We prove an analog of the removable singularity theorem for bounded harmonic functions on stratified sets. The harmonic functions are understood in the sense of the soft Laplacian. The result can become one of the main technical components for extending the well-known Poincaré–Perron’s method of proving the solvability of the Dirichlet problem for the soft Laplacian.

This study extends the CGAN-based MODIS cloud vertical-profile (64×64-scene, about 70km width×15km height) retrieval technique developed by Leinonen et al. (2019) [1] to construct seamless 3D cloud fields for the MODIS granules [2]. Firstly, the accuracy and spatial continuity of the Leinonen et al. [1] retrievals are statistically evaluated. Then, according to the characteristics of the retrieval error, a spatially overlapping-scene ensemble generation method and a bi-directional Ensemble Binning Probability Fusion (CGAN-BEBPF) technique are developed, which improves the CGAN retrieval accuracy and support to construct seamless 3D clouds. The CGAN-BEBPF technique involves three steps: cloud masking, intensity scaling, and optimal value selection. It ensures adequate coverage of the low reflectivity areas while preserving the high reflectivity cloud cores. The technique is applied to retrieve the 3D cloud fields of Typhoon Chaba and a multi-cell convective system. The cloud structures of the CGAN-BEBPF results are highly consistent with the ground-based radar observations. It can retrieve weak clouds at the top levels of convective storms and hurricanes that were missed by ground-based radars and fill the gaps of the ground-based radar lower-level PPI scan. Overall, CGAN-BEBPF can be automated to retrieve the rich informative 3D structures of typhoons and severe convective systems over the oceans along the MODIS swath, which certainly presents great value for improving maritime and near-shore typhoons and convection prediction and other cloud-sensitive applications.

The liver diseases are posing serious threat to human health associated with abusive use of allopathy medicines, especially in developing economies like Pakistan. The strengthening of detoxification system using natural products might be a suitable option to minimize the liver problems. In this current research, Zingiber officinale and Azadirachta indica were evaluated for their hepatoprotective role. For the purpose, 20 Sprague Dawley rats were equally divided into four groups i.e., negative control, paracetamol positive control, 2.5% ginger and 2% neem powder, respectively. During 42 days study, the levels of bilirubin and liver enzymes significantly elevated due to liver damage. Although, the values dropped (20-25%) in treatment group. Serum glucose and serum lipid profile increased in positive control group, but reduced (5-10%) in both ginger and neem groups. The serum protein profile declined in liver damage group, however, improved (~5%) in treatment groups. The hematological parameters slightly declined in liver damage group, but increased in ginger and neem groups. Moreover, the white blood cells differential count and renal parameters showed abnormal values in liver damage group, but improved after treatment. This study is promising and highlighted the hepato-protective role of neem and ginger that may open several avenues of research.

Estimating ground surface PM2.5 with fine spatiotemporal resolution is a critical technique for exposure assessments in epidemiological studies of its health risks. Previous studies have utilized monitoring, satellite remote sensing or air quality modeling data to evaluate the spatiotemporal variations of PM2.5 concentrations, but such studies rarely combined these data simultaneously. We develop a three-stage model to fuse PM2.5 monitoring data, satellite-derived aerosol optical depth (AOD) and community multi-scale air quality (CMAQ) simulations together and apply it to estimate daily PM2.5 at a spatial resolution of 0.1˚ over China. Performance of the three-stage model is evaluated using a cross-validation (CV) method step by step. CV results show that the finally fused estimator of PM2.5 is in good agreement with the observational data (RMSE = 23.00 μg/m^3 and R2 = 0.72) and outperforms either AOD-retrieved PM2.5 (R2 = 0.62) or CMAQ simulations (R2 = 0.51). According to step-specific CVs, in data fusion, AOD-retrieved PM2.5 plays a key role to reduce mean bias, whereas CMAQ provides all-spacetime-covered predictions, which avoids sampling bias caused by non-random incompleteness in satellite-derived AOD. Our fused products are more capable than either CMAQ simulations or AOD-based estimates in characterizing the polluting procedure during haze episodes and thus can support both chronic and acute exposure assessments of ambient PM2.5. Based on the products, averaged concentration of annual exposure to PM2.5 was 55.75 μg/m3, while averaged count of polluted days (PM2.5 > 75 μg/m3) was 81, across China during 2014. Fused estimates will be publicly available for future health-related studies.

This paper is devoted to the study of multi-dimensional integral transform with Fox H-function in the kernel in weighted spaces integrable functions in the domain R+n with positive coordinates. Mapping properties such as the boundedness, the range, the representations of the considered transformation are established.

We analyze damage propagation modeling of turbo-engines in a data-driven approach. We investigate subspace tracking assuming a low dimensional manifold structure and a static behavior during the healthy state of the machines. Our damage propagation model is based on the deviation of the data from the static behavior and uses the notion of health index as a measure of the condition. Hence, we incorporate condition-based maintenance and estimate the remaining useful life based on the current and previous health indexes. This paper proposes an algorithm that adapts well to the dynamics of the data and underlying system, and reduces the computational complexity by utilizing the low dimensional manifold structure of the data. A significant performance improvement is demonstrated over existing methods by using the proposed algorithm on CMAPSS Turbo-engine datasets.

Conditions under which compositions of component systems form a well-defined system-of-systems here are formulated at a fundamental level. Statement of what defines a well-defined composition and sufficient conditions guaranteeing such a result offers insight into exemplars that can be found in special cases such as differential equation and discrete event systems. For any given global state of a composition, two requirements can be stated informally as: 1) the system can leave this state, i.e., there is at least one trajectory defined that starts from the state, and 2) the trajectory evolves over time without getting stuck at a point in time. Considered for every global state, these conditions determine whether the resultant is a well-defined system and if so, whether it is non-deterministic or deterministic. We formulate these questions within the framework of iterative specifications for mathematical system models that are shown to be behaviorally equivalent to the Discrete Event System Specification (DEVS) formalism. This formalization supports definitions and proofs of the afore-mentioned conditions. Implications are drawn at the fundamental level of existence where the emergence of a system from an assemblage of components can be characterized. We focus on systems with feedback coupling where existence and uniqueness of solutions is problematic.

Quantitative determination of the critical size of inclined coal pillar in old goaf water affected area is of great significance for water damage prevention and safe mining. The critical size of inclined waterproof coal pillar is derived by using theoretical analysis, numerical calculation and field engineering practice for the stability of the waterproof coal pillar in old goaf water affected area of 1303 working face of Dananhu I mine in Xinjiang region. Firstly, a force model of inclined waterproof coal pillar was established to reveal the law that the critical size of the coal pillar increases with the increase of coal seam inclination under the action of asymmetric load. Then, numerical simulation was applied to reveal the dynamic evolution process of plastic deformation-destabilization-destruction of section coal pillar under the influence of mining and single-side water pressure, and the critical size of waterproof coal pillar in study area was determined to be 19.09m. Finally, measures such as pumping and pressure relief and slurry reinforcement were adopted to reduce the deformation rate of the roadway on side of waterproof coal pillar, which ensured the stability of the coal pillar and roadway and the safe recovery of the working face.

In this paper we describe a method which combines sentiment analysis with machine learning techniques and/or multivariate statistical analysis. By applying this methodology it is possible to classify a collection of texts into two or more groups or clusters. On the basis of a number of previously defined clusters, the novelty of the outlined approach is the use of the sentiment analysis results as input to the machine learning model or multivariate statistical analysis. Once the classifier has been obtained, we can assign a given text into one of the pre-established clusters. The groups or clusters can represent different time periods, classes of texts transcribed from different conversations, etc. The method is illustrated through an example taken from one of the two studies in which we have applied this methodology. In one of the studies, the method was used to classify press news of a volcanic eruption, while in the other study it was used to classify the conversations recorded between a chatbot with different kinds of speakers (humans or chatbots). This last study was the seminal work in which we introduced this methodology.

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

To facilitate the computation of the uniaxial compressive strength value of the cemented paste backfill under the condition of high sand-cement ratio, and to analyze the size effect of the cemented paste backfill with high sand-cement ratio under varied aspect ratios, the cemented paste backfill specimens with sand-cement ratios of 8:1-24:1 and aspect ratios of 0.5, 1, and 2 were prepared. Through response surface experiments and finite element analysis, the influence of three aspect ratios on the cemented paste backfill strength was explored, and the failure characteristics and size effect mechanisms of cemented paste backfill with various aspect ratios were analyzed. The model of strength conversion between cemented paste backfill with different aspect ratios was established. The results show that the compressive strength of the cemented paste backfill decreases with the increase of the aspect ratio under the same mass concentration and sand-cement ratio. When the aspect ratio is 0.5 and 1, the splitting failure is the main factor; when the aspect ratio is 2, the shearing failure is the main factor. The three-dimensional stress caused by the end effect of the cemented paste backfill is the main reason for the high strength when the aspect ratio is 0.5. Due to the one-dimensional stress zone generated in the middle of the cemented paste backfill with an aspect ratio of 2 in the laboratory, its strength value is closer to the actual uniaxial compressive strength value of the cemented paste backfill. The strength values of the cemented paste backfill with aspect ratios of 0.5 and 1 were higher than the actual uniaxial compressive strength values in the laboratory. The rationality of the theoretical analysis was verified by the ABAQUS analysis results. According to the experimental results, a strength conversion model between different aspect ratios is established, which can provide a reference for the design of the uniaxial compressive strength of the cemented paste backfill.

The main contribution of this paper is to develop a graphical tool to evaluate the suitability of a candidate parametric model to fit a data set. The practical motivation is to check the adequacy of the so called SAINT model proposed in Jarner and Kryger (2011). This model has been implemented in practice by an important European pension fund concerned with setting annuity reserves for all current or former employees of Denmark. So, one particular relevant question is whether this mortality model is actually fitting old-age. Our graphical test can be described as follows. First we transform the data by means of the parametric model which is being evaluated. Should the model be correct, the transformed data will be in accordance with an Exponential distribution with rate 1. Then we construct a family of local linear hazard estimates based on the data on the transformed scale. Finally we use the statistical tool SiZer to assess the goodness-of-fit of the Exponential distribution to the data on the transformed scale. If the model is correct the SiZer map should not reveal any significant feature in the family of kernel estimates. Our method allow us to establish a diagnostic regarding the validity of the SAINT model when describing mortality patterns in four different countries.

Additive Manufacturing (AM) provides an opportunity to fundamentally redesign components previously limited by conventional manufacturing techniques. A new process for this workflow of design, manufacture by Powder Bed Fusion (PBF) and validation is presented, to which a case study of a crank for a high performance racing bicycle is applied. Topology optimisation generated conceptually ideal geometry from which a functional design was interpreted. Design for AM considerations were employed to reduce build time, material usage and post-processing labour. PBF was employed to manufacture the parts, and the build quality assessed using Computed Tomography (CT). Static and dynamic functional testing was performed and compared to a Finite Element Analysis (FEA). CT confirmed good build quality of tall, complex geometry with no significant geometrical deviation from CAD over 0.5 mm. Static testing proved performance close to current market leaders, although failure under fatigue occurred after just 2495 ± 125 cycles, the failure mechanism was consistent in both its form and location. These physical results were representative of those simulated, thus validating the FEA. This research demonstrates a complete workflow from design, manufacture, post-treatment and validation of a highly loaded PBF manufactured component, offering practitioners with a validated approach to the application of PBF.

We present a case of serious and progressive neuropathy shortly following administration of the herpes zoster subunit (HZ/su) vaccine. The progressive neuropathy occurred a week following the vaccination and progressed until discharge 16 days post admittance. Mild patient’s symptoms persist. The development of neuropathy following HZ/su is exceedingly rare, with an attributable risk of 3 cases per million vaccines administered. A black box warning was issued for this indication, although diagnosis and treatment were not confirmed for this patient. Reporting cases like this is crucial for a comprehensive understanding of vaccine risks and to characterize the underlying etiology of these serious adverse events.

The degree of ore rock fragmentation of mine blasting is an important index for evaluating the effect of engineering blasting. In order to study the effect of millisecond delayed blasting on the internal fragmentation of the ore rock, LS-DYNA numerical simulation software was used to obtain the damage characteristics of the ore rock in the blasting area using the RHT model. The Mohr-Coulomb strength damage criterion was applied to analyze the internal stress state of the ore rock, and the effects of 25ms, 50ms and 75ms delay time conditions on ore rock fragmentation were studied. The results show that, within a certain range of delay time, the degree of rock fragmentation increases with the increase of delay time. The stress state in the ore rock due to blasting is strengthened with the increase of delay time in the direction of the blast hole, which shows that the stress concentration is strengthened from the excavation face to the interior of the ore rock. The damage and the degree of fragmentation show a characteristic relationship of 25ms＜50ms＜75ms, which is verified by carrying out blasting experiments on site.

To estimate the progressive collapse resistance capacity of a multi-column frame tube structure with the assembled truss beam composite floor (ATBCF), the pushdown analysis, and the nonlinear dynamic analysis as well, are conducted for such a structure by using the alternate load path (ALP) method. The bearing capacities of the remaining structures at three different work conditions, which are the side middle column failure, the edge middle column failure, and the corner column failure, are individually studied, and the collapse mechanism for the remaining structures is analyzed from the aspects of the internal force redistribution and the failure mode of the second defense line. Simultaneously, the influence of the column failure time on the dynamic response of the remaining structure and the dynamic amplification coefficient are discussed. The results show that the residual bearing capacity of the remaining structure with the bottom corner column failure is higher than that of the one with the side or edge middle column failure, while the latter has a stronger plastic deformation capacity. When the ALP method is adopted to operate the progressive collapse analysis, it is reasonable to take the column failure time as 0.1 times of the first-order vertical vibration period of the remaining structure, and it is suitable to set the dynamic amplification coefficient as of 2.0, which is the ratio of the maximum dynamic displacement to the static displacement of the remaining structure under the transient loading condition.

Aspalathin (ASP) can protect H9c2 cardiomyocytes against high glucose (HG)-induced shifts in myocardial substrate preference, oxidative stress and apoptosis. While the protective mechanism of aspalathin remains unknown, nuclear factor (erythroid-derived 2)-like 2 (Nrf2) has emerged as a key factor for intracellular responses against oxidative stress. Therefore, we hypothesized that aspalathin protects the myocardium against hyperglycemia-induced oxidative damage by up-regulating Nrf2 expression in H9c2 cardiomyocytes and diabetic (db/db) mice. Using an oxidative stress RT2 Profiler PCR array, ASP at a dose of 1 µM was demonstrated to protect H9c2 cardiomyocytes against HG-induced oxidative stress, but silencing of Nrf2 abolished this protective response of ASP and exacerbated cardiomyocyte apoptosis. Db/db mice and their non-diabetic (db/+) littermate controls were subsequently treated daily for 6 weeks with either a low (13 mg/kg) or high (130 mg/kg) ASP dose. Compared to nondiabetic mice the db/db mice presented increased cardiac remodeling and enlarged left ventricular wall that occurred concomitant to enhanced oxidative stress. Daily treatment of mice with ASP at a dose of 130 mg/kg for 6 weeks was more effective at reversing complications than both a low dose ASP or metformin, eliciting enhanced expression of Nrf2 and its downstream antioxidant genes. These results indicate that ASP maintains cellular homeostasis and protects the myocardium against hyperglycemia-induced stress through activation of Nrf2 and its downstream target genes.

Floods are among the most serious natural disasters worldwide; they cause enormous crop losses every year and threaten world food security. There are many studies focused on flood impact assessments for administrative districts but fewer on postdisaster impact assessments for specific crops. Therefore, this study used remote sensing data, including the normalized difference vegetation index (NDVI), elevation data, slope data, and rainfall data, combined with crop growing season data to construct a crop flood damage assessment index (CFAI). First, an analytic hierarchy process (AHP) was used to assign weights to the impact factors; then, the weighted composite score method was used to calculate the CFAI; and finally, the impact was classified as slight, moderate, or severe based on the magnitude of the CFAI . This method was used for the Missouri River floods of 2019 in the U.S. Due to the lack of measured data, the disaster vegetation damage index (DVDI) was used to validate the results. The combined approach achieved a high degree of consistency in spatial distribution. The CFAI can respond well to the degree of crop impact after flooding, providing new ideas and reference standards for agriculture-related departments.

The intentional damages to local Cultural Heritage sites carried out in recent months by the Islamic State have received wide coverage from the media worldwide. Earth Observation data provide important information to assess these damages in such non-accessible areas, and automated image processing techniques would be needed to speed up the analysis if a fast response is desired. This paper shows the first results of applying fast and robust change detection techniques to sensitive areas, based on the extraction of textural information and robust differences of brightness values related to pre- and post-disaster satellite images. A map highlighting potentially damaged buildings is derived, which could help experts at timely assessing the damages to the Cultural Heritage sites of interest. Encouraging results are obtained for two archaeological sites in Syria and Iraq.

Among the various types of gas sensors, the chemoresistive gas sensors based on metal oxides are considered the most attractive ones due to their simple operation, low costs and miniaturization. Thin films of V2O5 undoped and doped with TiO2 were studied as nitrogen dioxide, methane and hydrogen sensors. The experimental kinetics of the electrical conductivity changes due to introduction and removal of gas on metal oxide gas sensors were successfully explained in terms of the Langmuir adsorption theory.

Hybrid reinforcement in a composite material (CM), in many situations, allows better govern material mechanical properties. Polymer matrix CM plate is reinforced with small particles and macro-fibers. Particles are oil shale ash (OSA) powder, macro-fibers – basalt fiber threads im-pregnated by matrix material. With the goal to find out elastic properties of the matrix- polymer with OSA, samples with its different concentrations were experimentally fabricated and their elastic properties were experimentally measured. Obtained data were used in numerical model-ling. Was observed polymer matrix composite slab, reinforced with OSA and three-dimensional (3D) basalt fiber fabric. Fabric are forming threads impregnated by matrix (polymer resin mixed with OSA particles). Damage accumulation in the stretched slab was modelled. Impregnated thread, in the modelling, is observed as a macro-fiber (MF) with averaged elastic properties. With the load increase MF in the material began to break. At first, appeared isolated single breaks. A probabilistic sequential MF rupture accumulation model was accepted and numerical-ly realized. The geometry of the reinforcing knitted fabric was modeled using the Leaf and Glaskin approach. In the knitted fabric highly, stressed MF cross-sections are observed as po-tential location where thread breakage can occur. If one thread breaks, the cross-sections of the adjacent threads experience the overload caused by this rupture. More overloaded crossection in the adjacent overloaded thread breaks and we obtain two adjacent broken MF designated as a defect with the size j=2. The probabilities of obtaining defect consisting of at least 2, 3, … j adjacent broken MF in the plate were calculated. The calculations continued until the rupture process becomes unstable (j→∞). The overstresses on adjacent threads in the CM were calculat-ed using the FEM three-dimensional numerical approach. Analytical and stochastic methods were used for the load-carrying capacity determination and different size defects accumulation in the hybrid composite material (HCM) plate. Also, was calculated the probability of the plate rupture.

MRI shows in-vivo the three archetypal patterns of CNS volume loss underlying progressive ataxias, namely spinal atrophy (SA), cortical cerebellar atrophy (CCA) and olivopontocerebellar atrophy (OPCA). The MRI-based CNS atrophy pattern was reviewed in 128 progressive ataxias. A CNS atrophy pattern was identified in 91 conditions: SA in Freidreich’s ataxia, CCA in 5 acquired and 72 (24 dominant, 47 recessive,1 X-linked) inherited ataxias, OPCA in Multi-System Atrophy and 12 (9 dominant, 2 recessive,1 X-linked) inherited ataxias. The MRI-based CNS atrophy pattern may be useful for genetic assessment, identification of shared cellular targets, and repurposing therapies or enlargement of drugs indications in progressive ataxias.

Based on PVDF (piezoelectric sensing techniques), this paper attempts to study the propagation law of shock waves in brittle materials during the process of three-wavelength laser irradiation of polysilicon, and discusses the formation mechanism of thermal shock failure. The experimental results show that the vapor pressure effect and the plasma pressure effect in the process of pulsed laser irradiation lead to the splashing of high temperature and high density melt. With the decrease of the laser wavelength, the laser breakdown threshold decreases and the shock wave is weakened. Because of pressure effect of the laser shock, the brittle fracture zone is at the edge of the irradiated area. The surface tension gradient and surface shear wave caused by the surface wave are the result of coherent coupling between optical and thermodynamics. The average propagation velocity of laser shock wave in polysilicon is 8.47×103m/s, and the experiment has reached the conclusion that the laser shock wave pressure peak exponentially distributes attenuation in the polysilicon.

The development of computational acoustics allows simulation of sound generation and propagation in complex environment. In particular, meshfree methods are widely used to solve acoustics problems through arbitrarily distributed field points and approximation smoothness flexibility. As a Lagrangian meshfree method, smoothed particle hydrodynamics (SPH) method reduce the difficulty in solving problems with deformable boundaries, complex topologies, or multiphase medium. The traditional SPH method has been applied in acoustic simulation. This study presents the corrective smoothed particle method (CSPM), which is a combination of SPH kernel estimate and Taylor series expansion. The CSPM is introduced as a Lagrangian approach to improve accuracy in solving acoustic wave equations in the time domain. Moreover, a boundary treatment technique based on the hybrid meshfree and finite difference time domain (FDTD) method is proposed to represent different acoustic boundaries with particles. To model sound propagation in pipes with different boundaries, soft, rigid, and absorbing boundary conditions are built with this technique. Numerical results show that the CSPM algorithm is consistent and demonstrates convergence with exact solutions. Main computational parameters are discussed, and different boundary conditions are validated to be effective for benchmark problems in computational acoustics.

This work presents a novel approach for the study of the movement of droplets on inclined surfaces under the influence of gravity and chemical heterogeneities. The developed numerical methodology uses data-driven modeling to extend the applicability limits of an analytically derived reduced-order model for the contact line velocity. More specifically, while the reduced-order model is able to capture the effects of the chemical heterogeneities to a satisfactory degree, it does not account for gravity. To alleviate this shortcoming, datasets generated from direct numerical simulations are used to train a data-driven model for the contact line velocity, which is based on the Fourier neural operator and corrects the reduced-order model predictions to match the reference solutions. This hybrid surrogate model, which comprises of both analytical and data-driven components, is then integrated in time to simulate the droplet movement, offering a speedup of five orders of magnitude compared to direct numerical simulations. The performance of this hybrid model is quantified and assessed in different wetting scenarios, by considering various inclination angles and values for the Bond number, demonstrating the accuracy of the predictions as long as the adopted parameters lie within the ranges considered in the training dataset.

Urban rail transit hub platform is the most important area for passenger flow distribution. In order to calculate passenger flow volume in platform and evaluate platform service level during rush hours, this paper presents a method for modeling and simulation of passenger flow distribution in platform. Passenger flow distribution model (PFDM) is proposed based on the basic analysis and the superposition principle of passenger flow. Simulation design for PFDM is proposed by Anylogic, which contains simulation process and simulation model. Experiment results show that PFDM and simulation design are effective and accordant with the reality scenario, and the simulation precision is comparatively ideal. This research could provide a beneficial reference for train scheduling and operation management under the viewpoint of traffic safety and service level.

This study develops a progressive optimal fault-tolerant control method based on insufficient fault information. By combining passive and active fault-tolerant control manners during the process of fault diagnosis, insufficient fault information is fully used, and optimal fault-tolerant control effect is achieved. In addition, the fault-tolerant control method based on guaranteed robust cost control is introduced. The proposed progressive optimal fault-tolerant control method considers two aspects. First, as the amount of fault information continually increases, the performance index of the progressive optimal fault-tolerant controller improves. Second, at each moment, based on the corresponding insufficient fault information and prior knowledge, optimal fault-tolerant control is achieved according to current fault information. The process of progressive optimal fault-tolerant control converges to active fault-tolerant control when the fault is completely identified and the optimal fault-tolerant controller is no longer reconfigured until no more useful fault information can be provided. Furthermore, a progressive optimal fault-tolerant control algorithm based on the grid segmentation in the parameter uncertainty domain and the selection of different auxiliary center points is introduced. The simulation results verify the feasibility of the proposed algorithm and the validity of the proposed theory.

Studies on evolution have made significant progress in multiple disciplines, but evolutionary theories remain scattered and controversial. Here we deduce that, thermodynamically, many carbon-based entities (CBEs) on the Earth tend to absorb energy from widespread relatively temperate heat streams on the Earth flowing from the solar, geothermal, and other energy sources, to form higher-hierarchy CBEs (HHCBEs). This has been the driving force of evolution leading to accumulation of HHCBEs for billions of years. We further deduce three progressive mechanisms of evolution including natural selection from the driving force. We hence establish the CBE evolutionary theory (CBEET) which reinterprets the major aspects of evolution in a comprehensive and comprehensible way. The CBEET provides novel explanations for natural selection, origin of life (abiogenesis), macroevolution, sympatric speciation, and evolutionary tempos. It suggests that evolution is driven hierarchy-wise by thermodynamics and favors fitness and diversity. It elucidates that altruism, collaboration, and obeying rules with balanced freedom are important throughout the CBE evolution which includes chemical evolution, biological evolution, and animal group evolution. The CBEET refutes several erroneous views including negative entropy and survival of the fittest. It integrates with research advances in multiple disciplines and links up laws of physics, evolution in biology, and harmonious development of human society.

Soil and Water Assessment Tool (SWAT) was used to simulate five glacierized river basins that are global in coverage and vary in climate. The river basins included the Narayani (Nepal), Vakhsh (Central Asia), Rhone (Switzerland), Mendoza (Central Andes, Argentina), and Central Dry Andes (Chile) with a total area of 85,000 km2. A modified SWAT snow algorithm was applied in order to consider spatial variation of associated snow melt /accumulation by elevation band across each subbasin. In the previous studies, melt rates varied as a function of elevation resulting from an air temperature gradient while the snow parameters were constant throughout the entire basin. A major improvement of the new snow algorithm is separating the glaciers from seasonal snow based on their characteristics. Two SWAT snow algorithms were evaluated in simulation of monthly runoff from glaciered watershed: 1) the snow parameters are lumped (i.e. constant throughout the entire basin) and 2) the snow parameters are spatially variable based on elevation band-subbasin (i.e. modified snow algorithm). Applying the distributed SWAT snow algorithm improved the model performance in simulation of monthly runoff with snow-glacial regime, so that mean RSR decreased to 0.49 from 0.55 and NSE increased to 0.75 from 0.69. Improvement of model performance was negligible in simulation of monthly runoff from the basins with monsoon runoff regime.

Electrical contacts are important circuit components with diverse industrial applications, and their failure can lead to multiple unwanted effects. Hence, the behavior of electrical contacts is a widely studied topic in scientific literature based on various approaches, tools, and techniques. The present study proposes a numerical modeling and simulation approach based on the Holm contact theory to study the dependence between the electric potential and the temperature within an electrical contact. Structured in 5 sections, the research was conducted using COMSOL Multiphysics software and its solid-state mechanics, electric current, and heat transfer modules in order to highlight contact behavior from mechanical, electrical and thermal points of view: the von Mises stress, contact force, the electric field amplitude, the variation of the electrical potential along the current path, the temperature gradient, and dependence of temperature along the contact elements edges were obtained by simulation and graphically represented. The results are in line with the expectations and with past research that was studied in the literature review.

This study analyses regional development in one of the poorest provinces in China, Guizhou Province, between 2000 and 2012 using a multiscale and multi-mechanism framework. In general, regional inequality has been declining since 2000. In addition, economic development in Guizhou Province presented spatial agglomeration and club convergence, which shows how the development pattern of one core area, two-wing areas and a contiguous area at the edge of the province have been developed between 2006 and 2012. Multilevel regression analysis revealed that industrialization and investment level were the primary driving forces of regional economic disparity in Guizhou Province. The influences of marketization and decentralization on regional economic disparity were relatively weak. Investment level reinforced regional economic disparity and the development of core-periphery structure in the province. However, investment level actually weakened the regional economic disparity in Guizhou Province when the variable of time was considered. In addition, both the topography and urban–rural differentiation were the two main reasons for forming a core-periphery structure in Guizhou Province.

A power factor correction (PFC) rectifier is compulsory for any electronic circuitry connected to the electrical grid. The majority of PFCs that are utilized are based on a step-up converter that supplies high DC voltage at its output terminal. Electric vehicle battery voltage varies from a few hundred to almost a thousand volts. Thus, an additional conversion step is obligatory when designing an all-brand universal charger. This paper presents a new type of front-end versatile step up / down three voltage level PFC. The rectifier can operate in continuous conduction mode or discontinuous conduction mode. First, the rectifier’s principle of operation is described, and then an innovative rectifier is analyzed for continuous and discontinuous conduction modes. Finally, the proposed theory is experimentally validated in a multiplier-less dual loop mode at discontinuous conduction modes. It was shown that although there is no multiplier use in the control circuitry, the power factor is near unity. It was revealed that the rectifier could swing the output voltage from 50 V to 900 V while the input voltage was 230 Vrms. The innovative topology suits any standard PFC rectifier, three-level voltage supplement, and dual-stage low-voltage power supply.

Existing research indicates that mineralization of Carlin-type gold deposit is related to geological structures, but current studies mostly focus on the qualitative association, and lack quantitative analysis of the relationship between mineralization and structural features, which limits the potential for predictive mineral exploration. In this study, the Nibao Carlin-type gold deposit was selected as the research area to conduct three-dimensional geological modeling, spatial analysis, and three-dimensional prospectivity modeling. Spatial analysis revealed that the majority of gold ore bodies are situated near the main fault, with gold mineralization primarily occurring within a spatial range of approximately -80 meters to 20 meters from the main fault plane. This spatial distribution predominantly aligns in a SW-NE direction. Spatial characteristic extraction of fault slope indicators suggests that the slope angles are conducive to the migration and accumulation of ore-forming hydrothermal fluids, with ore body metal content rapidly increasing within an angle range of approximately 20° to 60°. The shape analysis of the mainfault indicates that the principal spaces of mineralization enrichment are within -10m to 20m of the fault plane undulations, where gold resources are concentrated in a relatively flat area. However, the degree of mineralization is more pronounced in the protruding sections than in the depressed areas. The areas with significant dip changes in faults display higher variations in orebody attitudes, with mineralization noticeably weaker in areas where the fault steepness changes markedly. Ore-bearing units exhibit significantly higher grades within the -17° to 3° interval of fault steepness transition. This interval also sees a rapid accumulation of gold resources. The Nibao gold mine's mineralization is derived from deep, ore-bearing hydrothermal fluids ascending along major fractures, precipitating, and accumulating in favorable ore-hosting spaces (faults, anticline cores) and lithological combinations (sedimentary tuffs, tuffs). Three deep potential gold targets were identified, highlighting the deep western edge of the F1 fault and the deep southeast-trending lower plate as prospective areas for future exploration. This study underscores the effectiveness of 3D prospective modeling as an indispensable tool for elucidating the spatial distribution of fault-controlled Carlin-type gold deposits and their tectonic determinants.

The coronavirus disease (COVID-19) is a respiratory tract infection caused by the new virus SARS-CoV-2. In March 2020, the World Health Organization (WHO) declared a worldwide COVID-19 pandemic. Since then, there has been a rush to find preventative or curative treatment. This arrived with the appearance of the COVID-19 vaccine, months after the declaration of the pandemic. In the setting of global COVID-19 immunization, there have been reports of rare skin immune-mediated diseases (IMD) after COVID-19 vaccination, particularly systemic sclerosis. Systemic sclerosis (SSc) is the most reported new onset IMD following messenger RNA (mRNA) COVID-19 vaccination. The pathogenesis of SSc is not fully understood, and the diagnosis is based on clinical symptoms. We report a 54-year-old male, with no prior history of autoimmune disorder, with rapid and progressive skin thickening (Modified Rodnan skin score 30/51), weeks after his mRNA COVID-19 vaccination. Antinuclear antibodies (ANA) showed a strongly positive nuclear fine-speckled pattern, but no SSc autoantibodies were found. The patient was diagnosed with SSc based on the persistence of autoantibodies and the clinical criteria according to the 2013 American College of Rheumatology/European League Against Rheumatism classification. Due to the proximity of the two events, we hypothesized a cross-reaction between COVID-19 vaccination and, in genetically predisposed patients, an emergence of systemic sclerosis. Our case suggests a potential relationship between the mRNA COVID-19 vaccine and new-onset autoimmune diseases. Physicians should be aware of this possible association.

Purpose: To assess clinical outcomes of corneal cross-linking (CXL) intervention in a population diagnosed with progressive keratoconus. Methods: This single-center retrospective cohort study included consecutive patients who underwent standard CXL or accelerated CXL for progressive keratoconus at a major teaching hospital in south Israel between January 2015 and December 2019. Patient medical files were reviewed to extract demographic, clinical, and tomographic data from outpatient clinic reports before treatment and one year post-operatively. Results: This study included 166 patients (representing 198 eyes), out of which 98 patients (123 eyes) were ethnically Bedouin, and 68 patients (75 eyes) were ethnically Jewish. Overall, 126 patients (144 eyes) had a follow-up of at least 12 months (16.84±5.76). The mean patient age was 20.62 ± 7.1 years old. There were significant baseline differences between the two ethnical groups in best-corrected visual acuity (BCVA; p < 0.001), uncorrected visual acuity (UCVA; p < 0.001), mean keratometry (p = 0.028), and corneal thickness (p < 0.001). Significant changes in BCVA, UCVA, and pachymetry parameters within each group were found after 12 months. Negative binomial regression analysis showed a maximal keratometry below 55D (RR=1.247, p < 0.001) and a standard CXL procedure (RR=1.147, p = 0.041) are significantly related to the stability of KC after 12 months. However, the effect size of the origin of patients is negligible (RR=1.047, p = 0.47). Conclusions: In this study, the Bedouin population suffered from more progressive keratoconus when compared to the Jewish population. CXL was significantly effective in improving BCVA and UCVA in both groups after 12 months of follow-up.

Progressive supranuclear palsy (PSP) is a sporadic parkinsonism tauopathy characterised by the deposition of aggregations of abnormal, hyperphosphorylated four-repeat tau (4R-tau). A revised clinical diagnostic criterion for PSP allows early presentations for the full spectrum of clinical phenotypes to be recognised enabling doctors to make a more accurate diagnosis. The major genetic risk factor for sporadic PSP is a common variant in the gene encoding microtubule-associated protein tau (MAPT). Research into the biochemical and pathological pathways of tau is vital to improve the chances of developing an effective diagnostic biomarker to monitor tau pathogenesis. Neuroimaging biomarkers, such as tau PET ligands, are proving the most successful tool in providing a differential diagnosis between neurodegenerative disorders. There are currently no effective treatments for PSP, however tau-directed therapies in the last five years have rapidly advanced. Latest tau therapies are proposed to have disease-modifying effects by reducing toxic aggregations of tau through manipulating tau gene expression. After encouraging results from long awaited trials, additional funding is being injected into this field and with new results expected, this proves an exciting area for scientific discovery. This paper reviews advances in pathophysiology, diagnosis, biomarkers and disease-modifying therapeutic treatments for PSP.

System-of-systems (SoS) evolution is a complex and unpredictable process. Although various principles to facilitate collaborative SoS evolution have been proposed, there is a lack of experimental data validating their effectiveness. To address these issues, we present an Agent-Based Model (ABM) for SoS evolution in the Internet of Vehicles (IoV) , serving as a quantitative analysis tool for SoS research. By integrating multiple complex and rational behaviors of individuals, we aim to simulate real-world scenarios as accurately as possible. To simulate the SoS evolution process, our model employs multiple agents with autonomous interactions and incorporates external environmental variables. Furthermore, we propose three evaluation metrics: evolutionary time, degree of variation, and evolutionary cost, to assess the performance of SoS evolution. Our study demonstrates that enhanced information transparency significantly improves the evolutionary performance of distributed SoS. Conversely, the adoption of uniform standards only brings limited performance enhancement to distributed SoSs. Although our proposed model has limitations, it stands out from other approaches that utilize Agent-Based Modeling to analyze SoS theories. Our model focuses on realistic problem contexts and simulates realistic interaction behaviors. This study enhances the comprehension of SoS evolution processes and provides valuable insights for the formulation of effective evolutionary strategies.

Preterm infants have increased body adiposity at term equivalent age and risk for adverse metabolic outcomes. The aim of the study was to define how nutrient intake may impact body composition (BC) of very low birth weight infants fed with early progressive enteral feeding and standard fortification. Eighty-six infants with

Viviparity is made possible by the placenta, a structure acquired relatively recently in the evolutionary history of eutherian mammals. Compared to oviparity, it increases the survival rate of the fetus, owing to the eutherian placenta. Questions such as “How was the placenta acquired?” and “Why is there diversity in placental morphology among mammalian species?” remain largely unsolved. Our present understanding of the molecules regulating placental development remains unclear, owing in no small part to the persistent obscurity surrounding the molecular mechanisms underlying placental acquisition. Many genes that characterize eutherian placental morphology likely evolved to be expressed at the fetal-maternal interface concurrently with those during embryogenesis. Therefore, identifying these genes, how they were acquired, and how they came to be expressed specifically at the fetal-maternal interface will shed light on some crucial molecular mechanisms underlying placental evolution. Exhaustive studies support the hypothesis that endogenous retroviruses (ERVs) could be evolutional driving forces for trophoblast cell fusion and placental structure in mammalian placentas including those of the bovine species. This review focuses on bovine ERVs (BERVs) and their expression and function in the placenta.

Developmental hazard evaluation is an important part of assessing chemical risks during pregnancy. Toxicological outcomes from prenatal testing in pregnant animals result from complex chemical-biological interactions, and while New Approach Methods (NAMs) based on in vitro bioactivity profiles of human cells offer promising alternatives to animal testing, most of these assays lack cellular positional information, physical constraints, and regional organization of the intact embryo. Here, we engineered a fully computable model of the embryonic disc in the compucell3d.org modeling environment to simulate epithelial-mesenchymal transition of epiblast cells and self-organization of mesodermal domains (chordamesoderm, paraxial, lateral plate, posterior/extraembryonic). Cell fate in the model is determined by an autonomous homeobox (HOX) clock driven by morphogenetic signals (e.g., FGF, WNT, ATRA, CDX). Executing the model renders a quantitative cell-level computation of mesodermal subpopulations and consequences of perturbation based on known embryogeny. For example, synthetic perturbation of the control network rendered altered phenotypes (cybermorphs) mirroring experimental mouse embryology, with 50% reductions in FGF4, FGF8 and BMP4 signaling resulting in 86%, 98% and 59% reductions, respectively in the posterior mesodermal population, while ATRA exposure also resulted in a 78% decrease in this population. This model enables integration of in vitro chemical bioactivity data for specific molecular targets with known embryology to test mechanistic veracity and quantitative prediction of altered development.

Abstract: The SARS-CoV-2 pandemic has reemphasized the urgent need for broad-spectrum antiviral therapies. We developed a computational workflow using scRNA-Seq data to assess cellular metabolism during viral infection. With this workflow we predicted the capacity of cells to sustain SARS-CoV-2 virion production in patients and found a tissue-wide induction of metabolic pathways that support viral replication. Expanding our analysis to influenza A and dengue viruses, we identified metabolic targets and inhibitors for potential broad-45 spectrum antiviral treatment. These targets were highly enriched for known interaction partners of all analyzed viruses. Indeed, phenformin, an NADH:ubiquinone oxidoreductase inhibitor, suppressed SARS-CoV-2 and dengue virus replication. Atpenin A5, blocking succinate dehydrogenase, inhibited SARS-CoV-2, dengue virus, respiratory syncytial virus, and influenza A with high selectivity indices. In vivo, phenformin showed antiviral activity against SARS-CoV-2 in a Syrian hamster model. Our work establishes host metabolism as druggable for broad-spectrum antiviral strategies, providing invaluable tools for pandemic preparedness.

Digital twins are an emerging technology that could be harnessed for the digitalization of the industry. Steel industry systems contain a large number of electro-hydraulic components, as proportional valves. An input-output model for a novel water proportional cartridge valve is derived from physical modelling based on fluid mechanics, dynamics, and electrical principles. The valve is a two-stage valve with two 2/2-way water proportional valves as the pilot stage and a marginally stable poppet-type cartridge valve as the main valve. The orifice equation, which is based on Bernoulli principles, is approximated by a polynomial which makes parameter estimation easier and makes modelling possible without measuring pressure of the varying control volume situated in the pilot stage part of the valve. Data were collected when the valve was operating as a component in a closed-loop system as part of a press-mill machine in a steel manufacturing plant. Parameter estimation was made by convex optimization with physical constraints to overcome problems caused by poor system excitation. For comparison, a simple linear model was derived and the least squares method used for parameter estimation. A thorough estimation of the parameters relative errors is presented. The model contains 5 parameters related to the design parameters of the valve. The modelled position output is in good agreement with experimental data for training and test data. The model can be used for real time monitoring of the valve’s status by the model parameters. One of the model parameters varies linearly with production cycles. Thus, aging of the valve can be monitored.

The behaviour of steel frame buildings under progressive collapse conditions depends on a combination of several parameters including the interplay between different collapse resistance mechanisms mobilized in different structural components. Previous studies have shown that the extent to which these mechanisms may contribute to progressive collapse resistance depends on the ability of the beam-column connections to undergo large inelastic deformations prior to reaching their deformation capacity limits. For this reason, and due to the important role of their flexural strength and tying capacity in the development of essential collapse resistance mechanisms, the response of beam-column connections is one of the most important features of progressive collapse performance. Based on the knowledge gained through previous studies on the mechanics of the problem, the role of the connections is critically reviewed by examining the results of several experimental studies conducted during the past decade. The factors that may adversely affect progressive collapse resistance – such as failure modes of certain connection types – are evaluated, and novel approaches of limiting these factors which are currently under development are reviewed. The assessment of these parameters leads to useful conclusions of practical significance, while highlighting the aspects of the problem that need further study and understanding.

Colistin is used in treatment against gram-negative bacteria. It causes toxic effects due to oxidative stress in a dose-dependent manner. Naringin is a flavonoid with strong antioxidant properties found naturally in many plants, especially citrus fruits. This study aimed to determine the protective effects of NRG in CLS-induced sciatic nerve injury. Thirty-five male Spraque rats were randomly divided into five groups: control, NRG, CLS, CLS+NRG50, and CLS+NRG100. CLS was administered orally at a 15mg/kg for seven days. NRG was administered orally at 50 or 100 mg/kg for seven days. CLS caused toxic damage in sciatic nerve tissue by increasing MDA and decreasing antioxidant capacity (SOD, CAT, GPx, and GSH); increasing NF-κB, TNF-α, nNOS release; increasing apoptotic factor (Caspase-3); decreasing HO-1 and nCAM; and inducing 8-OHdG, MAPK and GFAP. NRG, on the other hand, reversed all these changes in sciatic nerve tissue and reduced CLS-induced oxidative stress damage, inflammatory damage and apoptotic damage. These effects were more effective at the 100mg/kg dose of NRG than at the 50mg/kg dose. In sciatic nerve tissue, CLS-induced sciatic nerve toxicity with increased oxidative stress, inflammation, and apoptotic damage. NRG showed a protective effect against CLS-induced sciatic nerve toxicity.

Following the occurrence of a typhoon, quick damage assessment related to residents can facilitate quick dispatch of house repair and disaster insurance works. Employing a deep learning method, this study used aerial photos of the Chiba prefecture obtained following the Typhoon Faxai in 2019 to automatically detect and evaluate the roof damage. This study comprised three parts: training deep learning model, detecting the roof damage using trained model, and classifying the level of roof damage. The detection object comprised roof outline, blue tarps, and roof completely destroyed. The roofs were divided into three categories: roof without damage, roof with blue tarps and roof completely destroyed. the F value obtained using the proposed method was higher than those obtained using other methods. In addition, it can be further divided into 5 levels from level 0 to 4. Finally, the spatial distribution of the roof damage was analyzed using ArcGIS tools. The proposed method is expected to provide certain reference for the real-time detection of the roof damage after the occurrence of a typhoon.

Pump Sizing is the process of dimensional matching an impeller and stator to provide a satisfactory performance test result and good service life during the operation of progressive cavity pumps. In this process, historical data analysis and dimensional monitoring are done manually, consuming a large amount of man-hours and requiring a deep knowledge of progressive cavity pump behavior. This work presents the use of complex networks in the construction of a prototype to recommend interference during the Pump Sizing process in a Progressive Cavity Pump. For this, data from different expert applications were used in addition to individual control Excel spreadsheets to build the database used in the prototype. From the pre-processed data, complex network techniques and the Betweenness Centrality metric were used to calculate the degree of importance of each order confirmation, as well as to calculate the dimensionality of the rotors. The model was evaluated using the mean squared error (MSE), obtaining a MSE of 0.28 for the cases where there were recommendations for order confirmations. Based on the results obtained, it was realized that there is a similarity with the dimensional defined by design engineers during the Pump Sizing process.

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

There are several challenges on how to attain energy efficiency while maintaining balance among factors affecting energy consumption such as power rating and temperature setpoints of cooling units, room temperature and humidity, and indoor air quality (IAQ). A real-time energy and IAQ monitoring system were installed in an educational building to profile the operational power consumption of inverter-based air condition unit (ACU) installed in each room. Polynomial curve and neural fitting regression analysis were applied to the real-time power consumption, indoor temperature and humidity, and carbon dioxide (CO2) level data. The derived models were able to provide the stabilizing indoor thermal and air conditions of the room to reach steady state ACU power consumption. These collected data and calculated parameters can be used to define rules in automating control of cooling appliances for an efficient energy utilization. The regression approaches, using real-time data, have determined the influence of indoor heat conditions and carbon dioxide levels on ACU power consumption. These parameters were utilized to establish consistent values for temperature, humidity, and carbon dioxide levels under stable settings of inverter-based air conditioning units.

The alcoholic fermentation process with agitation/static, followed by the acetic fermentation submerged in banana was evaluated. Kinetics parameters of alcoholic fermentation for the maximum ethanol/glycerol metabolites in agitated process was 29 and 27 h, and in static 47 and 45 h, respectively. For acetic fermentation, the kinetics parameters were medium time of 39.9 h, acetic acid yield of 53.1% and acetic acid productivity of 0.216 g/Lh. Wines from agitated/static process presented 5.73 and 6.81% (v/v) of alcohol content, respectively. Wine obtained by the static process presented higher concentrations of volatile compounds. The vinegar showed 49.2 g/L of acetic acid and the esters concentrations were higher than in wine. The wine and vinegar minerals were consistent with amount observed in the pulp, with an increase in sulphur concentration after alcoholic fermentation and decrease after acetic fermentation. Products showed chemical and composition of sensory and nutritional interest.

Multiple sclerosis (MS) is a heterogeneous disease of the central nervous system that is governed by neural tissue loss and dystrophy during its progressive phase, with complex reactive pathological cellular changes. The immune-mediated mechanisms that promulgate the demyelinating lesions during the relapses of acute episodes are not characteristic of chronic lesions during progressive MS. This has limited our capacity to target the disease effectively as it evolves within the central nervous system white and gray matter, thereby leaving neurologists without effective options to manage individuals as they transition to a secondary progressive phase. The current review highlights the molecular and cellular sequelae that have been identified to cooperate and/or contribute to neurodegeneration that identifies individuals with progressive forms of MS. We emphasize the need for appropriate monitoring via known and novel molecular and imaging biomarkers that can accurately detect and predict progression for the purposes of newly designed clinical trials that may demonstrate efficacy of neuroprotection and potentially neurorepair. To achieve neurorepair, we focus on the modifications required in the reactive cellular and extracellular milieu, in order to enable endogenous cell growth as well as transplanted cells that can integrate and/or renew the degenerative MS plaque.

The contribution deals with the selection of progressive methods and technologies to improve the management of production processes in the industrial area. The first chapter of the contribution summarises the theoretical starting points for the management of production processes and their progressive concepts. Chapter 2 deals with the analysis of the current state of play in the use of methods and technologies in production management and summarise its problem areas. Chapter 3 presents the design of a system for selecting the appropriate method and technology to improve the management of the production process. The system presented is designed from data collection blocks, a benchmark for the performance of industrial enterprises' production processes, and data mining to gain knowledge of the impact of the methods and technologies used on the performance of production processes. The last block of the system allows the presentation of the obtained results, which sets out the recommendation for the choice of progressive method and technology. The system is partially verified in the last chapter of the contribution.

Agricultural greenhouse is largely answered in the agricultural sphere, despite the shortcomings it has, including overheating during the day and night cooling which sometimes results in the thermal inversion mainly due to its low inertia. The glasshouse dressed chapel is relatively more efficient than the conventional tunnel greenhouse. Its proliferation on the ground is more or less timid because of its relatively high cost[14-22]. Agricultural greenhouse aims to create a favorable microclimate to the requirements of growth and development of culture, from the surrounding weather conditions, produce according to the cropping calendars fruits, vegetables and flower species out of season and widely available along the year. It is defined by its structural and functional architecture, the quality thermal, mechanical and optical of its wall, with its sealing level and the technical and technological accompanying[12-13]. The greenhouse is a very confined environment, where multiple components are exchanged between key stakeholders and them factors are light, temperature and relative humidity[8]. This state of thermal evolution is the level sealing of the cover of its physical characteristics to be transparent to solar, absorbent and reflective of infrared radiation emitted by the enclosure where the solar radiation trapping effect otherwise called "greenhouse effect" and its technical and technological means of air that accompany. The socio-economic analysis of populations in the world leaves appear especially the last two decades of rapid and profound transformations These changes are accompanied by changes in eating habits, mainly characterized by rising consumption spread along the year[14]. To effectively meet this demand, greenhouse-systems have evolved, particularly towards greater control of production conditions (climate, irrigation, ventilation techniques, CO2 supply, etc ...). Technological progress has allowed the development of greenhouses so that they become increasingly sophisticated and of an industrial nature (heating, air conditioning, control, computer, regulation, etc ...). New climate driving techniques have emerged, including the use of control devices from the classic to the use of artificial intelligence[10-11] such as neural networks and / or fuzzy logic, etc... As a result, the greenhouse growers prefer these new technologies while optimizing the investment in the field to effectively meet the supply and demand of these fresh products cheaply and widely available throughout the year.

The point cloud is a form of three-dimensional data that comprises various detailed features at multiple scales. Due to this characteristic and its irregularity, point cloud analysis based on deep learning is challenging. While previous works utilize the sampling-grouping operation of PointNet++ for feature description and then explore geometry by means of sophisticated feature extractors or deep networks, such operations fail to describe multi-scale features effectively. Additionally, these techniques have led to performance saturation. And it is difficult for standard MLPs to directly "mine" point cloud. To address above problems, we propose the Detail Activation (DA) module, which encodes data based on Fourier transform after sampling and grouping. We activate the channels at different frequency levels from low to high in the DA module to gradually recover finer point cloud details. As training progresses, the proposed Point-MDA can uncover local and global geometries of point cloud progressively. Our experiments show that Point-MDA achieves superior classification accuracy, outperforming PointNet++ by 3.3% and 7.9% in terms of overall accuracy on the ModelNet40 and ScanObjectNN dataset, respectively. Furthermore, it accomplishes this without employing complicated operations, while exploring the full potential of PointNet++.

Statistical management policy and system plays significant role in development of any nation as it guides the planner to analyze the gaps, design programs and allocate budget in efficient manner. Globally, design and usage of statistical management system varies relying on country specific ability and resources availability. Government of Nepal has been managing database in individual as well as thematic manner. Policy wise, thematic database is in operation based on Statistics Act 1958 and individual database is in operation based on Birth, Death and Other Personal Related Events Registration Act 1976. Though civil registration related policies have been either updated or newly formed, sectoral statistic management related policy has not been updated since its first promulgation period (1958). With no policy supporting the consolidated statistical management system, government as well as non-government agencies has been investing millions of dollars annually to collect, compile and analyze individual related data and fulfill their respective objectives. It has forced the government services and performance to be paper-based, time-consuming and tedious thus creating dis-trust among citizen each passing day. Though, formulation of legal instrument promoted digital movement and mandated shift towards e-government modality, the process has been low. Theoretically, Technology Acceptance Model (TAM) state the need of having advanced and efficient technology to be in place in current setting to improve governance, efficiency, accountability, transparency and trust. Also, the same theory state the requirement of strong willingness and motivation from the users (government, non-government and citizen) to adopt the technology based policy and system. As the integrated statistical management policy emphasizes on technology-based system or digital system, the perspectives of Technology Acceptance Model also termed as Information System Theory aid in understanding the context, conceptualizing and addressing the needs with an establishment and implementation of digital system or technology based system. This policy paper applied the Technology Acceptance Model (TAP) to examine the underlying opportunities and challenges associated with an integrated statistical management policy. With existing gaps in existing policy, system and practice alongside an overview of opportunities and challenges, this paper recommends the need of stand-alone integrated statistical management policy to consolidate sectoral as well as civil related information through single system based on recommended policy.

Water resources provided by alpine glaciers are an important pillar for people living in the arid regions in the west of China. In this study, the HBV (Hydrologiska Byrans Vattenavdelning) light model was applied to simulate glacier mass balance and runoff in the Koxkar River Basin (KRB) on the south slope of Mt. Tumur, western Tianshan Mts.. Daily temperature and precipitation were calculated by multiple linear regressions and gradient-inverse distance weighting, respectively, based on in-situ observed data by automatic weather stations (AWSs) in the basin (2007–2009) and at four meteorological stations neighbering the basin (1959–2009). Observed daily air temperature and precipitation were taken as input data for the HBV model, which was calibrated using runoff in 2007/08 and 2009/10, and validated in 2008/09 and 2010/11. Generally, the model could simulate runoff very well. The annual glacier mass balance and runoff were calculated using the HBV model driven by interpolated meteorological data for the period of 1959–2009. The simulated glacier mass balance were reasonable when compared with those observed values at nearby glaciers, indicating a decrease trend of mass balance in the basin with an average value of –370.4 mm a^-1 since 1959. The annual runoff showed a slight increase trend (5.51 mm a^-1). Futher analysis indicated that the runoff is more sensitive to temperature than precipitation amuont in the Koxkar river basin.

Residual stress (RS) is the most challenging problem in metal additive manufacturing (AM) since the build-up of high tensile RS may influence the fatigue life, corrosion resistance, crack initiation, and failure of the additively manufactured components. While tensile RS is inherent in all the AM processes, fast and accurate prediction of stress state within the part is extremely valuable and would result in optimization of the process parameters in achieving a desired RS and control of the build process. This paper proposes a physics-based analytical model to rapidly and accurately predict the RS within the additively manufactured part. In this model, a transient moving point heat source (HS) is utilized to determine the temperature field. Due to the high temperature gradient within the proximity of the melt pool area, material experience high thermal stress. Thermal stress is calculated by combining three sources of stresses known as stresses due to the body forces, normal tension, and hydrostatic stress in a homogeneous semi-infinite medium. The thermal stress determines the RS state within the part. Consequently, by taking the thermal stress history as an input, both the in-plane and out of plane RS distributions are found from incremental plasticity and kinematic hardening behavior of the metal by considering volume conservation in plastic deformation in coupling with the equilibrium and compatibility conditions. In this modeling, material properties are temperature-sensitive since the steep temperature gradient varies the properties significantly. Moreover, the energy needed for the solid-state phase transition is reflected by modifying the specific heat employing the latent heat of fusion. Furthermore, the multi-layer and multi-scan aspects of metal AM are considered by including the temperature history from previous layers and scans. Results from the analytical RS model presented excellent agreement with XRD measurements employed to determine the RS in the Ti-6Al-4V specimens.

Rapidly progressive glomerulonephritis, the term coined for a subtype of nephritic syndrome, is characterized by a state of acute loss of renal function, along with those components of renal damage on urinalysis, over a relatively short period (a few weeks or months). It is the aftermath of an acute, severe glomerular injury. RPGN, famously known as Crescentic glomerulonephritis (cGN) is not a single disease entity but a pattern that can occur in a variety of glomerular diseases caused by different pathologic lesions leading to the development of necrosis in the glomerular capillaries seen in both, systemic and kidney-restricted diseases. While still little is known about the exact etiologic cause behind the primary injury to the capillary wall leading to the development of the condition, many mechanisms are put forth to describe the pathogenesis leading to the progression. Immunopathologic studies have demonstrated that rapidly progressive glomerulonephritis can result from glomerular antibody deposition, can be immune complex-mediated, or from some as yet undefined mechanism that doesn’t involve glomerular antibody deposition. The condition is often severe with poor prognosis as it develops so rapidly and with extensive glomerular involvement, the damage is almost irreversible. Many elements can be associated with the occurrence and progression of the disease but it often involves a disruptive immune response one way or the other. RPGN’s morphological hallmark, the extensive extra-capillary cellular proliferation into the bowman’s space, commonly known as crescent formation which is often observed by electron microscopic view of the tissue biopsy, is considered one of the great diagnostic approaches for the detection of the disease. Also, the severity of the disease is related to the nature and type of immunologic process causing the disease and (in part) to the degree of crescent formation, that can only be observed by histopathological studies. This activity attempts to explain the etiology and pathogenesis involved in the development leading to the rapid progression of the disease.

This review aims to explore the intricate relationship between epigenetic mechanisms, stress, and affective disorders, focusing on how early life experiences and coping mechanisms contribute to susceptibility to mood disorders. Epigenetic factors play a crucial role in regulating gene expression without altering the DNA (deoxyribonucleic acid) sequence, and recent research has revealed associations between epigenetic changes and maladaptive responses to stress or psychiatric disorders. A scoping review of 33 studies employing the PRISMA-S (Preferred Reporting Items for Systematic Reviews and Meta-Analyses – Statement) guidelines investigates the role of stress-induced epigenetic mechanisms and coping strategies in affective disorders' occurrence, development, and progression. The analysis encompasses various stress factors, including childhood trauma, work-related stress, and dietary deficiencies, alongside epigenetic changes such as DNA methylation and altered gene expression. Findings indicate that specific stress-related genes frequently exhibit epigenetic changes associated with affective disorders. Moreover, the review examines coping mechanisms in patients with bipolar disorder and major depressive disorder, revealing mixed associations between coping strategies and symptom severity. While active coping is correlated with better outcomes, emotion-focused coping may exacerbate depressive or manic episodes. Overall, this review underscores the complex interplay between genetic predisposition, environmental stressors, coping mechanisms, and affective disorders. Understanding these interactions is essential for developing targeted interventions and personalized treatment strategies for individuals with mood disorders. However, further research is needed to elucidate specific genomic loci involved in affective disorders and the clinical implications of coping strategies in therapeutic settings.

Online identity-based theft is known to be one of the most serious and growing threats to victims, such as individuals and organizations, over the last 10 years due to the enormous economic damage these crimes have caused. The availability of personal information on the Internet has increased the chances of this cyber-crime. Online identity theft crime is the result of a combination of cyber-crimes on the one hand and lack of awareness and training of users on the other hand to protect personal data on the other. Education and awareness, which also contributes to early detection, is the strongest tool for consumers to safeguard themselves from online identity fraud. This paper provides a comprehensive explanation of online identity theft, the various approaches that thieves use to attack individuals and organizations and the types of fraud involved in this cyber-crime. The aim of this research is to evaluate the need for a reformulation of the concept of identity theft in order to be compatible with the evolution of behaviors and fraud.

Macromolecular crystallography at cryogenic temperatures has so far provided the majority of the experimental evidence that underpins the determination of the atomic structures of proteins and other biomolecular assemblies by means of single crystal X-ray diffraction experiments. One of the core limitations of the current methods is that crystal samples degrade as they are subject to X-rays, and two broad groups of effects are observed: global and specific damage. While the currently successful approach is to operate outside the range where global damage is observed, specific damage is not well understood and may lead to poor interpretation of the chemistry and biology of the system under study. In this work, we present a phenomenological model in which specific damage is understood as the result of a single process, the steady excitation of crystal electrons caused by X-ray absorption, which acts as a trigger for the bulk effects that manifest themselves in the form of global damage and obscure the interpretation of chemical information from XFEL and synchrotron structural research.

Batch and transport experiments were used to investigate the remediation of a loamy sand soil contaminated with Cr(VI) using zero valent iron nanoparticles (nZVI) stabilized by the carboxy methyl cellulose (CMC-nZVI). The effect of pH, ionic strength (IS), and flow rate on the removal efficiency of Cr(VI) were investigated under equilibrium (uniform transport) and non-equilibrium (Two-site sorption) transport using the Hydrus-1D model. The overall removal efficiency ranged from 70 to over 90% based on the chemical characteristics of the CMC-nZVI suspension and the transport conditions. The concentration and pH of the CMC-nZVI suspension had the most significant effect on the removal efficiency and transport of Cr(VI) in the soil. The average removal efficiency of Cr(VI) was increased from 24.1 to 75.5% when the concentration of CMC-nZVI nanoparticles was increased from 10 to 250 mg L-1, mainly because of the increased total surface area at larger particle concentration. Batch experiments showed that removal efficiency of Cr(VI) was much larger under acidic conditions. The average removal efficiency of Cr(VI) reached 90.1 and 60.5 % at pH 5 and 7, respectively. The Two-site sorption model described quite well (r2 = 0.96-0.98) the transport of Cr(VI) in soil as compared to the uniform transport model (r2 = 0.81-0.98). The average retardation of Cr(VI) was 3.51 and 1.61 at pH 5 and 7, respectively, indicating earlier arrival for the breakthrough curves and shorter time to reach maximum relative concentration at lower pH. The Hydrus-1D model has proven to be an efficient tool for assessing the removal of Cr(VI) pollutants, offering innovative, cost-effective, and environmentally friendly remediation methodologies.

The rampant misuse of drones poses a serious threat to national security and human life. Currently, the CNN method has been widely used in drone detection. However, there is a challenge that traditional CNN cannot cope with, which is small drone targets often have reduced amplitude or even lost features in infrared images. This paper proposes a Progressive Feature Fusion Network (PFFNet) that gradually increases the response amplitude of the target in the deep network. The Feature Selection Model (FSM) is designed to improve the utilisation of the output coding graph and enhance the feature representation of the target in the network. A lightweight segmentation head is also designed to achieve progressive feature fusion with multi-layer outputs. Experimental results show that the proposed algorithm can achieve low duration and high accuracy in drone target detection. On the public dataset, the IoU is improved by 2.53% and the detection time is reduced by 81.03%.

A progressive FEA fatigue degradation model for composites was developed and implemented using a UMAT user material subroutine in Abaqus. Numerical results were compared to experimental strain field data from high frequency Digital Image Correlation (DIC) of split disk fatigue testing of pressure vessel cut outs with holes. The model correctly predicted the onset and evolution of damage in the matrix as well as the onset of fiber failure. The model use progressive failure analysis based on the maximum strain failure criterion, the cycle jump method and Miner sum damage accumulation rule. A parameter study on matrix properties was needed to capture the scatter in strain fields observed experimentally by DIC. S-N curve for the matrix material had to be lowered by 0% to 60% to capture the experimental scatter. The onset of local fiber failure had to be described by local S-N curves measured by DIC having 2.5 times greater strain than that of S-N curves found from standard coupon testing.

Atomic force microscopy (AFM) has been extensively used for the nanoscale characterization of polymeric materials. The coupling of AFM with infrared spectroscope (AFM-IR) provides another advantage to the chemical analyses and thus helps to shed light upon the study of polymers. In this perspective paper, we review recent progress in the use of AFM-IR in polymer science. We describe first the principle of AFM-IR and the recent improvements to enhance its resolution. We discuss then the last progress in the use of AFM-IR as a super-resolution correlated scanned-probe IR spectroscopy for chemical characterization of polymer materials dealing with polymer composites, polymer blends, multilayers and biopolymers. To highlight the advantages of AFM-IR, we report here several results in studying crystallization of both miscible and immiscible blends as well as polymer aging. Then, we demonstrate how this novel technique can be used to determine phase separation, spherulitic structure and crystallization mechanisms at the nanoscale, which have never been achieved before. The review also discusses future trends in the use of AFM-IR in polymer materials, especially in polymer thin film investigation.

This paper focused on impact localization of composite structures, which possess more complexity in the guided wave propagation because of the anisotropic behavior of composite materials. In this work, a composite plate was manufactured by using a compression molding process with proper pressure and temperature cycle. Eight layers of woven composite prepreg were used to make the plate. A structural health monitoring (SHM) technique is implemented with piezoelectric wafer active sensors (PWAS) to detect and localize the impact on the plate. There are two types of impact event are considered in this paper (a) low energy impact event (b) high energy impact event. Two clusters of sensors recorded the guided acoustic waves generated from the impact. The acoustic signals are then analyzed using a wavelet transform based time-frequency analysis. The proposed SHM technique successfully detect and localize the impact event on the plate. The experimentally measured impact locations are compared with the actual impact locations. An immersion ultrasonic scanning method was used to visualize the composite plate before and after the impact event. A high frequency 10 MHz 1-inch focused transducer was used to scan the plate in an immersion tank. Scanning results show that there is no visible manufacturing damage in the composite plate. However, clear impact damage was observed after the high-energy impact event.

Excess weight, which is seen as the background cause of most diseases, is gradually becoming the most important problem in the world. The cafeteria diet is robust diet that mimics the western diet and provides rapid weight gain associated with the obesity, diabetes, and cancer. The relationship between obesity and cancer is well known. For this reason, it is of great importance to reveal the first molecular and metabolic alterations that occur before entering the obesity process with any factor to understand the pathology of many diseases and to treat them in the early period. The beneficial effects of probiotics are attributed to their metabolite content with antioxidant properties or the postbiotic metabolites produced by them. In this study, the effects of different dietary practices on the hepatic antioxidant system of rats in developmental period were investigated by real-time PCR, enzyme activity measurements, and quantitative measurements of metabolites. After all, it was observed that CAF caused serious impairments by affecting the hepatic antioxidant system at gene and protein levels, and these impairments were relatively reversed with SCD Probiotics supplementation. In addition, regardless of the CAF diet, the regulatory effect of SCD Probiotics supplementation on the hepatic antioxidant system is remarkable.

This paper examines the most prominent “progressive” American religious groups’ (as defined by those that liberalized early on the issue of birth control, circa 1930) views of women between the first and second waves of the feminist movement (1930-1965). We find that some groups have indeed had a long and outspoken support for women’s equality. Using their modern-day names, these groups, the United Church of Christ, the Unitarian Universalist Church, and to a lesser extent, the Society of Friends, or Quakers, professed strong support for women’s issues, early, and often. However, we also find that prominent progressive groups –the Protestant Episcopal Church, the Methodist Episcopal Church and the United Presbyterian Church, were virtually silent on the issue of women’s rights – even as the second wave of the feminist movement was picking up steam – as late as 1965.

Climate warming is expected to have pronounced effects on Arctic and Subarctic ecosystems, especially regions underlain by discontinuous and relatively warm permafrost. The main goal of this research is to evaluate the vulnerability and dynamics of permafrost under climate warming across the various ecotypes in respect of ecosystem stability, socioeconomic impact, and for better understanding possible future environmental changes. We suggested the new version of the spatially distributed permafrost dynamics model (GIPL2-MPI), which is developed in the Geophysical Institute, University of Alaska Fairbanks. This model is based on the ecosystem approach to simulate the permafrost dynamics, which we are discussing in this paper. We combined ground-based observations and numerical freeze/thaw modeling using climate-ecosystem-permafrost interactions to understand the physical processes and mechanisms controlling permafrost physical state. We predict the changes in permafrost conditions using output from two GCMs (NCAR-CCSM4 and GFDL-CM3) and Five-Model Average Ensemble for the RCP-4.5 and RCP-8.5 scaled down to 1 by 1 km spatial resolution (https://uaf-snap.org/) across entire Alaska. Our result shows that by the end of the current century widespread near-surface permafrost degradation could begin everywhere in Alaska southward of the Brooks Range as well as across some spots at the North Slope Alaska.

The massive amount of human biological, imaging and clinical data produced by multiple and diverse sources, necessitates integrative modeling approaches able to summarize all this information into answers to specific clinical questions. In this paper, we present a hypermodeling scheme able to combine models of diverse cancer aspects regardless of their underlying method or scale. Describing tissue-scale cancer cell proliferation, biomechanical tumor growth, nutrient transport, genomic-scale aberrant cancer cell metabolism, and cell signaling pathways that regulate the cellular response to therapy, the hypermodel integrates mutation, miRNA expression, imaging, and clinical data. The constituting hypomodels as well as their orchestration and links are described. Two specific cancer types, Wilms tumor (nephroblastoma) and non-small cell lung cancer, are addressed as proof of concept study cases. Personalized simulations over the actual anatomy of a patient have been carried out. The hypermodel has also applied to predict tumor control after radiotherapy and the relationship between tumor proliferative activity and response to neoadjuvant chemotherapy. Our innovative hypermodel holds promise as a digital twin based clinical decision support system and the core of future in silico trial platforms, although additional retrospective adaptation and validation is necessary.

We examined Neurite orientation dispersion and density imaging in patients with migraine. We found that patients with medication overuse headache exhibited lower orientation dispersion than those without. Moreover, orientation dispersion at the body of the corpus callosum was statistically negatively correlated with migraine attack frequencies. These findings indicate that neurite dispersion is damaged in patients with chronic migraine. Our study results indicate the orientation preference of neurite damage in migraine.

The DNA Damage Response (DDR) is a complex signaling network that comes into play when cells experience genotoxic stress. Upon DNA damage, cellular signaling pathways are rewired to slow down cell cycle progression and allow recovery. However, when the damage is beyond repair, cells activate complex and still not fully understood mechanisms, leading to a complete proliferative arrest or cell death. A number of conventional and novel anti-neoplastic treatments rely on causing DNA damage or on the inhibition of the DDR in cancer cells. However, the identification of molecular determinants directing cancer cells toward recovery or death upon DNA damage is still far from complete, and it is object of intense investigation. SPRY-COntaining RAN binding Proteins (Scorpins) RANBP9 and RANBP10 are evolutionarily conserved and ubiquitously expressed proteins whose biological functions are still debated. RANBP9 has been previously implicated in cell proliferation, survival, apoptosis and migration. Recent studies also showed that RANBP9 is involved in the Ataxia Telangiectasia Mutated (ATM) signaling upon DNA damage. Accordingly, cells lacking RANBP9 show increased sensitivity to genotoxic treatment. Although there is no published evidence, extensive protein similarities suggest that RANBP10 might have partially overlapping functions with RANBP9. Like RANBP9, RANBP10 bears sites putative target of PIK-kinases and high throughput studies found RANBP10 to be phosphorylated following genotoxic stress. Therefore, this second Scorpin might be another overlooked player of the DDR alone or in combination with RANBP9. This review focuses on the relatively unknown role played by RANBP9 and RANBP10 in responding to genotoxic stress.

This study, which was supported by a quantitative research paradigm with two experiment and two control groups of 4x4 quasi-experimental design, aimed to find out the impact of a sustainable progressive STEAM (SP-STEAM Model) application model on 5th grade primary school students critical thinking dispositions and mathematic achievements in Northern Cyprus. The treatment model was applied to experimental groups for 14 weeks of time. Split-plot multiple group analysis of variance (Split-plot ANOVA) statistical technique was used to calculate between and within group significances regarding exogenous variable. SPSS24 software package was used for the analysis. Pre-test and post-test results derived from experiment and control groups revealed significant effect of SP-STEAM Model on 5th grade primary school students’ critical thinking dispositions as measured with CCTDI and mathematic achievements as measured with a dedicated ex-am. The results were discussed in detail under the light of related literature and suggestions for further studies were proposed.

We describe a 90-year-old male presenting with disequilibrium, loss of balance and difficulty walking for three days prior to initial presentation. Interestingly, he denied cough, fever or dyspnea prior to arrival. Over the course of 48 hours, the patient developed acute respiratory distress syndrome (ARDS) requiring intubation, diagnosed with COVID-19 infection and was treated in the intensive care unit where he died. Since the initial cases in Wuhan China in Dec 2019, the medical and epidemiological communities have learned much about the presenting features, symptomatology, epidemiology, transmission and common physical, laboratory and radiological findings of this disease. Although common symptoms are already established, it is very important to learn and record atypical symptoms or presentations of this highly contagious disease. By doing so, we will be able to recognize earlier atypical symptoms and prevent the environmental exposure to Health care workers and future patients as well. We report that Central disequilibrium may be such as initial presenting sign and symptom of impending respiratory failure from SARS-CoV-2 virus. These atypical findings such as presyncope may precede common respiratory complications of SARS-CoV-2.

To evaluate whether the extent of cardiac damage in severe aortic stenosis (AS) might be associated with the occurrence of conduction system disorders after TAVI. A total of 153 symptomatic patients with severe AS who had a TAVI were categorized before the procedure according to the extent of cardiac damage: stage 1 (13.5%): no or LV damage; stage 2 (58.8%): left atrial/mitral valve damage, atrial fibrillation (AF); stage 3 (27.7%): low-flow state, pulmonary vasculature/tricuspid valve/RV damage. After TAVI, 34% of patients exhibited LBBB and 10% high-degree atrioventricular block (HD-AVB). No patient in group 1 developed HD-AVD whereas new LBBB was frequent in groups 2 and 3. Twenty-one patients presented with resolutive paroxysmal AF with a higher rate for each group increment (group 1: n=0, 0%; group 2: n=11,15.7%; group 3: n=10, 30.3%) (p = 0.012). Patients in group 3 had the higher rate of permanent pacemaker implantation (PPMI) (group 1: n=1, 6.3%; group 2: n=7,10%; group 3: n=9, 27.3%) (p=0.012). No patient in group 1 died during the hospital stay. In conclusion, cardiac damage staging of AS might help identify patients at higher risk of conduction disorders and PPMI requirement after TAVI.

At present, the applications and efficacy of non-ionizing radiations (NIR) in oncotherapy are limited. In terms of potential combinations, the use of biocompatible magnetic nanoparticles as heat mediators has been extensively investigated. Nevertheless, the development of more efficient heat nanomediators that may exhibit high specific absorption rates still is an unsolved problem. Our aim was to investigate if externally applied magnetic fields and a heat-inducing NIR affect tumor cell viability. To this end, under in vitro conditions, different human cancer cells (A2058 melanoma, AsPC1 pancreas carcinoma, MDA-MB-231 breast carcinoma) were treated with the combination of electromagnetic fields (EMFs, using solenoids) and hyperthermia (HT, using a thermostated bath). The effect of NIR was also studied in combination with standard chemotherapy and targeted therapy. An experimental device combining EMFs and high intensity focused ultrasounds (HIFU)-induced HT was tested in vivo. EMFs (25 T, 4h) or HT (52C, 40 min) showed a limited effect on cancer cell viability in vitro. However, their combination decreased viability to approx. 16%, 50%, and 21% of controls values in A2058, AsPC1, and MDA-MB-231 cells, respectively. Increased lysosomal permeability, release of cathepsins into the cytosol and mitochondria-dependent activation of cell death are the underlying mechanisms. Cancer cells could be completely eliminated combining EMFs, HT and standard chemotherapy or EMFs, HT and anti-Hsp70-targeted therapy. As a proof of concept, in vivo experiments performed in AsPC1 xenografts showed that combination of EMFs, HIFU-induced HT, standard chemotherapy and a lysosomal permeabilizer induces a complete cancer regression.

Damage detection based on modal parameter changes becomes popular in the last decades. Nowadays are available robust and reliable mathematical relations to predict the natural frequency changes if damage parameters are known. Using these relations, it is possible to create databases containing a large variety of damage scenarios. Damage can be thus assessed by applying an inverse method. The problem is the complexity of the database, especially for structures with more cracks. In this paper, we propose two machine learning methods, namely the random forest (RF) and the artificial neural network (ANN) as search tools. The databases we developed contain damage scenarios for a prismatic cantilever beam with one crack and ideal and non-ideal boundary conditions. The crack assessment is made in two steps. First, a coarse damage location is found from the networks trained for scenarios comprising the whole beam. Afterward, the assessment is made involving a particular network trained for the segment of the beam on which the crack is previously found. Using the two machine learning methods, we succeed to estimate the crack location and severity with high accuracy for both simulation and laboratory experiments. Regarding the location of the crack, which is the main goal of the practitioners, the errors are less than 0.6%. Based on these achievements, we concluded that the damage assessment we propose, in conjunction with the machine learning methods, is robust and reliable.

Current bridge inspection practices rely on paper-based data acquisition, digitization, and multiple conversions in between incompatible formats to facilitate data exchange. This practice is time-consuming, error-prone, cumbersome, and leads to information loss. One aim for future inspection procedures is to have a fully digitized workflow that achieves loss-free data exchange, which lowers costs and offers higher efficiency. On the one hand, existing studies proposed methods to automatize data acquisition and visualization for inspections. These studies lack an open standard to make the gathered data available for other processes. On the other hand, several studies discuss data structures for exchanging damage information through out different stakeholders. However, those studies do not cover the process of automatic data acquisition and transfer. This study focused on a framework that incorporates automatic damage data acquisition, transfer, and a damage information model for data exchange. This enables inspectors to use damage data for subsequent analyses and simulations. The proposed framework shows the potentials for a comprehensive damage information model and related (semi-)automatic data acquisition and processing.

Damage to crops from wildlife interference is a common threat to food security among rural communities in or near Game Management Areas (GMAs) in Zambia. This study uses a two-stage econometric model and cross-sectional data from a survey of 2,769 households to determine the impact of land use planning on the probability and extent of wildlife-inflicted crop damage. The results show that crop damage is higher in GMAs as compared to non-GMAs, and that land use planning could be an effective tool to significantly reduce the likelihood of such damage. These findings suggest that there is merit in the current drive to develop and implement land use plans as means to minimize human-wildlife conflict such as crop damage. This is especially critical as Zambian conservation policies do not have an explicit provision for compensation in the event of damage from wildlife.

Several clinically useful anticancer drugs selectively kill cancer cells by inducing DNA damage; the genomic instability and DNA repair defects of cancer cells make them more vulnerable than normal cells to the cytotoxicity of DNA-damaging agents. Because epoxide-containing compounds can induce DNA damage, we have used the MTT assay to evaluate the selective cytotoxicity of three epoxyalkyl galactopyranosides against A549 lung cancer cells and MRC-5 lung normal cells. Compound (2S,3S)-2,3-Epoxydecyl 4,6-O-(S)-benzylidene-β-D-galactopyranoside (EDBGP) showed the highest selective anticancer activity and was selected for mechanistic studies. After observing that EDBGP induced cellular DNA damage (comet assay), we found that cells deficient in nucleotide excision repair were hypersensitive to the cytotoxicity of this compound; this suggests that EDBGP may induce bulky DNA adducts. EDBGP did not inhibit glycolysis (glucose consumption and lactate production). Pre-treatment of lung cancer cells with several antioxidants did not reduce the cytotoxicity of EDBGP, thereby indicating that reactive oxygen species do not participate in the anticancer activity of this compound. Finally, EDBGP was screened against a panel of cancer cells and normal cells from several tissues, including three genetically modified skin fibroblasts with increasing degree of malignancy. Our results suggest that epoxyalkyl galactopyranosides are promising lead compounds for the development of new anticancer agents.

The use of unmanned aerial aircrafts (UAVs) is governed by strict regulatory frameworks that prioritize safety. To guarantee safety, it is necessary to acquire and maintain situational awareness (SA) throughout the operation. Existing Canadian regulations require pilots to operate their aircrafts in visual line-of-sight. Therefore, the task of acquiring and maintaining SA primary falls to the pilots. However, the development of aerial transport is entering a new era with the adoption of a highly dynamic and complex system known as advanced air mobility (AAM), which involves UAV operating autonomously beyond visual line-of-sight. SA must therefore be acquired and maintained primarily by each UAV through specific technologies and procedures. In this paper, we review these technologies and procedures in order to decompose the SA of the UAV in the AAM. We then use the system modeling language to provide a high-level structural and behavioral representation of the AAM as a system having UAV as its main entity. In a case study, we analyze one of the flagships UAVs of our industrial partner. Results show that this UAV doesn’t have all the technologies and methodologies necessary to achieve all the identified SA goals identified for the safety of the AAM. This work is a theoretical framework intended to contribute to the realization of the AAM, and we also expect to impact the future design and utilization of UAVs.

High costs for the repair of concrete structures can be prevented if damage at an early stage of degradation is detected and precautionary maintenance measures are applied. To this end, we use numerical wave propagation simulations to identify simulated damage in concrete using convolutional neural networks (CNN). Damage in concrete subjected to compression is modeled at the mesoscale using the discrete element method. Ultrasonic wave propagation simulation on the damaged concrete specimens are performed using the rotated staggered finite-difference grid method. The simulated ultrasonic signals are used to train a CNN based classifier capable of classifying three different damage stages (microcrack initiation, microcrack growth and microcrack coalescence leading to macrocracks). The performance of the classifier is improved by refining the dataset via an analysis of the averaged envelope of the signal. The classifier using the refined dataset has an overall accuracy of 90%.

The physical and mechanical properties of rocks will change significantly after being subjected to high temperatures, which poses safety hazards to underground projects such as coal underground gasification. In order to investigate the effect of temperature on the macroscopic and microscopic properties of rocks, this paper has taken sandstone as the research object and conducted uniaxial compression tests on sandstone specimens at different temperatures (20-1000 ℃) and different heating rates (5-30 ℃/min) at 1200 ℃. At the same time, the acoustic emission (AE) test system was used to observe the acoustic emission characteristics of the rock damage process, and the microstructural changes after high temperature were analyzed with the help of a scanning electron microscope (SEM). The test results show that the effect of temperature on sandstone is mainly divided into three stages: Ⅰ (20-500 ℃) stage is the strengthening zone, the evaporation of water and the contraction of primary fissures, sandstone densification is enhanced. Specifically, the compressive strength and elastic modulus increase, the macroscopic damage mode is dominated by shear damage, and the fracture micro-morphology is mainly brittle fracture. Stage II (500-600°C) is the transition zone, 500°C is the threshold temperature for compressive strength and modulus of elasticity, and the damage mode changes from shear to cleavage damage, and the sandstone undergoes brittle-ductile transition in this temperature interval. Stage III is the physicochemical deterioration stage. The changes in physical and chemical properties make the sandstone compressive strength and modulus of elasticity continue to decline, the macroscopic damage mode is mainly dominated by cleavage damage, and the fracture microscopic morphology is more toughness fracture. The effect of different heating rates on the mechanical properties of sandstone was further studied, and it was found that the mechanical properties of the rock were more deteriorated under higher heating rates.

Transcranial direct current stimulation (tDCS) over the left Inferior Frontal Gyrus (IFG) was found to improve apraxia of speech (AOS) in post-stroke aphasia, speech fluency in adults who stutter, naming and spelling in primary progressive (PPA). This paper aims to determine whether tDCS over the left IFG coupled with AOS therapy improves speech fluency in patients with PPA more than sham. Eight patients with non-fluent PPA with AOS symptoms received either active or sham tDCS, along with speech therapy for 15 weekday sessions. Speech therapy consisted of repetition of increasing syllable-length words. Evaluations took place before, immediately after, and two months post-intervention. Words were segmented into vowels and consonants and the duration of each vowel and consonant was measured. Segmental duration was significantly shorter after tDCS than sham for both consonants and vowels. tDCS gains generalized to untrained words. The effects of tDCS sustained over two months post-treatment in trained words. Taken together, these results demonstrate that the tDCS over the left IFG facilitates speech production by reducing segmental duration. The results provide preliminary evidence that tDCS can maximize efficacy of speech therapy in non-fluent PPA with AOS.

Metal creep has been a subject of extensive study for more than 110 years, because it affects the useful life of engineering components operating at high temperatures. It is even more so with ever-increasing operating temperatures of propulsion/power-generation systems by the environmental regulations to reduce greenhouse emissions. This review summaries the recent development in creep modeling with regards to creep strain evolution, creep rate, creep ductility, creep life and fracture mode, attempting to provide a comprehensive mechanism-based framework to address all the important creep phenomena and the long-standing issue of long-term creep life prediction with microstructural evolution and environmental effects.

N6-methyladenosine (M6A) modification is the most abundant internal chemical modification on RNA molecules in eukaryotes and can affect mRNA metabolism and regulate RNA transcription, nuclear export, splicing, degradation, translation, ets, and has a significant impact on various aspects of physiology or pathobiology. Radiotherapy is the most common measure of tumor treatment, variety of cellular intrinsic mechanisms affect the responses of cells to ionizing radiation (IR) and effectiveness of cancer radiotherapy. In this review, we summarize and discuss the recent advances about the roles and mechanisms of RNA M6A methylation in cellular responses to radiation-induced DNA damage response, and outcomes of cancer radiotherapy. Insights into the RNA M6A methylation in radiation biology may facilitate the improvement of therapeutic strategies for cancer radiotherapy as well as radioprotection on normal tissues.

The fatigue delamination damage is one of the most important fatigue failure modes of laminated composite structures. However, the developments of the theoretical framework, mathematical/physical model and numerical simulation of fatigue delamination are still challenging issues, and there is a lack of systematic classification. This article reviews the experimental phenomena of the onset and propagation stage of delamination under fatigue loading, and summarizes the commonly used phenomenological models for laminated composite structures. The research methods, general modeling formulas and development prospects of the phenomenological models are presented in detail. In the light of the analysis of finite element model (FEM) for the laminated composite structures, a number of simulation methods of fatigue delamination crack propagation damage model are carefully classified. Furthermore, within the theoretical framework of strength theory model (SM), fracture mechanics model (FM), damage mechanics model (DM) and hybrid model (HM), the complete procedure, application, capability assessment, advantages and limitations of the models are also discussed in detail. Finally, several valuable suggestions are given for the research interests, and challenging technical problems that need to be overcome on the fatigue delamination prediction of laminated composite structures in the future.

The 2022-2023 Chilean summer showed increased temperatures and similar burned area, compared to the 2016-2017 season, where more than 500,000 hectares were compromised, mainly in the rural areas. After a brief review, it is revealed that the effects of forest fires on soil and hydrological properties are barely debated in Chile. Here, we showed a climatological analysis where temperature records in the 2016-2017 season were unusual, as well as another unexpected increase in the summer of 2022-2023, resulting in high-severity fires known as ‘mega-fires’ or “storm-fires”. Mega-fires affect forest plantations and native forests mainly from 33º S (Maule Region) to 39º S (Los Ríos Region) and they are expected to become frequent due to climate change, moving from the north to the south. We present an overview of the influence of wildfires on soil components in the most affected areas (inland, Coastal, and Andes ranges), their hydrological impacts, and potential erosion risk due to high winter precipitation. We propose several management practices that could help to prevent or mitigate these events, including pre-and post-fire interventions, such as afforestation and seeding, selective logging, mulching, erosion barriers, soil preparation, and dam monitoring. We argue that any effective plan in fire-prone and affected areas should include a combination of actions taken at the hillslope scale at integral ecosystem management, whose effectiveness should be monitored and verified regionally at the watershed scale.

This paper aims to provide an overall review of degradation, damage evolution and fatigue models in the literature of various engineering materials, mostly metals, and composites.

As a medical student (Granada University Medical School, Spain), interested in Pediatrics, expended countless hours at the hospital pediatric facilities and got to know many of the children and their medical problems. A particular case, still vivid on my mind, awaken my scientific curiosity. One day, walking and talking with a seven years old child he unexpectedly felt down unconscious with multiple, incontrollable and erratic muscular contractions involving face, body and extremities and salivating. I was overwhelmed thinking it was the child’ last hour. At the time, my knowledge of epilepsy was nil. Following the seizures, the child was up, talking and walking with me as if nothing has happened and without any knowledge of the event. What could have caused the brain motor cortex to suddenly discharge that amount of altered activity causing generalized and erratic muscular contractions remains inexplicable. I migrated to USA, become a Pediatric (Developmental) Pathologist and Director of the Pediatrics Autopsy Service (1962-1999) at the Dartmouth-Hitchcock Medical Center, New Hampshire. I carried out countless postmortem studied of children brains, normal (unaltered) as well as those altered by hemorrhagic, hypoxic-ischemic and/or traumatic damage. With an NIH Fellowship, I spend one year (1967-68) at the Cajal Institute (Madrid, Spain) studying Cajal’ old Golgi preparations and learning about the method. Some of my Golgi studies of children’ brains have been published: The Human Brain. Prenatal Development and Structure, Springer, Heidelberg, Germany, 2012. The present monograph explores the developmental neuropathology of selected perinatal cortical injuries through their acute, subacute and chronic stages. Including: a) how an altered neuronal activity evolves in a damaged cortical region; b) how it moves through the cortex (epileptic auras); and c) how it reaches the motor cortex to be discharged as erratic and incontrollable muscular contractions. Understanding these processes should provide insights into the pathogenesis of epilepsy secondary to perinatal brain damage.

Stunting, during the Covid-19 pandemic, is increasingly becoming a big problem in the world, especially in poor and developing countries. Observational studies have shown that stunting is associated with poor nutrition, especially a plant-based diet, inflammation, caused by infection, enteric dysfunction, an environment with clean water, inadequate sanitation and hygiene (WASH), and endogenous inflammation associated with excess adiposity. This causes nutritional interventions to be often unsuccessful (Kemenkes RI, 2018).The government intervenes to reduce stunting to the target party, which is divided into two categories. The first category is specific nutrition intervention, namely monitoring children under five at the posyandu, giving immunizations, giving vitamin A, giving Supplementary Foods (PMT), and others. The second category is sensitive nutrition interventions, namely the provision of drinking water and proper sanitation, postnatal family planning (KB) services, providing information related to stunting, food social assistance, conditional cash assistance, and others. (KEMEN-PMK, 2018). WHO states, that the impact of stunting can be divided into short-term and long-term impacts. The short-term impacts are; increased incidence of morbidity and mortality; cognitive, motor, and verbal development in children is not optimal; and increased healthcare costs. While the long-term impact; Posture that is not optimal as an adult (shorter than usual); Increased risk of obesity and other diseases; The decline in reproductive health; Less than optimal learning capacity and performance during school years; and Low productivity and work capacity (Kemenkes RI, 2018). The nursing goal is to help people achieve quality, holistic health. Implementation of Modeling and Role Modeling Theory is an option in implementing nursing care for children with stunting. “Modeling” is gaining an understanding of the client’s world from the client’s perspective. That is to build a “model” of the client’s worldview. “Role‑Modeling” is based on the assumption that all humans want to interact with others, they want to carry out selected roles in society. Role-Modeling is using the client’s model of the world to plan interventions that meet his or her perceived needs, grow, develop and heal. Role-Modeling requires that we aim to build trust, promote a positive orientation and a sense of control, affirm strengths and set specific mutual goals.

Student retention is one indicator of accountability in the implementation of educational programs. Achievement of student retention rates indicates the performance of the quality objectives of an institution or college. To get an accurate picture of the factors related to retention, we need to do modeling. The retention variable is the time response variable measured in semester units. One of the statistical analyzes that can be used to analyze response data in time is survival analysis. The selection of an accurate analytical method in modeling will produce valid conclusions and impact making policies that are right and on target. This paper presents alternative modeling of student retention in distance education using survival analysis. The method used is a literature review. This paper also briefly describes distance education, open and distance education, distance education students' characteristics, distance education student retention, and survival models for modeling student retention in distance education.

The objective of this study was to analyze the mental workload using EEG and eye tracking data and classify it using machine learning algorithms. The machine learning model was developed based on the simultaneous recording of eye tracking and EEG measurements during the experimental process. The experiments involved 15 university students, consisting of 7 women and 8 men. Throughout the experiments, the researchers utilized the n-back memory task and the NASA-Task Load Index (TLX) subjective rating scale to assess various levels of mental workload. The findings revealed that as the task difficulty increased, there was an increase in the diameter of both the right and left pupils, the number of fixations, the number and duration of saccades, and the number and duration of blinks. Conversely, variables related to fixation duration decreased. The EEG results indicated that theta power in the prefrontal, frontal, and front central regions increased with task difficulty. Additionally, alpha power increased in the frontal regions but decreased in the temporal, parietal, and occipital regions as the task became more challenging. Furthermore, low beta power significantly decreased in almost all brain regions as the task difficulty increased. In terms of the four-class classification problem, the mental workload level can be predicted with an accuracy rate of 76.59% using 34 selected features. This study has made a significant contribution to the literature by presenting a four-class mental workload estimation model that utilizes different machine learning algorithms.

Pregnancy is a complex biological process which is associated with significant metabolic and hemodynamic changes of the maternal body which allow a normal fetal growth. The inability to adapt to these changes can affect the cardiovascular health. During pregnancy, women may experience mood swings, anxiety and emotional ambivalence. These symptoms can lead to stress and have a negative impact on the mental well-being of expectant mothers. It is crucial to know the aspects that can influence the development of cardiovascular problems among pregnant women in order to manage these cases properly. The current narrative review explores and summarizes the evidence regarding the impact of stress and anxiety on cardiovascular health in pregnant women. Understanding the physiological adaptations that the body undergoes throughout pregnancy is crucial for educating and supporting individuals as well as healthcare professionals. This enables them to recognize any risk factors or pathological conditions that may exacerbate pregnant women's health. In order to reduce maternal morbidity and mortality from cardiovascular disease, a psychological and cardiovascular risk factor screening that is done either before or during pregnancy may be able to identify situations that call for particular medical and/or psychological therapies.

It has been more than three decades since the discovery of multifunctional factors, Non-POU Domain-Containing Octamer-Binding Protein, NonO and Splicing Factor, Proline-and Glutamine-Rich, SFPQ. Some of their functions, including their participations in transcriptional and posttranscriptional regulation as well as their contribution to paraspeckle subnuclear body organization have been well-documented. In this review, we focus on the roles of NonO and SFPQ in cell cycle with particular attention to their participation in nonhomologous end joining (NHEJ) and homologous recombination (HR) of DNA damage responses induced by irradiation. In these contexts, absence or malfunction of either or both lead to genome instability and ultimately to apoptosis, cellular senescence, and tumor development. This review also will shed light on NonO and SFPQ function in DNA/RNA/protein binding and their association with Ku heterodimer of the NHEJ pathway required for repair of DNA double-strand breaks (DSBs).

Methotrexate (MTX) is commonly used in intrathecal chemotherapy for patients with acute lymphocytic leukemia (ALL) to prevent central nervous system (CNS) involvement. However, the use of MTX-based chemotherapy can lead to rare yet severe complications, such as MTX-induced myelopathy. Here we report the case of MTX-induced myelopathy initially misdiagnosed as Guillain-Barre syndrome, leading to a delay in diagnosis and treatment. We present a case of a 39-year-old male with a history of B-cell acute lymphoblastic leukemia (B-ALL) who experienced bilateral foot paresthesia and progressive lower extremity weakness after intrathecal methotrexate (MTX) treatment. Initially suspected as Guillain-Barre syndrome (GBS) due to similar clinical features and nerve conduction studies. The patient received intravenous immunoglobulin (IVIG) treatment, but his condition worsened. A subsequent spine MRI revealed MTX-induced myelopathy. This case highlights the diagnostic challenge posed by the similarity in clinical presentation between MTX-induced myelopathy and GBS. Differentiating between these conditions is critical for appropriate management. Prompt recognition and treatment with folate metabolism compounds may mitigate neurological sequelae.

A new layout optimization method for offshore wind farms is proposed to minimize power deficits due to wake effect. The main engine of the algorithm is a gradient-descent method, in which an analogy is established between power deficit and repulsive electrostatic potential energy. To achieve a maximum dispersion in the initial solution, throughout the optimization process, new turbines are included in the centre of the concession area, and quickly expand outward looking for areas with less potential, in turn pushing the previous ones. When the optimization process ends, to avoid local maximums, it enters into a process of suppression of the set of locations that cause the greatest potential (power deficit). Then, a potential map of the entire area is created and a greedy algorithm includes new turbines to complete the layout with the final number of turbines. To speed up the search process and the creation of the potential map, two simplifications have been validated and added: the spatial distribution of velocities has been converted into its equivalent with point symmetry; and, for turbines affected by multiple wakes, the power deficit has been calculated by using a linear aggregation of powers, instead of the usual linear (or quadratic) aggregation of speeds. Owing to this type of aggregation, the process is accelerated in a factor of 90.

This paper presents an efficient topology optimization approach considering fail-safe requirements in the framework of the solid isotropic material with penalization method. Existing methods either are computationally extremely expensive or do not consider fail-safe requirements in the optimization. The current approach not only obtains the fail-safe optimization structure, but also significantly improves the computational efficiency of fail-safe topology optimization. In this method, the worst-case compliance is considered to minimize as the optimization objective, using the Kreisselmeier–Steinhauser function to approximate the non-differentiable max-operator. The sensitivity is derived by the adjoint method, and a general fail-safe optimality criteria method is developed to update design variables. The damage scenarios greatly reduces in the process of failsafe strategy optimization by employing the density-based filter method. Finally, the effectiveness and computational efficiency of this method for fail-safe optimization is verified by several numerical examples.

Critical infrastructures are those that are essential. For this type of infrastructure, it is necessary to implement analytical methodologies that will allow us to quickly obtain the susceptibility or resilience and possible damage generated in extreme precipitation episodes, through a holistic perspective in which the factors linked to hydrological risk intervene. In particular, urban hydraulic infrastructures are analyzed considering the degree of criticality, defined as the number of interactions on the different activities of the population. For this purpose, a hydrological risk analysis methodology is required. This methodology is focused on an integral approach of the system indicators to be analyzed and linked to the hydrological threat. This work proposes to delimit and analyze those factors that involve risk using an analytical expression. This model will estimate the damage to these infrastructures breaking down the factors involved in the risk equation and analyzing their variability according to the intrinsic characteristics linked to them as well as the interaction with external factors.

Background: The current review was conducted to determine the effectiveness of atenolol on all-cause mortality rate among Asian patients with chronic progressive diseases, mainly diabetes mellitus, primary hypertension, and coronary artery disease. Methods: We searched the COCHRANE, MEDLINE, TRIP, and EMBASE databases for published articles up to 31 March 2023. Studies that compared all-cause mortality rates among Asian patients who were on atenolol or other medications were included. Results: The review included 79603 Asian patients from three cohort studies. Out of the studied patients, 36046 were atenolol users and 43557 were non-atenolol users. The review revealed that atenolol users recorded lower all-cause mortality rates compared to non-users with a significant difference (OR= 0.57, CI= 0.44-0.75, P<0.001). The pooled estimate of the all-cause mortality rate was also lower among atenolol users (7.02%) compared to metoprolol tartrate users (13.15%) with a significant difference (OR= 0.50, CI= 0.47-0.53, p < 0.0001). Although the included studies were categorized as having a low risk of bias for most of the studied domains, significant heterogeneity was recorded across these studies (I2 =88%, P=0.001). Conclusion: This review found that atenolol when compared to a control or metoprolol tartrate, has a significant effect in reducing the all-cause mortality rate among Asian patients with chronic progressive disease.

Managers are often faced with the task of improving the management of the production 17process in order to maintain the sustainability of production efficiency in a highly competitive en-18vironment. The submitted contribution deals with the design of a system that will support them in 19the selection of progressive methods and technologies in order to improve the management of pro-20duction processes. Managers often follow new trends in this issue, but it is not easy to work on the 21knowledge that proven progressive technologies can bring them higher efficiency. The presented system is based on the use of knowledge of existing solutions of manufacturers of automotive components, where companies solve similar problems in production management. The mix of methods and technologies used in the management of production processes brings companies different results in the achieved efficiency. The proposed system for supporting the selection of the production management method and technology is designed from blocks of data collection, benchmarking of the performance of production processes of industrial enterprises, and further from data mining technology to obtain knowledge about the effect on efficiency from already implemented technologies. The last blocks help to examine the differences in the implementation of the same methods and technologies and allow to present the obtained results to the manager in the form of recommendations for choosing a suitable progressive method and technology.