regarding researching professional practice within one’s own institution are addressed.

The emphasis in the automotive industry towards sustainable mobility has led to a significant interest in hybrid-electric propulsion systems with high energy density batteries. Addressing the needs of this strategy, the battery market is exploring new technologies to improve the safety and lifespan of electric vehicles. To this end, there is a focus on the all-solid-state battery (ASSB) technology for its cycle capabilities. Filling the current void in the literature pertaining to accurate ageing models for ASSBs, in this work, we present an enhanced version of the numerical ageing model, originally developed for liquid electrolyte based batteries, to forecast the development of the solid electrolyte interface layer that is the major cause of battery capacity fading. The model has been tested on prototype batteries and reveals an accuracy of 99%. The capacity fade in ASSBs has been investigated under different conditions, and the enhanced ageing model has been validated using experimental data from these batteries. The findings suggest that there is potential for solid-state batteries to be commercialized, although significant work is needed to match the manufacturing level of lithium-ion batteries with liquid electrolytes.

In order to study the adsorption process and adsorption characteristics of shale oil at the macro scale, the isothermal adsorption experiments of illite and kerogen on heptadecane (oil phase) solution were carried out by infrared spectrophotometry and gas chromatography-mass spectrometry. The reaction mechanism of alkane solution concentration and temperature in the adsorption process was investigated. The adsorption characteristics of heptadecane on the surface and pores of illite and kerogen were elucidated by adsorption isotherm model and adsorption thermodynamic model. The results show that: The concentration of alkane solution contributes to the adsorption of illite and kerogen. As the concentration of the solution continues to increase, the adsorption rate gradually slows down. However, the temperature hardly affects the adsorption rate. At 25℃, 50℃ and 60℃, the adsorption process of illite on alkane solution is more consistent with Langmuir model and Freundlich model, respectively. The difference is that Langmuir model is suitable for describing the whole adsorption process of kerogen on alkane solution. 3) The adsorption process of illite and kerogen to heptadecane solution is an endothermic reaction. Heating helps to accelerate the migration rate of alkanes, which is conducive to the adsorption reaction. The research results clarify the adsorption characteristics of shale oil heavy components from the macro level, and fill the research gap in the application of solid-liquid isothermal adsorption physical experiments to the adsorption and occurrence of shale oil.

Colleges and universities play a crucial role in fostering innovation, making it essential to explore effective strategies for promoting innovation at the institutional policy level. This paper focuses on the establishment of intellectual property (IP) model cities as a starting point and conducts an empirical analysis using innovation data from 234 cities and 942 colleges and universities between 2007 and 2017. By constructing a multi-temporal double-difference model, this study reveals that the establishment of IP model cities effectively fosters innovation in colleges and universities. Further analysis demonstrates that this promotion effect is particularly significant in the western region, key cities, key colleges and universities, as well as in the fields of invention and utility model patents. These conclusions withstand a series of robustness tests, confirming their validity. In terms of the underlying mechanisms, it is found that the national IP pilot city policy has a substantial impact on university innovation by promoting research and development (R&D) investment and strengthening innovation cooperation. The insights provided by this study offer valuable policy recommendations for leveraging the innovation potential of the IP model city policy, thereby driving economic transformation, upgrading, and fostering innovation development in China.

While considering the sensitivity over the parameter optimization, it is essential to determine which parameters have the most significant implications on model performance. This study focuses on the baseflow recession constant as one of the independent basin parameters to forecast low flows, perform hydrograph analysis, and calibrate rainfall-runoff models for significant improvement. Prior studies examined that the optimization of data adjustment parameters can improve the hydrological model performance and determine the minimum acceptable data length for data scarce regions using the Xinanjaing model. However, it is essential to pay special attention to the sensitivity of the recession constant, which can also impact the model performance during the data scarcity. Therefore, this study extends the research to comprehend the recession constant sensitivity over data adjustment parameters in the shorter datasets leading to more reliable parameter estimation. In terms of that, this study explores how recession constant affects hydrological parameter estimation on annual scale while keeping data adjustment parameters constant in continuous hydrological modeling, employing the Xinanjiang (XAJ) model as a case study. This study considered two approaches of recession constant (cg); (i) assessing the relationship between cg and the data adjustment parameter (Cep), for the 28-year datasets, (ii) investigating the significant impacts of the sensitivity of cg over Cep in shorter datasets which can affect the estimation of the acceptable minimum data length in the data scarce basins. The study underscores the importance of the recession constant sensitivity for reliable continuous hydrological model predictions, especially in data-scared areas. The study’s outcomes enhance the understanding of the importance of parameter sensitivity and its relationship in conceptual hydrological modeling during the data limitations.

Using the Super-SBM method, this study calculates the carbon emission efficiency (CEE) of 30 provinces in mainland China from 2011 to 2019. Then, using the systematic GMM model, spatial Durbin model, and mediating effect model, it examines the direct effect, spatial effect, and influence mechanism of the digital economy (DE) on CEE. It was found that (1) the DE significantly promoted regional CEE, but had a negative effect on CEE in provinces with high economic correlation; (2) mechanism studies showed that the DE improved CEE by reducing energy intensity, promoting industrial upgrading and green technology innovation; (3) regional heterogeneity analysis found that the DE significantly improved CEE in eastern provinces, but not in central and western provinces. DE improves CEE in provinces with high level of economic development, but decreases CEE in provinces with low level of economic development.This paper provides some empirical and theoretical references for the development of DE to improve CEE.

In this paper, a novel strategy is adopted in a degradation model to affect the implied lifetime distribution. The multiplicative degradation model is utilized as a postulate in the model. It will be established that the implied lifetime distribution forms a classical mixture model. In this mixture model, time-to-failure is lying with some probabilities between two first passage times of the degradation process to reach two specified levels. Stochastic comparisons in the model under a change in the probabilities are studied. Several examples are provided to highlight the applicability of the results in the cases when typical degradation models are candidate.

During the dry season, glaciers contribute significantly to the water supply downstream, especially in areas with seasonal rainfall. It is important to have physical-based glacier models, which are more advanced in quantifying the future dynamics of glaciers and melt distribution. Toward this purpose, an investigation of different numerical modeling tools and their approach is conducted and planned further to incorporate a glacier module into a spatially distributed model called mHM (Mesoscale Hydrological Model, mhm-ufz.org). An open-source tool called MATILDA (Modeling Water Resources in Glacierized Catchments) is used beforehand to implement the glacier entities to the application catchment Maipo Basin in Chile. Due to limited time availability and source of data, the glacier melt of the MATILDA model is integrated into the mHM model to see what the implications would be if the glacier module was implemented from scratch in mHM. The analysis of the glacier hydrology resulting from the Maipo-Glacier model was carried out by comparing the modeled glacier flows with the total observed flows. To assess the credibility of the models, the results are compared based on three goodness-fit measures, specifically r^2Coefficient of Determination), NSE (Nash-Sutcliffe Efficiency), and KGE (Kling-Gupta Efficiency). Application of the mHM model in the Maipo basin in Central Chile is performed by using prepared input datasets, showing that the approach is viable as evidenced by a Kling-Gupta Efficiency of 0.80 and Nash-Sutcliffe Efficiency of 0.70 from the starting periods of 1950 to 2020. Overestimation by the mHM model occurs during the late summer season, particularly when temperature decreases. The MATILDA model is used on glacier melt distribution, forcing data is prepared from regional datasets, and a glacier profile is constructed from ice thickness datasets. According to the model, the average KGE result for the years 2012–14 and 2015–18 was respectively 0.44 and 0.41. Glacier melt produced by Matilda is in the range of 58-68 % of the total runoff and the glacier area decreased by 22-23% during the simulation period from 2012-14 and 2016-2018. A pilot sub-basin Olivares is used in the coupling mHM model with initializing MATILDA glacier melt. The new model is underestimated during the seasonal transition in the initial days of the simulation. A considerable portion of the ablation period might be missed as a result of the initial storage quality, true glacier extent, and height distribution being understated. As a conclusion, the mHM Model was capable of predicting runoff well in a mountainous region and could also serve as a monitoring tool for watershed management. There are differences in results between the two models because of the different spatial resolutions and methodologies. MATILDA have a glacier retreat routine that gives better result for the glacier characteristics such as. A new glacier dynamics model needs to be implemented in the future to develop intermediate complexity, bridge catchment, and glacier scales after using the mHM, MATILDA, and mHM with MATIDLA model for analysis of glacier and highlighting the model's deficiencies in the initial period of simulative assessments.

One of the most broadly used models for membrane fouling is the Hermia model, which separates this phenomenon into four blocking mechanisms, each with an associated parameter n. These mechanisms are complete blocking (n=2), intermediate blocking (n=1), standard blocking (n=3/2), and cake formation (n=3/2). The original model, which was obtained through experimental data, is given by an Ordinary Differential Equation (ODE) dependent on n. At the time, this ODE was only solved for these four values of n, which limits the effectiveness of the model when adjusted to experimental data. This paper aims to not only mathematically prove the original Hermia model but also to broaden the scope of this model for any real number n by using the original ODE, the equations of fluid mechanics, and the properties of single and multivariable calculus. The extended Hermia model (EHM) is given by a power law for any n≠2 and is given by an exponential function at n=2. To better test the model, we have performed the model fitting of the EHM and compared its performance to the original four pore-blocking mechanisms in 6 micro- and ultrafiltration examples. In all examples, the EHM performed consistently better than the four original pore-blocking mechanisms. Changes in the blocking mechanisms concerning transmembrane pressure (TMP), crossflow rate (CFR), crossflow velocity (CFV), membrane composition, and pretreatments are also discussed.

This study aimed to determine the feasibility of the development of an over-the-counter (OTC) screening model using machine learning for breast cancer screening in the Asian women population. Data were retrospectively collected from women who came to the Hospital Universiti Sains Malaysia, Malaysia. Five screening models were developed based on machine learning methods; random forest, artificial neural network (ANN), support vector machine (SVM), elastic-net logistic regression and extreme gradient boosting (XGBoost). Features used for the development of the screening models were limited to information from the patients’ registration form. The model performance was assessed across the dense and non-dense groups. SVM had the best sensitivity while elastic-net logistic regression had the best specificity. In terms of precision, both random forest elastic-net logistic regression had the best performance, while, in terms of PR-AUC, XGBoost had the best performance. Additionally, SVM had a more balanced performance in terms of sensitivity and specificity across the mammographic density groups. The three most important features were age at examination, weight and number of children. In conclusion, OTC models developed from machine learning methods can improve the prognostic process of breast cancer in Asian women.

A micromechanical model is implemented to indicate the progressive fatigue problem of a laminated composite with a central circular hole under fatigue loading based on a finite element model. The mechanical properties of the composite lamina are represented based on the characteristics of the fiber and the matrix through a micromechanics model. An appropriate algorithm is then adopted to simulate fatigue damage development in the composite lamina. According to this algorithm, the stress field of the composite subjected to fatigue load is initially obtained using the finite element method. Finally, the predicted results of the stresses in the constituents i.e. fiber and matrix are determined according to the micromechanical bridging model. Finally applying proper damage driving relations leads to damage degree in each element. The proposed model is proven to be successful in the observation of the fatigue behavior with stiffness degradation in each element of the composite in each cycle. Results are reported and validated using those micromechanical models and experimental data available in the literature.

Background: Outbreak of the Covid-19 is now an ongoing global health emergency. At the end of December 2019, the first infection was reported in Wuhan and the world did not pay attention to this extremely contaminated disease and plucked to react rapidly. The World is in an vulnerable state in disease spreading, facing a great loss of lives and socio-economic aspects also. That is why we have proposed a potential mathematical model with data analysis to predict and control the outcome of this pandemic. Methods: The model presented the epidemic dynamics of multiple compartments. We collected available online data of Spain. In primary step, we estimated the parameters using either the data analysis or reference papers. Then we did the data fitting analysis in comparison with the outcome of our mathematical results. The results of the system depended not only the parameters also on social consciousness. Results: It is found that disease progression in this model is determined by the basic reproductive ratio, $R_0$, the actual epidemic of $R_0$ and effective $R(t)$ of each day. If $R_0>1$, the number of latently infected individuals grows exponentially; endemic solution is stable while infection rate decays if $R_0<1$. The optimal control theory stated that vaccination and treatment strategies are highly effective for reducing both susceptible and infected population and to increase the recover rate high. In Spain, after state of alarm (quarantine) on 14 March 2020, reported cases increasing for 13 days only and from the 14th day, daily reported cases started to decline albeit with small fluctuation. Our proposed model approximates that the disease in Spain could be fully under control by after July 2020. Conclusion: Outbreak will be in control of health care system, reduce the death rate and will ensure social-economic stability.

In this paper, the seven traditional models of PV modules are reviewed comprehensively to find out the appropriate model to be reliable. All the models are validated using the Matlab code and make a graphical comparison. The accuracy and convergence of each model are evaluated using data of manufactured PV panels. Then, a novel model is proposed showing its consistent performance. The most three key parameters of single-diode model are self revised to adapt to various type of PV modules. This new method is verified in three types of PV panels' data measured by National Renewable Energy Laboratory (NREL), US. The validated data show promising results when the error RMSEs' range of the proposed model is under 0.36.

Model selection and model averaging have been the popular approaches in handling modelling uncertainties. Fan and Li(2006) laid out a unified frame work for variable selection via penalized likelihood. The tuning parameter selection is vital in the optimization problem for the penalized estimators in achieving consistent selection and optimal estimation. Since the OLSpost-LASSO estimator by Belloni and Chernozhukov (2013), few studies have focused on the finite sample performances of the class of OLS post-penalty estimators with the tuning parameter choice determined by different tuning parameter selection approaches. We aim to supplement the existing model selection literature by studying such a class of OLS post-selection estimators. Inspired by the Shrinkage Averaging Estimator (SAE) by Schomaker(2012) and the Mallows Model Averaging (MMA) criterion by Hansen (2007), we further propose a Shrinkage Mallows Model Averaging (SMMA) estimator for averaging high dimensional sparse models. Based on the Monte Carlo design by Wang et al. (2009) which features an expanding sparse parameter space as the sample size increases, our Monte Carlo design further considers the effect of the effective sample size and the degree of model sparsity on the finite sample performances of model selection and model averaging estimators. From our data examples, we find that the OLS post-SCAD(BIC) estimator in finite sample outperforms most of the current penalized least squares estimators as long as the number of parameters does not exceed the sample size. In addition, the SMMA performs better given sparser models. This supports the use of the SMMA estimator when averaging high dimensional sparse models.

Changes in agricultural yields due to climate change will affect land use, agricultural production volume, and food prices as well as macroeconomic indicators, such as GDP which is important as it enables one to compare the climate change impacts across multiple sectors. This study considered five key uncertainty factors and estimated macroeconomic impacts due to crop yield changes using a novel integrated assessment framework. The five factors are 1) land-use change (or yield aggregation method based on spatially-explicit information), 2) the amplitude of the CO2 fertilization effect, 3) the use of different climate models, 4) socioeconomic assumptions and 5) the level of mitigation stringency. We found that their global impacts on the macroeconomic indicator value were 0.02 - 0.06% of GDP in 2100. However, the impacts on the agricultural sector varied greatly by socioeconomic assumption. The relative contributions of these factors to the total uncertainty in the projected macroeconomic indicator value were greater in a pessimistic world scenario characterized by a large population increase and low income (0.6%) than in an optimistic scenario (0.00%).

The aim of this study has been to develop a novel two-level multi-objective genetic algorithm (GA) to optimize time series forecasting data for fans used in road tunnels by the Swedish Transport Administration (Trafikverket). Level 1 is for the process of forecasting time series cost data, while level 2 evaluates the forecasting. Level 1 implements either a multi-objective GA based on the ARIMA model or a multi-objective GA based on the dynamic regression model. Level 2 utilises a multi-objective GA based on different forecasting error rates to identify a proper forecasting. Our method is compared with using the ARIMA model only. The results show the drawbacks of time series forecasting using only the ARIMA model. In addition, the results of the two-level model show the drawbacks of forecasting using a multi-objective GA based on the dynamic regression model. A multi-objective GA based on the ARIMA model produces better forecasting results. In level 2, five forecasting accuracy functions help in selecting the best forecasting. Selecting a proper methodology for forecasting is based on the averages of the forecasted data, the historical data, the actual data and the polynomial trends. The forecasted data can be used for life cycle cost (LCC) analysis.

Bladder cancer is one of the most common malignant diseases in the urinary system and a highly aggressive neoplasm. The prognosis is not favourable usually and its evolution for particular patients is very difficult to find out. In this paper we propose a dynamic mathematical model that describes the bladder tumor growth and the immune response evolution. This model is customized for a single patient, determining appropriate model parameter values via model calibration. Due to the uncertainty of the tumor evolution, using the calibrated model parameters, we predict the tumor size and the immune response evolution over the next few months assuming three different scenarios: favourable, neutral and unfavourable. In the former, the cancer disappears; in the second a 5mm tumor is expected around the middle of August 2018; in the worst scenario, a 5mm tumor is expected around the end of May 2018. The patient has been cited around June 15th, 2018, to check the tumor size, if it exists.

Multiobjective optimization plays an increasingly important role in modern applications, where several criteria are often of equal importance. The task in multiobjective optimization and multiobjective optimal control is therefore to compute the set of optimal compromises (the Pareto set) between the conflicting objectives. The advances in algorithms and the increasing interest in Pareto optimal solutions have led to a wide range of new applications related to optimal and feedback control which results in new challenges such as expensive models or real-time applicability. Since the Pareto set generally consists of an infinite number of solutions, the computational effort can quickly become challenging which is particularly problematic when the objectives are costly to evaluate or when a solution has to be presented very quickly. This article gives an overview over recent developments in accelerating multiobjective optimal control for complex problems where either PDE constraints are present or where a feedback behavior has to be achieved. In the first case, surrogate models yield significant speed-ups. Besides classical meta-modeling techniques for multiobjective optimization, a promising alternative for control problems is to introduce a surrogate model for the system dynamics. In the case of real-time requirements, various promising model predictive control approaches have been proposed, using either fast online solvers or offline-online decomposition. We also briefly comment on dimension reduction in many-objective optimization problems as another technique for reducing the numerical effort.

A unified critical state model has been developed for both clean sand and silty sand using the modified Cam-clay model (MCC). The main feature of the proposed model is a new critical state line equation in the e-ln(p) plane that is capable of handling both straight and curved test results. With this feature, the error in calculating plastic volumetric strain is eliminated in theory. Another crucial feature of the model is the transformed stress tensor based on the SMP (spatially mobilized plane) criterion, which takes into account the proper shear yield and failure of soil under three-dimensional stresses. Additionally, the proposed model applies the intergranular void ratio with the fines influence factor for silty sand. Only eight soil parameters are required for clean sand, and a total number of twelve soil parameters are needed for silty sand.

Dyadic data contains co-occurrences of objects, which is often modeled by finite mixture model which in turn is learned by expectation maximization (EM) algorithm. Objects in traditional dyadic data are identified by names, causing the drawback which is that it is impossible to extract implicit valuable knowledge under objects. In this research, I propose the so-called attributed dyadic data (ADD) in which each object has an informative attribute and each co-occurrence of two objects is associated with a value. ADD is flexible and covers most of structures / forms of dyadic data. Conditional mixture model (CMM), which is a variant of finite mixture model, is applied into learning ADD. Moreover, a significant feature of CMM is that any co-occurrence of two objects is based on some conditional variable. As a result, CMM can predict or estimate co-occurrent values based on regression model, which extends applications of ADD and CMM.

The total enthalpies of the 16 different spin configurations that can be realized in the unit cell of the archetype spin crossover complex [Fe(phen)₂(NCS)₂] (phen=1,2-phenanthroline) were calculated applying periodic density functional theory combined with the Hubbard model and the Grimme-D2 dispersion correction (DFT+U/D2). The obtained enthalpy differences between the individual spin configurations were used to determine spin couplings of an Ising-like model and subsequent Monte Carlo simulations for this model allowed to estimate the phenomenological interaction parameter Г of the Slichter-Drickamer model, which is commonly used to describe the cooperativity of the spin transition. The calculational procedure described here, which led to an estimate of about 3 kJ/mol⁻¹ for Г, in good agreement with experiment, may be used to predict from first principles how modifications of spin crossover complexes can change the character of the spin transition from gradual to abrupt and vice versa.

Decades of research suggest that Mindfulness-Based Stress Reduction (MBSR) training supports a greater capacity to live with chronic medical conditions and contributes to lowering stress levels. This paper introduces a model for a Mindfulness-Based Recovery from Stroke (MBRfS) for promoting stroke recovery, informed by the lived experience of the author (a stroke survivor and certified MBSR instructor), the research literature regarding MBSR training, and the specific challenges of stroke recovery. Four themes emerged from the autoethnographic analysis that informed the proposed model: Readiness to accept the stroke event and the acquired brain injury; Navigating uncertainties of stroke recovery with awareness and self-responsibility for outcomes; Trusting the inherent wisdom of the body as a stroke recovery “teacher”; and Increased capacity to integrate complex emotions with self-compassion, and a sense of wholeness. A four component MBRfS model is offered, which consists of an integration amongst a modified MBSR framework, emergent attitudinal themes, and insights from the autoethnographic vignettes. The MBRfS model offers a path for providing participants with a supportive experience within stroke recovery. Recommendations and suggestions for future studies are offered to support the development of MBRfS for stroke survivors and their caregivers, as well as contributing to health care providers.

Populations with healthcare needs often reside in post-conflict settings where basic services needed to maintain good health may be non-existent or hard to access. Therefore, there is a need for better identification and reallocation of resources as part of the post-conflict health rehabilitation effort. This study applies an asset-based approach to explore the more optimal design of health services and to identify the resource constraints for basic health service delivery to the most vulnerable communities in eastern Congo. We implemented the asset mapping in two phases. Firstly, we combined a qualitative survey with community walks to identify the assets already present in the communities. Secondly, we conducted group discussions to map out assets that are the core of Asset-Based Community Development (ABCD) practice. We finally documented all assets in a Community Asset Spreadsheet. Overall, 209 assets were identified as available and potentially valuable resources for the communities in eastern Congo. Among them, 60 were local associations, 24 were land and physical environment, 43 were local institutions, 46 were individuals, 32 to economy and exchange, and only 6 were related to culture, history, and stories. Drawing upon the findings from the qualitative survey, community walks, and group discussions, we conclude that an important number of resources were in place for basic health service delivery. By activating the existing and potential resources, the most vulnerable populations in eastern Congo might have the required resources for basic health service delivery. Our findings support the use of an asset-mapping research method as appropriate to identify existing and potential resources for basic health services in a post-conflict setting.

This paper proposes two max-pooling engines, named the RTB-MAXP engine and the CMB-MAXP engine, with a scalable window size parameter for FPGA-based convolutional neural network (CNN) implementation. The max-pooling operation for the CNN can be decomposed into two stages, i.e., a horizontal axis max-pooling operation and a vertical axis max-pooling operation. These two one-dimensional max-pooling operations are performed by tracking the rank of the values within the window in the RTB-MAXP engine and cascading the maximum operations of the values in CMB-MAXP engine. Both the RBM-MAXP engine and the CMB-MAXP engine were implemented using VHSIC Hardware Description Language (VHDL) and verified by simulations. They have been employed for and tested in our CNN accelerator targeting at the CNN model YOLOv4-CSP-S-Leaky for object detection.

Traditional high pressure mechanical compressors account for over half of the car station’s cost, have insufficient reliability and are not feasible for a large-scale fuel cell market. An alternative technology, employing a two-stage, hybrid system based on electrochemical and metal hydride compression technologies, represents an excellent alternative to conventional compressors. The high-pressure stage, operating at 100-875 bar, is based on a metal hydride thermal system. A techno-economic analysis of the metal hydride system is presented and discussed. A model of the metal hydride system was developed, integrating a lumped parameter mass and energy balance model with an economic model. A novel metal hydride heat exchanger configuration is also presented, based on mini-channel heat transfer systems, allowing for effective high-pressure compression. Several metal hydrides were analyzed and screened, demonstrating that one selected material, namely (Ti_0.97Zr_0.03)_1.1Cr_1.6Mn_0.4, is likely the best candidate material to be employed for high-pressure compressors under the specific conditions. System efficiency and costs were assessed based on the properties of currently available materials at industrial levels. Results show that the system can reach pressures on the order of 875 bar with thermal power provided at approximately 150 °C. The system cost is comparable with the current mechanical compressors and can be reduced in several ways as discussed in the paper.

The research develops and validates new decomposition models for DNI estimations from Southern African data. The results demonstrated improved DNI estimation accuracy compared to the baseline models across all testing and validation datasets. These outcomes suggest that utilising a localised model can significantly enhance DNI estimations for Southern Africa and potentially for developing similar models in diverse geographic regions worldwide. Furthermore, clustered models highlighted the potential advantages of grouping data based on shared geographical and climatic attributes. This clustering approach could enhance decomposition model performance, particularly when local data is limited or data is available from multiple nearby stations. The Southern African decomposition model, which encompasses a wide spectrum of climatic regions and geographic locations, exhibited notable improvements over the baseline models despite occasional overestimation or underestimation. The overall metrics affirm the substantial advancement achieved with the Southern African model. This study focused on validating the model for hourly DNI in Southern Africa within a range of clearness index-intervals from 0.175 to 0.875. Implementing accurate decomposition models in developing countries can accelerate the adoption of renewable energy sources, diminishing reliance on coal and fossil fuels.

The paper presents a simplified laminar model with which the deformation of soil layers (base, subbase and subgrade courses) under flexible pavements due to repeated traffic load can be determined. In the first step, the cyclic strain amplitude is calculated using a nonlinear material model that is based on particle size distribution parameters (d50 and CU) and dependent on the mean normal stress, relative density and actual strain level. In the second step, the HCA (High Cycle Accumulation) model is used to calculate the residual settlement of each sublayer as a function of the number of cycles. It is shown that the developed model is suitable for describing different types of subgrades and pavement cross-sections. It is also demonstrated with finite element calculations that the developed model describes both the elastic and plastic strains sufficiently accurately. The developed model can predict the settlement and rutting of pavement structures with sufficient accuracy based on easily available particle size distribution parameters without the need of complex laboratory and finite element tests.

Industrial process control systems commonly exhibit features of time-varying, strong coupling, and strong nonlinearity. Obtaining accurate mathematical models of these nonlinear systems and achieving satisfactory control performance is still a challenging task. In this paper, data-driven modeling techniques and deep learning methods are used to accurately capture a category of smooth nonlinear system’s spatiotemporal features. The operating point of these systems may change over time, and their nonlinear characteristics can be locally linearized. We established the LSTM-CNN-ARX model by utilizing a fusion of long short-term memory (LSTM) network and convolutional neural network (CNN) to fit the coefficients of the state-dependent exogenous variable autoregressive (SD-ARX) model. Compared to other models, the hybrid LSTM-CNN-ARX model is more effective in capturing the nonlinear system’s spatiotemporal characteristics due to its incorporating the strengths of LSTM for learning temporal characteristics and CNN for capturing spatial characteristics. The model-based predictive control (MPC) strategy, namely LSTM-CNN-ARX-MPC, is developed by utilizing the model's local linear and global nonlinear features. The control comparison experiments conducted on a water tank system (WTS) show the effectiveness of the developed models and MPC methods.

Pancreatic cancer represents one of the difficult problems of contemporary medicine. The development illness evolves very slowly, takes place in special sake (stroma) and manifests clinically close to a final stage. Another feature of this pathology is a coexistence (symbiotic) effect between cancer cells and normal cells inside stroma. All these aspects make it difficult to understand the pathogenesis of pancreatic cancer and develop a proper therapy. The emergence of pancreatic pre-cancer and cancer cells represents a branching stochastic process engaging populations of 64 cells differing in the number of acquired mutations. In this study we formulate and calibrate the mathematical model of pancreatic cancer using the quasispecies framework. The mathematical model incorporates the mutation matrix, fineness landscape matrix and the death rates. Each element of the mutation matrix presents the probability of appearing a specific mutation in the branching sequence of cells representing the accumulation of mutations. The model incorporates the cancer cell elimination by effect CD8 T cells (CTL). The down-regulation of the effector function of CTLs and exhaustion are parameterized. The symbiotic effect of coexistence of normal and cancer cells is considered. The computational predictions obtained with the model are consistent with empirical data. The modelling approach can be used to investigate other types of cancers and examine various treatment procedures.

The use of cobalt-iron (VaCoFe) core is investigated as an alternative to silicon-iron (FeSi) on the design of interior permanent magnet synchronous motors (IPMSM). A spoke-type IPMSM geometry is optimized considering FeSi and VaCoFe cores for a torque range up to 40 N.m, providing a general comparative analysis between materials, considering the application of a 4-motor competition vehicle’s powertrain. A genetic optimization algorithm is applied over a hybrid analytical/finite-element model of the motor to provide sufficiently accurate electromagnetic and thermal results within a feasible time. VaCoFe can result in an estimated increase of up to 5 % in efficiency for the same torque, or up to 64 % torque increase for the same efficiency level. After optimization, and using a detailed time-dependent model, a potential 3.2 % increase in efficiency, a core weight reduction of 4.1 %, and a decrease of 9.6 % in the motor’s core volume was found for the VaCoFe at 20 Nm. In addition, for the same motor volume, the VaCoFe allows an increase of 51.9 % of torque with an increase of 1.1 % of efficiency, when compared with FeSi.

The cellular model we teach and have theorized assumes that the cell is the basic unit of multicellular living beings. This fundamental element has been the subject of many theories concerning its properties and the exchanges that exist with its environment. In this article, we demonstrate that certain functional aspects, in particular the electrical aspects related to diffusion, have not been correctly assumed or that certain initial conditions have been purely ignored and are in contradiction with physics, chemistry and thermodynamics.

Trachsummaroma is an eye infectious disease caused by Chlamydia Trachomatis bacterium, which may lead to irreversible blindness. The disease is spread directly or indirectly by contacting a contaminated material. It can also be transmitted through the disease vector known as “Musca sorbens” or “Bazaar fly”. To curtail the spread of the disease in a population, a meaningful information on the spread and possible control of the disease is required. Mathematical modeling provides efficient tools that can be used to understand and analyze the dynamics of the disease and its control. Several compartmental epidemic models have been proposed in the literature to study the dynamics of trachoma; including SI, SIR and SEIR. However, majority of the existing trachoma models consider only person to person transmission. Thus, the information provided by such models is insufficient since they did not capture the disease vector transmission. The current study proposed a novel SEIR-SEI model that consider both person-person and vector transmission dynamics. The threshold quantity, basic reproduction number R0 is obtained using the next generation matrix, and it was proved that the disease-free equilibrium is asymptotically stable when R0 < 1, and the endemic equilibrium is globally asymptotically stable when R0 > 1. Some simulation results with the aid of mesh plots for the reproductive number as a function of two different biological parameters were obtained. Furthermore, a comprehensive sensitivity analysis is conducted to identify the influence of the individual parameters on the R0. Numerical results show that the vector contact rate has the highest sensitivity with respect to R0, and the value of R0 increases with increase in, hence, the disease can be controlled by decreasing the vector contact rate. Similarly, improving the rate of environmental hygiene and facial cleanliness will decrease the size of R0 and result in the declination of the disease transmission. Moreover, a detailed parameter estimation of the model parameters and model fitting was presented with the use of field data cases from Northern Nigeria using least-square fitting method. The study provides alternative tools that can be used for planning trachoma control program to achieve global eradication of trachoma as a public heath challenge as targeted by WHO in 2030.

Mistrust between scientists and non-scientist stakeholders is a key challenge in fishery management. This problem is exacerbated with the use of complex models to support management: these models suffer from difficulties in communicating their results and a lack of confidence from end users. The European eel is an illustrative example; its complex life cycle raises problems of coordination and discussion among the multiple actors involved in the management of the species. The GEREM model has been proposed as a tool for estimating recruitment, but its complexity, which is essential for addressing the characteristics of the species, makes it difficult to understand and accept by all stakeholders. In the context of the SUDOANG project, we proposed a co-parametrization of this assessment model to tackle this mistrust. Through the use of various questionnaires and appropriate statistical analyses, stakeholders were involved in two important choices for the model (zone definition and prior construction). Regular workshops and presentations were organised to explain the model rationales and to gather feedback and expectations. The results show that stakeholders have very similar perceptions of the potential definitions of sub-areas of recruitment in south-western Europe, and these perceptions are consistent with the underlying environmental conditions. On the other hand, the stakeholders have contrasting opinions about the exploitation rates of fisheries in different river basins, and the use of their knowledge currently has little effect on GEREM estimates. More importantly, the overall approach of this study is thought to have reconstructed the trust and confidence among participants.

The Rasch model is one of the most prominent item response models. In this article, different item parameter estimation methods for the Rasch model are compared through a simulation study. The type of ability distribution, the number of items, and sample sizes were varied. It is shown that variants of joint maximum likelihood estimation and conditional likelihood estimation are competitive to marginal maximum likelihood estimation. However, efficiency losses of limited-information estimation methods are only modest. It can be concluded that in empirical studies using the Rasch model, the impact of the choice of an estimation method with respect to item parameters is almost negligible for most estimation methods. Interestingly, this sheds a somewhat more positive light on old-fashioned joint maximum likelihood and limited information estimation methods.

Karst wetlands are being seriously damaged, and protecting it has become an important matter. Karst vegetation is the essential component of wetland and plays an important role in in the ecological functions of wetland ecosystems. Classifying karst vegetation is important for karst wetlands protection and management. This paper addressed to classify karst vegetation in Huixian National Wetland Park, located in China using the improved SegNet Deep-Learning Algorithm and UAV images. This study proposed a method to fuse single-class SegNet models using the maximum probability algorithm for karst vegetation classification, and compared with object-based RF classification and multi-class SegNet classification, respectively. This paper evaluated the performance of multi-class SegNet model and fusion of single-class SegNet model with different EPOCH values for mapping karst vegetation. A new optimized post-classification algorithm was proposed to eliminate the stitching traces caused by SegNet model prediction. The specific conclusions of this paper include the followings:(1) fusion of four single-class SegNet models produced better classification for karst wetland vegetation than multi-class SegNet model, and achieved the highest overall classification accuracy (87.34%); (2) The optimized post-classification algorithm was able to improve prediction accuracy of SegNet model, and it could eliminate splicing traces; (3) The karst wetland vegetation classifications produced by single-class SegNet model outperformed multi-class SegNet model, and improved classification accuracy(F1-Score) between 10%~25%;(4)The EPCOH values and textural feature important impact on karst wetland vegetation classifications. The SegNet model with EPCOH 15 achieved greater classification accuracy(F1-Score) than the model with EPOCH 5 or 10. The textural feature improved improves the capability of the SegNet model for mapping karst vegetation;(5) Fusion of single-class SegNet models and object-based RF model could provide high classifications results for karst wetland vegetation, and both achieved greater 87% overall accuracy.

Essential oils (EO) are a great antimicrobial resource against bacterial resistance in public health. Math models are useful describing the growth, survival, and inactivation of microorganisms against antimicrobials. We evaluated the antimicrobial activity of M. armillaris EO obtained from plants placed in the province of Buenos Aires (Argentina) against Staphylococcus aureus. Minimum Inhibitory and Bactericidal Concentrations were close and decreased slightly acidifying the medium from pH 7.4 to 6.5 and 5.0. This result was also evidenced by applying a sigmoid model, where the time and EO concentration necessaries to achieve 50% of the maximum effect decreased when medium was acidified. Moreover, at pH 7.4, applying the Gompertz model, we found that subinhibitory concentrations of EO decreased the growth rate and the maximum population density, and increased the latency period respect to the control. Additionally, we established physicochemical parameters for quality control and standardization of M. armillaris EO. Mathematical modelling allowed us to estimate key parameters in the behavior of S. aureus and Melaleuca armillaris EO at different pHs. This is interesting in situations where the pH changes are relevant, such as the control of intracellular infections in public health or the development of preservatives for food industry.

The COVID-19 epidemic is an emerging infectious disease of the viral zoonosis type caused by the corona-virus strain SARS-CoV-2, is classified as a human-to-human communicable disease and is currently a pandemic worldwide. In this paper, we propose conceptual mathematical models the epidemic dynamics of four compartments. We have collected data from the Djibouti health ministry. We define the positivity, boundedness of solutions and basic reproduction number. Then, we study local and global stability and bifurcation analysis of equilibrium to examine its epidemiological relevance. Finally, we analyze the fit of the data in comparison with the result of our mathematical results, to validate the model and estimating the important model parameters and prediction about the disease, we consider the real cases of Djibouti from 23th March to 10th June, 2020.

p-Hydroxyphenylpyruvate dioxygenase (HPPD) is not only the useful molecular target in treating life-threatening tyrosinemia type I, but also an important target for chemical herbicides. A combined in silico structure-based pharmacophore and molecular docking based virtual screening were performed to identify novel potential HPPD inhibitors. The complex based pharmacophore model (CBP) with 0.721 of ROC used for screening compound showed remarkable ability to retrieve known active ligands from decoy molecule. The ChemDiv database was screened using CBP-Hypo2 as a 3D query, and the best-fit hits subjected to molecular docking with two methods of LibDock and CDOCKER in Accelrys Discovery Studio 2.5(DS 2.5) to discern interactions with key residues at the active site of HPPD. 4 Compounds with top rank in HipHop model and well-known binding model were finally chosen as identification of lead compounds with potentially inhibitory effects on active site of target. The results provided powerful insight to the development of novel HPPD inhibitors herbicides using computational techniques.

This study aimed to evaluate the effectiveness of decellularized nerves for promoting nerve regeneration by transplanting them from the sciatic nerve of a rabbit to that of a rat. The Sciatic nerves were harvested from New Zealand White Rabbits (n=4). A 1-cm segment of each nerve was decellularized by Sondell’s protocol and preserved at –4℃ in Roswell Park Memorial Institute (RPMI) 1640 medium for one week. Subsequently, a defect of a similar size was created in the sciatic nerves of rats. The decellularized nerves treated with detergent were then transplanted into these defects in the rats. The study included the three following groups: Group 1 (n=4) was the decellularized xenogeneic nerve group; Group 2 (n=7) was the negative control group in which the defect remained un-repaired; and Group 3 (n=5) was composed of the positive control group where the nerve was immediately transplanted from one rat to another. The sciatic functional index (SFI) was considered to evaluate the motor function by comparing it with the normal side at 1, 2, 3, and 4 weeks, and 1, 2, and 3 months postoperatively. Furthermore, electrophysiological and immunohistochemical assessments were conducted at 4 months. Immunohistochemically, at 16 weeks postoperative, the experimental group did not show statistically significant differences compared to controls in terms of schwann cell comparison, yet exhibited favorable outcomes when compared to the positive control group. Additionally, analysis of motor function test confirmed recovery of latency and nerve conduction velocity to levels similar to those of the normal side. While the nerve allograft demonstrated superior nerve regeneration in motor function test at 3 months, markedly low values were observed in all groups. When reconstructing peripheral nerves using detergent-based decellularized xenogenic nerves, analysis of immuno-histochemistry and motor nerve conduction study reveals a promising degree of nerve regeneration. However, when compared with fresh allograft group at 3 months after operation, the sciatic functional index was lower. Significantly lower values observed across all groups compared to previous assessments indicate the need for further research into potentially influential alternative factors.Histologically, at 16 weeks postoperative, the experimentally treated group did not show statistically significant differences compared to the controls in terms of a Schwann cell comparison, but exhibited favorable outcomes when compared to the positive control group. Additionally, an analysis of the motor function testing confirmed recovery of the latency and nerve conduction velocity to levels similar to those of the normal side. While the nerve allograft demonstrated superior nerve regeneration in the motor nerve examination at 3 months, notably low values were observed in all the groups. When reconstructing the peripheral nerves using detergent-based decellularized xenogeneic nerves, an analysis of immunohistochemistry and motor nerve conduction study reveals a promising degree of nerve regeneration. However, when compared with the fresh allograft group at 3 months post-operation, the sciatic functional index was lower. The significantly lower values observed across all groups compared to those of the previous assessments indicate the need for further research of potentially influential alternative factors.

Adaptation to the impacts of human induced global heating is inevitable. For a socially just and ecological sustainable adjustment to these new circumstances, we need to draw from diverse knowledge systems while, simultaneously, foster a social-ecological transformation. We approach these concerns through the concept of community-based adaptation (CBA) and discuss remedies. Synergies with feminist knowledge systems and indigenous ways of living show the transdisciplinary essence of CBA. By abstracting from its origin and overlapping disciplines, revealing the social metabolism as a common problem, CBA is prone to achieve more equitable and sustainable adjustments. Transdisciplinarity itself and the current related CBA literature point beyond mere adjustments and call for a redesign of the social metabolism, thereby shifting from adaptation to global heating towards adaptation with nature by focusing on social provisioning. An engagement with transformative adaptation and heterodox economics further shows the potential of CBA to serve as an instrument in support of a social-ecological transformation by addressing root causes of vulnerability and by fostering epistemic justice. Reflecting these points back on the epistemological and ontological assumptions of CBA, the viewpoint proposes critical realism as a guiding philosophy of science to move forward.

Opportunistic networks, an evolution of mobile Ad Hoc networks (MANETs), offer decentralized communication without relying on preinstalled infrastructure, enabling nodes to route packets through different mobile nodes dynamically. However, due to the absence of complete paths and rapidly changing connectivity, routing in opportunistic networks presents unique challenges. This paper proposes a novel probabilistic routing model for opportunistic networks, leveraging nodes' meeting probabilities to route packets towards their destinations. The model dynamically builds routes based on the likelihood of encountering the destination node, considering factors such as last meeting time and acknowledgment tables to manage network overload. Additionally, an efficient message detection scheme is introduced to alleviate high overhead by selectively deleting messages from buffers during congestion. Furthermore, the proposed model incorporates cross-layer optimization techniques, integrating optimization strategies across multiple protocol layers to maximize energy efficiency, adaptability, and message delivery reliability. Through extensive simulations, the effectiveness of the proposed model is demonstrated, showing improved message delivery probability while maintaining reasonable overhead and latency. This research contributes to the advancement of opportunistic networks, particularly in enhancing connectivity and efficiency for Internet of Things (IoT) applications deployed in challenging environments.

The numerical implementation of the controllers allows the use of very complex algorithms with ease. However, in practice, due to its proven advantages, the PID controller (and its variants) is widely used in industrial control systems as well as in many other applications that require continuous control. Most of the methods for tuning the parameters of PID controllers are based on time-invariant linear models of the processes, which in practice can lead to poor performance of the control system. The paper presents an application of reinforcement learning algorithms in tuning of PID controllers for the control of some classes of continuous nonlinear systems. Tuning the parameters of PID controllers is done with the help of Twin Delayed Deep Deterministic Policy Gradients (TD3) algorithm which presents a series of advantages compared to other similar methods from Machine Learning dedicated to continuous state and action spaces. TD3 algorithm is an off-policy Actor-Critic based method and was used as it does not require a system model. The presented technique is applied for control of a biotechnological system which has a strongly nonlinear dynamic. The proposed tuning method is compared to the classical tuning methods of PID controllers. The performance of the tuning method based on the TD3 algorithm is demonstrated through simulation illustrating the effectiveness of the proposed methodology.

Promoting the sustainable development of resource-based cities is of great significance to the ecological protection and high-quality development of the Yellow River Basin. Combining the environmental status and economic development, we objectively evaluate the ecological safety level of resource-based cities in the Yellow River Basin, compare and analysis the main factors affecting the ecological safety of different resource-based cities, and formulate precise and differentiated ecological safety protection measures in river basins to achieve high-quality development. Taking the Yellow River Basin as an example, this paper analyses the ecological level of the Yellow River Basin based on the theory of sustainable development, calculates the ecological safety level of 30 resource-based cities in the Yellow River Basin from 2006 to 2020 by TOPSIS model, and makes convergence analysis by classification. Through the DID empirical test of sustainable development planning policy on ecological safety factors, distinguish the development characteristics of different resource-based cities. The results show that: (1) the ecological safety level of resource-based cities in the Yellow River Basin is generally developing well, and there are differences among different types of resource-based cities; (2) the kinetic energy of sustainable development of resource-based cities in the Yellow River Basin is mainly industrial transformation, and the power of science and technology is insufficient; (3) local governments pay limited attention to environmental protection, and the sustainable development planning of resource-based cities in 2013 mainly promotes the adjustment of industrial structure of resource-based cities.

Use of MS Excel as a platform for teaching simple iterative solution of partial differential equations runs into issues of reliability of the computation because of the large number of calculations required. This article documents the problem and an empirical solution. The cause of this unreliability was found by detailed examination of the numerical results from three simultaneous iterations. Computers of several types from different manufacturers were found to fail occasionally in updating variables in the numerical iteration. A simple work-around is suggested and was tested on several platforms.

This work proposes a physics-based identification technique based on genetic algorithms. The main objective is to obtain a parametric matrix A that describes the time-invariant linear model of the longitudinal dynamics of an aircraft. This is achieved by proposing a fitness function based on the properties of the transition matrix and taking advantage of some of the capabilities of the genetic algorithm, mainly those of restricting the search ranges of the unknowns. In this case, such unknowns are related to the type of aircraft and flight conditions that are considered during the identification process. The proposed identification method is validated with a reliable nonlinear model that can be found in the literature, as well as with the calculation of the trim condition and linearization generally used in aircraft dynamics. In summary, this study suggests the genetic algorithm provided with the adequate fitness function, could be an appealing alternative for aircraft model identification, even when only a limited amount of data is available. Furthermore, in some cases, linearization using genetic algorithm can be more efficient than classical methods.

Public parks are a community resource with an important role in improving liveability, physical, and mental wellbeing. However, exercise facilities that are suitable for people with disabilities typically have been neglected in public park design. As such, people with disabilities often are unable to independently or safely use the park. To address this shortcoming, the objective of this paper was to employ a pragmatic research through designing process in developing the design for an inclusive park. We used a mixed-methods approach in the research that included review of previous studies, semi-structured interviews, and questionnaire surveys with stakeholders were applied as design integration. Persons with disabilities specifically were consulted to express their views on all matters of inclusive park design.

Background: Lung cancer is still the most lethal malignancy in the world from the report of Cancer Statistics in 2021. Platinum-based chemotherapy combined immunotherapy is the first-line treatment in lung cancer patients. However, the 5-year survival rate always affected by the adverse reaction and drug resistance caused by platinum-based chemotherapy. DNA damage and repair system is one of the important mechanisms which can affect the response to chemotherapy and clinical outcome in lung cancer patients. Objective: The objective of this study is to find the relationship between the polymorphisms of DNA repair genes with the prognosis in platinum-based chemotherapy in lung cancer patients. Patients and Methods: We performed genotyping in 17 single nucleotide polymorphisms (SNPs) of Excision Repair Cross-Complementation group (ERCC) genes and X-ray Repair Cross-Complementing (XRCC) genes of 345 lung cancer patients by Sequenom MassARRAY. We used Cox proportional hazard models, state and plink to analyze the associations between SNPs and the prognosis of lung cancer patients. Results: We found that the ERCC5 rs873601 was associated with the overall survival time in lung cancer patients treat by platinum-based chemotherapy (p=0.031*). We also discovered that the polymorphisms in rs873601 was significantly associated with the prognosis in age more than 55 years, Small Cell Lung Cancer (SCLC) and smoking patients, Long Intergenic Non-protein Coding RNA (PNKY) rs2444933 in age less than 55 years, SCLC, metastasis and stage III/IV/ED patients, Short Tau Inversion Recovery (STIR1) rs3740051 in SCLC and metastasis patients, PNKY rs1869641 in SCLC patients, XRCC5 rs1051685 in non-metastasis patients, respectively. Conclusion: The ERCC5 rs873601(G>A) maybe a valuable biomarker for predicting the prognosis in lung cancer patients treated with platinum-based chemotherapy. Statements and Declarations: The authors declare that they have no conflict of interest.

Background and Objectives: Treatment for antineutrophil cytoplasmic antibody-associated vasculitis (AAV) must deal with immunosuppression as well as infections associated with compromised immune system, such as tuberculosis (TB). Our aim was to overcome the gap in the literature concerning the risk of incidental TB after diagnosis of AAV. Materials and Methods: This retrospective population-based cohort study was based on the data from the National Health Insurance Research Database in Taiwan. We used a novel algorithm to identify patients with newly diagnosed granulomatous polyangiitis (GPA) or microscopic polyangiitis (MPA) between January 1, 2000 and December 31, 2012. The primary outcome was risk of incidental TB. Cox proportional hazard models were used to evaluate the association between AAV and incidental TB. Results: A total of 2,257 patients with AAV and a propensity-score matched cohort of 9,028 patients were studied. Overall, patients with AAV were at a 1.48x higher risk of contracting incidental TB than were patients in the matched cohort (adjusted HR 1.48; 95% confidence interval [CI], 1.02-2.15). Note that the highest risk of contracting incidental TB was in the first two years following a diagnosis of AAV, with a nearly 1-fold increase in risk (adjusted HR, 1.91; 95% CI, 1.01-3.60). Female AAV patients were 3.24x more likely than females without AAV to develop TB (adjusted HR 3.24; 95%CI, 1.85-5.67). Conclusion: Patients with AAV face an elevated risk of contracting incidental TB, particularly within the first two years after AAV diagnosis. The risk of contracting TB is higher among female AAV patients than among females without AAV.

Articular cartilage injuries are found in up to 60 % of patients who undergo an arthroscopic knee procedure, those that totally affect articular cartilage (grade IV) have limited regenerative capac-ity and extended time for recovery, 3-D scaffolds represent a novel solution to address this type of injury. Our purpose was to analyze MRI findings and functional status of patients that underwent repair of chondral defects either by microfractures or Hyaluronan (HA) 3-D Scaffold. We con-ducted a retrospective study of patients with chondral defects. The outcomes analyzed in this study included anatomical changes envaulted by the Henderson score (based on MRI findings) at baseline, 6, and 12 months after surgery; and improvement in functionality, evaluated by the Modified Cincinnati Knee Rating System (MCKRS) at baseline and 6 months after surgery. Clini-cal and demographic characteristics were similar for both groups. There was a statistically sig-nificant improvement in Henderson score for the 3-D scaffolding treated group at 6 months, ver-sus the microfracture group (p < 0.0001). Improvement in functionality, measured by MCKRS was more frequently found in the 3-D scaffold treated group. In conclusion the use of HA 3-D scaf-folding was superior with faster recovery, evident after 6 months of the surgery, that progress to full recovery in all patients after a year of surgery. Future studies with a randomized design might help to sustain our findings. This study provides a level III of evidence.

The study focuses on solving urban challenges such as temperature reduction, urban stormwater management, noise reduction, air quality improvement, and CO2 concentrations reduction, and suggests terrestrial and freshwater habitat types (HTs) found in Europe, as innovative forms of nature-based solutions (NBS) for them. Establishment of native HTs in different urban environments to solve urban challenges would enhance biodiversity at different levels and integrate this aspect in urban planning. This contribution builds on the recognition that surfaces of vegetated terrain are the most versatile NBS in addressing the broadest range of environmental problems in urban areas and that the processes running within these green spaces offer the key to socio-ecological improvements of such areas. Employing narrative literature review, qualitative content analysis, and interdisciplinary expert discussion this paper defines why and how native HTs of unchanged nature can be implemented as NBS in the urban environment, indicate potential HTs for specific urban challenges and presents an approach to the inclusion of HTs as NBS into spatial planning documents at national, regional, and local levels. The proposed planning approach attributes added value to HT, and by linking the concepts of NBS and HT integrates them into urban planning.

Oxygen based heterocyclic moieties hold an ample range of therapeutic activities. Heterocyclic molecules are nominated as vital components of an extensive array of structural motifs with both biological and pharmaceutical significance. The oxygen-based scaffolds act as anticancer candidates and are also present in numerous phytomolecules viz; irinotecan, camptothecin, topotecan, taxol, taxotere, podophylotoxin, etoposide, daunorubicin and teniposide. The architectural design of numerous structural motifs for amelioration of cancer has become progressively amplified in recent past years. Until now presently there is no strategic treatment which is so capable that can cure cancer from its roots. Henceforth, it is very indispensible to design novel anticancer structural motifs with least side effects. The oxygen containing heterocyclic scaffolds includes flavonoids, pyrans, xanthones and coumarins are of utmost importance in medical chemistry for the mitigation of cancer. This assemblage offers several recent developments as anticancer oxygen containing heterocyclic molecules all-round the globe and attracted the structural motifs of auspicious molecules, along with their mechanistic insights, IC50 values, structure–activity relationships, and molecular docking studies. The encouraging properties discovered by these oxygen-based scaffolds unconditionally engaging them at frontline in invention of potential drug candidates. Consequently, these probably will be of amazing attention to scientists working on the design and synthesis of antitumor candidates.

River monitoring has the potential to grow substantially with present-day available affordable and locally sourced hardware. Yet river monitoring networks are still under pressure due to lack of resources, difficulties with maintenance, and rapidly changing conditions which in part may be due to climate change. We advocate that to turn around this trend, monitoring stations must rely on local people, work with locally available devices, work without contact with water and operate through openly available knowledge. River observations with camera videos have this potential. IP cameras, drones or smartphones are widely available as observation platforms. The scientific methods are well established in literature. Yet current attempts to establish scalable open-source software solutions that can be operated anywhere are lacking. In fact, currently available software is either research-oriented, more aimed at incidental observations, is restricted to single-use licenses, entirely proprietary, or restricted to operations through a third-party Software as a Service. To overcome this obstacle, we present OpenRiverCam, a free and open-source software ecosystem for river observations that can fulfill a wide variety of use cases and business cases through a well-documented and simple to use Application Programming Interface, service workflows, cloud scalability and interoperability, and options to extend to several applications within the hydrological, hydrodynamic, environmental and geospatial domains. We demonstrate its current technical abilities through three different case studies that all originate from a user perspective. We discuss the future developments to meet further requirements, which include documentation, widely available training materials and embedding in curricula, and further hardware and software developments.

Nurses’ competency towards Evidence-based Practice (EBP) has been extensively investigated by several studies worldwide. However, factors affecting the competence of Greek nurses working in the NHS have not been fully investigated in terms of EBP. Thus, this study aimed to explore the impact of individual qualifications of nurses on their competence towards EBP. Data from 473 registered nurses working in 10 hospitals of in the Greek National Health Service (NHS) were collected between October and December 2022 using a convenience-sampling method in a cross-section design. The Greek version of the 35-item (5-point Likert scale) Evidence-Based Practice Competency Questionnaire for Professional Registered Nurses (EBP-COQ Prof) was used to assess the competence level of nurses focusing on the attitudes, skills, and knowledge as well as the utilization of EBP in clinical practice. One-way ANOVA and Pearson coefficient tests were applied to compare possible differences among variables (two or more groups) as appropriate. A multi-factorial regression model was applied to explore participants’ qualifications, including demographics (MSc degree, gender, English language knowledge, etc.) as independent variables, and to control for potential confounding effects toward EBP competency. P-values &lt;0.05 were considered statistically significant. The mean age of 473 participants (402 women and 71 men) was 44.7 ± 9.2 years old. The mean value of competence subscales was found as follows: attitudes 3.9±0.6, knowledge 3.7±0.6, skills 3.1±0.8, and utilization 3.4±0.7. A multivariate regression analysis revealed that associates of “Master's degree” (t=3.039, p=0.003), “Writing an academic article” (3.409, p=0.001), “Working in a University clinic” (2.203, p=0.028) and, “Computer Skills” (2.404, p=0.017) are positively affecting ‘Attitudes’, ‘Knowledge’, ‘Skills’ and ‘Utilization’. Respectively. The research data suggest that nurses working in Greek NHS are limited in competence towards EBP in comparison to other European countries. Therefore, Vocational, Educational and Training programs tailored to EBP enhancement are crucially important.

Advances in technology and information systems have had a major impact on people's lives and activities. Smartphone use plays an important role in many areas, including housing governance. One of the problems faced by housing administrators is administrative management, which is still manual. To overcome this, it is proposed to use a web-based application. A literature study was conducted to support the development of this application. Data collection through interviews and observations was carried out to obtain system requirements. The Agile Scrum method is used in making applications. These applications have non-functional requirements such as security, availability, performance, usability, and compatibility. Implementation using PHP and MySQL. Testing is done to ensure the application functions properly. This application allows administrators to manage citizen data, payment of contributions, billing, and print financial reports. Residents can upload proof of transfer and print proof of payment. It is hoped that this application will facilitate housing administration, improve the quality of life in the community, and create a better housing environment.

The erythropoietin-producing hepatocellular carcinoma (Eph) receptors are the largest receptor tyrosine kinases family. EphB4 is essential for cell adhesion and motility during embryogenesis. Pathologically, EphB4 is overexpressed and contributes to poor prognosis in various tumors. Therefore, sensitive monoclonal antibodies (mAbs) should be developed to predict the prognosis for multiple tumors with high EphB4 expression, including breast and gastric cancers. This study aimed to develop highly sensitive and specific anti-EphB4 mAbs for several applications using the Cell-Based Immunization and Screening (CBIS) method. EphB4-overexpressed Chinese hamster ovary (CHO)-K1 (CHO/EphB4) cells were immunized into mice, and we established an anti-EphB4 mAb (clone B4Mab-7), which is applicable for flow cytometry, western blotting, and immunohistochemistry. B4Mab-7 reacted with endogenous EphB4-positive breast cancer cell lines, MCF-7 and MDA-MB-468, but did not react with EphB4-knockout MCF-7 (BINDS-52) in flow cytometry. Dissociation constant (KD) values were determined to be 2.9 × 10‑9 M, 1.3 × 10‑9 M, and 3.3 × 10‑9 M by flow cytometric analysis for CHO/EphB4, MCF-7, and MDA-MB-468 cells, respectively. B4Mab-7 detected the EphB4 protein bands from breast cancer cells in western blotting, and stained breast cancer tissues immunohistochemistry. Altogether, B4Mab-7 demonstrated high sensitivity and specificity against EphB4 in various applications.

Lomefloxacin (F1) and pefloxacin (F2) have a broad spectrum of antimicrobial activities against Gram-positive and Gram-negative bacteria. In this study, we investigated the complexation mode, morphological and biological properties of four metal-based complexes of F1 and F2 molecules with Mg(II), Ca(II), Zn(II), and Fe(III) metal ions. These complexes were prepared at ~60–70°C in a neutral medium using a 5% NH3 solution at pH ~7-8 with a 1:1 ratio. Multiple physicochemical methods were employed to characterize the binding mode between F1 and F2 with the metal ions under investigation. The results of these methods suggested that the gross formula of the complexes obtained with the metal ions were [MgF1(H2O)Cl]2H2O, [CaF1(H2O)Cl]3H2O, [ZnF1(H2O)Cl], [FeF1(H2O)2Cl2]Cl2H2O, [MgF2(H2O)Cl]2H2O, [CaF2(H2O)Cl]3H2O, [ZnF2(H2O)Cl], and [FeF2(H2O)2Cl2]Cl2H2O. The microscopic characterizations indicated that the Ca(II)-F1 complex had an interesting surface topography. Its particles had a homogenous, short, rod-like shaped structure that clustered together to form a tree shape. Using the Kirby-Bauer disc diffusion protocol, the synthesized metal-based complexes were screened in vitro against different Gram-positive and Gram-negative bacterial and fungal species. The antimicrobial profile of the Fe(III)-F1 complex indicated that it had remarkable inhibitory activity against all the tested bacterial and fungal species with potency equal to that of the standard drugs (streptomycin and ketoconazole).

Ocean wave energy is a significantly well-liked renewable energy source. The Internet of Things (IoT) offers a great opportunity for development of the ocean wave energy research. The IoT technology significantly enhanced the performance of the ocean wave energy converter (WEC). The data obtained from IoT devices reduce the risk of using WEC. However, Integrating IoT devices with the WEC is a daunting task because controlling the WEC needs real-time data. This paper investigated the performance of WEC and estimate the lifetime of the modules. IoT devices integrated with various sensors determine different parameters and conditions of the ocean. The IoT platform is used for wireless voltmeters, wireless AC current meters, wireless voltage detection, wireless resistance sensors, wireless temperature sensor, wireless accelerometer, wireless pressure meters, and wireless water level sensors to determine the real-time condition of the ocean wave. These devices are also proven to be reliable and accurate.

This paper presents a novel DQ-based multicarrier Pulse Width Modulation PWM for a single-phase, three-level PV-powered grid-connected F-type inverter. The main control objective in the proposed inverter is to regulate the grid current with low total harmonic distortion and load power components compensation. Despite the F-type inverter’s advanced advantages, there are only a few works addressing the control issue in the literature yet. The proposed control and switching methods aim to achieve both DC side voltage balance and lowest switching losses. The proposed scheme has been designed based on a modified multi-carrier PWM switching algorithm. Consequently, the proposed control method able to satisfy the requirements of DC side voltage balance and achieve lower switching losses. A further advantage of the proposed control and switching methods is that they retain the main advantage of the F-Type inverter, which is that only 25% of the power switches are exposed to full DC voltage. This is an important advantage since it reduces the overall cost of the inverter and improves its reliability. Overall, the proposed modified multi-carrier PWM switching algorithm appears to be a promising approach for controlling the F-Type inverter, offering improved performance and efficiency compared to other control methods. The theoretical model was verified through simulation using MATLAB/Simulink. According to the simulation results, the grid current and dc capacitor voltages are successfully managed in all operational situations.

The current concerns of both society and materials industries about the environmental impact of thermoset composites, as well as new legislation, have led the scientific sector to search for more sustainable alternatives to reduce the environmental impact of thermoset composites. Until now, to a large extent, sustainable reinforcements have been used to manufacture more sustainable composites and thus contribute to the reduction of pollutants. However, in recent years, new alternatives have been developed such as thermosetting resins with bio-based content and/or systems such as recyclable amines and vitrimers that enable recycling/reuse. Throughout this review, some new bio-based thermoset systems as well as new recyclable systems and sustainable reinforcements are described and a brief overview of the biocomposites market and its impact is shown.

Latterly, the development of green synthesized polymeric nanoparticles with anticancer studies has been an emerging field in academia, and in the pharmaceutical and chemical industry. Vegetable oils are potential substitutes for petroleum derivatives, as they present themselves as a clean and environmentally friendly alternative and are available in high quantities at relatively low prices. Biomass-derived chemicals can be converted into monomers with unique structures, generating materials with new properties for the synthesis of sustainable monomers and polymers. In this way, the production of bio-based polymeric nanoparticles appears as a great application of green chemistry for biomedical uses. There is an increasing demand for biocompatible and biodegradable materials for specific applications in biomedical as cancer therapy, encouraging scientists in working on research towards designing polymers, with enhanced properties and clean processes, containing oncology active pharmaceutical ingredients (APIs). The nanoencapsulation of these APIs in bio-based polymeric nanoparticles can control the release of the substances, increase bioavailability, reduce problems with volatility and degradation, reduce side effects, and increase treatment efficiency. Thus, this review aims to discuss the use of green chemistry for bio-based nanoparticle production and its application in anticancer medicine. The use of vegetable oils for the production of renewable monomers and polymers will be discussed, bringing castor oil as an ideal candidate for such application, as well as more suitable methods for the production of bio-based nanoparticles and some oncology APIs available for anticancer application.

We present a new model formulation for a class of capacitated lot-sizing problem considering setup costs, product returns, and remanufacturing (CLSP-RM). We investigate a broad class of instances that fall into two groups, in the first group we can reformulate the problem with a relaxation and test whether the original problem is solvable. The relaxation gives near optimal solutions and the solution of this class does not give any difficulty to known solvers such as Cplex, Gurobi or Xpress. The second group of instances are of category NP and will be solved with a simple period-by-period simulation

The Pandemic in 2019 forced a digital adaptation with direct consequences on all educational stakeholders. On behalf of teachers and trainers, while many regarded these changes with some scepticism, others embraced the opportunity to integrate technology into their teaching methods and as learning resources. As translation trainers, it is essential to follow and understand the translation market. Thus, the exponential changes that digital technology has brought to this sector over the years impose constant shifts in teaching and learning methods and resources. In fact, translators require vast competencies, amongst which is the flexibility to adapt. In translation training Project-Based Learning (PBL) has been established as an essential teaching and learning method, as it has proven to foster the development of competencies as it simulates the translator's work environment. Thus, the need to adapt new strategies reinforced PBL and its benefits. PBL, however, similar to a freelance translator, places the student in the centre of the learning process, where self-regulation becomes essential, as it is necessary to analyse the market/situation and be flexible enough to adapt to the context accordingly. As of 2018-2019, technical translation courses at ISCAP have implemented PBL as their main teaching and learning method. At the same time, a study on student self-regulation began. The purpose was to understand student perception on their own self-regulation competence and its development or lack thereof after one year of PBL. Results indicate that PBL is seen as a useful simulation of the translation labour market and that it does enhance many essential competences, amongst which is student self-regulation.

Background: Grades in clinical courses matter. They are often used to determine clinical academic awards, scholarships, and—most importantly—interns’ suitability for graduate medical education opportunities. Aware of these stakes, clinic preceptors may feel pressure to grade too leniently or uniformly. A fair method of adjusting for differences in preceptor bias is then needed. Approach: The authors propose a technique that employs the advantages of both criterion- and normative-based grading to adjust for differences in both grader leniency and uniformity. Evaluation: The technique produces fair adjustments to any raw assign grades, and the authors demonstrate how easily this process can be administered in any clinical setting where multiple preceptors are evaluating interns. Implications: This work provides a grading framework that is transparent to all stakeholders but places responsibilities at the appropriate level. That is, clinic performance evaluations are left to clinic preceptors but grading to clinic academic managers.

This paper presents a robust method based on graph topology to find the topologically correct and consistent subset of inter-robot relative pose measurements for multi-robot map fusion. However, the absence of good prior gives a severe challenge to distinguish the inliers and outliers, and wrong loop closures can seriously corrupt the fused global map. Existing works mainly rely on the consistency of spatial dimension to select inter-robot measurements, which does not always hold. In this paper, we propose a fast inter-robot loop closure selection method that integrates the consistency and topology relationship of measurements, which both conform to the continuity characteristics of similar scenes and spatiotemporal consistency. The traditional high-dimensional consistency matrix is decomposed into the sub-matrix blocks corresponding to the overlapping trajectory area. Building on this logic, a clustering method involving topology correctness of inter-robot loop closures is introduced to split the entire measurement set into multiple clusters. We define the weight function to find the maximum cardinality subset with topologically correct and consistent, then convert the weight function to a maximum clique problem in the graph and solve it. We evaluate the performance of our method in a simulation and in a real-world experiment. Compared to state-of-the-art methods, the results show that our method can achieve competitive performance in accuracy while reducing computation time by 75%.

Clinicians are key in reclaiming the medical arts ceded to clinically irrelevant technology and thereby aligning patient with fast-changing biological realities. Narrowing the chasm between virtual and real perceptions of health hazards requires: 1) becoming acutely aware of the habitat loss aggravating the pervasive dissemination of chemicals via conventional food, air, and consumer products and the proliferation of non-ionizing radiation; and 2) making strategic use of slow, system 2 thinking so as to respond wisely to the rampant epidemics of chronic low-dose toxicity disregarded or misdiagnosed for half a century. To respond adaptively, take a moment during each patient encounter to add chronic ambient poisoning to the differential diagnosis and investigate subtle symptoms and signs of irritation in vulnerable organ systems. Enacting adaptive response across our profession could ease the suffering of millions, help avert the sixth extinction, and contribute to continuation of evolved life as we know it.

Time and cost-efficient techniques are essential to avoid extra conventional experimental studies with large date-set to characterize the mechanical properties of composite materials. Correlation between the structural performance and mechanical properties could be captured through the efficient predictive models. Several ensembled Machine Learning (ML) methods were implemented in this study, to materially characterize carbon nanotube (CNT)-reinforced cement-based composites. Proposed models were compared with each other to represent the accuracy of each method. The Flexural and Compressive Strength (target values) of CNT reinforced composites were predicted based on the data-rich framework provided in previous experimental investigations. These data were utilized for training of the proposed models by employing SciKit-Learn library in Python, followed by hyper-parameter tuning and k-fold cross-validation method for obtaining an efficient model to predict the target values. Random Forest (RF) and Gradient Boosting Machine (GBM) were developed for this purpose. The findings of this study would be useful for prospective composite designers in case of sufficient experimental data availability for ML model training.

Water is a limited resource that needs to be properly managed and distributed to the ever-growing population of the world. Rapid urbanization and development have drastically increased the overall water demand worldwide. Ageing water distribution networks are vulnerable to deterioration and leakage, thereby causing an estimated annual loss of about 48 trillion liters of water. To address these issues, efficient and reliable leakage detection and management techniques are necessary. In this paper, the results of the experiments performed on a looped water distribution network in AnSeong, Korea are discussed. Transient-based techniques were used and physical data were collected for the detection and localization of leakages in the experimental water pipes. The results obtained from the experiments demonstrated the applicability of transient techniques for leak analysis in looped water distribution networks.

In the present study, the corrosion resistance of amorphous coating and composite coatings in 3.5 wt.% NaCl, 0.5 M H₂SO₄ and 10 wt.% NaOH solution were studied. The composite coatings exhibit superior corrosion resistance. When the content of AT13 （Al₂O₃–13 wt.% TiO₂）was 15 wt.%, the composite coating has the lowest corrosion current density (1.75×10^-6 A cm^-2), which is 5.14×10^-5 A cm^-2 for Fe-based metallic glassy coating, and the highest corrosion potential (-411 mV), which is -580 mV for Fe-based metallic glassy coating. The breakdown potential of the passivation film in 3.5 wt.% NaCl solution was much higher than that of 316L.The long-time immersion corrosion tests carried out on different coatings showed that the corrosion protection effect of coating was enhanced with the increase of the amount of AT13 added.

In the field of industrial process monitoring, more and more interest is being shown in specific process categories. These include time-varying processes, that is, those processes whereby the response one receives as output from the system depends on when the input signal is sent into it. There are many reasons for this process variability and such contexts are not always analyzed with this operational characteristic at their core. At the same time, interest in certain categories of techniques is also becoming more prominent, to meet certain application needs. Among these, clustering and unsupervised techniques in general are gaining ground. This is largely due to the difficulty of finding fault data with which to train, for example, supervised models. On the other hand, the clustering technique, on which this contribution focuses, also makes it possible to compensate for the lack of complete knowledge of the structure of the process itself. With these two considerations in mind, this contribution proposes a literature review on the topic of clustering applied in time-varying contexts, in the maintenance field. The aim is to present an overview of the main fields of study, the role of clustering in this context and the main clustering techniques used.

Decision-making using evidence-based practice (EBP) is generally universally accepted by nurses. Such acceptance may affect the personnel’s behaviour towards patients, which is also demonstrated by taking into consideration the patient’s preferences, including the patient’s spiritual needs, in the care plan. The provision of such care requires the development of an attitude of approval and an adequate level of communicative competence, which will enable the actual implementation of the EBP. The purpose of our study was to assess the perception of spirituality and the nurse’s role in providing spiritual care, as well as the perception of the significance of communication skills in the approval of EBP in professional practice. A multi-centre cross-section study was conducted on a population of 1176 participants (459 undergraduate (Bachelor programme, BP) and 717 postgraduate students (Master programme, MP)) from 10 medical universities in Poland. Three tools were used in the study to evaluate the participants’ approach: Evidence-Based Practice Competence Questionnaire (EBP-COQ), The Spirituality and Spiritual Care Rating Scale (SSCRS), and Communication Skills Attitude Scale (CSAS). Structural equation modelling was used for the analysis. An analysis of structural equations revealed the presence of positive relationships of the attitude to spiritual care and the role of communicative competences with the approach to EBP regardless of the cohort. A significant difference was found related to the influence of age on the attitude toward learning communicative competences. The approval in this respect was observed to decrease with age in the MP group. Increasing approval of EBP requires strengthening the approach to activity-centred spiritual care, with the simultaneous development of a positive attitude towards learning communicative competences. The model reveals the need to integrate a humanistic approach with EBP, which can be achieved by planning different interventions in different groups of recipients: nurses, academic teachers and students.

There is great interest in developing biodegradable biopolymer-based packaging materials whose functional performance is enhanced by incorporating active compounds into them, such as light blockers, plasticizers, crosslinkers, diffusion blockers, antimicrobials, antioxidants, and sensors. However, many of these compounds are volatile, chemically unstable, water-insoluble, matrix incompatible, or have adverse effects on film properties, which makes them difficult to directly incorporate into the packaging materials. These challenges are being overcome by incorporating the bioactive compounds into nanoparticles, which are then introducing into the packaging materials. The presence of these nanoencapsulated active compounds in biopolymer-based coatings or films can greatly improve their functional performance. This article reviews the different kinds of nanocarriers available for loading active compounds into these types of materials, and then discusses their impact on the optical, mechanical, thermal, barrier, antioxidant, and antimicrobial properties of the packaging materials. Furthermore, this article highlights the different kinds of bioactive compounds that can be incorporated into biopolymer-based packaging.

Background: Simulation plays a crucial role in health studies, as it helps medical students apply their theoretical knowledge in real-life situations. Moulage is one of the techniques that helps in making simulation more realistic or high-fidelity. It uses special effects to emulate wounds for a better understanding of what the wound is like visually. Still, moulage is expensive, time-consuming, resource-intensive, and requires the training of staff, which is why we need to find low-cost substitutes for moulage materials. Method: When searching the database “PubMed” for the terms “Low-cost and Medical moulage”, we retrieved 222 studies, out of which when excluding results not related to low-cost, we obtained 62 studies, from which when removing studies that do not contain information regarding moulage, we found two papers, after referring to citations and cited articles of those papers, we ended up with six studies. Based on the selected articles and additional articles sourced from their reference list, a total of 11 studies were included in the review. Results: We understand that moulage is a technique that helps make simulations come alive, but the resources required to use it are at times, expensive, which is why we need to find methods to do low-cost moulage, and many studies address that it can be as simple as using homemade ingredients. Students from a previous study have talked about their opinions regarding the realistic component of moulage and whether if it is any different from other moulages. Most of the students agreed that the moulage ranked well in face and content validity. However, further innovations must be introduced in the field to be widely spread and lead to newer opportunities. Conclusion: Although the research done under moulage is limited, it is accepted that moulage is helpful for simulation-based studies and that low-cost moulage can help make medical studies a better experience for students studying it. Students have a favorable opinion on the realistic aspect of the low-cost moulage applied to them. Newer methods can be introduced to moulage, and it can be implemented in low-income countries.

We report on an immunofluorescent paper-based assay for the detection of severe acute respiratory symptom coronavirus 2 (SARS-CoV-2) humanized antibody. The paper-based device was fabricated by using lamination technique for easy and optimized handling. Our approach utilises a two-step strategy that involves (i) initial coating of the paper-electrode with recombinant SARS-CoV-2 nucleocapsid antigen to capture the target SARS-CoV-2 specific antibodies, and (ii) subsequent detection of SARS-CoV-2 antibodies using fluorophore-conjugated IgG antibody. The fluorescence readout was observed with fluorescence microscopy. The images were processed and quantified using a MATLAB program. The assay can selectively detect SARS-CoV-2 humanized antibodies spiked in PBS and healthy human serum samples with the relative standard deviation of approximately 6.4% (for n = 3). It has broad dynamic ranges (1 ng to 50 ng/µL in PBS and 5 to 100 ng/µL in human serum samples) for SARS-CoV-2 humanized antibodies with the detection limits of 2 ng/µL (0.025 IU/mL) and 10 ng/µL (0.125 IU/mL) in PBS and human serum samples, respectively. We believe that our assay has the potential to be used as a simple, rapid, and inexpensive paper-based diagnostic device with a portable fluorescent reader to provide point-of-care diagnosis. This assay can be used for rapid examination of a large batch of samples toward clinical screening of SARS-CoV-2 specific antibodies as a confirmed infected active case or to evaluate the immune response to a SARS-CoV-2 vaccine.

Background: It is clear that breastfeeding is the gold standard of infant feeding because of the many advantages it offers to both the child and the mother. Objective: to identity the main reasons for cessation breastfeeding declares by the mother themselves during the first year. Design: A prospective cohort study was conducted, recruiting 969 newborns in a third level hospital in Spain. The main maternal variables studied were: maternal age, parity, educational level, work occupation, smoking habit, gestational age, birth, weigh, feeding type, and duration of breastfeeding. All the participants were followed for a year to determinate the duration of breastfeeding and to know the reason of the abandonment. Results: At 6 months, the percentage of maternal lactation was cut in half and only 24.6% of these mothers maintain. Mainly 15.80% of the mothers decide to give up the exclusive maternal lactation of their own free desire, and 15.41% because they suspect hypogalactia. The work cause is the third reason of abandonment in both cases. Conclusions: Our results show the need to improve the health policies of promotion, protection and support the initiation of breastfeeding. In particular, our results show the importance of the work factor with particular emphasis on improving conciliation measures.

Fossil-based plastics are significant contributors to global warming through CO₂ emissions. For more sustainable alternatives to be successful, it is important to ensure that consumers become aware of the benefits of innovations such as bio-based plastics, in order to create demand and a willingness to initially pay more. Given that consumer attitudes and (inaccurate) beliefs can influence the uptake such new technologies, we investigated participants’ attitudes towards fossil-based and bio-based plastic, their perceived importance of recycling both types of plastic, their willingness to pay, and their perceptions of bio-based plastic in four studies (total N = 961). The pre-registered fourth study experimentally manipulated information about bio-based plastic and measured willingness to pay for different types of plastic. The results suggest participants hold very favourable attitudes and are willing to pay more for bio-based products. However, they also harbour misconceptions, especially overestimating bio-based plastic’s biodegradability, and they find it less important to recycle bio-based than fossil-based plastic. Study 4 provided evidence that educating consumers about the properties of bio-based plastic can dispel misconceptions, retain a favourable attitude and a high willingness to pay. We found mixed evidence for the effect of attitudes on willingness to pay, suggesting other psychological factors may also play a role. We discuss how attitudes and misconceptions affect the uptake of new sustainable technologies such as bio-based plastics and consumers’ willingness to purchase them.

TWIST1 is a transcription factor that affects cell behavior during development and cell differentiation. Yet, it is better known for its roles in neoplasia through regulation of cell plasticity. The pathological contributions of TWIST1 in tumor initiation, angiogenesis, invasion, metastasis, and chemo-resistance have been the focus of much research. To-date, the only way to quantitatively measure the abundance of TWIST is by immunoblots. Yet, no bioassay exists that can detect TWIST1 activity. Thus, we present here a TWIST1 cell-based assay that allows measuring the amount of active TWIST1 non-invasively in living cells. The bioassay was characterized against previously described TWIST1 “inhibitors”, as well as by epigenetic modulators of TWIST1 gene expression. Moreover, we tested multiple cell lines, showing that the level of TWIST1 mRNA resembles that of the bioassay. We show that prostate cancer cells (PC3) undergoing EMT, migrate out of 3D-spheroids and have increased TWIST1 activity. This fast and reliable system to detect active TWIST1 in different biological conditions allows a detailed analysis of this factor, as well as it can be used for drug discovery, since TWIST1 is a potential target for cancer chemotherapeutics.

Ovarian cancer is the most frequent cause of deaths in gynecologic malignancies. Many possible mechanisms have been proposed via RNAseq and DNAseq technique recently. However, the driving factors are still obscure. The possible reasons are attributed to the incomplete human reference. This study integrated the canonical mapping-based and mapping-free protocols to extract reliable variations and novel events. We eventually obtained 450 reliable SNVs from the WES data and novel events from the RNAseq data, including 154 SNVs, 462 intron events, two repeats and six splice events. We identified six differentially expressed genes and six contigs that are significantly related to survival prognosis. The recurrent SNVs in significantly differentially expressed genes can be validated in an independent cohort of 20 Chinese ovarian cancer patients.

This paper discusses devastating urban floods in the year 2019 that caused human and socioeconomic losses in many countries, including Iran. The main question addressed by this paper is the choice between two flood management models, namely, the optimal and nature-based flood management or the existing hazardous situation that damage the ecosystem and natural resources. The analysis of this paper will find the main responsible factors in the mentioned floods in Iran. For this reason, it examines the impacts of the existing flood management that neglects the ecosystems, environmental components, and nature. The method of this research includes theoretical studies, case studies with the help of structured interviews, and observations. A benchmarking technique compares the two alternatives. The comparisons use seven indicators abstracted from successful global experiences and local knowledge. Finally, this research presents a model for optimal flood management that is applicable everywhere in the world.

The air pollutant NO2 is derived largely from transportation sources and is known to cause respiratory disease. A substantial reduction in transport and industrial processes around the globe from the novel SARS-CoV-2 coronavirus and subsequent pandemic resulted in sharp declines in emissions, including for NO2. Additionally, the COVID-19 disease that results from the coronavirus may present in its most severe form in those who have been exposed to high levels of air pollution. To explore these links, we compared ground-based NO2 sensor data from 11 US cities from a two-month window (March-April) over the previous five years versus the same window during 2020 shutdowns. NO2 declined roughly 12-41% in the 11 cities. This decreased coincided with a sharp drop in vehicular traffic from shutdown-related travel restrictions. To explore this link more closely, we gathered more detailed traffic count data in one city, Indianapolis, Indiana, and found a strong correlation between traffic counts/classification and vehicle miles travelled, and a moderate correlation between NO2 and traffic related data. This finding indicates that we can use such analysis in targeting reduction in pollutants like NO2 by examining and manipulating traffic patterns, thus potentially leading to more population-level health resilience in the future.

Paper-based microfluidic analysis devices (μPADs) have attracted attention as a cost-effective platform for point-of-care testing (POCT), food safety, and environmental monitoring. Recently, three-dimensional (3D)-μPADs have been developed to improve the performance of μPADs. For accurate diagnosis of diseases, however, 3D-μPADs need to be developed to simultaneously detect multiple biomarkers. Here, we report a 3D-μPADs platform for the detection of multiple biomarkers that can be analyzed and diagnosed with a smartphone. The 3D-μPADs were fabricated using a 3D digital light processing printer and consisted of a sample reservoir (300 µL) connected to 24 detection zones (of 4 mm in diameter) through 8 microchannels (of 2 mm in width). With the smartphone application, eight different biomarkers related to various diseases were detectable in concentrations ranging from normal to abnormal conditions: glucose (0–20 mmol/L), cholesterol (0–10 mmol/L), albumin (0–7 g/dL), alkaline phosphatase (0–800 U/L), creatinine (0–500 µmol/L), aspartate aminotransferase (0–800 U/L), alanine aminotransferase (0–1000 U/L), and urea nitrogen (0–7.2 mmol/L). These results suggest that 3D-µPADs can be used as a POCT platform for simultaneous detection of multiple biomarkers.

Taking a typical forest underlying surface as the research area, this study employed the unmanned aerial vehicle (UAV) photogrammetry to explore more accurate canopy parameters including tree height and canopy radius, which were used to improve the Noah-MP land surface model conducted in Dinghushan Forest Ecosystem Research Station (CN-Din). While the canopy radius was fitted as a Burr distribution, the canopy height of CN-Din forest followed a Weibull distribution. The replacement of the parameters by these observed UAV would result in the Noah-MP model. It was found that the influence on the simulation of the energy fluxes could not be negligible, and the main influence of these canopy parameters was on the latent heat flux which could decrease up to -11% in the midday while increase up to 15% in the nighttime. Additionally, this work indicated that the description of the canopy characteristics for the land surface model should be improved to accurately deliver the heterogeneity for the underlying surface.

In this study, a novel task of printing speed optimization for continuous fiber composites is investigated. Using continuous fibers is an innovative approach to reinforce products made by fused filament fabrication (FFF) additive manufacturing (AM) technology. In the printing process of composites with continuous fibers, the printing speed is critical because of its significant effect on the geometric shape of the samples, especially their corners. During optimization in this research, continuous glass fiber (CGF) and polylactic acid (PLA) filaments were utilized as reinforcing phase and matrix, respectively, and were simultaneously fed into the extrusion-based polymer 3D printer to form PLA/CGF composites. The optimization was carried out by calculating the temperature changes of the deposited rasters in the presence and absence of fibers as a first step and then determining the special relationship between the printing speeds and rasters temperature changes. Finally, the optimal and the maximum printing speed was computed based on a hypothesis, which is proved by the results of high-quality printed composites with different geometric shapes.

Eco-friendly and low-cost water-based nanolubricants containing rutile TiO2 nanoparticles (NPs) were developed for accelerating their applications in industrial-scale hot steel rolling. The lubrication performance of developed nanolubricants was evaluated in a 2-high Hille 100 experimental rolling mill at a rolling temperature of 850 ℃ in comparison to that of pure water. The results indicate that the use of nanolubricant enables to decrease the rolling force, reduce the surface roughness and the oxide scale thickness, and enhance the surface hardness. In particular, the nanolubricant consisting of 4 wt% TiO2, 10 wt% glycerol, 0.2 wt% sodium dodecyl benzene sulfonate (SDBS) and 1 wt% Snailcool exhibits the best lubrication performance by lowering the rolling force, surface roughness and oxide scale thickness up to 8.1%, 53.7% and 50%, respectively. The surface hardness is increased by 4.4%. The corresponding lubrication mechanisms are attributed to its superior wettability and thermal conductivity associated with the synergistic effect of rolling, mending and laminae forming that are contributed by TiO2 NPs.

How a species is represented by marketers of animal-based products both reflects and shapes how consumers think about that animal. By examining the explicit statements, and implicit messages encoded in the imagery on supermarket egg boxes, this paper explores how hens are represented by whole egg retailers. Content analysis reveals two prominent messages purveyed through eggbox graphics, namely those pertaining to hen welfare and human health. The later disenfranchises hens from their products by focusing on the nutritional value of eggs, whereas the former reflects a public concern for the welfare of egg-laying hens. Although claims of improvements in welfare practices are undoubtedly exploited as marketing tools, they serve to raise awareness and drive competitors to adopt similar practices. Welfare claims are a direct response to public concerns about the plight of hens, and may positively influence industry welfare standards. However, idyllic depictions displayed on eggboxes also lull consumers into the belief that those eggs are an ethically sound food choice, regardless of the actual standard of living experienced by the hens.

Participation by local communities in wildlife conservation projects have long been advocated since it is socially just and is effective to reach conservation and development goals. Socio–economic variables that drive participation and impact of participation have been studied, but the contextual process that stir up local community participation remains understudied. In this paper, we studied factors facilitating community participation in three wildlife conservation projects in Northeast India. Through ethnographic fieldwork at these sites we identified conservation actors and examined interactions between them.We found common modes of participation at these sites and these were related to gaining material incentives, providing labour, attending consultative workshops. Levels of interaction and coercion were found to be different in three sites. Three critical factors that drive participation were: (1) trigger, (2) negotiation and (3) sustenance. Trigger factors kickstart participation through establishment of a crisis narrative and facilitation by external actors. Negotiation factors emerge from day–to–day interaction between local community and external actors and involve effective entry stage activities, income opportunity, mediating voices within the community and intra–community dynamics. Sustenance factors affect the long term participation by community in the conservation project and involve tangible/intangible results, capability development of locals, funding and availability to critical information. In our paper we argue that investment of time and fund to understand the stakeholders and their concept of participation, periodic feedback sessions, capacity development of locals for self–mobilization, innovative information dissemination and securing long term funding are necessary for effective local community participation.

The aim of this study was to introduce a new activity-based balance index by using accelerometer data. Twenty-seven junior soccer players from the Iranian premier league were selected. Four functional tests, consisting of one leg stance, dynamic Y balance, running and dribbling tests, were conducted to assess the players’ balance, activity and skill. During these four tests, besides their relative scores, the acceleration of their body center was also recorded. Activity-based balance index (ABI) was calculated using these acceleration data. The results showed positive correlations between ABI and both static and dynamic balance scores. Additionally, negative correlations were found between ABI and dribbling scores, which demonstrate the agility required for this skill. It seems that this new index achieves the evaluation of both the balance and the skill level of soccer players. Perhaps this is a new way of talent identification and also a re-development of balance tests from traditional to modern.

In the software development process, the decision-maker (DM) has a range of problems inherent to its function. Wrong choices during software planning can bring great risk to the project. Therefore, the planning of software releases to be delivered to the customer should be well done. This is not an easy task because releases are made up of many requirements that contain complex variables that must be considered, such as precedence, cost, requirement stability, among other features that make the requirements-selection process challenging. To make this process less exhaustive, DM can use tools that facilitate this work. In software engineering, we can find fields of research specialists in this context, such as Search-Based Software Engineering (SBSE). The SBSE makes use of advanced metaheuristics to search for optimal solutions or the closest to it. In this work, we try to use another field of research to solve this same problem type, the Verbal Decision Analysis (VDA). To do this, we elaborate a workflow that will use the same source data, execute two solutions using the two search fields (SBSE and VDA) and compare the results. In the end, we evaluated and commented on the results.

Fragment-based drug discovery (FBDD) has become a major strategy to derive novel lead candidates for various therapeutic targets, as it promises efficient exploration of chemical space by employing fragment-sized (MW < 300) compounds. One of the first challenges in implementing a FBDD approach is the design of a fragment library, and more specifically, the choice of its size and individual members. A diverse set of fragments is required to maximise the chances of discovering novel hit compounds. However, the exact diversity of a certain collection of fragments remains underdefined, which hinders direct comparisons among different selections of fragments. Based on structural fingerprints, we herein introduced quantitative metrics for the structural diversity of fragment libraries. Structures of commercially available fragments were retrieved from the ZINC database, from which libraries with sizes ranging from 100 to 100,000 compounds were selected. The selected libraries were evaluated and compared quantitatively, resulting in interesting size-diversity relationships. Our results demonstrated that while library size does matter for its diversity, there exists an optimal size for structural diversity. It is also suggested that such quantitative measures can guide the design of diverse fragment libraries under different circumstances.

The objective to fast-track the mapping and registration of large numbers of unrecorded land rights globally, leads to the experimental application of Artificial Intelligence (AI) in the domain of land administration, and specifically the application of automated visual cognition techniques for cadastral mapping tasks. In this research, we applied and compared the ability of rule-based systems within Object Based Image Analysis (OBIA), as opposed to human analysis, to extract visible cadastral boundaries from Very high resolution (VHR) World View-2 image, in both rural and urban settings. From our experiments, machine-based techniques were able to automatically delineate a good proportion of rural parcels with explicit polygons where the correctness of the automatically extracted boundaries was 47.4% against 74.24% for humans and the completeness of 45% for machine, as against 70.4% for humans. On the contrary, in the urban area, automatic results were counterintuitive: even though urban plots and buildings are clearly marked with visible features such as fences, roads and tacitly perceptible to eyes, automation resulted in geometrically and topologically poorly structured data, that could neither be geometrically compared with human digitised, nor actual cadastral data from the field. These results provide an updated snapshot with regards to the performance of contemporary machine-drive feature extraction techniques compared to conventional manual digitising.

Over half of adult Americans now have diabetes or prediabetes and worse, this epidemic is now world-wide and shows no signs of slowing, with rates of both diabetes and diabetes-related health complications still rising. When advising patients with diabetes on food choices, many providers rely on nutrition guidelines provided by the American Diabetes Association (ADA), and these guidelines influence other recommendations across the globe. Given the alarming trends in diabetes, it is paramount to review the treatment guidelines to ensure they are based on rigorous and accepted scientific methods. Our review included the evidence cited by the ADA in support of its claims and recommendations for eating patterns to combat diabetes (see Description of Eating Patterns, Supplemental Appendix), as presented in the most current edition of the annual ADA’s Standards of Medical Care in Diabetes (2018 Standards). Our review also includes sources cited in the latest edition of the ADA’s Nutrition Therapy Recommendations for Adults with Diabetes (2014 Recommendations), which also informed the 2018 Standards. In October 2018 low carbohydrate was named as a recommended eating pattern by the ADA and European Association for the Study of Diabetes (EASD)⁵but only citations from the 2014 and early 2018 ADA documents were reviewed.

Background: The aim of this study was to investigate the risk of obstructive sleep apnea (OSA) among ankylosing spondylitis (AS) patients in a nationwide population. Methods: We conducted a nationwide cohort study between 2003 and 2013 using the Taiwan National Health Insurance Research Database. The AS cohort included 2210 patients who were newly diagnosed between 2003 and 2013. Randomly selected non-AS controls were matched at a 1:4 ratio based on age, sex and index date. The endpoint of OSA was occurrence or the end of 2013. Cumulative incidences, hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated after adjusting for age, gender, comorbidities and co-medications. Multivariate analyses were performed using Cox proportional hazards model. Due to violation of the proportionality assumption, landmark analysis was conducted to explore the risk of OSA during specific follow-up periods. Results: The adjusted HR (aHR) of OSA for the AS group was 2.826 (95% C.I. = 1.727–4.625) compared to the control group. On landmark analysis, aHR was 7.919 (95% C.I. = 3.169–19.792) for AS group 0–24 months from index date, and decreased to 1.816 (95% C.I. = 0.944–3.494) at ≥ 24 months from index date. On subgroup analyses increased risks of OSA in AS group compared to the control group were found for both males and females (aHRs were 4.533 (95% C.I. = 1.441–14.262) and 2.672 (95% C.I. = 1.522–4.692) for females and males, respectively). On age stratified analysis, there was significant risk only for the 40–59 age group with aHR of 3.913 (95% C.I. = 1.890–8.102). Conclusions: A higher risk of developing OSA was found among newly diagnosed AS cohort during the 12-year follow-up period, especially within 2 years after AS index date and in the 40–59 age group.

The current focus on afforestation in climate policy runs the risk of compromising both longterm carbon storage and human adaptation. It also works against efforts to stem the tide of biodiversity loss. We outline why an emphasis on diverse, intact natural ecosystems—as opposed to tree plantations with fast-growing exotic species—will help nations deliver the goals of the Paris Agreement and much more.

With the development of IoT technology, various information resources, such as social resources and physical resources, are deeply integrated for different comprehensive applications. Social networking, car networking, medical services, video surveillance and other forms of IoT information Service model gradually change people's daily life. Facing the vast amounts of IoT information data, IoT search technology is used to quickly find accurate information to meet real-time search needs of users. However, IoT search requires to use a large number of user privacy Information, such as personal health information, location information, social relations information, to provide personalized services. User privacy information will meet security problems if an effective access control mechanism is missing during the IoT search process. Access control mechanism can effectively monitor the access activities of resources, and ensure authorized users to access information resources under legitimate conditions. This survey examines the growing literature on access control for IoT search. Problems and challenges of access control mechanism are analyzed to facilitate the adoption of access control solutions in real-life settings. This paper aims to provides theoretical, methodological and technical guidance for IoT search access control mechanism in large-scale dynamic heterogeneous environment. Based on the literature study, we also analyzed future development direction of access control in the age of IoT.

In this work, the decennial experience of Policumbent student team at Politecnico di Torino is summarized by focusing on the acquired knowledge in design of Human Powered Vehicles (HPVs) and on soft skills developed by both students and staff. Policumbent was funded by the authors at the end of 2008 in order to gather engineering students interested in design and construction of HPVs. In the last decade, the team has grown from 10 up to 50 students enrolled per year, exploring a range of HPV design for sports and mobility. Even when focusing on sport vehicles and extreme HPVs for speed record, such kind of projects allows students to familiarize with important concepts related to sustainable mobility: the amount of resistive forces and dissipated power, the role of vehicle weight and the impact of acceleration on the overall energetic balance as far as fundamental concepts about energy consumption, efficiency and emissions of the ``human engine'' in comparison with other kind of engines. By touching with hands such topics in the framework of a ``human-centred'' design project, the students have opportunity to develop awareness about the impact of design choices on sustainability of any kind of vehicle for transportation. Also, the paper retraces the team evolution path by focusing on a thorough analysis of what factors contributed to the success of this project.

Hot rolling tests of a low-alloy steel were conducted at a rolling temperature of 850 ℃ under different lubrication conditions, including benchmarks (dry condition and water) and water-based nanolubricants containing different concentrations of nano-TiO₂ from 1.0 to 8.0 wt%. The effects of nanolubricants on rolling force, surface roughness, thickness of oxide scale and microstructure were systematically investigated through varying nano-TiO₂ concentrations. The results show that the application of nanolubricants can decrease the rolling force, surface roughness and oxide scale thickness of rolled steels, and refine ferrite grains. In particular, the nanolubricant containing an optimal concentration (4.0 wt%) of nano-TiO₂ demonstrates the best lubrication performance, owing to the synergistic effect of lubricating film, rolling, polishing and mending generated by nano-TiO₂.

Neisseria meningitidis causes most cases of bacterial meningitis. Meningococcal meningitis is a public health burden to both developed and developing countries throughout the world. There are a number of vaccines (polysaccharide-based, glycoconjugate, protein-based and combined conjugate vaccines) that are approved to target five of the six disease-causing serogroups of the pathogen. Immunization strategies have been effective at helping to decrease the global incidence of meningococcal meningitis. Researchers continue to enhance these efforts through discovery of new antigen targets that may lead to a broadly protective vaccine and development of new methods of homogenous vaccine production. This review describes current meningococcal vaccines and discusses some recent research discoveries that may transform vaccine development against N. meningitidis in the future.

This paper presents a novel 3-degrees-of-freedom (3-DOF) haptic master with rubber bands for self-resetting. The mechanical design avoids coupling between three directions mechanically by using three perpendicular axis intersecting at one point. Bevel gear transmission is adopted to increase the compactness of the overall structure. VR-based interactive system is designed and built by incorporating the proposed haptic master. The proposed haptic device can generate force feedback along 3-degree-of-freedom motion using motors and provide command signals to the avatar in the virtual environment. In order to analyze the performance of the developed device in terms of haptic feedback operation, ergonomics assessments are designed and experimentally implemented. Preliminary studies on the influencing factor including the guidance force, the reset force, the speed of the avatar and the arm the length have been conducted. The results of this paper are of great significance for the design of the haptic master and interactive system.

Smoothed particle hydrodynamics (SPH) is regarded as a pure Lagrangian approach, which can solve fluid dynamics problems without the creation of mesh. In this paper, a paralleled SPH solver is developed to solve particle-based computational acoustics (PCA). The aim of this paper is to study the feasibility of using SPH to solve acoustic problems and to improve the efficiency of solving processes by paralleling some procedures on GPU during calculating. A stand SPH code running serially in a CPU is proposed to solve wave equation. This is a wave propagating in a two-dimensional domain. After finishing the computation, the results are compared with the theoretical solutions and they agree well. So its feasibility is verified. There are two main methods for searching neighbor particles: all-pair search method and linked-list search method. Both methods are used in different codes to simulate an identical problem and their runtimes are compared to investigate their searching efficiencies. The runtime results show that linked-list search method has a higher efficiency, which can save a lot of searching time when simulating problems with huge amounts of particles. Furthermore, the percentages of different procedures’ runtimes in a simulation are also discussed to find the most consuming one. Then, some codes are modified to run in different GPUs and their runtimes are compared with those of serial ones on a CPU. Runtime results show that the paralleled algorithm can be more than 80 times faster than the serial one. The result shows that GPU paralleled SPH computing can achieve desirable accuracy and speed in solving acoustic problems.