In this review article the study of the development of relativistic cosmology and introduction of inflation in it is carried out. We study the properties of standard cosmological model developed in the framework of relativistic cosmology and the geometric structure of spacetime connected coherently with it. We examine the geometric properties of space and spacetime ingrained into the standard model of cosmology. The big bang model of the beginning of the universe is based on the standard model which succumbed to failure in explaining the flatness and the large-scale homogeneity of the universe as demonstrated by observational evidence. These cosmological problems were resolved by introducing a brief acceleratedly expanding phase in the very early universe known as inflation. Cosmic inflation by setting the initial conditions of the standard big bang model resolves these problems of the theory. We discuss how the inflationary paradigm solves these problems by proposing the fast expansion period in the early universe.

Despite advances in understanding of the molecular pathogenesis of glioma, outcomes remain dismal. Developing successful treatments for glioma requires faithful in vivo disease modeling and rigorous preclinical testing. Murine models, including xenograft, syngeneic, and genetically engineered models, are used to study gliomagenesis, identify methods of tumor progression, and test novel treatment strategies. Since the discovery of highly recurrent isocitrate dehydrogenase (IDH) mutations in lower-grade gliomas, there is increasing emphasis on effective modeling of IDH mutant brain tumors. Improvements in preclinical models that capture the phenotypic and molecular heterogeneity of gliomas are critical for the development of effective new therapies. Herein, we explore the current status, advancements, and challenges with contemporary murine glioma models.

The exact dynamics of emergence remains one of the most prominent outstanding questions for the field of complexity science. I first discuss various perspectives on emergence in various contexts, then offer a different perspective on understanding emergence in a graph-theoretic representation. From the discussion, an observer’s choice in state space seems to have an effect for that observer to detect emergent behavior. To test these ideas, I analyze the dynamics of all possible spatial state spaces near the critical temperature in an Ising model. As a result, state space topologies that appear more deterministic flip more bits than topologies that appear more random, which is contrary to our intuitions about randomness. In addition, the size of different state spaces constrain a system’s ability to explore various states within the same time frame. These results are important to understanding emergent phenomena in biological systems, which are layered with various state spaces and observational perspectives.

This paper presents an analysis of precipitation extremes over the East African region. The study employs six extreme precipitation indices defined by the Expert Team on Climate Change Detection and Indices (ETCCDI) to evaluate possible climate change. Observed datasets and CMIP6 simulations and projections are employed to assess the changes during the two main rainfall seasons of March to May (MAM) and October to December (OND). The study evaluated the capability of CMIP6 simulations in reproducing the observed extreme events during the period 1995 – 2014. Our results show that the multi-model ensemble (herein referred to as MME) of CMIP6 models can depict the observed spatial distribution of precipitation extremes for both seasons, albeit with some noticeable exceptions in some indices. Overall, MME's assessment yields considerable confidence in CMIP6 to be employed for the projection of extreme events over the study area. Analysis of extreme estimations shows an increase (decrease) in CDD (CWD) during 2081 – 2100 relative to the baseline period in both seasons. Moreover, SDII, R95p, R20mm, and PRCPTOT demonstrate significant OND estimates compared to the MAM season. The spatial variation for extreme incidences shows likely intensification over Uganda and most parts of Kenya, while reduction is observed over the Tanzania region. The increase in projected extremes during two main rainfall seasons poses a significant threat to the sustainability of societal infrastructure and ecosystem wellbeing. The results from these analyses present an opportunity to understand the emergence of extreme events and the capability of model outputs from CMIP6 in estimating the projected changes. More studies are encouraged to examine the underlying physical features modulating the occurrence of extremes incidences projected for relevant policies.

The formation of rock fractures in nature has a certain relationship with water seepage. In order to analyze the shape of rock fracture, we propose a rock deformation theoretical model in three-dimensional space considering with the condition of seepage water pressure, and establish the mass conservation equation, seepage equation, surrounding rock displacement equation, so as to deduce the rock deformation control equation under seepage pressure action. The numerical analysis of the nonlinear quadratic partial differential equation obtains the fracture deformation distribution in the example, and verifies with the calculated fracture morphology. And we further use this principle to analyze the fracture morphology in nature and prove the rationality of the theory.

The masonry building Heritage is appreciated for its aesthetic and historical value all around the world. The widespread presence of curved elements, such as arch, vault and dome express the relevant constructive abilities in the different historical epochs. These curved elements are characterized by architectural beauty, structural strength (especially against the gravity loads), thermal comfort and fire resistance. On the other hand, curved structures required scaffolding in order to be erected. The design, the construction and the dismantling of the scaffolds is typically time-consuming and expensive. In addition, the on-site working risk is related to time-interferences (e.g. in manpower working, at the same time, over and under scaffold). This technology dates back to the Era of the Roman Empire and it is currently still used, despite its limitations and disadvantages. In the present paper, an innovative technique (recently patented), aiming for the construction of a curved structural member without scaffolds, is proposed and illustrated. It consists in a Hinged Lifting Arch (HLA), using FRP (Fiber Reinforced Polymer) bonded strips. In details, a series of blocks are cut following an arch geometry and then aligned on the ground-floor in order to bond a composite on their top surface. Moreover, the impregnation of the polymeric adhesive is not allowed at the extremities of each block. The fiber sheet is applied continuously along the entire extrados. In this sense, hinges are introduced, in fact, the FRP-connected blocks are able to easily rotate, in the opposite direction, around the contact ends (i.e. hinge). Finally, the middle block is lifted-up and the arch takes the desiderated shape. In the first experimental demonstration, the natural calcareous stone was used, even if the proposed technique is totally material-independent. Moreover, an analytical model is proposed and discussed for designing the proper aspect ratio of the blocks in order to ensure the full mutual contact when the HLA is totally lifted up. The advantages of the proposed technique are related to the absence of scaffolds and improved seismic strength against horizontal loads thanks to the presence of the FRP, which limits the occurrence of hinges at the extrados.

In India the first case of coronavirus disease 2019 (COVID-19) reported on 30 January 2020, and thereafter cases were increasing daily after the last week of Feb. 2020. COVID-19 identified as family member of coronaviridae where previously Middle East Respiratory Syndrome MERS and Severe Acute Respiratory Syndrome SARS belongs to same family. The COVID-19 attacks on respiratory system signing fever, cough and breath shortness, in severe cases may cause pneumonia, SARS or some time death. The aim of this study work is to develop model which predicts the epidemic peak for COVID-19 in India by using the real-time data from 30 Jan to 10 May 2020. There are uncertainties while identifying the population information due to the incomplete and inaccurate data, we initiate the most popular model for epidemic prediction i.e Susceptible, Exposed, Infectious, & Recovered SEIR initially the compartmental model for the prediction. Based on the solution of the state estimation problem for polynomial system with Poisson noise, we estimate that the epidemic peak may reach the early-middle July 2020, initializing recovered R0 to 0 and Infected I0 to 1. The outcomes of the model will help epidemiologist to isolate the source of the disease geospatially and analyze the death. Also government authorities will be able to target their interventions for rapidly checking the spread of the epidemic.

Italy was the first country in Europe which imposed control measures of travel restrictions, quarantine and contact precautions to tackle the epidemic spread of the novel coronavirus (SARS-CoV-2) in all its regions. While such efforts are still ongoing, uncertainties regarding SARS-CoV-2 transmissibility and ascertainment of cases make it difficult to evaluate the effectiveness of restrictions. Here, we employed a Susceptible-Exposed-Infectious-Recovered-Dead (SEIRD) model to assess SARS-CoV-2 transmission dynamics, working on the number of reported patients in intensive care unit (ICU) and deaths in Sicily (Italy), from 24 February to 13 April. Overall, we obtained a good fit between estimated and reported data, with a small fraction of unreported SARS-CoV-2 cases (19.5%; 95%CI=0%-34.7%) before 10 March lockdown. Interestingly, we estimated that the first set of restrictions reduced transmission rate in the community by 42% (95%CI=38%-46%), and that more stringent measures adopted on 23 March succeeded to drastically curb the transmission rate by 84% (95%CI=80%-88%). Thus, our estimates delineated the characteristics of SARS-CoV2 epidemic before restrictions taking into account unreported data. Further modeling after the adoption of control measures, moreover, indicated that restrictions reduced SARS-CoV2 transmission considerably.

Worldwide the steel corrosion is one of the greatest deterioration problems for reinforced concrete structures. Comparing some literature experimental results with a complex FEM model, the present paper points out the principal aspects those characterize the static behavior of RC elements damaged by corrosion. Moreover, the non-dimensional abaci defined for some specific case studies finalized to the evaluation of the residual flexural strength of corroded elements highlight the dangerousness of the corrosion degradation if the failure of the element is governed by the steel

Here, we have discovered a X-ray excited long afterglow phosphor β-NaYF₄: Tb³⁺. After the irradiation of X-ray, the green emission can persist for more than 240 h. After 36 h, the afterglow intensity arrived at 0.69 mcd•m^-2, which can clearly be observed by naked eyes. Even after 84 h, the afterglow emission brightness still reached 0.087 mcd•m^-2. Also, combined with the results of thermoluminescence and photoluminescence, the super long afterglow emission of β-NaYF₄: Tb³⁺ can be ascribed to the tunneling model associated with F centers. More importantly, the super long green afterglow emission of β-NaYF₄: Tb³⁺ has been successfully used as in vivo light source to activate g-C₃N₄ for photodynamic therapy（PDT）and bacteria destruction. Furthermore, super long persistent luminescence of β-NaYF₄: Tb³⁺ could be repeatedly charged by X-ray for many circulations, which indicates that the phosphors have high photo stability under repeated cycles of alternating X-ray irradiation.

The practice of spatial econometrics revolves around a weighting matrix, which is often supplied by the user on previous knowledge. This is the so called $\mathbf{W}$ issue. Probably, the aprioristic approach is not the best solution although, nowadays, there few alternatives for the user. Our contribution focuses on the problem of selecting a $\mathbf{W}$ matrix from among a finite set of matrices, all of them considerer appropriate for the case. We develop a new and simple method based on the Entropy corresponding to the distribution of probability estimated for the data. Other alternatives, which are common in current applied work, are also reviewed. The paper includes a large Monte Carlo to calibrate the effectiveness of our approach compared to the others. A well-known case study is also included.

Network Simulators (NS) is typically used to study services and applications in complex scenarios due to the infeasibility of deploying real testbeds. This is the case of Mobile Ad hoc Networks (MANETs) which can be extensively used in emergency situations or to gather information without infraestructure. An important factor to get realiable results in the MANETs’ simulations is the wireless channel module of NSs. In this report, we explain how thw wireless channel is implemented in NS-3 and it can be configured.

The aim of this study was to investigate the effect of discoursal approach on Iranian intermediate EFL learners’ reading comprehension ability. Quick Placement Test (QPT) was used to select 60 intermediate EFL learners as the participants of this study. Then, they were randomly divided into experimental and control groups. Each consisted of 30 learners. Prior to the treatment, the participants of both groups were given a pretest to ensure their reading comprehension. The experimental group was exposed to the Hymes’ model. In the control group the researcher used placebo for teaching reading skill. Then a posttest was administered to both groups. An Independent samples t-test between posttests of the study and a paired-samples t-test between the pretest and posttest of the groups of the study were run. The results of the study revealed and emphasized that the Hymes’ model improved the learners’ reading comprehension.

Fear can sometimes paralyze us, and it can sometimes be exciting; for some people, fear is so crippling it can significantly mix up their lifes! We understand a little bit about how the brain acts when we are afraid, mainly by studying the brains of animals. Recently, surprising findings were made using a humble animal, the zebrafish – a small aquarium fish that in the past has helped scientists figure out how our organs develop. Zebrafish are useful because they develop quickly, reproduce richly, and have brains which are similar to ours. They also produce what we call an “alarm substance” that alerts shoalmates when one of them has been injured; when they smell this substance in the water they act as if they are very scared. When this happens, they release serotonin in their brains, a neurotransmitter that acts as a light switch, making them less afraid but more cautious – as if trying to figure out if a predator is there or not. Hopefully, finding more about how the zebrafish brains process this serotonin signal can help scientists develop better treatments for mental disorders that are associated with fear.

This paper introduces a surrogate model to reduce the huge computational load in the process of simulation-optimization and uncertainty analysis. First, the groundwater numerical simulation model was established, calibrated and verified in the northeast of Hetao Plain. Second, two surrogate models of simulation model were established using support vector regression (SVR) method, one (surrogate model A, SMA) was used to describe the corresponding relationship between the pumping rate and average groundwater table drawdown, and another (surrogate model B, SMB) was used to express the corresponding relationship between the hydrogeological parameter values and average groundwater table drawdown. Third, an optimization model was established to search an optimal groundwater exploitation scheme using the maximum total pumping rate as objective function and the limitative average groundwater table drawdown as constraint condition, the SMA was invoked by the optimization model for obtaining the optimal groundwater exploitation scheme. Finally, the SMB was invoked in the process of uncertainty analysis for assessing the reliability of optimal groundwater exploitation scheme. Results show that the relative error and root mean square error between simulation model and the two surrogate models are both less than 5%, which is a high approximation accuracy. The SVR surrogate model developed in this study could not only considerably reduce the computational load, but also maintain high computational accuracy. The optimal total pumping rate is 7947 m3/d and the reliability of optimal scheme is 40.21%. This can thus provide an effective method for identifying an optimal groundwater exploitation scheme and assessing the reliability of scheme quickly and accurately.

There is no simple method to measure glomerular filtration rate (GFR) in swine, an established model to study renal disease. We developed a protocol to measure GFR in conscious swine with the plasma clearance of iohexol. We used two groups: testing and validation, of 8 animals each. Ten milliliters of iohexol (6.47 g) were injected by the marginal auricular vein and blood samples (3 ml) were collected from the orbital sinus at different points after injection. GFR was determined considering two models: two-compartments (CL2: all samples) and one-compartment (CL1: the last six samples). In the testing group, CL1 overestimated CL2 by ~30%: CL2=245±93 and CL1=308±123 ml/mn. This error was corrected by a first order polynomial quadratic equation to CL1, which was considered the simplified method: SM=-47.909+(1.176xCL1)–(0.00063968xCL1²). SM showed narrow limits of agreement with CL2, and a concordance correlation of 0.97 and a total deviation index of 14.73%. Similar results were obtained for the validation group. This protocol is reliable, reproducible, can be performed in conscious animals, uses a single dose of the marker, and requires a reduced number of samples avoiding urine collection. Finally, it portends a significant improvement in animal-welfare conditions and handling necessities in experimental trials.

The fundamental challenge for the structural design of complicated shapes and lightweight structures such as Tensile Membrane Structures (TMS) is a reliable estimation of lateral loading such as wind load. Wind pressure coefficients can’t be correctly calculated by standards for a large range of intricate forms of structures. Computational Fluid Dynamics (CFD), a strong computational technique for evaluating wind pressure distribution on complicated geometry, can be used to determine wind-related parameters. The average wind related-distributions for the tensile membrane surface are validated with a few contributions from prior studies, which are important to validate with benchmarks in experimental wind tunnel experiments. For the double-curved surface, the results show good agreement between experimental tests and CFD simulations. In this study, CFD simulation was used to calculate the mean surface pressure coefficient (Cp) for generic shapes and tensile membrane forms using the steady Reynolds-Averaged Navier-Stokes (RANS) technique. Wind force coefficients and the effect of wind directions are also explored across a wide range of classical and tensile forms. The purpose of the current research is to look at the effect of wind assault angle on the Cp distribution for tensile membrane structures and general shapes.

For the in-house validation of a droplet digital PCR method, a factorial experimental design was implemented. This design serves different purposes. On the one hand, it is an efficient design in relation to the workload involved in achieving a desirable level of reliability of variance estimates. On the other hand, it allows a partitioning of total variance into different components, thus providing information regarding the dominant sources of random variation. The statistical modelling reflects the actual measurement mechanism, establishing relationships between nominal target DNA copies per well, the range of variation of copy numbers per droplet, probability of detection values, and estimated numbers of copies.

In the future, the pursuit of high-quality economic development and a focus on ecological environmental protection in China will inevitably result in significant conflicts between land use and ecological land use. The challenge lies in achieving sustainable high-quality development while simultaneously protecting the ecological environment, optimizing the land use structure, and promoting a harmonious relationship between humans and the land. These challenges are faced by all regions. Land use conflicts primarily occur in peri-urban areas characterized by prominent economic development and urban agglomeration. Previous studies have mainly focused on analyzing the effects of land use on habitat quality during intense urbanization. However, it is important to recognize that land pressure encompasses economic, ecological, and social aspects. To gain a comprehensive understanding of the spatial conflict of land use and the impact on habitat quality in Ankang, a city that has been advocating ecological protection for the past two decades, this study aims to objectively analyze the spatial trends in land use changes in such cities. Additionally, it aims to provide insights for the harmonious development of land use in eco-region-oriented cities. Using the SSP-RCP scenarios provided by CMIP6, this paper employs a system analysis method, PLUS model, InVEST model, and land use conflict measurement model to dynamically simulate the future habitat quality and spatial conflict patterns of land use in Ankang City. The study explores the spatial coupling effect of both factors under different scenarios. The results indicate the following:(1) Under different future shared socio-economic path scenarios, land use intensity and land conflict levels follow the order of SSP585 (high forcing scenario), SSP370 (medium to high forcing scenario), SSP245 (medium forcing scenario), and SSP126 (low forcing scenario), with intensity and conflict decreasing accordingly. (2) The overall development trend in Ankang City reveals an intensification of land use conflicts and a decrease in habitat quality. The expansion rate of construction land is increasing and exhibiting aggregation, while agricultural land area is expanding and forest land area is continuously decreasing. (3) Land use intensity exhibits a significant positive correlation with land conflict levels, while land conflict levels demonstrate a significant negative correlation with habitat quality. These findings suggest that land use has had some impact on the ecological environment, with indications of habitat degradation. Even in Ankang, where ecological development is highly valued, the city will gradually face conflicts between ecological protection and economic development in future scenarios. The study highlights that Ankang's future development space will be constrained within the context of environmental protection, leading to greater land use conflicts in urban and surrounding areas. Consequently, the quality of habitats will inevitably decline. Therefore, it is crucial to allocate sufficient space for economic development while simultaneously prioritizing ecological protection. This approach will ensure a healthy economic development trajectory and foster a harmonious relationship between humans and the land.

The stability of deep soft rock tunnels is of particular concern under seepage conditions. Aiming at thoroughly discussing the seepage actions and their consequences on the support scheme of such structures, the host rocks of the Weilai Tunnel situated in Guangxi province of China are taken as the research substance. Emphasizing on seepage conditions, stresses, displacements and plastic zone radii along the surrounding rocks of such tunnel are adequately examined taking into consideration the Mogi-Coulomb strain criterion and the elastic-plastic theory. Subsequently, based on strain-softening model, the coupled actions of seepage and softening on the rocks surrounding the tunnel are studied. According to the results, it is revealed that the support scheme of deep soft rock tunnels must be of highest resistance possible to better decrease the plastic zone and the tangential stress along the host rocks. Moreover, throughout the surrounding rocks, the dissemination of pore water pressure is strongly affected by the uneven permeability coefficient under anisotropic seepage states. Combined effects of softening and seepage are very dangerous for the surrounding rocks of deep-buried tunnels. To better guarantee reasonable longevity of such tunnels, long-term monitoring of their support structures with reliable remote sensors is strongly recommended.

As an irreplaceable ecological barrier, the ecological conservation developing area (ECDA) is vital for the integrated construction of urban and rural areas, and optimization and adjustment of industrial structure. Developing green agriculture is the foundation and guarantee for improving the rural ecological environment and meeting the increasing needs of farmers′ income. Issued by the Chinese government, No. 1 Central Document stated the necessity of promoting agricultural green development (AGD) and the related technologies in dealing with ecological pressure and resource shortages, especially for large cities like Beijing. However, few empirical studies have conducted on spatiotemporal variations of AGD in ECDA of large cities. Based on the green agricultural traits of Beijing and the accessible data, we evaluate AGD and analyze its spatial and temporal heterogeneity of Beijing ECDA by constructiing a framework with 13 indicators. The weight coefficients of AGD indicators were calculated by the projection pursuit method, with the district panel data from 2006 to 2016. The results demonstrated that energy consumption is a vital factor of green agriculture production, and agricultural output value per unit of the arable land area is the key to the green agricultural revenue. From 2006 to 2016, the AGD index of ECDA had an increasing trend till 2012, followed by a decreasing tendency. The AGD index of the northern region is higher than the southern of ECDA. The obstacle degree model was used to verify AGD limiting factors. They were poor infrastructure, slow agritourism, low labor productivity, and low resource utilization efficiency that varied by districts in ECDA. Given these findings, our study is conducive to the AGD evaluation at the district (county) level for ECDA of large cities and also provides important policy implications.

Online and offline shopping trip have different impacts on various aspects of urban life, such as e-commerce, transportation systems, and sustainability. Therefore, it is important to evaluate the factors that influence their choices. We use a hybrid machine learning model that combines a gray wolf optimization algorithm and a deep convolutional neural network to estimate shopping trip based on a survey of 1,000 active e-commerce users who made successful orders in both online and offline services in the last 20 days of 2021 in areas 2 and 5 of Tehran. The gray wolf optimization algorithm performs feature selection and hyperparameter tuning for the deep convolutional neural network, which is a powerful deep learning model for image recognition and classification. The results show that our model achieves an accuracy of 97.81% with an MSE of 0.325 by selecting seven out of ten features. The most important features are delivery cost, delivery time, product price, car ownership. In addition, comparing the performance of the proposed method with other methods showed that the proposed algorithm with an accuracy of 97.81%, the accuracies of the single deep learning model, MLP neural network, decision tree, and KNN models were 95.63%, 90.0%, 86.49%, and 80.16%, respectively.

A numerical study for the effect of crest width, breaking parameter and trunk permeability on hydrodynamics and flow behavior in the vicinity of rubble-mound, permeable, zero-freeboard breakwaters (ZFBs) is presented. The modified two dimensional Navier-Stokes equations for two-phase flows in porous media with a Smagorinsky model for the subgrid scale stresses were solved numerically. An immersed-boundary/level-set method was used. The numerical model was validated for the cases of wave propagation over a submerged impermeable trapezoidal bar and over a low-crested permeable breakwater. Five cases of breakwaters were examined, and the main results are: (a) The size of the crest width, B, does not notably affect the wave reflection, vorticity and currents in the seaward region of ZFBs, while wave transmission, currents in the leeward side, and mean overtopping discharge, all decrease with increasing B. A non-monotonic behavior of the wave setup is also observed. (b) As the breaking parameter decreases, wave reflection, transmission, currents, mean overtopping discharge, and wave setup decrease. This observation is also verified by relevant empirical formulas. (c) As the ZFB trunk permeability decreases, an increase of the wave reflection, currents, wave setup, and a decrease of wave transmission and mean overtopping discharge is observed.

Given the high number of vehicle-crash victims, it has been established as a priority to reduce this figure in the transportation sector. For this reason, many of the recent researches are focused on including control systems in existing vehicles, to improve their stability, comfort and handling. These systems need to know in every moment the behavior of the vehicle (state variables), among others, when the different maneuvers are performed, to actuate by means of the systems in the vehicle (brakes, steering, suspension) and, in this way, to achieve a good behavior. The main problem arises from the lack of ability to directly capture several required dynamic vehicle variables, such as roll angle, from low-cost sensors. Previous studies demonstrate that low-cost sensors can provide data in real-time with the required precision and reliability. Even more, other research works indicate that neural networks are efficient mechanisms to estimate roll angle. Nevertheless, it is necessary to assess that the fusion of data coming from low-cost devices and estimations provided by neural networks can fulfill the reliability and appropriateness requirements for using these technologies to improve overall safety in production vehicles. Because of the increasing of computing power, the reduction of consumption and electric devices size, along with the high variety of communication technologies and networking protocols using Internet have yield to Internet of Things (IoT) development. In order to address this issue, this study has two main goals: 1) Determine the appropriateness and performance of neural networks embedded in low-cost sensors kits to estimate roll angle required to evaluate rollover risk situations. 2) Compare the low-cost control unit devices (Intel Edison and Raspberry Pi 3 Model B), to provide the roll angle estimation with this artificial neural network-based approach. To fulfil these objectives an experimental environment has been set up composed of a van with two set of low-cost kits, one including a Raspberry Pi 3 Model B, low cost Inertial Measurement Unit (BNO055 - 37€) and GPS (Mtk3339 - 53€) and the other having an Intel Edison System on Chip linked to a SparkFun 9 Degrees of Freedom module. This experimental environment will be tested in different maneuvers for comparison purposes. Neural networks embedded in low-cost sensor kits provide roll angle estimations very approximated to real values. Even more, Intel Edison and Raspberry Pi 3 Model B have enough computing capabilities to successfully run roll angle estimation based on neural networks to determine rollover risks situation fulfilling real-time operation restrictions stated for this problem.

This paper presents a limited scope dynamic probabilistic risk assessment (D-PRA) on the survivability of commercial of the shelf (COTS) drones tasked with surveilling areas with varying radiation levels after a nuclear accident. The D-PRA is based on a discrete-dynamic event tree (D-DET) approach, which couples with the OpenEPL error propagation framework to model sequences leading to Loss of Mission (LOM) scenarios due to component failures in the drone’s navigation system. Radiation effects are simulated by calculating the total ionizing dose (TID) against the permissible limit per component, and errors are propagated within the electronic hardware and software blocks to quantify navigation system availability per radiation zone. The proposed methods are integrated into the traditional event tree/fault tree approach and the most vulnerable components are radiation-hardened (RAD-HARD) to the extent specified by a predefined mission success criterion. The results demonstrate the usefulness of the proposed approach in performing trade studies for incorporating COTS components into RAD-HARD drone designs.

The DEP force is usually calculated from the object’s point of view using the interaction of the object’s induced dipole moment with the inducing field. Recently, we described the DEP behavior of high- and low-conductive 200-µm 2D spheres in a square 1x1mm chamber with a plane-versus-plane electrode configuration from the system’s point of view. Here we extend our previous considerations to the plane-versus-plane and pointed-versus-pointed electrode configurations. The trajectories of the sphere center and the corresponding DEP forces were calculated from the gradient of the system’s overall energy dissipation for given starting points. The dissipation’s dependence on the sphere’s position in the chamber is described by the numerical “conductance field”, which is the DC equivalent of the capacitive charge-work field. While the plane-versus-plane electrode configuration is field-gradient free without an object, the presence of the highly or low-conductive spheres generates structures in the conductance fields, which result in very similar DEP trajectories. For both electrode configurations, the model describes trajectories with multiple endpoints, watersheds, and saddle points, very high attractive and repulsive forces in front of pointed electrodes, and the effect of mirror charges. Because the model accounts for inhomogeneous polarization within the objects, the approach allows the modeling of the complicated interplay of attractive and repulsive forces near electrode surfaces and chamber edges. Non-reversible DEP forces or asymmetric magnitudes for the highly and low-conductive spheres in large areas of the chamber indicate the presence of higher-order moments, mirror charges, etc.

Physics theory has yet to settle on specific descriptions for new elementary particles, for dark matter, and for dark energy forces. Our work extrapolates from the known elementary particles. The work suggests well-specified candidate descriptions for new elementary particles, dark matter, and dark energy forces. This part of the work does not depend on theories of motion. This work embraces symmetries that correlate with motion-centric conservation laws. The candidate descriptions seem to explain data that prior physics theory seems not to explain. Some of that data pertains to elementary particles. Our theory suggests relationships between masses of elementary particles. Our theory suggests a relationship between the strengths of electromagnetism and gravity. Some of that data pertains to astrophysics. Our theory seems to explain ratios of dark matter effects to ordinary matter effects. Our theory seems to explain aspects of galaxy formation. Some of that data pertains to cosmology. Our theory suggests bases for inflation and for changes in the rate of expansion of the universe. Generally, our work proposes extensions to theory in three fields. The fields are elementary particles, astrophysics, and cosmology. Our work suggests new elementary particles and seems to explain otherwise unexplained data.

Silicon-based photonic integrated circuit (PIC) is a research focus in producing high-density photonics. One of the potential applications of silicon PIC is the sensing and measurement system. In this work, we use the one-dimensional photonic crystal (1D-PhC) cavity design which and utilize it at the PIC level design. The 1D PhC design used as the compact model has the same characteristics as experimentally demonstrated in previous works. The compact model is made from the S-parameter extraction of the 1D-PhC device which is done by using Lumerical FDTD software. The PIC design integrates the 1D-PhC device as a sensing component with a PN-phase shifter (PN-PS) to function as a refractive index (RI) sensor calibration or tuning circuit. A custom design of PN-PS device is used by simulating and extracting the bias voltage-effective index (bias-Neff) data by using Lumerical DEVICE and MODE into the circuit simulator. The circuit-level simulation is done by using Lumerical Interconnect software. Finally, we show the GDSII layout design of the 1D-PhC based photonic sensor calibration circuit with an analysis of generic silicon PIC design rules. The designed PIC is applicable for the bio-sensing applications and photonic SOC component. This work also shows the promise of PIC design approach for further PIC development.

This study evaluates the accessibility of primary care physicians (PCPs) in Eastern Quebec using an enhanced two-step floating catchment area (E2SFCA) methodology. The E2SFCA methodology considers both the population supply of PCPs and the travel time between PCPs and dissemination areas, and was found to be effective in identifying possibly underserved areas. The availability of primary care services is contingent upon the presence of adequate road infrastructure, and populations residing in areas with limited access to main road networks may experience compromised access to primary care. The study concludes that the E2SFCA methodology can facilitate the identification of areas experiencing a shortage of PCPs, and this information can be used to inform the development of regional medical workforce programs and support the establishment of rural residency initiatives.

Microarray gene expression-based detection and classification of medical conditions have been prominent in research studies over the past few decades. However, extracting relevant data from the high-volume microarray gene expression with inherent nonlinearity and inseparable noise components raises significant challenges during data classification and disease detection. So, this paper proposes a two-level strategy involving feature extraction and selection methods before the classification step. The feature extraction step utilizes Short Term Fourier Transform (STFT), and the feature selection step employs Particle Swarm Optimization (PSO) and Harmonic Search (HS) metaheuristic methods. The classifiers employed are Non-Linear Regression, Gaussian Mixture Model, Softmax Discriminant, Naive Bayes, SVM (Linear), SVM (Polynomial), and SVM (RBF). The two-level extracted relevant features are compared with raw data classification results, including Convolutional Neural Network (CNN) Methodology. Among the methods, STFT with PSO feature selection and SVM (RBF) classifier produced the highest accuracy of 94.47%.

A tumbler screen type residual film–impurity mixture wind separator as a key equipment for secondary utilization of farmland residual film. During the working process, the proportion of impurities in the separated waste mulch film intermittently increases, resulting in poor working stability of the device, which may hamper long-term operation. In this study, the material inside the separation unit was continuously monitored, and the main factor affecting separator per-formance was determined to be the challenges in the effective depolymerization of some residual film–impurity mixtures. The principles of agglomeration and depolymerization of the residual film–impurity mixtures were analyzed using computational fluid dynamics (CFD) and discrete element method (DEM) flow-solid coupling simulation methods. The key factor affecting the disaggregation of the mixture was the collision force between the residual film–impurity mixture and the trommel screen. The collision force was maximum when the residual film–impurity mixture first collided with the trommel screen when it was fed into the separation device. As determined by force analyses, the key factors affecting the collision force of the process were the material feeding amount and the structure of the inlet. Furthermore, simulations were carried out for different inlet structure forms; the evaluation index was the maximum collision force of the residual film–impurity mixture agglomerate on the trommel screen. The best disaggregation effect was obtained with a square feed inlet and at a feeding rate of 202 kg/h. A prototype was built using these specifications for verification. The average value of the ratio of impurities in the re-sidual film was 6.966%, the coefficient of variation was 7.38%, and the dispersion of statistical results was small. The ratio of impurities in the residual film was kept constant during the con-tinuous operation of the wind separator. Thus, this study analyzed the agglomerate disaggregation process and provided theoretical insights for deter-mining the optimal structures of the inlets of various cleaning devices and the feeding volumes.

Pin-on-plate and pin-on-disk wear tests are typically used for assessing the wear behavior of a given material coupling. Such a behavior is frequently described by a wear coefficient k, which is estimated, according to the Archard’s law, as the ratio between the measured wear volume V and the product of the applied normal force F and the sliding distance s, i.e. k=V/(F s). This study demonstrates that such a relationship is correct for pin-on-plate but not for pin-on-disk, particularly for flat-ended pins. Both analytical and finite element models of the two tests were developed, considering three different distances pin-disk axis. As results, wear volumes, pressure and wear depth maps were examined and compared for the same sliding distance. Though this study is focused only on the initial transient part of the wear tests, some interesting aspects arose: i) rotational effect in pin-on-disk tests affects k estimation, especially when the pin is near to the disk axis; ii) a simple analytical function is defined to correct the wear volume estimation for pin-on-disk tests; iii) due to the different sliding distances of contact points in pin-on-disk tests, pressure redistribution occurs with higher values on the inner side (closer to disk axis); the opposite trend is observed in wear depth maps. From a computational point of view, the pin-on-disk problem, only apparently simple, required very small time increments to correctly describe the circular trajectories of points, with high computational costs. Numerical strategies are currently under investigation to extend the study to the steady state phase of both tests.

Digital technologies have changed the way supply chain operations are structured. In this article, we conduct systematic syntheses of literature on the impact of new technologies on supply chains and the related cyber risks. A taxonomic/cladistic approach is used for the evaluations of progress in the area of supply chain integration in the Industrial Internet of Things and Industry 4.0, with a specific focus on the mitigation of cyber risks. An analytical framework is presented, based on a critical assessment with respect to issues related to new types of cyber risk and the integration of supply chains with new technologies. This paper identifies a dynamic and self-adapting supply chain system supported with Artificial Intelligence and Machine Learning (AI/ML) and real-time intelligence for predictive cyber risk analytics. The system is integrated into a cognition engine that enables predictive cyber risk analytics with real-time intelligence from IoT networks at the edge. This enhances capacities and assist in the creation of a comprehensive understanding of the opportunities and threats that arise when edge computing nodes are deployed, and when AI/ML technologies are migrated to the periphery of IoT networks.

Based on recent interest and on the importance of the ongoing climate change catastrophe, this article provides the basics of global carbon cycle modelling as required for the assessment of the degree of carbon neutrality of biomass energy, and its underlying dynamics. It is aimed at clari-fying the question “Are biomass fuels carbon neutral?”. The "Combined Energy and Biosphere Model" (CEBM) computes annual carbon flows including growth and decay of plants on 2.52.5° grid elements of the continents’ surface and offers detailed results on the changes of after implementation of large-scale biomass energy strategies world-wide. The main (and possibly unexpected) effect is the long-term depletion of the soil organic compartment after extraction of biomass fuels. When comparing CEBM model runs using (i) biomass energy sources and (ii) carbon-free energy sources (such as solar or wind), it becomes quantitatively clear already on the theoretical level (i.e., even without taking into account effi-ciency losses) that biomass is only “half as carbon neutral” as ideally assumed, to express a rule of thumb – mainly because of soil carbon depletion. Still, biomass energy will play an important role when fighting global warming, even if efforts to lower energy demand are preferable as a fundamental strategy.

Autonomous unmanned aerial vehicles (UAVs) require highly reliable navigation information. Generally, navigation systems with the inertial navigation system (INS) and global navigation satellite system (GNSS) have been widely used. However, the GNSS is vulnerable to jamming and spoofing. The terrain referenced navigation (TRN) technique can be used to solve this problem. In this study, to obtain reliable navigation information even if a GNSS is not available or the degree of terrain roughness is not determined, we propose a federated filter based INS/GNSS/TRN integrated navigation system. we also introduce a TRN system that combines batch processing and an auxiliary particle filter to ensure stable flight of UAVs even in a long-term GNSS-denied environment. As an altimeter sensor for the TRN system, we use an interferometric radar altimeter (IRA) to obtain reliable navigation accuracy in high altitude flight. In addition, a parallel computing technique with general-purpose computing on graphics processing units (GPGPU) is applied to process a high resolution terrain database and a nonlinear filter in real time on board. Finally, we verify the performance of the proposed system through software-in-the-loop (SIL) tests and captive flight tests in a GNSS unavailable environment.

Sensory Processing Sensitivity (SPS) is a trait describing inter-individual differences in sensitivity to environments, both positive and negative ones. SPS has attracted growing societal interest. However, (neuro)scientific evidence is lagging behind. We critically discuss how to measure SPS, how it relates to other theories of Environmental Sensitivity and other temperament and personality traits, how SPS interacts with environments to influence (a)typical development, what the underlying aetiologies and mechanisms are, and its relation to mental disorders involving sensory sensitivities. Drawing on the diverse expertise of the authors, we set an agenda for future research to stimulate the field. We conclude that SPS is a heritable, evolutionarily conserved trait, linked to increased risk for psychopathology and stress-related problems in response to negative environments, as well as to greater benefits (e.g., intervention responsivity, positive mood) in positive environments. We need advances in objective assessment of SPS, understanding mechanisms, differentiating it from (seemingly) related mental disorders, to exploit the potential of SPS to improve mental health, preserve human capital, and prevent adverse effects.

The aim of the work is to study the effect of the 3D printing process on the microstructural and hydrophilic properties of polylactic acid (PLA) samples with various model printing patterns obtained from the black Filament PLA by sequentially applying polymer layers using the FDM (Fused Deposition Modeling) method. X-ray phase analysis revealed partial crystallization of PLA polymer chains in printed samples, which occurs under thermal and mechanical action on the original amorphous PLA filament during 3D printing to varying degrees, depending on the geometry of the pattern and the morphology of its surface. At the same time, IR spectroscopy data indicate the preservation of all intrastructural chemical bonds of polylactide. Measured at the original installation, the values of the wetting edge angles on the surface of the printed samples are in the range φ = 50-60°, which is significantly less than the right angle. This indicates the hydrophilic properties of all samples’ surface. At the same time, the influence of different geometries of model drawings in printed samples was found not only on the morphology of the samples’ surface according to SEM data, but also on its wettability.

Long-span steel structure trusses are widely used in factory buildings, but with the growth of service time and the increase of dynamic load fatigue, a considerable part of the long-span truss with dynamic load appears serious transverse cracks at the bottom of the middle span and oblique deformation of the abdomen in the operation process. The U-shaped cracks at the bottom and belly, as well as the mid-span down deflection of the main truss, reduced the functional function of the factory building truss structure, and had to limit the original crane load, which affected the normal safety and durability of the structure. Therefore, the application principle of the variable axial force cable system in the long-span factory building truss structure and 3D3S software modeling [1] were used. Analyzing and studying the reinforcement method of large-span powerhouse truss can provide practical experience for subsequent similar projects. In view of the above phenomenon, the large-span powerhouse trusses of Hongcheng Powerhouse 1 and No.2 located in Tonglu, Zhejiang Province are used as the research object, and the variable axial force cable method is proposed to strengthen and lift the load. Considering the span of large-span powerhouse truss, the cable system with 22m controlling force of 400kN is proposed to be selected for powerhouse 1, and the cable system with variable axial force of 24m is proposed to be selected for Powerhouse 2. The force model of large-span truss is established by using the finite element method commonly used to analyze the force of truss. Under two working conditions, The influence of reinforcement effect is analyzed and compared from three aspects: stiffness, bearing capacity and stability. And the phenomenon of uneven stress distribution is analyzed. The stress distribution characteristics of each node are understood by simulating the most disadvantageous node plates with the greatest internal force before and after reinforcement.

Urban transit planning is going through a transition to greater private investment in many parts of the world and is now on the agenda in Australia. After showing examples of private investment in transit globally the paper focuses on historical case studies of private rail investment in Western Australia. These case studies mirror the historical experience in rapidly growing railway cities in Europe, North America and Asia (particularly Japan), and also the land grant railways that facilitated settlement in North America. The Western Australian experience is noteworthy for the small but rapidly growing populations of the settlements involved, suggesting that growth, rather than size, is the key to successfully raising funding for railways through land development. The paper shows through the history of transport, with particular reference to Perth, that the practice of private infrastructure provision can provide lessons for how to enable this again. It suggests that new partnerships with private transport investment as set out in the Federal Government City Deal process, should create many more opportunities to improve the future of cities through once again integrating transit, land development and private finance.

The article is focused mainly on verifying the suitability of data from experimental milling heat-treated beech wood and on investigation of the effect of technical and technological parameters of milling on the energy consumption of this process. The independent parameters of machining process are cutting speed, feed speed, rake angle, and hydrothermal modification of experimental wood material. Based on analysis of variance it can be argued that the greatest statistically significant effect on energy consumption have cutting speed and rake angle of the tool while the feed speed had the least influence. The measured data of cutting power during milling were used to build a regression model and validate it, while the most suitable type of model with correlation of 87 % is Classification and Regression Tree followed by model created by Random Forests method.

Art's unique style and creativity are essential in defining a work's identity, conveying emotions, and shaping audience perception. Recent advancements in diffusion models have revolutionized art design, animation, and gaming, particularly in generating original artwork and visual identities. However, traditional creative processes face challenges such as slow innovation, high costs, and limited scalability. Consequently, deep learning has emerged as a promising solution for enhancing painting-style creative design. In this paper, we present the Painting-Style Design Assistant Network (PDANet), a groundbreaking network architecture designed for advanced style transformation. Our work is supported by the Painting-42 dataset, a meticulously curated collection of 4,055 artworks from 42 illustrious Chinese painters, capturing the aesthetic nuances of Chinese painting and offering invaluable design references. Additionally, we introduce a lightweight Identity-Net, designed to enhance large-scale text-to-image (T2I) models by aligning internal knowledge with external control signals. This innovative Identity-Net seamlessly integrates image prompts into the U-Net encoder, enabling the generation of diverse and consistent images. Through extensive quantitative and qualitative evaluations, our approach has demonstrated superior performance compared to existing methods, producing high-quality, versatile content with broad applicability across various creative domains. Our work not only advances the field of AI-driven art but also offers a new paradigm for the future of creative design.

The Covid19 pandemic and the current wars in some countries have put incredible pressures on the global economy. Challenges for the U.S. include not only economic factors, major disruptions and reorganizations of the supply chains, but also those of national security and global geopolitics. This unprecedented situation makes predicting economic crisis for the coming years crucial yet challenging. In this paper, we propose a method based on various machine-learning models to predict the probability of a recession for the US economy in the next year. We collect the U.S’ monthly macroeconomics indicators and recession data from January of 1983 to December of 2023 to predict the probability of an economic recession in 2024. The performance of the individual economics indicator for the coming year was predicted separately, and then all of the the predicted indicators were used to forecast a possible economic recession. Our results showed that the U.S. will face a high probability of being in a recession period in the last quarter of 2024.

This study explores an optimal strategy for muscle force estimation by integrating simplified cost functions with detailed muscle models, demonstrating significant advantages over traditional complex methods. Using elbow flexion as a case study, we characterize the interplay between muscle models and cost functions, showing that accurate muscle force estimations can be achieved effectively without electromyography (EMG) data while also substantially reducing computational demands. Validated against established bio-mechanical data, our models confirm their accuracy in predicting muscle behavior. This approach has profound implications for enhancing rehabilitation protocols and athletic training, providing a more accessible and adaptable tool for muscle force analysis. It broadens the applicability of muscle force estimation in clinical and sports settings and paves the way for future innovations in bio-mechanical research.

This study investigates the ergodic behavior of brain waves through an innovative multidisciplinary approach, integrating insights from neuroscience, physics, economics, and biology. By examining the statistical properties and dynamic characteristics of alpha (8-13 Hz), beta (13-30 Hz), theta (4-8 Hz), and delta (0.5-4 Hz) brain waves, we argue that the ergodic nature of these neural oscillations can be comprehensively understood and quantified using analytical tools from diverse scientific disciplines. Our research begins with a thorough review of current neurophysiological understanding of brain wave generation and propagation. We then introduce the concept of ergodicity, originating from statistical physics, and explore its applicability to neural systems. The study employs advanced time series analysis techniques and stochastic process models, borrowed from economics and physics, to analyze large-scale electroencephalographic (EEG) datasets. Key findings suggest that brain waves exhibit ergodic properties under specific physiological conditions, with implications for understanding neural information processing and brain state transitions. We propose a novel framework that characterizes the ergodic behavior of brain waves across different frequency bands and cognitive states. This framework provides new insights into the relationship between local neural dynamics and global brain function.Furthermore, we discuss the biological implications of ergodic brain waves, considering evolutionary perspectives and potential adaptive advantages. Our analysis reveals how the ergodic properties of neural oscillations may contribute to the brain's ability to balance stability and flexibility, a crucial aspect of adaptive behavior and cognition. The study's findings have significant implications for multiple fields. In neuroscience, our framework offers new tools for analyzing brain function and may inform the development of innovative therapeutic interventions for neurological disorders. In physics and complex systems theory, it provides a concrete biological example of ergodic behavior in a highly complex, non-linear system. For the field of economics, it demonstrates the successful application of financial modeling techniques to biological time series data. This research contributes to a more holistic understanding of the brain as a complex, adaptive system operating at the intersection of physics, biology, and information processing. It also opens new avenues for future research, including investigations into the relationship between ergodic properties of brain waves and consciousness, cognitive flexibility, and the emergence of complex behavior from neural ensembles.

Artificial intelligence technologies based on deep convolutional neural networks and large lan-guage models have made significant breakthroughs in many tasks such as image recognition, target detection, semantic segmentation and natural language processing, but also face the problem of contradiction between the high computational capacity of the algorithms and the limited deployment resources. Quantization, which converts floating-point neural networks into low-bit-width integer networks, is an important and essential technique for efficient deploy-ment and cost reduction in edge computing. This paper analyze various existing quantization methods, showcase the deployment accuracy of advanced techniques, and discuss the future challenges and trends in this domain.

We developed a novel machine learning (ML) algorithm with the goal of producing transparent models (i.e. understandable-by-humans) while also flexibly accounting for nonlinearity and interactions. Our method is based on ranked sparsity, and allows for flexibility and user-control in varying the shade of the opacity of black-box machine learning methods. The main tenet of ranked sparsity is that an algorithm should be more skeptical of higher-order polynomials and interactions \textit{a~priori} compared to main effects, and hence the inclusion of these more complex terms should require a higher level of evidence. In this work, we put our new ranked sparsity algorithm (as implemented in the open-source R package, `sparseR`) to the test in a predictive model "bakeoff" (i.e. a benchmarking study of ML algorithms applied "out-of-the-box," that is, with no special tuning). Algorithms were trained on a diverse set of simulated and real-world data sets from the Penn Machine Learning Benchmarks database, addressing both regression and binary classification problems. We evaluate the extent to which our human-centered algorithm can attain predictive accuracy that rivals popular black-box approaches such as neural networks, random forests, and support vector machines, while also producing more interpretable models. Using out-of-bag error as a meta-outcome, we describe the properties of data sets in which human-centered approaches can perform as well as or better than black-box approaches. We find that interpretable approaches predicted optimally or within 5% of the optimal method in most real-world data sets. We provide a more in-depth comparison of the performances of random forests to interpretable methods for several case studies, including exemplars in which algorithms performed similarly, and several cases when interpretable methods underperformed. This work provides a strong rationale for including human-centered transparent algorithms such as ours in predictive modeling applications.

Soil moisture can be an important indicator of climate change in humid and semi-arid areas. This indicator can more efficiently propose different public policies related to climate change than just using precipitation and temperature data. Given the above, the objective of this study is to evaluate changes in soil moisture in the state of Pernambuco during the period 1961-2021, using the System of Hydrological Response Units for Pernambuco. In this study, two river basins in the state of Pernambuco that represent the different climatic conditions of the state will be chosen. The results show that in the coastal region, there is a tendency towards more saturated soils, and in the semi-arid region, there is a tendency towards drier soils. With these results, it is possible to conclude that public policy decisions for the economy, environment, and society must consider this vital water balance variable.

Although there is a vast body of neuroscientific research on cognitive behavioral therapy (CBT), many therapists are confused about how to incorporate the evidence into their clinical practice. This narrative review translates neuroscientific research on CBT for depression into a therapist-friendly formulation model. Four brain networks that are particularly relevant to depression, including the Central Executive Network, Default mode Network, Salience Network, and Affective Network, were identified based on their interactions and described in relation to depressive symptoms. Additionally, it clarified how each CBT technique works on a brain map. Assessment, intervention selection, and patient psychoeducation based on this model will facilitate the clinical application of neuroscience in CBT.

The crab’s fishery of the genus Callinectes is one of the most important fisheries in Mexico. Sonora and Sinaloa are the states in the eastern coast of the Gulf of California, the mainland coast. Sinaloa encompass the greatest production in Mexico of these species, in particular the Bahía Santa Maria La Reforma (BSMR), supply the most important catches. An administrative mandatory document for the fishery is the Management Plan of Crab. One of the main weaknesses found in it for Sonora and Sinaloa is the lack of fisheries assessment in both states. For this reason, a structured size method called CASA (Catch at Size Analysis) was applied in the BSMR, in the seasons 2000, 2011 and 2014 to C. bellicosus. The first catch size (CW50%) estimated for females were 97.5, 102.5 and 100 mm, while in males it was estimated at 117.5, 107.5 and 102.5 mm. In the specific case of fishing mortality (F), the algorithm allowed us to detect that in the larger sizes the greater fishing pressure is applied for both, females and males, yielding a weighted exploitation rate of 0.047 (2000), 0.119 (2011) and 0.426 (2014) for females and 0.045 (2000), 0.295 (2011) and 0.132 (2014) for males, all below 50% (E= 0.50), crab C. bellicosus in BSMR is not at risk of over exploitation.

In this work, quiescence is added to the Susceptible-Infectious-Recovered (SIR) model with demography. In order to investigate consequences of quiescence in the infection process in more depth, we use stochastic simulations on the stochastic version of model that we built. This method provides a more accurate picture of the dynamics of infectious diseases by taking into consideration the inherent randomness in the disease processes. We examine the effects of quiescence on the number of infected people using simulations. The results, presented in histograms depicting the distribution of infected individuals, reveal a notable trend: the mean number of infected individuals is higher when quiescence is incorporated into the dynamics. These finding emphasizes the dynamic influence of quiescence on infectious disease spread. The higher mean number of infections during periods of quiescence highlights the need for public health strategies that are flexible enough to focused interventions during these times to reduce the possibility of an increase in infections.

The realm of natural language processing has always been fascinated by the intricacies of sentence semantics. The emergence of context-aware word representations, especially from pre-trained models like ELMO and BERT, has revolutionized several semantic tasks including but not limited to, question answering, text categorization, and sentiment analysis. Despite these advancements, the integration of supplementary knowledge, particularly syntactic, to augment semantic comprehension in models remains an area ripe for exploration. This paper introduces the Syntactic Enhancement Network (SEN), a pioneering method for synergizing syntactic elements with established pre-trained language models. Our approach encompasses a dual-phase evaluation: initially, we delve into the syntactic augmentation of both RNN-based and Transformer-based language models; subsequently, we test our SEN's proficiency in two specific domains: the task of sentence completion and the extraction of biological relationships. The results are striking, with SEN achieving a stellar 91.2% accuracy in sentence completion—surpassing baseline models by a substantial 37.8%. In the context of biological relation extraction, SEN demonstrates competitive prowess with a 75.1% F1 score.

Cyberaggression is an important problem today; it can affect adolescents in different ways. Therefore, reliable and valid measures are necessary to better study the phenomenon. The aim of the present study was to generate validity and reliability evidence for a Spanish-language cyberaggression scale in a population of adolescents from northwestern Mexico. The results of this study contribute to the research and focus on cyberaggression in adolescents in Mexico. The measure in this article differs from other measures by detecting the different roles, including the bystander, contributing to the state of knowledge about cyberaggression, ratifying the presence of this actor that has not been sufficiently explored in the cyber context.

Amyotrophic Lateral Sclerosis (ALS) stands as a debilitating neurodegenerative condition characterized by the progressive degeneration of motor neurons. Despite extensive research in various model animals, the cellular signal mechanisms of ALS remain elusive, impeding the development of efficacious treatments. Among these models, a well-characterized and diminutive organism, Caenorhabditis elegans (C. elegans), has arisen as a potent tool for investigating the molecular and cellular dimensions of ALS pathogenesis. This review summarizes the contributions of C. elegans models to our comprehension of ALS, emphasizing pivotal findings pertaining to genetics, protein aggregation, cellular pathways, and potential therapeutic strategies. We analyze both the merits and constraints of C. elegans system in the realm of ALS research, and point towards future investigations that could bridge the chasm between C. elegans foundational discoveries and clinical applications.

AbstractTo overcome the negative impacts on the environment and other problems associated with fossil fuels have forced many countries to inquire into and change to environmentally friendly alternatives that are renewable to sustain the increasing energy demand. Solar energy is one of the best renewable energy sources with the least negative impacts on the environment. Different countries have formulated solar energy policies to reduce dependence on fossil fuel and increasing domestic energy production by solar energy. According to the 2010 BP Statistical Energy Survey, the world cumulative installed solar energy capacity was 22928.9 MW in 2009, a change of 46.9% compared to 2008. In this study, a PV generation system has been modeled and installed considering uncertain whether based on the hourly wind speed data of New York City (NYC) of year 2014. Regression models has been used to forecast the hourly, weekly, and monthly wind speed of NYC year 2014. Design of experiment (DOE) has been used to determine the optimal panel size (area), the battery capacity size, and other levels of factors.

Many relevant health social actors have voiced concerns about the danger of self-medication in times of growing use of over-the-counter medicines and, in some contexts, unregulated selling of them. Previous research has examined the incidence of parental self-medication as well as the use and abuse of antibiotics without professional advice. However, these studies have limited evidence on the role of family doctors and the perceived severity of self-medication in the case of parents. Based on Health Belief Model, our research tested the effects of exposure to medical information on the perceived level of self-treating the children without medical advice. Specifically, we aimed to assess whether exposure to information warning about the risks of treating children without a medical prescription influences parents' attitudes toward administering medicines to their children without medical advice. The results showed that our respondents evaluated the practices of self-medication negatively, especially when it came to treating their children without medical advice. However, their attitudes towards self-medication varied depending on their beliefs about administering certain medications. Additionally, the results showed that the perceived level of satisfaction with the family physician could be a significant factor in controlling and avoiding the treatment of children without professional advice.

An attempt has been made to explore the relative contributions of moisture feedback processes on tropical intraseasonal oscillation or Madden Julian Oscillation (MJO). We focused on moisture feedback processes, including evaporation wind feedback (EWF) and moisture convergence feedback (MCF), which integrates the mechanisms of convective interactions in the tropical atmosphere. The dynamical framework considered here is a moisture-coupled, single-layer linear shallow-water model on an equatorial beta-plane with zonal momentum damping. With this simple approach, we aimed to recognize the minimal physical mechanisms responsible for the existence of the essential dispersive characteristics of the MJO, including its eastward propagation(k>0), the planetary-scale (small zonal wavenumbers) instability, and the slow phase speed of about ≈ 5 m/sec. Further, we extended our study to determine each feedback mechanism’s influence on the simulated eastward dispersive mode. Our model emphasized that the MJO-like eastward mode is a possible outcome of the combined effect of moisture feedback processes without requiring additional complex mechanisms such as cloud radiative feedback and boundary layer dynamics. The results substantiate the importance of EWF as an energy source for developing eastward moisture mode with a planter-scale instability. Moreover, our model endorses that the MCF alone could not produce instability without surface fluxes, although it has a significant role in developing deep convection. It is found that the MCF exhibits a damping mechanism by regulating the frequency and growth rate of the eastward moisture mode at shorter wavelengths.

The optimization design of buildings is very important the energy consumption, carbon emissions ,and sustainable development of buildings. The low-temperature granary has low grain storage temperature and high energy consumption indexes. The design scheme of roof insulation for low-temperature granary should be determined in actual building design processes by considering economy, carbon emissions, and outdoor climate, comprehensively. In this paper, the low-temperature granary roof insulation for different ecological grain storage zones in China are optimized by using a new low-carbon optimization design method. The low-carbon optimization design method can response to the economical issue, emission reduction issue, and outdoor climate issue, simultaneously. The application results of the optimization design method in ecological grain storage zones in China indicate that outdoor climate has significant impacts on the economic performance and carbon reduction effect of roof insulation. The considering of carbon emission cost can apparently increase economic efficiency of roof insulation. The optimal economic thickness of expanded polystyrene (EPS) in Urumqi, Harbin, Zhengzhou, Changsha, Guiyang and Haikou cities is 0.025 m, 0.037 m, 0.085 m, 0.097 m, 0.072 m and 0.148 m, respectively. The different outdoor climates of seven ecological grain storage areas in China have important influences on the comprehensive economic performances of low-temperature granary roof insulation. The design of low-temperature granary roof insulation in Haikou city has the best economic performances among the seven ecological grain storage zones in China.

Interferon-beta is one of the most widely prescribed disease-modifying therapies for multiple sclerosis patients. However, this treatment is only partially effective, and a significant proportion of patients do not respond to this drug. This paper proposes an alternative fuzzy logic system based on the opinion of a neurology expert to classify relapsing-remitting multiple sclerosis patients: high, medium, and low responder to interferon-beta. Also, a pipeline prediction model trained with biomarkers associated to interferon-beta response is proposed for predicting whether patients are potential candidates to be treated with this drug, in order to avoid ineffective therapies. The classification results shows that the fuzzy system presents a 100% efficiency compared with an unsupervised hierarchical clustering method (52%). So, the performance of the prediction model is evaluated, and a 0.8 testing accuracy is achieved. Hence, a pipeline model including data standardization, data compression, and a learning algorithm, can be a useful tool for getting reliable predictions about the response to interferon-beta.

Prostate cancer is a common cancer among men and typically progresses slowly for several decades before becoming aggressive and spreading to other organs, leaving few treatment options. While large animals have been studied, the dog's prostate is anatomically similar to humans and has been used to study spontaneous prostate cancer. However, most research currently focuses on the mouse as a model organism due to the ability to genetically modify their prostatic tissues for molecular analysis. One hallmark in this research was the identification of the prostate-specific promoter Probasin, allowing for prostate-specific expression of transgenes. This has led to generations of mice with aggressive prostatic tumors through overexpression of the SV40 oncogene. The Probasin promoter is also used to drive Cre expression and allow researcher to generated prostate specific loss-of-function studies. Another landmark in the process of modeling prostate cancer in mice was orthoptic delivery of viral particles. This technology allows selective overexpression of oncogenes from lentivirus or the use of CRISPR to generate complex loss of function studies. These genetic modified models are complemented by classical xenografts of human prostate tumor cells in immune deficient mice. Overall, pre-clinical models have provided a portfolio of model systems to study and address complex mechanisms in prostate cancer for improved treatment options. This review will focus on the advances of each technique.

Background: Aedes albopictus, the Asian Tiger Mosquito, has become a prevalent pest in Italy, causing severe nuisance and posing a threat of transmission of arboviruses introduced by infected travellers. In this study, we investigated the influence of weather parameters on the seasonal population density of Aedes albopictus. Methods: A Bayesian approach was employed to identify the best meteorological predictors of species trend, using the eggs collected monthly from 2010 to 2022 by the Emilia-Romagna regional monitoring network. Results: The findings show that the winter-spring period (January to May) plays a crucial role in the size of the first generation and seasonal development of the species. Conclusions: A temperate winter and a dry and cold March, followed by a rainy and hot spring and a rainy July, seem to favour the seasonal development of Ae. albopictus.

Magnesium matrix composites have attracted significant attention due to their lightweight nature and impressive mechanical properties. However, the fabrication process for these alloy compo-sites is often time-consuming, expensive, and labor-intensive. To overcome these challenges, this study employed machine learning (ML) techniques to predict the mechanical properties of magnesium matrix composites. Regression models were utilized to forecast the yield strength of magnesium alloy composites reinforced with various materials. The study incorporated previous research on matrix type, reinforcement type, heat treatment, and mechanical working. The re-gression models employed in this study included decision tree regression, random forest re-gression, extra tree regression, and XGBoost regression. Model performance was assessed using metrics such as RMSE and R2. The XGBoost Regression model out-performed others, exhibiting an R2 value of 0.94 and the lowest error rate. Feature importance analysis indicated that the rein-forcement particle form had the greatest influence on the mechanical properties. The study iden-tified the optimized parameters for achieving the highest yield strength, which was 186.99 MPa. Overall, this study successfully demonstrates the effectiveness of ML as a valuable tool for opti-mizing the production parameters of magnesium matrix composites.

Portfolio optimization is a mathematical formulation whose objective is to maximize returns while minimizing risks. A lot of improvement in the model has been made, including adding practical constraints. With the growing of shares trading, the problem becomes dimensionally very large. In this paper, we propose the usage of modified Biogeography-Based Optimization to solve the large scale constrained portfolio optimization. Results indicate the effectiveness of the method used.

Electromagnetic (EM) waves, used for purposes such as geophysical characterization, impact properties to be measured. This paper describes the effects of radio frequency (RF) waves on the hydraulic conductivity of glass beads and natural sand. A series of tests were conducted using a customized, rigid-wall, cylindrical permeameter inside a resonant cavity made of Plexiglas covered with electrically conductive transparent films. Constant head ASTM-D2434 [1] tests were performed to measure the samples’ hydraulic conductivity. RF stimulation was performed using a magnetically coupled loop antenna at a constant frequency of 726 MHz and input power levels of 10, 25, 40, and 50 Watts. The hydraulic conductivity of both natural sand and glass-bead samples increased with RF stimulation. Furthermore, measurement of the electric-field component of RF waves was also performed to illustrate the pattern of the electric field as well as evaluate the RF impact on the hydraulic conductivity tests. The electric field was numerically simulated and validated against experimentally measured electric fields. A finite-difference numerical model was developed in MATLAB to analyze the seepage flow, which was then validated against the experimental results. An optimization scheme was then used to develop a governing equation for the RF impact on hydraulic conductivity.

Although ChatGPT promises wide-ranging applications, there is a concern that it is politically biased; in particular, it has a left-libertarian orientation. Nevertheless, in light of recent trends in attempts to reduce such biases, this study re-evaluated the political biases of ChatGPT using political orientation tests and the application programming interface. Moreover, the effects of the languages used in the system as well as gender and race settings were evaluated. The results indicated that ChatGPT had less political bias than previously thought; however, they did not entirely discount the political bias. The languages used in the system and the gender and race settings may induce political biases. These findings enhance our understanding of the political biases of ChatGPT and may be useful for bias evaluation and designing ChatGPT’s operational strategy.

When the majority of museums and other cultural institutions were shut down due to the pandemic, mixed museum visits became a hot issue. After the epidemic, mixed visits, in the opinion of many experts, would become the new norm for experiencing cultural content. Diverse types of merging online and onsite visits are already being investigated by researchers, not only to avoid the spread of a disease but also to enable visits of people who were previously excluded, such as persons in remote geographic areas or people with mobility challenges. The current work takes into account the Contextual Model of Museum Learning to define potential use scenarios for visits from people of different cultural backgrounds and offers an evaluation of current practices.

Electricity generation from renewable energy sources is emerging as a result of global carbon emission reduction policies. However, most renewable energy sources are non-dispatchable and cannot be adjusted to meet the fluctuating electricity demands of society. A flexible operation process has been proposed as an effective solution to compensate for the unstable nature of renewable energy sources. Thermal load fluctuations during flexible operation may cause creep-fatigue damage to the high-temperature components of thermal power plants, as they are designed with a focus on creep damage under a constant power level. This study investigated the residual life of high-temperature components, such as a superheater tube and a reheater header, to failure under flexible operation conditions using finite element analysis and empirical models. First, we determine an analytical solution for the straightened superheater tube under thermal conditions and compare it with the numerical solution to verify the numerical models. Through the verified finite element model, the creep-fatigue life of the reheater header is estimated by considering flexible operation factors and employing the Coffin-Manson and Larson-Miller models. Although fatigue damage increases with decreasing minimum load and ramp rate, we confirmed that creep damage significantly affects the residual life during flexible operation. In addition, a surrogate model was proposed to evaluate the residual life of the reheater as a function of the flexible operation factors using the machine learning methodology, based on the results of finite element methods. It can be used to predict its residual life without performing complex thermal-structure analysis and relying on empirical models for fatigue and creep life. We expect our findings to contribute to the efficient operation of thermal power plants by optimizing the flexible operation factors.

During the past few years many projects and initiatives were undertaken deploying and testing automated vehicles for public transportation and logistics. However in spite of their ambition, all of these deployments stayed on the level of elaborated experimentation deploying no more than 4 maximum 5 AVs in rather small sites (few Kms of roads) and never really reached the level of large scale “commercial” deployment of transport services. The reasons for this are many, but the most important being the lack of economically viability and commercially realistic models, the lack of scalability of the business and operating models, and the lack of inclusive citizen/user centric services required for the large end-user acceptation and adoption of the solutions. In this paper, based on the experience gained in the H2020 AVENUE project, we present the missing pieces of the puzzle, ad which will be addressed in the Horizon Europe project ULTIMO.

Objective: We aimed to clarify the differences in the association between re-admission for heart failure (HF) and left atrial (LA) overload indices by sex in heart failure and a preserved ejection fraction (HFpEF). Methods: We analysed 898 HFpEF patients hospitalized for acute decompensated HF. Blood tests and transthoracic echocardiography were performed before discharge. The primary endpoint was re-admission for HF during the first year. Results: The ratio of diastolic elastance to arterial elastance (p=0.014), a relative index of LA pressure overload, in men and LA volume index (LAVI, p=0.020) in women were significant for re-admission for HF during the first year in the multivariable Fine-Gray analysis. Stroke volume (SV)/LA volume (LAV), another index for LAV overload, was not a significant prognostic factor of re-admission for HF during this time. Conclusion: LA overload was an important prognostic factor for HF re-readmission during the first year after enrolment in patients with HFpEF, but the indices relating to LA overload differed by sex.

The High-Resolution Computed Tomography (HRCT) detection and diagnosis of diffuse lung disease is primarily based on the recognition of a limited number of specific abnormal findings, pattern combinations or their distributions, as well as anamnesis and clinical information. Since texture recognition has a very high accuracy percentage if a complex network approach is used, this paper aims to implement such a technique customized for diffuse interstitial lung diseases (DILD). The proposed procedure translates HRCT lung imaging into complex networks by taking samples containing a secondary lobule, converting them into complex networks and analyzing them in 3 dimensions: emphysema, ground glass opacity and consolidation. This method was evaluated on a 60 patient lot and the results show a clear quantifiable difference between healthy and affected lungs. By deconstructing the image on three pathological axes, the method offers an objective way to quantify DILD details which, so far, have only been analyzed subjectively.

This article analyzes the perspectives of professional training of the public administration staff, from the viewpoint of the sustainability criteria, starting from the experiences of the online courses run in the last two years. In order to understand the nature, type and magnitude of the changes caused by the Covid-19 pandemic in the field of professional training for public administration, we performed a survey among public institution employees, by applying a questionnaire. The instrument included qualitative elements, to allow the framing of statistical results. The article is grounded on the hypothesis that the professional training activities in the last two years were organized preponderantly online and it aims to analyze the sustainability of the new teaching systems/methods. The limitations of the study are given by the fact that each administrative system has structure particularities and its own legal framework regarding the professional training of public administration staff, what makes that the model proposed cannot be applied in all countries. The study provides a model of online professional training for public administration staff, sustainable, based on the experience accumulated in the last two years.

COVID-19 outbreak started in the Chinese city of Wuhan and spread around the globe in months due to its high contagious level. Hence, this disease posed a great challenge to researchers and mathematicians. Numerous mathematical models have been suggested to visualize the spreading speed and trends of this pandemic. In this paper, a comparison of a widely established and accepted method, the Susceptible-Exposed-Infected-Quarantined-Recovered-Death-Insusceptible (SEIQRDP) model with a newly-proposed fractional-order SEIQRDP is studied. Densely populated Countries of Asia (Pakistan, India, and Bangladesh) have been chosen as data sets and both algorithms have been applied to their data. The same comparison technique has also been used on the data of two polar countries, New Zealand and Russia, that validated our findings. Error comparison of both algorithms has been recorded in a tabulated form which shows multi-fold erroneous trends over WHO data of selected countries.

Biology is about structure. Structures within structures. Organs within animals, tissues within organs, cells within tissues, and molecules, often proteins within cells. The structures are so complex that they can only be described by numbers. No numbers are of more importance than those that describe proteins. The numbers that describe coordinates of its atoms, often determined by Patterson functions (which are inverse Fourier Transforms of intensities) of crystal diffraction. Without these numbers, structural biology would hardly exist. Without numbers, engineering would not exist. Numbers are surely needed by the engineers who produce the x-rays diffracting from atoms of protein crystals. Devices of engineering have function. They are built to implement equations. Engineering devices use structures to implement equations, when power is supplied at the right places, that produces appropriate flows. Flows are the essence of life. Equilibrium means death in most living systems. Flows in biological structures are hard to analyze because we do not know input output equations in advance. Sometimes we do not know their function. Flows, forces, and structures of life (like those of engineering) are related by field equations of conservation laws, partial differential equations, constrained by location and properties of structures. Constraints are boundary conditions located on the complicated domain of biological structure. Dealing with this complexity is simplified if one systematically analyzes structure using the most general field theory known, electricity described by the Maxwell equations, without significant known error. Currents are involved because flows of biology usually involve migration of charges, convection of water and solutes, diffusion of ions that form the plasma of life, and their interactions. Interactions can dominate function. Here I show how a few complex structures can be understood in engineering detail. This approach may be useful in dealing with biological and medical issues in many other cases. In one limited case—the clearance of a toxic waste (potassium ions) from the optic nerve—this approach seems to have succeeded.

Phase transitions- both in the classical and in the quantum version- are the perfect playground for appreciating universality at work. Indeed, the fine details become unimportant and a classification in very few universality classes is possible. Very recently, a striking deviation from this picture has been discovered: some antiferromagnetic spin chains with competing interactions show a different set of phase transitions depending on the parity of number of spins in the chain. The aim of this article is to demonstrate that the same behavior also characterizes the most simple quantum spin chain: the Ising model in a transverse field. By means of an exact solution based on a Wigner-Jordan transformation, we show that a first order quantum phase transition appears at zero applied field in the odd spin case, while it is not present in the even case. A hint of a possible physical interpretation is given by the combination of two fact: at the point of the phase transition, the degeneracy of the ground state in the even and the odd case substantially differ, being respectively 2 and 2N, with N the number of spins; the spin of the most favorable kink states changes at that point.

Unhealthy diet is an important health problem in the region of the Americas, and Uruguay does not escape this reality. Nutritional problems in Uruguayan school-age children are overweight and obesity. Caloric intake is excessive for 60% of children and 28% of calories come from ultra-processed products (UPP) [CODICEN 2021] [MIDES 2020) (Köncke, Toledo, 2021). In 2018, an evaluation of food intake was conducted in a representative sample of public schools in the city of Montevideo. Food and preparations were categorized according to the NOVA system, later they were analyzed according to the Pan American Health Oganization nutrient profile model (PAHO NPM). Only 0.52% of children consumed exclusively natural foods, unprocessed, minimally processed or culinary ingredients. Twenty-five percent of children consumed ≥ 4 products categorized with excessive content of free sugars, total fat or saturated fat according to the PAHO NPM; in the case of excessive sodium this was 40%. In general, children who included products with excessive free sugars, sodium or saturated fat in their diet exceeded the limits established by PAHO and as a result their diet is of poorer nutritional quality compared to children who did not consume such products.

Natural disasters are showing an increase in the magnitude, frequency, and geographic distribution. Studies have shown that individuals’ self-sufficiency, which largely depends on household preparedness, is very important for hazard mitigation in at least the first 72 hours following a disaster. However, for factors that influence a household’s disaster preparedness, though there are many studies trying to identify from different aspects, we still lack an integrative analysis on how these factors contribute to a household’s preparation. This paper aims to build a classification model to predict whether a household has prepared for a potential disaster based on their personal characteristics and the environment they located. We collect data from the Federal Emergency Management Agency’s National Household Survey in 2018 and train four classification models - logistic regression, decision trees, support vector machines, and multi-layer perceptron classifier models- to predict the impact of personal characteristics and the environment they located on household prepare for the potential natural disaster. Results show that the multi-layer perceptron classifier model outperforms others with the highest scoring on both recall (0.8531) and f1 measure (0.7386). In addition, feature selection results also show that among other factors, a household’s accessibility to disaster-related information is the most critical factor that impacts household disaster preparation. Though there is still room for further parameter optimization, the model gives a clue that we could support disaster management by gathering publicly accessible data.

The vibrational excitation cross section of a diatomic molecule by positron impact is obtained using wavepacket propagation techniques. The dynamics was carried on a two-dimensional potential energy surface which couples a hydrogen-like harmonic oscillator to a positron via a spherically symmetric correlation polarization potential. The cross section for the excitation of the first vibrational mode is in good agreement with previous reports. Our model suggests that a positron couples to the target vibration by responding instantly to an interaction potential which depends on the target vibrational coordinate.

Severe thunderstorms are often accompanied by strong vertical air currents, temporary wind gusts, and heavy rainfall. The development of this atmospheric phenomenon over tropical shallow water zones, such as bays, can lead to intensification of atmospheric disturbances and produce a small-scale storm surge. Here, the storm surge that occurred on 19 March 2017 in the Persian Gulf coastal area has been investigated. Air temperature, precipitation, mean sea level pressure, wave height, wind direction, wind speed, geopotential height, zonal components, meridional winds, vertical velocity, relative humidity, and specific humidity obtained from the European Centre for Medium-Range Weather Forecasts (ECMWF (and Global Forecast System (FNL) were used to implement the Weather Research and Forecasting (WRF) model. The results showed that the main cause of the storm surge was the occurrence of a supercell thunderstorm over the Persian Gulf. The formation of this destructive phenomenon resulted from a downburst under Cumulonimbus cloud and high-velocity air subsidence, after collision with the sea surface coinciding with the high tide. This caused a severe, yet temporary, gust, which in turn caused the creation of the four waves of 3.1 m height along the coast of Bandar Dayyer.

The most common and taught membrane theory assumes that the membrane behaves as a kind of electrical capacitance that is exposed to an electrical current generated by an ionic flow. If this statement is verifiable, it can be confirmed by the laws of physics, mathematics and in particular electricity. We will demonstrate that this hypothesis is not verified and that it is necessary to modify biophysics according to already established and experimentally verified principles of physics.

Modern agent-based models (ABM) and other simulation models require evaluation and testing of many different parameters. Managing that testing for large scale parameter sweeps (grid searches) as well as storing simulation data requires multiple, potentially customizable steps that may vary across simulations. Furthermore, parameter testing, processing, and analysis are slowed if simulation and processing jobs cannot be shared across teammates or computational resources. While high-performance computing (HPC) has become increasingly available, models can often be tested faster through the use of multiple computers and HPC resources. To address these issues, we created the Distributed Automated Parameter Testing (DAPT) Python package. By hosting parameters in an online (and often free) "database", multiple individuals can run parameter sets simultaneously in a distributed fashion, enabling ad hoc crowdsourcing of computational power. Combining this with a flexible, scriptable tool set, teams can evaluate models and assess their underlying hypotheses quickly. Here we describe DAPT and provide an example demonstrating its use.

We study the problem of predicting time series data using the autoregressive integrated moving average (ARIMA) model in an online manner. Existing algorithms require model selection, which is time consuming and inapt for the setting of online learning. Using adaptive online learning techniques, we develop algorithms for fitting ARIMA models with fewest possible hyperparameters. We analyse the regret bound of the proposed algorithms and examine their performance using experiments on both synthetic and real world datasets

Gauge fields control the dynamics of fermions, also a back reaction of fermions on the gauge field is expected. This back reaction is investigated within the vortex picture of the QCD vacuum. We show that the center vortex model reproduces the string tension of the full theory also with the presence of fermionic fields.

New analysis regarding the structure of center vortices and their color structure is presented: Using data from gluonic SU(2) lattice simulation with Wilson action a correlation of fluctuations in color space to curvature of vortex fluxes was found. Finite size effects of the S2-homogeneity hint at color homogeneous regions on the vortex surface.

The aim of this study was to identify MFIs specific determinants of capital structure of selected micro finance institutions in Ethiopia. The researcher employed quantitative research approach with explanatory research design. The result of regression analysis showed that out that variables like growth, profitability, firm size, age, and asset tangibility have positive and statistically significant effect on leverage ratio. Whereas, profitability has statistically significant and negative effect on capital structure. Based on the findings of the study, the researcher concluded that the firm specific determinants of capital structure of micro finance institutions in Ethiopia were growth, profitability, firm size, age, and asset tangibility.

In central Senegal malaria incidences have declined from 2000 to 2010 in response to scaling-up of control measures and then remained stable, making elimination improbable. Additional control measures are needed to reduce transmission. We simulated chemoprophylaxis interventions targeting malaria hotspots, using a meta-population mathematical model based on differential equation framework and incorporating human mobility. The model was fitted to weekly malaria incidences from 45 villages. Three approaches for selecting intervention targets were compared: a) villages with malaria cases during the low transmission season of the previous year; b) villages with highest incidences during the high transmission season of the previous year; c) villages with highest connectivity with adjacent populations. Our modeling, considering human mobility, showed that the intervention strategies targeting hotspots would be effective in reducing malaria incidence in both targeted and untargeted areas. But whatever the intervention, pre-elimination stage (1-5 cases per 1,000 per year) would not be reached without simultaneously increasing vector control by more than 10%. Targeted interventions allow increasing overall malaria control and elimination potential.

In this paper, drug treatment and vaccination are compared healthy and economically. In this comparison, in addition to comparing economic costs, it has paid attention to patients' recovery. To reach the goal, the prevalence of Covid-19 virus in New York is modeled by using the SEIR dynamic model. The SEIR dynamic model is used to model the period of epidemic diseases. Then, three disease control scenarios are evaluated economically. Treatment and vaccination are two important issues in controlling epidemic diseases. To control the disease, each of these two methods has economic costs and benefits. The main purpose of this paper is to compare the two methods of vaccination and treatment economically. Although the results of the study show that vaccination can be effective in preventing the epidemic of the disease than treatment, but due to the high cost and genetic mutation of the virus, vaccination is not the optimal method for both health and the economy in the long term.

We extend our high-resolution MRI-based Finite Element (FE) head model, previously presented and validated in [1–3], by considering the heterogeneities of the white matter structures captured through the use of Magnetic Resonance Elastography (MRE). This approach imparts more sophistication to our numerical model and yields results that more closely match experimental results. It is found that the peak pressure more closely matches the experiments as compared to the heterogeneous case. Qualitatively, we find differences in stress wave propagation near the corpus callosum and the corona radiata, which are stiffer on average than the global white matter. We are able to study the effects of these stiff structures on transient stress wave propagation within the cerebrum, something that cannot be done with a homogenized material model.

The crux of the paper is to present a detailed analysis of COVID-19 data which is available on global basis. This analysis is performed using some specific package of R software. It provides various insights from the data and help to understand the current status of this pandemic in India so that effective measures can be formulated by policymakers. These insights include global summary of this disease, growth rate of this pandemic and performance of SIR model for the given global data. The analysis has been presented in different tables and graphs to understand the outputs of the problem in a more detailed point of view.

The outburst of the CoVid-19 pandemic has raised several questions leading to a complex system in terms of modeling. Indeed, the modeling of the epidemic, at the level of a country, needs considering each of the different sources of contamination as well as the public health authorities' strategy, in a specific way. With this in mind, in the present paper, we develop a mathematical model of the CoVid-19 epidemic in Senegal. In the model, the population is subdivided into five compartments: susceptible, infected but asymptomatic, symptomatic, quarantined, and recovered immune people. In addition, due to its important impact on the propagation of the disease, we add one more variable: the number of infected objects. %\vspace{1mm} Therefore, the model corresponds to a system of six non-linear ordinary differential equations we submit to an analytical study to prove the relevancy of the model, simulate the evolution of the epidemic, and retrieve epidemiological parameters, namely the infection rate and the basic reproduction number, ${\cal R}_0$. Based on the senegalese territory CoVid-19 data, we simulate various scenarios as for the evolution of the epidemic in the country, in order to predict the peak and its magnitude with regard to the application of barrier measures. We also explore the option of collective immunity with special protection for vulnerable people. In doing so, non-available parameters are identified using some mathematical identification technics.

In attempting to predict the further course of the novel coronavirus (COVID-19) pandemic caused by SARS-CoV-2, mathematical models of different types are frequently employed and calibrated to reported case numbers. Among the major challenges in interpreting these data is the uncertainty about the amount of undetected infections, or conversely: the detection ratio. As a result, some models include assumptions about the percentage of detected cases among total infections while others completely neglect undetected cases. Here, we illustrate how model projections about case and fatality numbers vary significantly under varying assumptions on the detection ratio. Uncertainties in model predictions can be significantly reduced by representative testing, both for antibodies and active virus RNA, to uncover past and current infections that have gone undetected thus far.

Based on K-ε Standard Wall turbulence model (2-Equation) and Navier-Stokes (N-S) equations defined for incompressible fluids, fluid flow behaviour around hyperloop pods in an evacuated tube was simulated using ANSYS fluent solver assuming steady state and two dimensional conditions. In this research, to develop the case studies, using combination of different head and tail shape profile, four kind of hyperloop pods were developed with the aid of SolidWorks. These four pods have been investigated for their aerodynamic behaviour as four different case scenarios. The results of simulation depicts that an atmospheric pressure of 100 Pa with blockage ratio of 0.36 in tube provides the best possible aerodynamic behaviour for the designed hyperloop pod models. This research finds that overall aerodynamic behaviour of hyperloop pods can be varied by changing the head and tail shape profile of pods and a particular combination of head and tail shape profile can provide optimally best aerodynamic capabilities. Thus, this research paper provides a novel method of obtaining best aerodynamic capabilities in hyperloop pods by designing head profile optimally in combination with tail profile. This outcome will provide major contribution towards the development of hyperloop pods in future with better aerodynamic behaviour resulting in lesser electrical energy required to propel the hyperloop pods in evacuated tube.

Several particles are not observed directly, but only through their decay products. We consider the possibility that they might be fakeons, i.e. fake particles, which mediate interactions but are not asymptotic states. A crucial role to determine the true nature of a particle is played by the imaginary parts of the one-loop radiative corrections, which are affected in nontrivial ways by the presence of fakeons in the loop. The knowledge we have today is sufficient to prove that most non directly observed particles are true physical particles. However, in the case of the Higgs boson the possibility that it might be a fakeon remains open. The issue can be resolved by means of precision measurements in existing and future accelerators.

Magnetic properties of soft magnetic composites are highly sensitive to the processing conditions. In this paper we focus on the possibility to model this effect using the Jiles-Atherton-Sablik theory. It is assumed that the effect of varying compaction pressure may be described by direct introduction of stress-dependent term in the model equations. The values of model parameters are kept constant. Verification of the proposed approach is carried out using measurement data from self-developed iron-based composite cores.

This paper proposes a novel deterministic methodology for estimating the optimal sampling frequency (SF) of water quality monitoring systems. The proposed methodology is based on employing two-dimensional contaminant transport simulation models to determine the minimum SF considering all the potential changes in the boundary conditions of a water body. A two-dimensional contaminant transport simulation model (RMA4) was implemented to estimate the distribution patterns of the total dissolved solids (TDS) within the Al-Hammar Marsh in the southern part of Iraq for 30 cases of potential boundary conditions. Using geographical information system (GIS) tools, a spatiotemporal analysis approach was applied to the results of the RMA4 model to determine the minimum SF of the monitoring stations with an accuracy level of detectable change in TDS concentration (ALC) of 5%, 10% and 15%. The proposed methodology specified a minimum and maximum SF for each monitoring station (MS) that ranged between 12 and 33 times per year, respectively. Additionally, increasing the ALC to 10% and 15% increase the minimum SF for some MSs by approximately 18% and 21%, respectively. However, the proposed methodology includes all the potential values and cases of boundary conditions, which increases the certainty of monitoring the system and the efficiency of the SF schedule. Moreover, the proposed methodology can be effectively applied to all types of surface water resources.

This article attempts to formulate a mathematical model for a potential explanation regarding the unavoidable impact of a rigid body's peculiar shape on the seamless flow over it. The solid body completely immersed in a Newtonian fluid and respectively has a relative open circuit flow on it will typically experience various observable phenomena like flow separation, flow transition, down-wash, stalling at the higher angle of attack, stalling velocity and how cambered airfoil can typically generate lift at a zero incidence angle. This article respectively represents an understanding of the laminar flow over a rigid body's external surface with due respect to its distinctive shape and size. This working paper formulates a more realistic and simplified mathematical model for open circuit laminar flow over a body, based on the historical data of aerodynamics and theoretical mechanics. This is intended to properly estimate forces on the continuous surface of the body in a laminar flow, to properly explain, understand and predict mentioned phenomena. Most of all the mechanism of streamline formation and its deformation with due regards to flow, shape and size of the body in an open-circuit laminar are formulated mathematically to enhance better design theory which can reduce experimentation while designing a streamlined body.

As climate is not only a valuable tourism resource but also a factor influencing travel experience, estimating climate change can provide implications to sustainable development of the tourism industry. This study develops Climate Volatility Index (CVI) using GARCH model and estimates the relationship between CVI and Japanese tourism demand for Korea using a tourism demand model, based on data from January 2000 to December 2013. Time lag is applied based on a decision making process regarding travel destinations. The result shows that an increase in volatility of climate change leads to a decrease in tourism demand.

A diagnostic method is presented for analysing the large-scale behaviour of the Met Office Unified Model, which is a comprehensive atmospheric model used for weather and climate prediction. Outside the boundary layer, on scales larger than the radius of deformation, semigeostrophic theory will give an accurate approximation to the model evolution. In particular, the ageostrophic circulation required to maintain geostrophic and hydrostatic balance against prescribed forcing and a rate of change of the geostrophic pressure can be calculated. In the tropics the balance condition degenerates to the weak temperature gradient approximation. Within the boundary layer the semigeostriptic approximation has to be used because friction and rotation are equally important. Assuming the calculated pressure tendency and ageotriptic circulation match the observed model behaviour, the influence of the large-scale state and the nature of the forcing on the model response can be deduced in a straightforward way. This process is illustrated by comparing predictions of the ageotriptic circulation from the theory and the model. It is then used to show that the effects of latent heat release can be included by modifying the static stability, and to show the effect of an idealised tropical heat source on the subtropical jet. Finally the response of the ageotriptic flow to boundary layer heating in the tropics is demonstrated. These illustrations show that the model behaviour on large scales conforms with theoretical expectations, so that the results of the diagnostic can be used to aid the development of further improvements to the model.

The non-existence of a land ownership database in most of the developing countries moves the inhabitants to the occupation of public lands. Some of this situation are the origin to areas of informal housing, commerce and agriculture and in the end into new ​​informal settlements. Informal settlements become a serious problem in developing countries. The most common typology of informal settlements is that they are the population settled in public lands without any infrastructures and against the administrator's will. Thought this action the result in an uncontrolled land occupation process that promotes new informal areas without any proper built-up utilities, located in risk areas on the territory, barely ensuring the minimum requirements for a heaty living of the population and in various cases incentives to an informal economy. The process of build a cadastral map in informal settlement areas is a fundamental base to support the future transformation of illegal areas and to regulate the occupation of new subdivision planning and into the creation of new expansion areas. In this paper, it is presented a methodology developed to be applied to support a new register of land and to management. The transformation of informal settlement areas. The model to register the land tenure has been associated with allows the process application to multiple typology of informal settlements. The model to register land tenure has developed on a series of qualitative and quantitative data that determine the identification and classification of the buildings and its physical and functional description. The model was developed using Geographic Information System and with an initial survey of existing land titles of possession and public proposals to develop new expansion areas. A case study of the method is presented, where the land management model was implemented - Chã da Caldeiras in Ilha do Fogo an informal settlement in Cape Verde. The results are a great acceptance of the proposal by the population and local authorities and the starting of the implementation phase.

The recent demand for reducing pollutant and CO2 emissions from internal combustion engines has created an urgent need for the development of ultra-lean burn engines that ensure combus-tion stability and reducing knocking tendency. One of the most promising methods is the Pre-chamber Spark Ignition (PCSI) system, where a jet of high-energy reactive gases, produced by pilot combustion in a pre-chamber, ignites the main combustion event in the cylinder. Given the intricate phenomena inherent to PCSI systems, conducting 3D CFD studies is imperative for a comprehensive analysis and optimal design. Furthermore, the detailed CFD model, combined with the calibrated 0-D/1-D model, is anticipated to yield a significant amount of new data that would be difficult to obtain through experimental approaches, making it essential for advancing our understanding and optimization of these systems. However, recently published papers re-port that some state-of-the-art models developed for conventional SI operation may not be pre-dictive under the challenging conditions of TJI systems, particularly under lean conditions. This review elucidates the reasons behind the widespread adoption of CFD in the optimal design of PCSI engines. It presents significant examples and delves into the potential and challenges of employing CFD not just as a predictive tool but also as a design instrument for enhancing PCSI engine performance.

The move to a low carbon economy is very important for enhancing international competitiveness. The eco-innovation is the critical factor of the green paradigm. This study is designed to investigate deeply the determinants of eco-innovation of manufacturing firms in Korea by suggesting anticipated regulations, self-regulations, and industry-specific characteristics as external factors and open information sources as internal factors. The data used in the analysis is 1946 sample firms from Korean Innovation Survey 2010 based on the Oslo Manual. Using the multi-variate probit analysis and the zero-inflated negative binomial (ZINB) regression analysis, we have found out that the anticipated regulations and self-regulations have significant influences both on eco-process innovation and eco-product innovation, while industrial characteristics have no effects. The empirical results also show that the breadth of information sources has a positive effect on businesses in implementing eco-innovations. Our findings show that the Korean government should provide a good platform where firms can better understand the future trends of environmental policies, particularly policies on anticipated and self-regulations. At the same time, Korean firms should establish a voluntary system to control environmental activities so that they can improve eco-innovations through integrating external information.

Human Face and facial parts are the most significant parts as it reveals a person’s true identity. It plays an important role in various biometric applications like crowd analysis, human tracking, photography, cosmetic surgery, etc. There are many techniques are available to detect a facial image. Among them, skin detection is the most popular one. The aim of this paper is to detect first the person's identity from facial image and finally check any spot present the the detected person. The first step is to detect the maximum skin region based on a combination method of RGB and HSV color space model. Next it is to verify the skin areas of human through machine learning approach. The Aggregated Channel Features (ACF) detector is used to identify the different facial parts like eye pairs, nose, and mouth. Bootstrap aggregation decision tree classifier is applied to classify the person’s identity based on Histogram Oriented Gradient (HOG) features value. The experimental results show that the proposed method gives the average 97% accuracy.