This article is about a framework for determining the degree of realism of any given 1 passenger ride motion discomfort measurement formula. After providing some context and reviewing 2 evidence of deficiency in currently popular ride motion discomfort measurement formulas, the article 3 outlines the research program that needs to be carried out in order to establish such a framework. 4 The research begins with gathering recordings of uncomfortable ride motion episodes encountered in 5 a chosen type of passenger transport service. It then has test subjects compare the episodes via a ride 6 motion simulator and adjust their amplitudes pair wise until they cause equal discomfort. It explains 7 how to take the pair wise amplitude adjustments and determine amplitude adjustments that bring all 8 of the motion episode recordings to a common level of discomfort so that they form a normalized set. 9 Then, the lower the scatter of the scores assigned by any given discomfort measurement formula to 10 the members of that set, the more realistic that formula will be for the chosen service.

In this paper, a new approach to retrieve semantic images based on shape and geometric features of image in conjunction with multi-class support vector machine is proposed. Zernike moment as shape feature is to verify the invariance of objects for silhouette image. In addition, a set of geometrical features is to explore the objects shape using two features of rectangularity and circularity. Then the extracted features are normalized and employed for multi-class support vector machine either for learning or retrieving processes. The retrieving process relies on three main tasks which namely Query Engine, Matching Module and Ontology Manger, respectively. Query Engine is to build the input text or image query using SPARQL language. The matching module extracts the shape and geometric features of image’s objects and employ them to Ontology Manger which in turn inserts them in ontology knowledge base. Benchmark mammals have been conducted to empirically conclude the outcome of proposed approach. Our experiment on text and image retrieval yields efficient results to problematic phenomena than previously reported.

This paper illustrates an experimental activity for the closed-loop position control of an actuator made using shape memory alloy (SMA) wire. A solution with the self-sensing effect was implemented to miniaturize the systems, i.e., without external sensors. A proportional control algorithm was initially used, demonstrating the idea's feasibility: the wire can behave simultaneously as an actuator and sensor. An experimental investigation was subsequently conducted for the optimization of the developed actuator. As for the material, a Flexinol wire, Ni-Ti alloy, with a diameter of 0.150 mm and a length of 200 mm, was used. Preliminarily, a characterization of the SMA wire at constant and variable loads was carried out: the characteristics detected are elongation vs. electric current and elongation vs. electrical resistance. The control system is PC-based with a data acquisition card (DAQ). A drive board has been designed and built to read the wire's electrical resistance and power it by pulse width modulation (PWM). A notable result is that the actuator works with good precision and in dynamic conditions, even when it is called to support a load up to 65% different from that for which the electrical resistance-length correlation has previously been experimentally obtained, on which the control is based. This opens up the possibility of using the actuator in a counteracting configuration with a spring, which makes hardware implementation and control management simple.

This review provides a comprehensive and updated information on chalcones, presenting their sources, methods of identification and properties putting emphasis on applications in agricultural industry. Vast exploitation of synthetic pesticides led to the weed and pest resistance issue, causing economic loss but more importantly health risk due to pesticide overuse. Because one of the aim of the European Green Deal 2030 is the Farm to Fork strategy which targets on reducing by 50% the use of chemical pesticides by 2030, and push towards natural pesticides which are more environmentally friendly and restore natural resources, chalcones and their derivatives seem to fit perfectly. They have herbicidal, fungicidal, bactericidal and antiviral properties. Moreover, chalcones are naturally occurring substances, known for their favorable health effect for years, working on many areas. This review is complementary to the previous ones describing chalcones use in agriculture, updates them but additionally shows methods of detection.

This paper addresses the input coupling problem for the shape memory alloy (SMA) actuated parallel platform with completely unknown nonlinear dynamics. The problem means that in a parallel platform with multiple SMA actuators acting together, the displacement of one of the actuators will simultaneously affect the remaining actuators to change. Due to the coupling problem and nonlinear characteristics of the SMA actuators, it is not easy to control the platform to achieve bi-directional swing with completely unknown nonlinear dynamics. Therefore, a model-free adaptive sliding mode control method is proposed to improve the control accuracy of the complex motion of the platform, avoiding the complex modeling process of the SMA actuated parallel platform. Based on the model-free adaptive control (MFAC) theory, a multiple input and multiple output (MIMO) dynamic linearized mathematical model of the parallel platform is established using the online input and output data of the system. Then, a sliding mode controller is designed based on the dynamic model. A rigorous theoretical analysis proves that the proposed model-free adaptive sliding mode controller is able to stably control the SMA actuated parallel platform, and its tracking error is uniformly ultimately bounded (UUB). Finally, experiments are conducted on the established SMA actuated parallel platform. The results show that the proposed model-free controller is feasible and effective, and can control the system well to achieve the complex motion of bi-directional swing.

In this paper, a new type of self-excited thermomechanical oscillator containing an oscillating shape memory alloy (SMA) filament with two symmetrically arranged spheres is investigated. The self-excitation of the oscillations is due to a heater of constant temperature, which causes periodic contractions of the filament when it approaches it. The contracted filament moves away from the heater a distance sufficient to cool it. Under the action of the weight of the spheres, the cooled filament re-approaches the heater, causing the above processes to repeat periodically. On the basis of experimental studies, approximating functions of the heater's heat field distribution are derived. A dynamic model of the oscillator has been created, in which the minor and major hysteresis in the SMA alloy and the distribution of the heat field around the heater have been taken into account. By numerical solutions of the differential equations, the laws of motion of the spheres are obtained. The displacements of the spheres in two perpendicular directions were measured using an experimental system. The obtained experimental results validate the proposed dynamic model and its assumptions with a high degree of confidence. Conclusions are drawn about the stochastic nature of the oscillations due to the hysteresis properties of the SMA and the temperature variation of the natural frequency of the oscillating system.

In this work, we'll present the power spectra density, auto-correlation function, dentrended fluctuation analysis and multifractal spectrum are performed to the daily light curves of Blazars monitored by Fermi LAT. Some signs of power law are found.

To better understand the shape memory materials and self-healing materials, a new series of liquid-crystalline shape memory polyurethane (LC-SMPU) composites, named SMPU-OOBAm, were successfully prepared by incorporating 4-octyldecyloxybenzoic acid (OOBA) into the PEG-based SMPU. The effect of OOBA on the structure, morphology and properties has been carefully investigated. The results demonstrate that SMPU-OOBAm have liquid crystalline properties, triple-shape memory properties and self-healing properties. The incorporated OOBA promotes the crystallizability of both soft and hard segments of SMPU, and the crystallization rate of the hard segment of SMPU decreases when the OOBA-content increases. Additionally, the SMPU-OOBAm forms a two-phase separated structure (SMPU phase and OOBA phase), and it shows two-step modulus changes upon heating. Therefore, the SMPU-OOBAm shows triple-shape memory behavior, and the shape recovery ratio decreases with an increase in the OOBA content. Finally, SMPU-OOBAm shows self-healing properties. The new mechanism can be ascribed to the heating-induced “bleeding” of OOBA in the liquid crystalline state and the subsequent re-crystallization upon cooling. This successful combination of liquid crystalline properties, triple-shape memory properties and self-healing properties make the SMPU-OOBAm composites with many promise applications in smart optical devices, smart electronic devices and smart sensors.

Hierarchical tungsten oxide assemblies such as spindle-like, flowers with sharp petals, nanowires and regular hexagonal structures are successfully synthesized via a solvothermal reduction method by simply adjusting the reaction conditions. On the basis of the experimental results, it is determined that the reaction time significantly influences the phase transition, microstructure and photocatalytic activity of the prepared samples. The possible mechanisms for the phase transition and morphology evolution process have been systematically proposed. Moreover, the as-prepared products exhibit significant morphology-depended photocatalytic activity. The flower-like W18O49 prepared at 6 h possesses large specific surface area (150.1 m²g^-1), improved separation efficiency of electron-hole pairs and decreased electron-transfer resistance by the photoelectrochemical measurements. As a result, the flower-like W₁₈O₄₉ prepared at 6 h exhibits the highest photocatalytic activity for the degradation of Methyl orange aqueous solution. The radical trap experiments showed that the degradation of MO was driven mainly by the participation of h⁺ and •O²⁻ radicals.

(1) Background: The aim of this study was to investigate the circadian rhythms of tongue features according to the effects of physiological phases over a 24-hour period. (2) Methods: Fifteen healthy participants aged 20 to 69 years were recruited. The participants did not have current chronic diseases or past diseases and had to meet the inclusion and exclusion criteria. The participants stayed at the Gil Hospital for a duration of 2 nights and 3 days. On the first day, at 18:00, they consumed their allocated portions of food and water and then completed a questionnaire. At approximately 21:00, their tongue images were acquired using a computerized tongue image acquisition system, following which they slept for 8 hours, commencing at 23:00. Measurements were taken from 07:00 through 21:00 on the second day, and the final acquisition was taken at 07:00 on the following morning, resulting in a total of eight images. The circadian rhythm was authenticated and quantified utilizing the single cosinor analysis, a technique for periodic regression analysis for fitting a 24-hour cosine curve. (3) Results: Cosinor analysis revealed that all tongue features were significantly related to circadian rhythm. (4) Conclusions: The results of this study may be important for considering the time of day at which the tongue is observed and tongue status is evaluated.

In this paper, we present an experimental procedure to enhance the dimensional accuracy of fabrication via Stereolithography (SLA) of features at the micro-scale. Using a custom benchmark part, deviations in feature dimensions (micro-pores diameters and depths) were detected and measured by confocal microscopy. Samples characterization and experimental observations allowed the identification of inaccuracy sources, mainly due to the laser beam scanning strategy and to the incomplete removal of uncured liquid resin in post-processing (i.e., IPA washing). As a technology baseline, the measured dimensional errors on pores diameters were up to -46%. A compensation method of the nominal laser spot diameter (85m) was defined and implemented resulting in relevant improvements in dimensional accuracy of 9.8% and 11.3% on full-open pores diameter and depth, respectively. Further investigation was performed on a customized test part to adjust the calibration procedure. The measurements revealed that the estimate of the laser beam spot size is 968m. Measurements on micro-pores having different sizes revealed that a constant compensation parameter (i.e., C=85, 96, 120m) is not fully effective at the micro-scale, where average errors still remain at -24%, -18.8%, and -16% for compensations equals to 85, 96 and 120m, respectively. A further experimental campaign allowed the identification of an effective nonlinear compensation law where the compensation parameter depends on the micro-feature size C= f (D). Results show a sharp improvement in dimensional accuracy on micro-pore fabrication, with errors consistently below +8.2%. The proposed compensation method can be extended for the fabrication of any micro-features without restrictions on the specific technology implementation. Concerning the inaccuracy introduced by the laser path, experimentation with a customized part allowed the optimization of the part orientation to reduce the errors and their differences along the X and Y machine axes.

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

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.

GaN ultraviolet photodetector with metal-semiconductor-metal structure is achieved by growing on a periodic trapezoid column-shape patterned sapphire substrate using metalorganic chemical vapor deposition. Under 5-V reverse bias, the photodetector fabricated on such patterned sapphire substrate exhibits a lower dark current, a higher photocurrent, and a 476 % enhancement in the maximum responsivity as compare with those of the photodetector fabricated on conventional flat sapphire substrate. It is also found that the much larger UV-to-visible rejection ratio and the fact that responsivity drops in a smaller cut-off region are observed from photodetector fabricated by using a periodic trapezoid column-shape patterned sapphire substrate. These phenomena may all be attributed to the reduction of threading dislocation density and the improved quality of GaN film, as well as the internal reflection and/or scattering effect on the interface between GaN film and the periodic trapezoid column-shape pattern of the substrate.

The features of a class of cubic curves with a shape factor are analyzed by means of the theory of envelope and topological mapping. The effects of the shape factor on the cubic curves are made clear. Necessary and sufficient conditions are derived for the curve to have one or two inflection points, a loop or a cusp, or to be locally or globally convex. Those conditions are completely characterized by the relative position of the edge vectors of the control polygon and the shape factor. The results are summarized in a shape diagram, which is useful when the cubic parametric curves are used for geometric modeling. Furthermore we discussed the influences of the shape factor on the shape diagram and the ability for adjusting the shape of the curve.

Molecular analysis of growing teratoma syndrome has not been extensively studied. Here, we report a 14-year-old boy with a growing mass during treatment for a mixed germ cell tumor of the pineal region. Tumor markers were negative; thus, growing teratoma syndrome was suspected. A radical resection via the occipital transtentorial approach was performed, and histopathological examination revealed a teratoma with malignant features. Methylation classifier analysis confirmed the diagnosis of teratoma, and DMRT1 loss and 12p gain were identified by copy number variation analysis, potentially elucidating the cause of growth and malignant transformation of the teratoma. The patient remains in remission after intense chemoradiation treatment as a high-risk germ cell tumor.

Membrane potential generation and maintenance are fundamental parts of membrane theory. However, since its inception, theories and assumptions have remained simple, whereas the amount of knowledge available has only grown. Unfortunately, this simplification and reductionism lead to its loss of validity on both microscopic and macroscopic scales. This article shows that assumptions made outside the context of factual reality lead to contradictions that cannot be compared with science. Membrane theory must be thoroughly revised, taking into account all forgotten and unanswered assumptions and questions to conform to facts and science to maintain credibility.

This study was to identify clinical cancer biomarkers for recurrent gastric cancer survivors using artificial intel-ligence. From a hospital-based cancer registry database in Taiwan, the datasets of the incidence of recurrence and clinical risk features were included in 2476 gastric cancer survivors. We benchmarked Random forest with MLP, C4.5, AdaBoost, and Bagging algorithms on metrics and leveraged synthetic minority oversampling technique (SMOTE) for imbalanced dataset issues, cost-sensitive learning for risk assessment, and SHapley Additive ex-Planations (SHAP) for feature importance analysis in this study. Our proposed Random forest outperformed the other models with an accuracy of 87.9%, a recall rate of 90.5%, an accuracy rate of 86% and F1 of 88.2% on re-current category by a 10-fold cross-validation in a balanced dataset. We identified clinical features of recurrent gastric cancer, which are the top five features, stage, number of regional lymph node involvement, Helicobacter pylori, BMI(body mass index), and gender, which significantly affect the prediction model's output, are worth paying attention to in the following causal effect analysis. Using an artificial intelligence model, the risk factors for recurrent gastric cancer could be identified and cost-effectively ranked according to their feature importance. In addition, they should be crucial clinical features to provide physicians with the knowledge to screen high-risk patients in gastric cancer survivors as well.

Forest ecosystems make a greater contribution to carbon (C) stocks than any other terrestrial ecosystem. To understand the role of regional forest ecosystems in global climate change and carbon exchange, forest C stock and its spatial distribution within the small (2,300 km2) Liuxihe River basin were analyzed to determine the different contributors to the C stock. Forest C stocks were quantified by measuring the biomass of trees, understory vegetation, litter and roots, as well as soil organic C, using data from field samples and laboratory experiments. The results showed that forests stored 38.04 Tg C in the entire basin, with secondary and planted forests accounting for 89.82% and 10.18%, respectively, of the stored C. Five types of forests, a subtropical evergreen broad-leaved forest, a subtropical coniferous and broad-leaved mixed forest, a subtropical coniferous forest, a timber forest, and a non-wood forest, stored 257.55 ± 15.01, 218.92 ± 9.59, 195.24 ± 18.29, 177.42 ± 17.55, and 117.86 ± 6.04 Mg C ha−1, respectively. In the forest ecosystem C stocks of the basin, soils averagely contribute about 73.78%, not including root underground biomass. It provides a comprehensive method for forest ecosystem carbon investigation and forest management in small basin scale.

Membracis mexicana (Hemiptera: Membracidae) is distributed in four biogeographic provinces of Mexico, field observations indicate that there are different forms of this species but so far it has not been recorded how the different forms are and how they are distributed. The aim of this study was to quantify the phenotypic and genetic variation of M. mexicana and determine if the config-uration of biogeographic provinces impacts the distribution of this variation. To do this we ana-lyzed 307 photographs using 19 landmarks and geometric morphometrics to quantify the pheno-typic variation in helmets. We sequenced five molecular markers for 205 individuals to describe the phylogeographic pattern. As a result, we identified three morphological configurations of the helmet of M. mexicana and two genetic lineages. The morphotypes are 1) large and wide helmet with small dorsal spots, 2) small and narrow helmet with large dorsal spots, and 3) small and narrow helmet with small spots. Genetic lineages are distributed at southeast and western of Mexico. The western lineage corresponds to two helmet morphotypes (1 and 2) and the southeast lineage to morphotype 3. We found that the larger helmets correspond the western lineage and are distributed in Transmexican Volcanic Belt and Pacific lowlands provinces whereas the smallest helmets correspond to the southeast lineage and are present in Veracruzan and Yucatan Peninsula provinces.

The UNUSUAL FLORAL ORGANS (UFO) gene is an essential regulatory factor of class B genes and plays a vital role in the process of inflorescence primordial and flower primordial development. We investigated the role of UFO genes in soybean to better understand the development of floral organs through gene cloning, expression analysis, and gene knockout. In situ hybridization demonstrated predominant expression of the GmUFO1 gene in the flower primordium. Phenotypic observation of GmUFO1 knockout mutant lines (Gmufo1) showed obvious alteration in floral organ number and shape, and mosaic organ formation. In contrast, GmUFO2 knockout mutant lines (Gmufo2) showed no obvious difference in floral organs. However, the GmUFO1 and GmUFO2 double knockout lines (Gmufo1ufo2) showed a more severe mutant phenotype than the Gmufo1 lines. Gene expression analysis also showed differences in the expression of major ABC function genes in the knockout lines. Based on the phenotypic and expression analysis, our results suggest the major role of GmUFO1 in the regulation of flower organ formation in soybean and that GmUFO2 does not have any direct effect, but it might have an interaction role with GmUFO1 in the regulation of flower development. In conclusion, the present study identified UFO genes in soybean and improved our understanding of floral development that could be useful for flower design in hybrid soybean breeding.

The common dandelion uses a bundle of drag-enhancing bristles (the pappus) that enables seed dispersal over formidable distances; however, the scaling laws of aerodynamic drag underpinning pappus-mediated flight remains unresolved. In this paper, we study the aerodynamic shape of dandelion, derive the scaling law of resistance, determine the Vogel exponent. In particular, we find that the total drag coefficient is proportional to the -2/3 power of the dandelion pappus Reynolds number, and obtain the terminal velocity of the dandelion seed under gravitation field.

In large-capacity energy storage systems, instructions are decomposed typically using an equalized power distribution strategy, where clusters/modules operate at the same power and durations. When dispatching shifts from stable single conditions to intricate coupled conditions, this distribution strategy inevitably results in increased inconsistency and hastened system aging. This paper presents a novel differentiated power distribution strategy comprising three control variables: the rotation status, the operating boundaries for both Depth of Discharge (DOD) and C-rates (C) within a control period. The proposed strategy integrates an aging cost prediction model developed to express the mapping relationship between these control variables and aging costs. Additionally, it incorporates the multi-colony particle swarm optimization (Mc-PSO) algorithm into the optimization model to minimize aging costs. The aging cost prediction model consists of three functions: predicting health features (HFs) based on the cumulative charge/discharge throughput quantity and operating boundaries, characterizing HFs as comprehensive scores, and calculating aging costs using both comprehensive scores and residual equipment value. Further, we elaborated on the engineering application process for the proposed control strategy. In the simulation scenarios, this strategy prolonged the service life by 14.62%, reduced the overall aging cost by 6.61%, and improved module consistency by 21.98%, compared with the traditional equalized distribution strategy. In summary, the proposed strategy proves effective in elongating service life, reducing overall aging costs, and increasing the benefit of energy storage systems in particular application scenarios.

The shape memory alloys belong to the smart materials thanks to their thermomechanical proprieties' reply to thermal or to mechanical loading. These materials can change shape, stiffness, displacement, natural frequency, and many mechanical characteristics in response to stress or to heat such as conduction, convection or radiation. However, heating by convection or conduction are the most useful and studied methods unlike radiation. Therefore, this paper aims to study the radiation effect on the shape memory alloy behavior

In this paper, we present the idea of Self Supervised learning on the Shape Completion and Classification of point clouds. Most 3D shape completion pipelines utilize autoencoders to extract features from point clouds used in downstream tasks such as Classification, Segmentation, Detection, and other related applications. Our idea is to add Contrastive Learning into Auto-Encoders to learn both global and local feature representations of point clouds. We use a combination of Triplet Loss and Chamfer distance to learn global and local feature representations. To evaluate the performance of embeddings for Classification, we utilize the PointNet classifier. We also extend the number of classes to evaluate our model from 4 to 10 to show the generalization ability of learned features. Based on our results, embedding generated from the Contrastive autoencoder enhances Shape Completion and Classification performance from 84.2% to 84.9% of point clouds achieving the state-of-the-art results with 10 classes.

Beetles are by far one of the most successful and diverse insect species. A part of this success is attributed to their elytra which provide various functions such as protection to their bodies from mechanical forces and the harmful environmental factors. In this study, Stag beetle (Lucanus cervus) elytra were first examined for their overall flexural properties and were observed to have a localized shape retaining snap-through mechanism which could play a crucial role in energy absorption, e.g. during battles and falls from heights. The snap-through mechanism was validated using theoretical calculations and also finite element simulations. Elytra were also characterized to examine their puncture and wear resistance. Our results show that elytra resisted puncture up to a force of 1.8±0.4 N and have puncture resistance compared to that of commercially available puncture resistant gloves. The measured values of modulus and hardness of elytra exocuticle were 10.3±0.8 GPa and 0.7±0.1 GPa. Using the hardness to modulus ratio as an indicator of wear resistance, the estimated value was observed to be in the range of wear resistant biological materials. Thus, our study demonstrates different mechanical properties of the stag beetle elytra which can be explored to design shape retaining bio-inspired composites with enhanced puncture and wear resistance.

Metabolic Associated Steatohepatitis Liver Disease (MASLD) currently impacts a quarter of the global population and its prevalence is expected to increase in the future. As a result, the incidence of hepatocellular carcinoma associated with MASLD is also on the rise. Notably, hepatocellular carcinoma in this group does not always develop alongside liver cirrhosis, often leading to a more advanced stage at diagnosis. The challenge lies in accurately identifying patients who are at a higher risk of hepatocellular carcinoma to tailor screening processes effectively. Additionally, several therapeutic approaches are being explored to prevent hepatocellular carcinoma, although there are yet no universally accepted guidelines

GM2 gangliosidoses are a group of pathologies characterized by GM2 ganglioside accumulation into the lysosome due to mutations on the genes encoding for the β-hexosaminidases subunits or the GM2 activator protein. Three GM2 gangliosidoses have been described: Tay-Sachs disease, Sandhoff disease, and the AB variant. Central nervous system dysfunction is the main characteristic of GM2 gangliosidoses patients that include neurodevelopment alterations, neuroinflammation, and neuronal apoptosis. Currently, there is not approved therapy for GM2 gangliosidoses, but different therapeutic strategies have been studied including hematopoietic stem cell transplantation, enzyme replacement therapy, substrate reduction therapy, pharmacological chaperones, and gene therapy. The blood-brain barrier represents a challenge for the development of therapeutic agents for these disorders. In this sense, alternative routes of administration (e.g. intrathecal or intracerebroventricular) have been evaluated, as well as the design of fusion peptides that allow the protein transport from the brain capillaries to the central nervous system. In this review, we outline the current knowledge about clinical and physiopathological findings of GM2 gangliosidoses, as well as the ongoing proposals to overcome some limitations of the traditional alternatives by using novel strategies such as molecular Trojan horses or advanced tools of genome editing.

The QCD Lagrangian is based on quark and gluonic fields -- not squarks nor gluinos. However, one can show that its hadronic eigensolutions conform to a representation of superconformal algebra, reflecting the underlying conformal symmetry of chiral QCD. The eigensolutions of superconformal algebra provide a unified Regge spectroscopy of meson, baryon, and tetraquarks of the same parity and twist as equal-mass members of the same 4-plet representation with a universal Regge slope. The predictions from light-front holography and superconformal algebra can also be extended to mesons, baryons, and tetraquarks with strange, charm and bottom quarks. % The pion $q \bar q$ eigenstate has zero mass for $m_q=0.$ % % A key tool is the remarkable observation of de Alfaro, Fubini, and Furlan (dAFF) which shows how a mass scale can appear in the Hamiltonian and the equations of motion while retaining the conformal symmetry of the action. When one applies the dAFF procedure to chiral QCD, a mass scale $\kappa$ appears which determines universal Regge slopes, hadron masses in the absence of the Higgs coupling. One also predicts the form of the nonperturbative QCD running coupling: $\alpha_s(Q^2) \propto e^{-{Q^2/4 \kappa^2}}$, in agreement with the effective charge determined from measurements of the Bjorken sum rule. One also obtains viable predictions for spacelike and timelike hadronic form factors, structure functions, distribution amplitudes, and transverse momentum distributions. % The combination of conformal symmetry, light-front dynamics, its holographic mapping to AdS$_5$ space, and the dAFF procedure thus provide new insights, not only into the physics underlying color confinement, but also the nonperturbative QCD coupling and the QCD mass scale.

Hox gene collinearity (HGC) is a multiscalar property of many animal phyla particularly important in embryogenesis. It relates entities and events occurring in Hox clusters inside the chromosome DNA and in embryonic tissues. These two entities differ in linear size by more than four orders of magnitude. HGC is observed as spatial collinearity (SC) where the Hox genes are located in the order (Hox1, Hox2, Hox3 …) along the 3’ to 5’ direction of DNA in the genome and a corresponding sequence of ontogenetic units (E1, E2, E3, …) located along the Anterior – Posterior axis of the embyo. Expression of Hox1 occurs in E1. Hox2 in E2, Hox3 in E3… Besides SC, a temporal collinearity (TC) has been also observed in many vertebrates. According to TC first is Hox1 expressed in E1, later is Hox2 expressed in E2, followed by Hox3 in E3,… Lately doubt has been raised whether TC really exists. A biophysical model (BM) was formulated and tested during the last twenty years. According to BM, physical forces are created which pull the Hox genes one after the other driving them to a transcription factory domain where they are transcribed. The existing experiments support this BM description. Symmetry is a physical-mathematical property of Matter that was explored in depth by Noether who formulated a ground-breaking theory that applies to all sizes of Matter. This theory applied to Biology can explain the origin of HGC as applied not only to animals developing along the A/P axis but also to animals with circular symmetry.

The article describes a classification solution for amber stones. The problem of classifying amber is known for a long time among jewelers and artisans of amber art. Existing solutions can classify amber pieces according to color, but a need to classify by shape and texture is not satisfied up to now. The proposed solution is capable of classifying the gemstones according to a shape. Amber can be considered as a specific object since the form is difficult to define unambiguously. Data for amber experiments was gathered from amber art craftsmen. In the proposed solution amber form can be classified into 10 different classes (7 classes chosen during the experiment).

Paraffin wax stores energy in the form of latent heat at a nearly constant temperature during melting and releases this energy during solidification. This effect is used in industrial energy storage. An unusual change in the shape of a melted droplet of paraffin wax placed on a relatively cold glass plate is studied. As the droplet solidifies, its upper surface becomes nearly flat and a dimple is formed in the center of this surface, making the droplet look like a fruit (pumpkins are more commonly shaped like this, but the authors prefer apples). A series of experiments, as well as physical and numerical modeling of the droplet's thermal state, taking into account the formation of a mushy zone between liquidus and solidus, made it possible to understand the role of gravity and gradual increase in viscosity and density of paraffin wax on changing the droplet shape and, in particular, to clarify the mechanism of formation of the dimple on its upper.

An airfoil is the geometric shape of a cross-section of a wing. When it is exposed to the flow of a fluid over its surface, it generates both lift and drag forces through a pressure distribution. Designing an efficient airfoil for aerodynamic applications is a complex challenge in the field of aerodynamic engineering, with the aim of achieving an optimal lift-to-drag ratio. The success of airfoil design hinges on the effective reduction of drag force under specific conditions, while simultaneously attaining the desired lift without encountering significant flow separation, such as boundary layer shedding. Furthermore, the design must adhere to lifting force limitations and meet structural requirements. In the field of airfoil design and optimization, literature review articles provide a valuable resource by offering an overview of the latest advancements in specific areas. These areas encompass a wide range of topics including the design and testing of adaptive geometry wings, such as morphing wing technology as well as the application of shape optimization techniques to enhance airfoil performance. Our goal is to provide researchers with an in-depth review of relevant articles published to date, examining the applied approaches utilized to tackle the complex problem of airfoil design and explore the methodology employed to address it, from a broad perspective on the topic.

Parametric models have been widely used in pertinent literature for reconstructing, modifying and representing a wide range of airfoil and/or hydrofoil profile geometries. Design spaces corresponding to these models can be exploited for modeling and profile-shape optimization under various performance criteria. Accuracy requirements, along with the need for modeling local features, often lead to high-dimensional design spaces that hinder the process of shape optimization and design through analysis. In this work, we propose a shape-informed dimensional reduction approach that attempts to tackle this deficiency by producing low-dimensional latent design spaces that can be efficiently used in shape representation and optimization. Furthermore, geometric moments are introduced in an attempt to cost-effectively capture analysis-relevant information that is generally expensive to produce. Specifically, geometric integral properties, although intrinsic features of the shape, are quite commonly related to performance indicators employed in performance optimization and therefore provide a cost-effective physics-informed component in the description of the design in the latent space. To this end, we employ the generalized Karhunen-Loeve expansion to produce a shape- and physics-informed subspace retaining the highest possible geometric variance and robustness, that is, lack of invalid designs. At the same time, a series of shape discretizations, encoding the foil's shape profile, are examined with regards to their effect on the resulting latent space's quality and efficiency. Our results demonstrate a significant reduction in the dimensionality of the original design space while maintaining a high representational capacity and a large percentage of valid geometries that facilitate fast convergence to optimal solutions in performance-based optimization.

Shape of supracondylar fracture of the humeral of pediatric patients is analysed with Procrustes method. XR–images of fractures are considered both in anterio-posterior (AP) view (66 images) and in a lateral (L) view (24 images). Applying Procrustes method for both views mean images are constructed and compared. Variability of shapes is quantified with a shape principal component analysis. Non-parametric tests (permutational and bootstrap) do not indicate statistical difference between Procrustes mean shapes in anterio-posterior and lateral projections. It is shown, however, that AP and L shapes of humeral fractures differ in their variability quantified by shape principal components.

This paper reports a rational design of temperature-responsive nanofiber meshes with shape-memory effect. The meshes were fabricated by electrospinning a poly(ε-caprolactone) (PCL)-based polyurethane with different contents of soft and hard segments. The effects of PCL diol/hexamethylene diisocyanate (HDI)/1,4-butanediol (BD) molar ratio in terms of the contents of soft and hard segments on the shape-memory properties were investigated. Although the mechanical property improved with increasing hard segment ratio, optimal shape-memory properties were obtained with a PCL/HDI/BD molar ratio of 1:4:3. At a microscopic level, the original nanofibrous structure was easily deformed into a temporary shape, and recovered its original structure when the sample was reheated. A higher recovery rate (>89%) was achieved even when the mesh was deformed up to 400%. Finally, the nanofiber meshes were used to control the alignment of human mesenchymal stem cells (hMSCs). The hMSCs aligned well along the fiber orientation. The proposed nanofibrous meshes with the shape-memory effect have the potential to serve as in vitro platforms for the investigation of cell functions as well as implantable scaffolds for wound-healing applications.

In particle physics, spin is an intrinsic form of angular momentum carried by the elementary particles. It is believed that spin is a solely quantum-mechanical phenomenon, which does not have a counterpart in classical mechanics. We have derived the simplest equations of spin and magnetic moment of a charged elementary particle. These formulas enable us to understand the physical nature of the spin and the structure of elementary particles. It is shown that the electron and muon are in the shape of a thin ring with a circular cross section (torus), and the proton is in the form of a ring with a non-circular cross section (toroid). The connection between the photon’s spin of and the propagation of electromagnetic waves is considered.

Noctuidae is known to have high species diversity, although the genomic diversity of Noctuidae species have not been studied extensively. Investigation of transposable elements (TEs) in this family can improve our understanding on the genomic diversity of Noctuidae. In this study, we annotated and characterized genome-wide TEs in ten noctuid species belonging to seven genera. With multiple annotation pipelines, we constructed a consensus sequence library containing 1,038 –2,826 TE consensus. The genome content of TEs showed high variation in the ten Noctuidae genomes, ranging from 11.3% to 45.0%. The relatedness analysis indicated that the TE content, especially the content of LINEs and DNA transposons, are positively correlated with the genome size (r=0.86, p-value=0.001). We identified SINE/B2 as a lineage-specific subfamily in Trichoplusia ni, a species-specific expansion of LTR/Gypsy subfamily in Spodoptera exigua, and a recent expansion of SINE/5S subfamily in Busseola fusca. We further revealed that of the four TE classes, only LINEs showed phylogenetic signal with high confidence. We also examined how the expansion of TEs contributed to the evolution of noctuid genomes. Moreover, we identified a total of 56 horizontal transfer TE (HTT) events among the ten noctuid species and at least three HTT events between the nine Noctuidae species and 11 non-noctuid arthropods. One of HTT events caused by a Gypsy transposon might have caused the recent expansion of Gypsy subfamily in the S.exigua genome. By determining the TE content, dynamics, and HTT events in the Noctuidae genomes, our study emphasized that TE activities and HTT events had substantial impacts on the Noctuidae genome evolution.

The study of a flexible body immersed in a flowing medium is one of best way to find its aerodynamic shape. This Letter revisited the problem first studied by Alben, Shelley and Zhang (Nature 420, 479-481, 2002). The aerodynamic shape of the fibre is found by simpler approach and universal drag scaling laws of the flexible fibre in flowing medium are proposed by using dimensional analysis. The Alben scaling laws is being generalized and confirmed to be universal. Our study show that the Alben number is a measurement of maximum curvature of the fibre forced by dynamic pressure. A complete Maple code is provided for finding aerodynamic shape of the fibre in the flowing medium.

There is an accelerating requirement for ocean sensing where autonomous vehicles can play an essential role in assisting engineers, researchers, and scientists with environmental monitoring and collecting oceanographic data. This paper is performed to develop a rigid sail for the autonomous sailing drone. Our study aims to numerically analyze the aerodynamic characteristics of curvy twin sail and compare it with wing sail. Because racing regulations limit the sail shape, only the two-dimensional geometry was open for an optimization. Therefore, this study’s first objective was to identify the aerodynamic performance of such curvy twin sails. Simultaneously, a secondary objective was to estimate the effect of the sail’s spacing and shapes. A viscous Navier-Stokes flow solver is used for the numerical aerodynamic analysis. The 2D aerodynamic investigation is a preliminary evaluation. The results have shown that the curvy twin sail designs have improved lift, drag, and driving force coefficient compared to the wing sails. The spacing between the port and starboard sails of curvy twin sail is an important parameter. The spacing is 0.035L, 0.07L, and 0.14L shows the lift coefficient reduction because of dramatically stall effect, while flow separation is improved with spacing is 0.21L, 0.28L, and 0.35L. Significantly, the spacing 0.28L shows the maximum high pressure at the lower area and the small low pressure area at leading edges, so the highest lift is generated.

In this study, seventeen faceted gem-quality chrysoberyls exhibiting an attractive canary yellow color were investigated by a series of gemological, spectral, and chemical methods. Microscopic observation reveals distinct growth lines and inclusions, including CO2 fluids, carbon, and mineral crystals such as calcite, quartz, sillimanite, and mica, identified by the Raman spectrum. The FTIR spectrum exhibits the 2405 and 2160 cm-1 feature peaks and a 3223 cm-1 peak in all samples, which can be accompanied by the 3112 cm-1 shoulder, 3301, and 3412 and 3432 cm-1 peaks. The UV-Vis spectrum shows a Fe-related peak at 440 nm, along with the 650–660 nm band and the absorption band in the blue zone of the visible light. The chemical results of EDXRF reveal a V-poor, Cr-poor, and Fe-rich feature. The spectral and chemical results could contribute to explaining the origin of the canary yellow color, which originates from the abundant amount of Fe with very little influence from Cr or V.

Machine learning methods have been a standard approach to select features that are associated with an outcome and build a prediction model when the number of candidate features is large. LASSO has been one of the most popular approaches to this end. LASSO approach selects features with large regression estimates, rather than based on statistical significance, associating the outcome, by imposing L1-norm penalty to overcome the high dimensionality of the candidate features. As a result, LASSO may select insignificant features while possibly missing significant ones. Furthermore, from our experience, LASSO has been found to select too many features. By selecting features that are not associated with the outcome, we may have to spend more cost to collect and manage them in the future use of a fitted prediction model. Using the combination of L1- and L2-norm penalties, elastic net (EN) tends to select more features than LASSO. The overly selected features that are not associated with the outcome act like white noise, so that the fitted prediction model loses the prediction accuracy. In this paper, we propose to use the standard regression methods (without any penalizing approach) with stepwise variable selection procedure to overcome these issues. Unlike LASSO and EN, this method selects features based on statistical significance. Through extensive simulations, we show that this maximum likelihood estimation based method selects very small number of features while maintaining a high prediction power, while LASSO and EN make a large number of false selections to result in loss of prediction accuracy. Contrary to LASSO and EN, the regression methods combined with a stepwise variable selection method is a standard statistical method, so that any biostatistician can use it to analyze high dimensional data even without advanced bioinformatics knowledge.

Nowadays, speed up development and use of digital devices such as smartphones have put people at risk of internet crimes. The evidence of present crimes in a computer file can be easily unreachable by changing the prefix of a file or other algorithms. In more complex cases, either file divided into different parts or the parts of a file that has information about the file type are deleted, where the file fragment recognition issue is discussed. The known files are divided into different fragments, and different classification algorithms to solve the problems of file fragment recognition. A confusion matrix measures the accuracy of type recognition. In the present study, first, the file is divided into different fragments. Then, the file fragment features, which are obtained from Binary Frequency Distribution (BFD), are reduced by 2 feature reduction algorithms; Sequential Forward Selection algorithm (SFS) as well as Sequential Floating Forward Selection algorithm (SFFS) to delete sparse features that result in increased accuracy and speed. Finally, the reduced features are given to 3 classifier algorithms, Multilayer Perceptron (MLP), Support Vector Machines (SVM), and K-Nearest Neighbor (KNN) for classification and comparison of the results. In this paper, we proposed the algorithm of file type recognition that can recognize 6 types of useful files ( pdf, txt, jpg, doc, html, exe), which may distinguish a type of file fragments with higher accuracy than the similar works done.

This observational study was ordered by the Medical Practitioners’ Chamber in Warsaw. The objective of the study was to evaluate the health status of physicians in relation to their occupational duties. Professional burnout was considered relative to different features of personality. This study was initially carried out from 2005–2008, but further analysis of burnout and personality was carried out from 2017–2018. The research tools were anonymous, validated questionnaires. The sample size was based on the size of the population— the registry of the Regional Chamber of Medical Practitioners— and literature on burnout prevalence. The respondents’ work places were randomly selected from the Mazovian District register. The test on burnout was completed by 378 respondents, while 62 subjects completed a personality test. Results showed that burnout syndrome was an occupational problem for healthcare workers. Professional burnout affected as many as 42% of respondents(n = 158). It affected two age groups in particular: physicians up to 31 years old and individuals aged 41-50. Moreover, neuroticism was found to be significantly related to burnout syndrome. In conclusion, burnout syndrome is common among medical practitioners, and neuroticism may be correlated with burnout syndrome.

We present a brief history of the construction of models of the universe, followed by calculations of quantitative characteristics of basic geometric and kinematic properties of the Standard Cosmological Model ($\Lambda$CDM). Using the Friedmann equations of uniform space, we derive equations characterizing a $\Lambda$CDM model that describes a universe corresponding to current observational data. The equations take into account the effects of radiation and ultra-relativistic neutrinos. It is shown that the universe at very early and late stages can be described to sufficient accuracy by simple formulas. Certain important moments of cosmic evolution are determined: the times when densities of the gravitational components of the universe become equal, when they contribute equally to the gravitational force, when the accelerating expansion of space begins, and several others. The dependences of different distances on redshift and the scale factor of space are derived. The distance to the sphere that expands with the speed of light (the Hubble distance), and its current and future acceleration, are found. Concepts of a horizon, second inflation, and second horizon are discussed. We consider the remote future of the universe and the opportunity, in principle, of connection with extraterrestrial civilizations.

Background: Few studies have assessed built environment correlates of active commuting in low-and-middle-income countries, but the different context could yield distinct findings. This cross-sectional study investigated associations between home neighbourhood environment characteristics and active commuting in Chennai, India. Methods: Adults (N = 370, 47.2% female, mean age = 37.9 years) were recruited from 155 wards in the metropolitan area of Chennai in southern India between January and June 2015. Participants self-reported their usual mode of commute to work, with responses recoded into three categories: (1) multi-modal or active commuting (walking and bicycling); (2) public transit; and (3) private transport. Environmental attributes around participants’ homes were assessed using the Neighborhood Environment Walkability Scale-India (NEWS-India). Associations between environmental characteristics and likelihood of active commuting and public transit use were modelled using logistic regression with private transport (driving alone or carpool) as the reference category, adjusting for age, gender, and household car ownership. Results: Consistent with other international studies, participants living in neighbourhoods with a mix of land-uses and a transit stop within a 10-minute walk from home were more likely to use active commuting (both p < 0.01). Land-use mix was significantly associated with the use of public transit compared to private transport (aOR = 5.2, p = 0.002). Contrary to findings in high-income countries, the odds of active commuting were reduced with improved safety from crime (aOR = 0.2, p = 0.003), aesthetics (aOR = 0.2, p = 0.05) and street connectivity (aOR = 0.2, p = 0.003). Conclusions: Different environmental attributes were associated with active commuting, suggesting that these relationships are complex and may distinctly differ from those in high-income countries. Unexpected inverse associations of perceived safety from crime and aesthetics with active commuting emphasize the need for high quality epidemiologic studies with greater context-specificity in the study of physical activity in LMICs. Findings have public health implications for India and suggest that caution should be taken when translating evidence across countries.

Quantum logic is a well-structured theory, which has recently received some attention because of its fundamental relation to quantum computing. However, the complex foundation of quantum logic borrowing concepts from different branches of mathematics as well as its peculiar settings have made it a non-trivial task to device suitable applications. This article aims to propose for the first time an approach to use quantum logic in image processing at hand of a process for shape classification. We show how to make use of the principal component analysis to realize quantum logical propositions. In this way we are able to assign a concrete meaning to the rather abstract quantum logical concepts, and we are able to compute a probability measure from the principal components. For shape classification we consider encrypting given point clouds of different objects by making use of specific distance histograms. This enables to initiate the principal component analysis. At hand of experiments, we explore the possibility to distinguish between different geometrical objects and discuss the results in terms of quantum logical interpretation.

The objective of the proposal is to find the optimal geometric shape of a photovoltaic panel that maximizes the collection of solar radiation. The optimal shape will be determined based on direct radiation, as it is the most relevant radiation for the photovoltaic process. To achieve this goal, we will use computer simulations to evaluate the performance of different geometric shapes under different conditions of solar radiation. Specifically, we will simulate the behavior of a photovoltaic panel with different shapes and orientations, and we will compare their energy yield over a given period of time. The simulations will be based on a mathematical model that takes into account the incidence angle of solar radiation on the panel, as well as other factors such as the reflection and absorption of radiation, and the temperature of the panel. The simulations will be carried out for different locations, with different solar radiation conditions, and for different periods of time, in order to obtain a comprehensive assessment of the performance of the different shapes. The results of the simulations will be analyzed to identify the optimal shape of the photovoltaic panel. This shape will be compared with the performance of flat panels, both static and with tracking systems, in order to evaluate the potential improvement in energy yield.

The integration of robotics and image processing has led to the realization of robot autonomy in dynamic environments through the provision of visual feedback. This paper presents the application of parallel and open-link robots in palletizing and shape drawing tasks as enhanced by visual feedback from image processing. In determining the set of joint angles that could be used to reach the desired position and orientation of the end effector, the geometric approach in which the spatial geometry of the robotic arms was decomposed into several plane geometry problems was employed. Image processing techniques were used to enhance the performance of the robotic manipulators. In one approach, Color-based segmentation was used to distinguish between different objects in the workspace by using predefined color markers as references in the L*a*b color space. Classification of each pixel in the workspace image was then done by calculating the Euclidean distance between that pixel and a predefined color marker. A second approach employed Edge detection to identify the boundaries of objects within the workspace image by employing the Hough Transform mathematical model to detect the abrupt changes in the image brightness pixel-wise. The pixel locations from Hough were then sorted sequentially to outline the detected object. The integration of image processing with the robotic tasks was expected to improve the precise detection of the position of objects as well as the outline of geometric shapes. The incorporation of visual feedback allowed for dynamic robot manipulation in which prior knowledge of the workspace was not requisite. This led to improved pick and place as well as shape detection as applied in palletizing and shape drawing tasks actuated by the parallel and serial link manipulators, respectively.

3D Zernike moments based on 3D Zernike polynomials have been successfully applied to the field of voxelized 3D shape retrieval and have attracted more attention in biomedical image processing. As the order of 3D Zernike moments increases, both computational efficiency and numerical accuracy decrease. Due to this phenomenon, a more efficient and stable method for computing high-order 3D Zernike moments was proposed in this study. The proposed recursive formula for computing 3D Zernike radial polynomials combines the recursive calculation of spherical harmonics to develop a voxel-based algorithm for the calculation of 3D Zernike moments. The algorithm was applied to the 3D shape Michelangelo's David with a size of 150×150×150 voxels. As compared to the method without additional acceleration, the proposed method uses a group action of order sixteen orthogonal group and saving unnecessary iterations, the factor of speed-up is 56.783±3.999 when the order of Zernike moments is between 10 and 450. The proposed method also obtained an accurate reconstructed shape with the error rate (normalized mean square error) of 0.00 (4.17×10^-3) when the reconstruction was computed for all moments up to order 450.

We proposed in this work the introduction of a new vision of stochastic processes through geometry induced by dilation. The dilation matrices of a given process are obtained by a composition of rotation matrices built in with respect to partial correlation coefficients. Particularly interesting is the fact that the obtention of dilation matrices is regardless of the stationarity of the underlying process. When the process is stationary, only one dilation matrix is obtained and it corresponds therefore to Naimark dilation. When the process is nonstationary, a set of dilation matrices is obtained. They correspond to Kolmogorov decomposition. In this work, the nonstationary class of periodically correlated processes was of interest. The underlying periodicity of correlation coefficients is then transmitted to the set of dilation matrices. Because this set lives on the Lie group of rotation matrices, we can see them as points of a closed curve on the Lie group. Geometrical aspects can then be investigated through the shape of the obtained curves, and to give a complete insight into the space of curves, a metric and the derived geodesic equations are provided. The general results are adapted to the more specific case where the base manifold is the Lie group of rotation matrices, and because the metric in the space of curve naturally extends to the space of shapes, this enables a comparison between curves’ shapes and allows then the classification of processes’ measure.

In many image processing and computer vision applications, the main aim is to describe image contents. So, different visual properties such as color, texture and shape are extracted to make aim. In this respect, texture information play important role in image description and visual pattern classification. Texture is referred to a specific local distribution of intensities that is repeated throughout the image. Since now different operations or descriptors have been proposed to analysis texture characteristics. In the multi object images specific texture operators usually doesn’t provide accurate results. So, in many cases, combination of texture operators are used to achieve more discriminant features. In this paper, some combination methods are survived to analysis effect of combinational texture features in image content description. Also, in the result part, different related methods are compared in terms of accuracy and computational complexity.

The aim of the study was to assess the relationship between the shape of the anterior-posterior curvature of the spine and body composition in school-children. The study included 257 children, aged 11-12. Correct spinal curvature was established in 106 (41.08%) subjects. Other types included: decreased kyphosis and correct lordosis - 40 participants (15.50%), correct kyphosis and decreased lordosis - 24 individuals (9.30%), increased kyphosis and correct lordosis - 17 subjects (6.59%), correct kyphosis and increased lordosis - 22 children (8.53%), decreased kyphosis and decreased lordosis - 32 people (12.40%), decreased kyphosis and increased lordosis - 4 of the examined subjects (1.55%), increased kyphosis and lordosis - 13 people (5.04%). In addition, 134 (51.94%) demonstrated scoliotic posture and 8 (3.10%) scoliosis. There were significant relationships between the shape of the anterior-posterior curvatures and body composition in school-children. Those with a strong body build (predominance of mesomorphs) were generally characterised by the correct formation of these curvatures. In contrast, lean people (with the predominance of ectomorphic factors) were more likely to experience abnormalities. No correlations with body composition were observed in the group with scoliotic posture or scoliosis.

Equiatomic CuZr alloy undergoes a martensitic transformation from the B2 parent phase to martensitic phases (P21/m and Cm) below 150 °C. We clarified the effect of the thermal cycling on the morphology and crystallography of martensite in equiatomic CuZr alloy using a transmission electron microscopy. The 10th cycled specimens consisted of different multiple structures at the maximum temperature of DSC measurement: 400 °C and 500°C, respectively. At the maximum temperature 400 °C of DSC measurement, it is composed of the fine plate-like variants, and a lamellar eutectoid structure consisting of Cu10Zr7 and CuZr2 phases on the martensitic variant. Concerning the maximum temperature 500 °C of DSC measurement, it is observed the martensitic structure and the lamellar structure in which the martensitic phase was completely eutectoid transformed. The formation of this lamellar eutectoid structure due to thermal cycling leads to the shift of forward and reverse transformation peaks to low and high temperature side. In addition, new forward and reverse transformation peaks indicating a new transformation appeared by thermal cycling, and the peaks remained around -20 °C. This new martensitic transformation behavior is also discussed.

Effect of abrasive shape on the three-body abrasion behaviors of monocrystalline silicon was investigated by Molecular dynamics modelling. The axial ratio of abrasive particle was varied from 1.00, i.e., a complete sphere, to 0.40 to mimic abrasive shape. The particle’s movement turns toward sliding from rolling when the axial ratio becomes less than a critical value 0.46. In the abrasion process, the friction force and normal force showed an approximately sinusoid-like fluctuation for the rolling ellipsoidal particles, while the front cutting of particle caused that friction force increased and became larger than normal force for sliding particles. The phase transformation process was tracked under different particle’ movement patterns. The Si-II and Bct5 phase producing in loading process can partially transform to Si-III/Si-XII phase and backtrack to original crystal silicon under pressure release, which also occurred in the abrasion process. The secondary phase transformation showed difference for particles’ rolling and sliding movements after three-body abrasion. The rolling of particle induced the periodical and inhomogeneous deformation of substrates, while the sliding benefited producing high-quality surface in CMP process. This study aiming to construct more precise model to understand the wear mechanism benefits evaluating the MEMS wear and CMP process of crystal materials.

Visual imagery, i.e., the mental experience of an object occurring in the absence of external visual stimulus, might encompass diverse content according to different observers. Further, subjectively experienced time is encoded in the later entorhinal cortex. Starting from these two observations, and considering Einstein’s account of spacetime, we show how, in terms of special relativity, the content of visual imagery is not stationary and fixed, rather depends on the standpoint of the observer. We elucidate how the subjective definition of time (perceived by our mind as static) might give rise to modifications in the length of the imagined object which are experimentally assessable and quantifiable. In particular, based on recent neuroscientific literature, we show how changes in our mental time windows are able to squeeze the visual content of mental imagery.

Proteins play a major role in biosensors in which they provide catalytic activity and specificity in molecular recognition. The immobilization process is however far from straightforward as it often affects the protein functionality. An extensive interaction of the protein with the surface or a significant surface crowding can lead to changes in the mobility and conformation of the protein structure. This review will provide an insight of how the analysis of the physico-chemical features of the protein surface features before the immobilization process can help to identify the optimal immobilization approach to preserve the functionality of the protein when on the surface of the biosensor.

Due to the proliferation of genetic testing, pathogenic germline variants predisposing to hereditary hematological malignancy syndrome (HHMS) have been identified in an increasing number of genes. Consequently, the field of HHMS is gaining recognition among clinicians and scientists worldwide. Patients with germline genetic abnormalities often have poor outcomes and are candidates for allogeneic hematopoietic stem cell transplantation (HSCT). However, HSCT using blood from a related donor should be carefully considered because of the risk that the patient may inherit a pathogenic variant. At present, we now face the challenge of incorporating these advances into clinical practice for patients with myelodysplastic syndrome (MDS) or acute myeloid leukemia (AML) and optimizing the management and surveillance of patients and asymptomatic carriers, with the limitation that evidence-based guidelines are often inadequate. The 2016 revision of the WHO classification added a new section on myeloid malignant neoplasms, including MDS and AML with germline predisposition. The main syndromes can be classified into three groups. Those without pre-existing disease or organ dysfunction; DDX41, TP53, CEBPA, those with pre-existing platelet disorders; ANKRD26, ETV6, RUNX1, and those with other organ dysfunctions; SAMD9/SAMD9L, GATA2, and inherited bone marrow failure syndromes. In this review, we will outline the role of the genes involved in HHMS in order to clarify our understanding of HHMS.

In this paper, we investigate an exact Universe which is observational viable and filled with binary mixture of perfect fluid and cosmological constant $\Lambda$. Owning the non-uniform expansion of cosmos, we have considered redshift drift $\dot{z} = -(1+z)H_{0}+H(z)$ and performed statistical test to obtain the best fit value of model parameters of derived Universe with its observed values. Here $H_{0}$ and $z$ denote the present value of Hubble constant and redshift respectively. We estimate the best fit values of Hubble constant and density parameters are $H_{0} = 68.58 \pm 0.84$ km/s/Mpc, $(\Omega_{m})_{0} = 0.26 \pm 0.010$ and $(\Omega_{\Lambda})_{0} = 0.71 \pm 0.025$ by bounding the derived model with latest observational Hubble data (OHD) while with joint Pantheon data and OHD, its values are $H_{0} = 71.93 \pm 0.58$ km/s/Mpc, $(\Omega_{m})_{0} = 0.272 \pm 0.06$ and $(\Omega_{\Lambda})_{0} = 0.74 \pm 0.09$. The analysis of deceleration parameter and jerk parameter show that the Universe in derived model is compatible with $\Lambda$CDM model.

Helicobacter pylori is a gastric pathogen that infects nearly half of the global population and is recognized as a group 1 carcinogen by the Word Health Organization. The global rise in antibiotic resistance has increased clinical challenges in treating H. pylori infections. Biofilm growth has been proposed to contribute to H. pylori’s chronic colonization of the host stomach, treatment failures, and the eventual development of gastric diseases. Several components of H. pylori have been identified to promote biofilm growth, and several of these may also facilitate antibiotic tolerance, including the extracellular matrix, outer membrane proteins, the coccoid morphology, modulated metabolism, efflux pumps, and virulence factors. Recent developments in therapeutic approaches targeting H. pylori biofilm have shown that synthetic compounds, such as small molecule drugs and plant-derived compounds, are effective at eradicating H. pylori biofilms. These combined topics highlight the necessity for biofilm-based research in H. pylori, to improve current H. pylori targeted therapeutic approaches and alleviate relative public health burden. In this review we discuss recent discoveries that have decoded the life cycle of H. pylori biofilms and current biofilm targeted treatment strategies.

CNN-based off-the-shelf features have shown themselves as a good baseline for trademark retrieval. However, in recent years, the computer vision area was transitioning from CNNs to a new architecture – Vision Transformer. In this paper, we investigate the performance of off-the-shelf features extracted with vision transformers and explore the effects of pre, post-processing, and pre-training on big datasets. We propose a method of joint usage of global and local features, which leverages the best aspects of both approaches. Experimental results on METU Trademark Dataset show that off-the-shelf features extracted with ViT-based models outperform off-the-shelf features from CNN-based models. The proposed method achieves the mAP value of 31.23, surpassing previous state-of-the-art results. We assume that the proposed approach for the trademark similarity evaluation will allow one to improve the protection of such data with the help of artificial intelligence methods. Moreover, this approach will allow one to identify cases of unfair use of such data and form an evidence base for litigation.

The inositol triphosphate associated proteins IRAG1 and IRAG2 are cGMP kinase substrate proteins, which regulate intracellular Ca2+. Previously, IRAG1 was discovered as a 125 kDa membrane protein at the endoplasmic reticulum, that is associated with the intracellular Ca2+ channel IP3R-I and the PKGIβ and inhibits IP3R-I upon PKGIβ-mediated phosphorylation. IRAG2 is a 75 kDa membrane protein homologue of IRAG1 and was recently also determined as a PKGI substrate protein. Several (patho-)physiological functions of IRAG1 and IRAG2 were meanwhile and recently elucidated in a variety of human and murine tissues, e.g. of IRAG1 in various smooth muscles, heart, platelets and other blood cells, of IRAG2 in pancreas, heart, platelets and taste cells. Hence, lack of IRAG1 or IRAG2 leads to diverse phenotypes in these organs, e.g. smooth muscle and platelet disorders or secretory deficiency, respectively. This review aims to highlight the recent research regarding these both regulatory proteins to envision its molecular and (patho-)physiological tasks and to unravel its functional interplay as possible (patho-)physiological counterparts.

in this work formulations of "environmentally compatible" silicone-based antifouling synthesized in the laboratory and based on copper and silver on silica/titania oxides have been characterized, capable of replacing the non-ecological antifouling paints currently on the market. The texture properties and the morphological analysis of these powders with an antifouling action indicate that their activity is linked to the nanometric size of the particles and to the homogeneous dispersion of the metal on the substrate. The presence of two metal species on the same support limits the for-mation of nanometric species and therefore the formation of homogeneous compounds. The presence of the antifouling filler (specifically the one based on TiO2 and Ag) facilitates the achievement of a higher degree of cross-linking of the pure resin, and therefore a better compactness and completeness of the coating. This leads to a consequent better degree of adhesion to the tie-coat and therefore to the steel support used for the construction of the boats.

Global demand for sleep-tracking wearables, or consumer sleep technologies (CSTs), is steadily increasing. CST marketing campaigns often feature a scientific component, but the scientific relevancy and monetary value of CST features within the sleep research community remains unquantified. Sleep medicine experts were recruited through social media and nonprobability sampling techniques to complete a survey identifying sleep metrics and device features that are most desirable to the scientific community. A hypothetical purchase task (HPT) estimated economic valuation for devices with different features by price. Forty-six (N=46) respondents with an average of 10±6 years’ experience conducting research in real-world settings completed the online survey. Total sleep time was ranked as the most important measure of sleep followed by objective sleep quality while sleep architecture/depth and diagnostic information were ranked as least important. Experts preferred wrist-worn devices that could reliably determine sleep episodes as short as 20 minutes. Economic value was greater for hypothetical devices with longer battery life. These data set a precedent to determine how scientific relevance of a product impacts the potential market value of a CST device. This is the first known attempt to establish consensus opinion or economic valuation for scientifically-desirable CST features and metrics using expert elicitation.

Face verification for unrestricted faces in the wild is a challenging task. This paper proposes a method based on two deep convolutional neural networks(CNN) for face verification. In this work, we explore to use identification signal to supervise one CNN and the combination of semi-verification and identification to train the other one. In order to estimate semi-verification loss at a low computation cost, a circle, which is composed of all faces, is used for selecting face pairs from pairwise samples. In the process of face normalization, we propose to use different landmarks of faces to solve the problems caused by poses. And the final face representation is formed by the concatenating feature of each deep CNN after PCA reduction. What's more, each feature is a combination of multi-scale representations through making use of auxiliary classifiers. For the final verification, we only adopt the face representation of one region and one resolution of a face jointing Joint Bayesian classifier. Experiments show that our method can extract effective face representation with a small training dataset and our algorithm achieves 99.71% verification accuracy on LFW dataset.

We report on magnetic studies of inhomogeneous commercial and synthesized amorphous carbon (a-C) and a-C doped with sulfur (a-CS) powders which exhibit (i) peculiar magnetic behavior and (ii) traces of two superconducting (SC) phase ~ Tc=33 and at 65 K. (i) The temperature dependence of zero-field-cooled (ZFC) curves measured up to room temperature show a well distinguish elusive peaks around 50-80 K, their origin is not yet known. These peaks are totally washed-out in the second ZFC sweeps and in the FC branches as well. As a result, in the vicinity of the peaks, the FC curves lie below the ZFC peaks (FC<ZFC), a phenomenon which is rarely observed. These magnetic anomalies are intrinsic properties of a-C and a-CS materials (ii) SC was observed in three different a-C sources: (a) The commercial a-C powder contains 0.21% of sulfur and it is suggested that two different a-CS phases (at 33 and 65 K) are the origin of the two SC states observed. The compositions of these two phases are not yet unknown. The small SC volume fractions of the 33 K phase can be enhanced by a solid reaction with additional sulfur at 250 ºC. (b) The synthesized a-C powder (obtained from decomposition of sucrose) is not SC. However, when mixed with sulfur and heated at 400 ºC under a protective atmosphere, the a-CS powder obtained also show traces of a SC phase at TC= 42 K. (c)The same occurs in a-C thin films. The as-grown films are not SC but a SC phase at Tc = 34 K emerges after the films were reacted with sulfur at elevated temperatures. It is concluded therefore, that all SC phases observed are due to different unknown a-CS phases. Since the a-C and a-CS powders possess SC and magnetic states, we believe that these powders resemble the high TC curates and Fe-As based systems in which the SC and the magnetic states are closed related to each other.

Tablets are the most used solid dosage form in medical treatments. The realistic modeling 1 of such specific object shapes in computer simulations has been demonstrated already by the author 2 for bi-convex round tablets but needs to be extended for a wider range of tablet shapes. Moreover, 3 the number of tablets must be large enough to simulate industrial drum coaters, which are typically 4 used for film coating processes. Whilst this long lasting process where tablets flow continuously, 5 tablets are loaded by frequent changes of individual contacts. Using a tailored simulation tool named 6 pharmAssist , written for GPUs (modern massively parallel processors in graphics cards and HPC 7 accelerators), we implemented the widely used oblong tablet shape. We are able to simulate flows 8 involving industrial scale number of such tablets. Data mining allows for statistical analyzis of 9 pair-wise contacts of different geometric features. Our simulation results serves as an observation 10 space, which cannot be addressed by experimental analyzis and will help to avoid degradation of 11 tablets like chipping or other defect phenomena. This paper reflects current development work in 12 order to extend the DEM with a new shape class, which comprises the modelling of compacted or 13 extruded objects, which have a smooth exterior, a flat band and sharp contour edges

Efficient natural convection-cooled heat sinks are vital to the future of electronics cooling due to their low energy demand in the absence of an external pumping agency in comparison to other cooling methods. The present study is aimed to identify the most effective fin design for enhancing heat transfer in natural convection applications. Initially, a baseline case with rectangular fins was considered in the present study and it was optimized with respect to fin spacing. This optimized baseline case is then validated against the semi-empirical correlation proposed by Elen-baas (1942) [2]. Upon good agreement, the validated model is used for comparative analysis of different heat sink configurations with rectangular, trapezoidal, curved, and angled fins. The optimised fin spacing obtained for the baseline case is also used for the other heat sink configura-tions and then the fin designs are further optimized for better performance. However, for the an-gled fin case, the optimized configuration proposed by Zhang et al. (2020) [3] is adopted in the present study. This study is carried out with Ansys Fluent for a Rayleigh number of 2.4 × 10^6. The proposed novel curved fin design with a shroud defined as Case C4 showed a 4.1% decrease in the system’s thermal resistance with an increase in the heat transfer coefficient of 4.4% when compared to the optimized baseline fin case. The obtained results are further non-dimensionalized with proposed scaling in terms of the baseline case for the two novel heat sink cases (trapezoidal, curved).

Shape-memory hydrogels (SMH) are as multifunctional, actively-moving polymers of interest in biomedicine. In loosely crosslinked polymer networks gelatin chains may form triple helices, which can act as temporary netpoints in SMH, depending on the presence of salts. Here, we show programming and initiation of the shape-memory effect of such networks based on a thermomechanical process compatible with the physiological environment. The SMH were synthesized by reaction of glycidylmethacrylated gelatin with OEG α,ω-dithiols of varying crosslinker length and amount. Triple helicalization of gelatin chains is shown directly by wide-angle X-ray scattering and indirectly via the mechanical behavior at different temperatures. The ability to form triple helices increased with the molar mass of the crosslinker. Hydrogels had storage moduli of 0.27-23 kPa and Young’s moduli of 215-360 kPa at 4 °C. The hydrogels were hydrolytically degradable, with full degradation to water soluble products within one week at 37 °C and pH = 7.4. A thermally-induced shape-memory effect is demonstrated in bending as well as in compression tests, in which shape recovery with excellent shape recovery rates Rr close to 100% were observed. In the future, the material presented here could be applied e.g. as self-anchoring devices mechanically resembling the extracellular matrix.

Based on a measuring system to determine the statistical heterogeneity of individual polygons we propose a method to use polygonal shape patterns as a source of data in order to determine the Shannon entropy of biological organizations. In this research, the term entropy is a particular amount of data related with levels of spatial heterogeneity in a series of different geometrical meshes and sets of random polygons. We propose that this notion of entropy is important to measure levels of information in units of bits, measuring quantities of heterogeneity in geometrical systems. In fact, one important result is that binarization of heterogeneity frequencies yields a supported metric to determine geometrical information from complex configurations. Thirty-five geometric aggregates are tested; biological and non-biological, in order to obtain experimental results of their spatial heterogeneity which is verified with the Shannon entropy parameter defining low particular levels of geometrical information in biological samples. Geometrical aggregates (meshes) include a spectrum of organizations ranging from cell meshes to ecological patterns. Experimental results show that a particular range (0.08 and 0.27) of information is intrinsically associated with low rates of heterogeneity. We conclude it as an intrinsic feature of geometrical organizations in multi-scaling biological systems.

The human gut microbiome includes beneficial, commensal and pathogenic bacteria that possess antimicrobial resistance (AMR) genes and exchange these predominantly through conjugative plasmids. Escherichia coli is a significant component of the gastrointestinal microbiome and is typically non-pathogenic in this niche. In contrast, extra-intestinal pathogenic E. coli (ExPEC) including ST131 may occupy other environments like the urinary tract or bloodstream where they express genes enabling AMR and host adhesion like type 1 fimbriae. The extent to which non-pathogenic gut E. coli and infectious ST131 share AMR genes and key associated plasmids remains understudied at a genomic level. Here, we examined AMR gene sharing between gut E. coli and ST131 to discover an extensive shared preterm infant resistome. In addition, individual ST131 show extensive AMR gene diversity highlighting that analyses restricted to the core genome may be limiting and could miss AMR gene transfer patterns. We show that pEK499-like segments are ancestral to most ST131 Clade C isolates, contrasting with a minority with substantial pEK204-like regions encoding a type IV fimbriae operon. Moreover, ST131 possess extensive diversity at genes encoding type 1, type IV, P and F17-like fimbriae, particular within subclade C2. The type, structure and composition of AMR genes, plasmids and fimbriae varies widely in ST131 and this may mediate pathogenicity and infection outcomes.

The existing segmentation-based scene text detection methods mostly need complicated post-processing, and the post-processing operation is separated from the training process, which greatly reduces the detection performance. The previous method, DBNet successfully simplified post-processing and integrated the post-processing into a segmentation network. However, the training process of the model took a long time for 1200 epochs and the sensitivity to texts of various scales was lacking, leading to some text instances being missed. Considering the above two problems, we design the text detection Network with Binarization of Hyperbolic Tangent(HTBNet). First of all, we propose Binarization of Hyperbolic Tangent (HTB), optimized along with which, the segmentation network can expedite the initial convergent speed by reducing the amount of epochs from 1200 to 600. Because features of different channels in the same scale feature map focus on the information of different regions in the image, to better represent the important features of all objects in the image, we devise the Multi-Scale Channel Attention(MSCA). Meanwhile considering that multi-scale objects in the image cannot be simultaneously detected, we propose a novel module named Fused Module with Channel and Spatial(FMCS), which can fuse the multi-scale feature maps from channel and spatial dimension. Finally we adopt cross entropy as the loss function, which measures the difference between predicted values and ground truths. The experimental results show that HTBNet compared with lightweight models has achieved competitive performance and speed on Total-Text(F-measure:86.0%, FPS:30) and MSRA-TD500 (F-measure:87.5%, FPS:30).

Shape-shifting polymers usually require not only reversible stimuli-responsive ability, but also strong mechanical properties. A novel shape-shifting photochromic hydrogel system was designed and fabricated through embedding hydrophobic spiropyran (SP) into double polymeric network (DN) by micellar copolymerization. Here, sodium alginate (Alg) and poly acrylate-co-methyl acrylate-co-spiropyran (P(SA-co-MA-co-SPMA)) were employed as the first network and the second network respectively to realize high mechanical strength. After soaked in the CaCl2 solution, the carboxyl groups in the system undergo metal complexation with Ca2+ to enhance the hydrogel. Moreover, after the hydrogel is exposed to UV-light, the closed isomer of spiropyran in the hydrogel network can be converted into an open zwitterionic isomer MC, which is considered to interact with Ca2+ ions. Interestingly, Ca2+ and UV-light responsive programmable shape of the copolymer hydrogel can recover to its original form by immersing in pure water. Since its excellent metal ion and UV-light stimuli-responsive and mechanical properties, the hydrogel has potential applications in the field of soft actuators.

Cu-Sn shape-memory microwires were fabricated by a glass-coated melt spinning method. Effects of Sn content on the microstructure and mechanical property of microwires were investigated. The phase transforms from martensite to austenite with Sn content increasing from 14.0 atomic percent (at.%) to 16.5 at.%, and highly ordered intermetallic phase, δ, formed with higher Sn content. The fracture stress, σf, and the critical stress for martensitic transformation, σMs, increases with Sn content increasing. The mechanical property as well as superelasticity were improved greatly by high cooling rate of the glass-coated melt spinning method.

A plant’s health and productivity is influenced by its associated microbes. Although the common microbiome is often thought to be the most influential, significant numbers of rare or uncommon microbes (eg. specialized endosymbionts) may also play an important role in the health and productivity of certain plants in certain environments. To help identify rare/specialized bacteria and fungi in the most important angiosperm plants, we contrasted microbiomes of the shoots, roots and rhizospheres of Arabidopsis, Brachypodium, maize, wheat, sugarcane, rice, tomato, coffee, common bean, cassava, soybean, switchgrass, sunflower, Brachiaria, barley, sorghum, and pea. Plants were grown inside sealed jars on sterile sand or field soil. About 95% and 86% of fungal and bacterial diversity inside plants was uncommon, however judging by read abundance, up to half of the mycobiome consists of uncommon fungal cells, while less than 11% of bacterial endophytes are rare. Uncommon seed transmitted microbiomes consisted mostly of Proteobacteria, Firmicutes, Bacteriodetes, Ascomycetes and Basidiomycetes that most heavily colonized shoots, to a lesser extent roots and least of all rhizospheres. Soil served as a more diverse source of rare microbes than seeds, replacing or excluding the majority of the uncommon seed transmitted microbiome. With the rarest microbes, their colonization pattern could either be the result of stringent biotic filtering by most plants, or uneven/stochastic inoculum distribution in seeds or soil. Several strong plant-microbe associations were observed such as seed transmission to shoots, roots and/or rhizospheres of Sarocladium zeae (maize), Penicillium (pea and Phaseolus), and Curvularia (sugarcane), while robust bacterial colonization from cassava field soil occurred with the cyanobacteria Leptolyngbya into Arabidopsis and Panicum roots, and Streptomyces into cassava roots. Some abundant microbes such as Sakaguchia in rice shoots or Vermispora in Arabidopsis roots appeared in no other samples, suggesting they were infrequent, stochastically deposited propagules from either soil or seed (impossible to know based on the available data). Future experiments with culturing and cross inoculation of these microbes between plants may help us better understand host preferences and their role in plant productivity, perhaps leading to their use in crop microbiome engineering and enhancement of agricultural production.

In this article authors shows chosen problems of technical state diagnosis with the use of identification and technical diagnostics methods such as experimental modal analysis. Relations between methods of dynamic state evaluation and methods of technical state evaluation were indicated. Example modal analysis results illustrate the complexity of projecting dynamic state researches into diagnostic researches of state evaluation.

Single-level Principal Components Analysis (PCA) and multi-level PCA (mPCA) methods are applied here to a set of (2D frontal) facial images from a group of 80 Finnish subjects (34 male; 46 female) with two different facial expressions (smiling and neutral) per subject. Inspection of eigenvalues gives insight into the importance of different factors affecting shapes, including: biological sex, facial expression (neutral versus smiling), and all other variations. Biological sex and facial expression are shown to be reflected in those components at appropriate levels of the mPCA model. Dynamic 3D shape data for all phases of a smile made up a second dataset sampled from 60 adult British subjects (31 male; 29 female). Modes of variation reflected the act of smiling at the correct level of the mPCA model. Seven phases of the dynamic smiles are identified: rest pre-smile, onset 1 (acceleration), onset 2 (deceleration), apex, offset 1 (acceleration), offset 2 (deceleration), and rest post-smile. A clear cycle is observed in standardized scores at an appropriate level for mPCA and in single-level PCA. mPCA can be used to study static shapes and images, as well as dynamic changes in shape. It gave us much insight into the question “what’s in a smile?”

Background: Pulmonary sequestration (PS), generally diagnosed using computed tomography pulmonary angiography (CTPA), is a rare congenital developmental malformation of the lung that is characterized by nonfunctional lung tissue independent of the normal lung tissue. This study summarizes the imaging features of the supplying arteries and draining vessels in patients with PS with an aim to assist with timely clinical diagnosis and operation guidance. Materials and Methods: A total of 55 patients with PS diagnosed on CTPA from multiple clinical centers were retrospectively analyzed. Data included demographic characteristics, imaging features, disease location, isolation type, and the features of supplying and draining vessels, as shown on CTPA images. Results: Of the 55 patients reviewed, 3 (5.45%) were children, 3 (5.45%) were adolescents, and 49 (89.09%) were adults, with a mean age of 44 years. Fifty-four (98.18%) patients had intralobar sequestration and one (1.82%) had extralobar sequestration. PS was noted 3.5 times more frequently in the left lower lobe than in the right lower lobe. For the supplying arteries, 47 (85.45%) were derived from the descending thoracic aorta, 1 (1.82%) from the abdominal aorta, 7 (12.73%) from the celiac axis, and 1 (1.82%) from the bronchial artery. The draining vessels were the pulmonary veins in 49 patients (89.09%), the umbilical vein in 1 (1.82%), the venae intercostal in 1 (1.82%), the pulmonary arteries in 11 (20.00%), and not shown on the images in 2 (3.64%). Conclusion: Clinical presentations of PS are non-specific and can be easily missed or misdiagnosed. However, CTPA can help improve the diagnostic accuracy and identify the supplying arteries and draining vessels, which significantly contribute to surgical planning.

Mitochondrial carriers facilitate the transfer of small molecules across the inner mitochondrial membrane (IMM) to support mitochondrial function and core cellular processes. In addition to the classical mitochondrial carrier family SLC25, the past decade led to the discovery of additional protein families that exhibit IMM localization and transporter-like properties. These include mitochondrial pyruvate carriers, sideroflexins and mitochondrial cation/H+ exchangers that have been linked to vital physiological functions and disease. Their structures and transport mechanisms are still largely unknown and understudied. Protein sequence analysis per se can often pinpoint hotspots that are of functional or structural importance. In this review, we summarize current knowledge about sequence features of mitochondrial transporters with a special focus on the newly included SLC54, SLC55 and SLC56 families of the SLC solute carrier superfamily. Taking a step further, we combined sequence conservation analysis with transmembrane segment and secondary structure prediction methods to extract residue positions and sequence motifs that likely play a role in substrate binding, binding site gating or structural stability. We hope that our review will help guide future experimental efforts by the scientific community to unravel the transport mechanism and structure of these novel mitochondrial carriers.

The building blocks of a scientific facility based on particle beams comprise magnets and electro-magnetic devices. The design of an overall accelerator usually imposes a demanding accuracy on their exact position and orientation. Their non-geometrical functional features are frequently either not clearly defined – what is the « magnetic axis » of a magnet –, or not explicitly used along their lifecycle. Improving the process to establish and use these functional features would contribute to meeting demanding challenges. The European Spallation Source (ESS) is aiming at providing a powerful proton linear accelerator and a target system to produce pulsed neutrons. The challenging complex design and integration yielded to introducing a tool for all stakeholders along the lifecycle: the "situation features", as defined in ISO GPS (Geometrical Product Specifications) standards. This paper deals with the beyond-mechanics non-geometrical use cases and generalizes the use of the association operation to theoretically exact features. The examples of the functional specification, fiducialization (measure of the real features with respect to external marks) and installation phases of quadrupole magnets is developed. Perspectives of generic use are highlighted.

Grain shape and chalkiness are important appearance quality traits of rice, which also affect the yield and milling quality. In this study, 190 F2 families derived from the cross between Hua 5178S and Hua 611 were sequenced to construct a genetic linkage map. Combined with F2 and F2:3 generations grain shape and chalkiness phenotype, a total of 15 QTL were detected. There are 8 grain shape QTL distributed on chromosomes 2, 5, 6, 8, and 10; 7 chalkiness QTL distributed on chromosomes 2, 3, and 6. A QTL cluster affecting multiple chalkiness traits was found on chromosome 6, qWBR6.1, qWCR6 and qCR6 were selected for genetic effect verification. It was found that qWBR6.1, qWCR6 and qCR6 explained 13% and 24%, and 29% of the phenotypic variations respectively, and qWCR6 and qCR6 were co-mapped. This study laid a foundation for further cloning of rice appearance quality genes.

Hydroxyapatite (HAP) is the main mineral component of bones and teeth. It is widely used in medicine as a bone filler and coating for implants to promote new bone growth. Ion substitutions into the HAP structure highly affect its properties. One of the important substituents is manganese. This paper presents the new results obtained by high-precision density functional theory calculations using hybrid functionals, for studies of the Mn/Ca substitutions in HAP in a wide manganese concentration range within 2x2x2 supercell model. The experimental data on the mechanochemical synthesis of HAP-Mn samples with different Mn concentrations are also presented. The comparison between experiment and theory showed good agreement: the HAP-Mg unit cell parameters and volume decrease with increasing degree of Mn/Ca substitution. It is shown that electronic energy levels appear inside the band gap Eg of HAP, which depend on the concentration of Mn and change the photo-excitation energy of HAP, making its effective value Eg* less than the band gap Eg, and generally changing the photoelectronic properties of HAP. It was established that the energies of formation of the substitutions Mn/Ca depend on the position of the Ca atom (Ca1 or Ca2) and on the concentration of manganese. It has been shown that the replacement of calcium cations with manganese occurs predominantly at the Ca2 position.

Microsoft has implemented several measures to defend against macro viruses, including the use of Anti-malware Scan Interface (AMSI) and automatic macro blocking. Nevertheless, evidence shows that threat actors have found ways to bypass these mechanisms. As a result, phishing emails continue to apply malicious macros as their essential attack vector. In this paper, we analyze 77 obfuscation features from the attacker’s point of view and extract 46 suspicious keywords in macro. We first combine the above two types of features to train machine learning models on a public dataset. Then, we carry out the same experiment on self-constructed dataset, a collection of newly discovered samples, to see if our proposed method could discover the unseen malicious macros. Experimental results demonstrate that, comparing with the existing researches, our proposed method has a higher detection rate and better consistency. Furthermore, ensemble multi-classifiers with distinct feature selection further improve the detection performance.

Diffuse intrinsic pontine glioma (DIPG) is recognized as a pediatric brainstem cancer with a 0% survival rate. On a molecular basis, DIPG commonly results from mutations in histone H3, specifically a mutation in the H3K27M gene, that promotes tumorigenesis and results in presentation of this fatal brainstem tumor. DIPG is challenging to treat, as surgical intervention is inefficacious due to the location where the glioma resides. To date, traditional treatments such as radiation, chemotherapy, and immunotherapy have not increased survival rates and have only been successful at relieving symptoms. Future therapeutic approaches such as proton beam radiation, Chimeric Antigen Receptor T Cell (CAR-T) immunotherapy, and alternative epigenetic pharmaceuticals are under investigation for potential benefits. Various clinical trials have also explored these treatment procedures to discover potential increases in survival rates in both animal and human studies. In this review, we will evaluate the pathology and molecular characteristics of DIPG, the current and future approaches to DIPG treatment; and, lastly, we will discuss clinical trials that have been completed to develop successful treatment options.

Fabry disease (FD) is a X-linked inheritable storage disease caused by deficiency of al-pha-galactosidase causing lysosomal overload of sphingolipids. FD cardiomyopathy is character-ized by left ventricular (LV) hypertrophy and should be considered in differential diagnosis with all the other causes of LV hypertrophy. An early diagnosis of FD is very important because the enzyme replacement therapy (ERT) may change the fate of patients by blocking both cardiac and systemic involvement and improving prognosis. Diagnosis may be relatively easy in young patients with the typical signs ans symptoms of FD, but in male patients with late onset of disease and in females, diagnosis may be very challenging. Morphological and functional aspects are not specific for FD, which cannot be diagnosed or excluded by echocardiography. Cardiac magnetic resonance (CMR) with tissue characterization capability, is the preferred technique for the differential diagnosis of LV hypertrophy. The finding of decreased myocardial T1 value in LV hypertrophy is very specific for FD. Late gadolinium enhancement (LGE) is found in late stage of disease but it is useful to predict the cardiac response to ERT and to stratify the prognosis.

Lung cancer is a deadly disease if not diagnosed in its early stages. However, early detection of lung cancer is a challenging task due to the shape and size of its nodules. Radiologists need support from automated tools for precise opinion. Automated detection of the affected lungs nodule is difficult because of the shape similarity among healthy tissues. Over the years, several expert systems have been developed that help radiologists to diagnose lung cancer. In this article, we propose a framework to precisely detect lungs cancer by classifying it between benign and malignant nodules. The framework is tested using the subset of the publicly available at the Lung Image Database Consortium image collection (LIDC-IDRI). Multiple techniques including filtering and noise removing are applied for pre-processing. Subsequently, the OTSU and the semantic segmentation are used to accurately detect the unhealthy lungs nodules. In total, 13 nodules features were extracted using Principal Components Analysis (PCA) algorithm. Four optimal features are selected based on the classification performance. In the classification phase, 9 different classifiers are used along with two types of validation schemes i.e. train test holdout validation with 70-30 data split and 10 fold cross-validation. Our experiments show that the proposed system provides 99.23\% accuracy using logic boost classifier.

Thin twin-roll cast strips from a model Al-Cu-Mg-Li-Zr alloy with a small addition of Sc were prepared. A combination of a fast solidification rate and a favorable effect of Sc microalloying impacts the size and distribution of primary phase particles and the dimension of eutectic cells. Small interdendritic spacing in the range of 10-15 um enables substitution of the initial long-term homogenization annealing by a two-step annealing at 300 and 450 °C and a short solution treatment at 530 °C before quenching and the final age-hardening procedure. A dense dispersion of small Al3(Sc,Zr) precipitates intensively inhibiting recrystallization forms during this treatment. Further refining and fragmentation of the structure were performed using constrained groove pressing before the solution treatment. Small pre-straining of quenched specimens and subsequent age-hardening simulating T8 temper were used to promote the formation of a homogeneous distribution of strengthening particles and to suppress preferential heterogeneous precipitation on subgrain and grain boundaries. Microstructural features and microhardness evolution during annealing were critically examined and confronted with microstructural characteristics observed on reference mold cast alloy or alloys without Sc alloying. The proposed treatment represents a model near net shape procedure for manufacturing thicker sheets for the aerospace industry.

In this paper, we investigate some elementary properties of two kinds modified Szasz type basis functions, depending on non-negative parameters. We study some shape preserving properties of these operators concerning monotonicity, convexity, starlikeness, semi-additivity and smoothness.

Liquid crystal elastomers (LCEs) are shape morphing materials whose large and reversible shape transformation results from the coupling between the mobile anisotropic properties of liquid crystal (LC) units and the rubber elastic of polymer networks. Their shape changing behaviors under the stimuli are largely directed by LC orientations, and therefore various strategies have been developed to spatially modulate the LC alignments. But most of the methods suffer from complex fabrication technologies or intrinsic limitations in applicability. To address this issue, programmable complex shape changes in some types of LCEs, such as polysiloxane side-chain LCEs or thiol-acrylate main-chain LCEs, etc, have been achieved by using a mechanical alignment programming process coupled with two-step crosslinking. Here, we report a polysiloxane main-chain LCE with programmable 2D and 3D shape changing performances achieved by mechanically programming the polydomain LCE between the two crosslinking steps. The resulting LCEs exhibited reversible thermal induced shape transformation between the initial and programmed shapes due to the two-way memory between the first network structure and the second network structure. Our work would be potential for expanding the applications of LCE materials in actuators, soft robotics and smart structures where arbitrary and easily programmed shape morphings are needed.

A method was developed to perform shape optimization of a tidal stream turbine hydrofoil using a multi-objective genetic algorithm. A bezier curve parameterized the refrence hydrofoil profoil NACA 63815. Shape optimization of this hydrofoil maximized its lift-to-darg ratio and minimized its pressure coefficient, thereby increasing the turbines power output power and improving its cavitation characteristics. The Elitist Non-dominated Sorting Genetic Algorithm (NSGA-II) was employed to perform the shape optimization. A comparative study of two-and three-dimensional optimizations was carried out. The effect of varing the angle of attack on the quality of optimized results was also studied. predictions based on two-dimensional panel method results was also studied. Preditions based on a two-dimensional panel method and on a computational fluid dynamics code were compared to experimental measurments.

Thermal shape memory polymers (SMPs) find increasing applications in biology and medicine due to their promising potential for improved biocompatibility. In such applications, the inevitable penetration of small molecules from the ambient fluid into the polymer influences the shape recovery process and often leads to a reduction of the temperature, at which shape recovery takes place. We show here via molecular dynamics simulations that the size of additive molecules plays a key role for this process. While the effect of concentration on the recovery rate is monotonic in the investigated range, a non-monotonic dependence on the size of additive molecules emerges at temperatures close to the glass transition. This work thus identifies the additives’ size to be a qualitatively novel parameter for switching the recovery process in polymer-based shape memory materials.

In this study, we report a new epoxy acrylate based shape memory polymer(SMP) fabricated by Liquid crystal display (LCD) Stereolithographic 3D printing. The printed 3D object has a high resolution and high transparency in visible light region. The uniaxial tensile tests showed enhanced tensile toughness and tunable mechanical properties. The fix-recovery and cycle tests indicated high shape recovery properties including high shape recovery rate and excellent cycling stability. In addition, a smart electrical valve actuator was fabricated that can be used in fast heat or electricity responsive electrical circuits. LCD 3D printing provides a low-cost and high efficient way to fabricate fast responsive SMP, which can be used in wide applications in various fields on aerospace engineering, biomedical devices, soft robots and electronic devices.

The diffusion of New Ways of Working (NWW) is an important trend in contemporary organi-zations. Many related empirical studies have been produced, but none have focused on differences in employees’ perceptions of the opportunity to use NWW according to organization sector (pri-vate, semi-public, public). This study, based on neoinstituionalism and HR attributions theory, investigated these differences via a survey (n=2693) of employees at private (n=358), semi-public (n=204), and public (n=2131) organizations. The results show that public employees reported less opportunity to use NWW than their private and semi-public counterparts. Furthermore, private sector employees were more likely to attribute well-being and productivity benefits to NWW than their public sector and semi-public counterparts. We also show that institutional and organiza-tional variables specific to the characteristics of organizations in the three sectors partially ex-plain the degree of perceptions with respect to the opportunity to use NWW.

We construct a novel family of summation-integral type hybrid operators in terms of shape parameter $\alpha\in \lbrack 0,1]$ in this paper. Basic estimates, rate of convergence, and order of approximation are also studied using the Korovkin theorem and the modulus of smoothness. We investigate the local approximation findings for these sequences of positive linear operators utilising Peetre's K-functional, Lipschitz class, and second-order modulus of smoothness.

Owing to their unique physicochemical properties, nanoparticles are used in a variety of ways in the field of cancer treatment, including imaging, drug delivery, and photothermal and photodynamic therapies. The fascinating properties of nanoparticles are determined by their size, morphology, and constituent elements, and various synthetic methods and post-synthetic techniques have been applied to control these factors. Herein, we present examples of shape and composition control through galvanic replacement, a technique that exploits redox potential differences between elements to induce spontaneous ion-exchange and highlight its specific contributions to cancer treatment applications. The present article identifies the recent advances in nanoparticle formation techniques and discusses the future outlook of the field.

The possibility of releasing gold nanoparticles (GNP) into the environment has been rapidly increasing with the wide spread and flourishing application of gold nanoparticles (GNPs) in a wide range of areas. Consequently, environmental effects of GNP, especially toxicities to living organisms have drawn great attention. However, their toxicological characteristics still remain unclear. Fungi, as the decomposers of the ecosystem, interact directly with the environment and critically control the overall health of the biosphere. Thus, their sensitivity to GNP toxicity is particularly important. The aim of this study was to evaluate the role of shape and size of GNPs on their toxicities to fungi, which could help reveal the ecotoxicity of GNPs. Aspergillus niger, Mucor hiemalis and Penicillium chrysogenum were chosen for toxicity assessment, and spherical and star/flower-shaped GNPs sized from 0.7 nm to large aggregates of 400 nm have been synthesised. After exposure to GNPs and their corresponding reaction agents and incubation for 48 hours, the survival rates of each kind of fungus was calculated and compared. The results indicated that fungal species was the major determinant of the variation of survival rates, whereby A. niger was most sensitive and M. himalis was least sensitive to GNP exposure. Additionally, larger and non-spherical GNPs had relatively stronger toxicities.

Obesity is related to the establishment of chronic inflammation and metabolic diseases, but it can be positively influenced by the regular practice of physical activity. The study aimed to compare the anthropometric, metabolic, and inflammatory parameters of physically active Military Police Officers (MPOs) with those who are less physically active. Sixty male MPOs, low activity (n=28) and physically active (n=32) participated. The following parameters were measured: plasma cytokine levels, C-reactive protein (CRP) levels, circulating glucose triglyceride (TAG) and high-density lipoprotein cholesterol (HDL-C) levels, and plasma glutamic oxalacetate transaminase (GOT), glutamic-pyruvate transaminase (GPT), and gamma-glutamyl transferase (GGT) activities. The physically active group presented lower body fat and reduced TAG and IL-8 levels compared to the low activity group. Moreover, a negative correlation between SPE and SBP, DBP, BPM was detected for the physically active group (p<0.05) but not in the low activity group. Furthermore, the physically active group's work time (WT) values were not correlated with the important metabolic markers SBP, DBP, BPM, GLU, TAG (p>0.05) but could be in the low activity group (p<0.05). These findings highlight the fundamental protective role of physical activity in controlling body composition, subclinical inflammation, and cardiovascular risk in MPOs.

Deep networks-based hashing has gained significant popularity in recent years, particularly in the field of image retrieval. However, most existing methods only focus on extracting semantic information from the final layer, disregarding valuable structural information that contains important semantic details crucial for effective hash learning. To address this limitation and improve image retrieval accuracy, we propose a novel deep hashing method called Deep Supervised Hashing by Fusing Multiscale Deep Features (DSHFMDF). Our approach involves extracting multiscale features from multiple convolutional layers and fusing them to generate more robust representations for efficient image retrieval. Experimental results on CIFAR10 and NUS-WIDE datasets demonstrate that our method surpasses the performance of state-of-the-art hashing techniques.

Additive manufacturing (AM) technologies show potential for the development of functionally integrated lightweight designs, biomimetic structures and material savings. Typically, as-built surfaces show powder particle agglomerations and re-entrant features, leading to rough surfaces, which are associated with poor fatigue performance. To benefit from the full range of advantages with special focus on aerospace applications, critical features for crack initiation when subjected to fatigue loading need to be identified and mitigated. A first step toward achieving this goal is the surface texture characterisation based on the quantification of surface features. In this paper, selected areal height, functional and feature parameters from ISO 25178-2:2022 are generated and process-specific features are examined for as-built AlSi7Mg0.6 from laser powder bed fusion (PBF-LB). A connection with the particle size distribution of the used powder is demonstrated. It is shown that surface feature analysis opens up opportunities to use physically meaningful surface characteristics in future quality assurance and part qualification processes.

We proposed a deep learning based chemometric data analysis technique. We trained L2 regularized sparse autoencoder end-to-end for reducing the size of the feature vector to handle the classic problem of curse of dimensionality in chemometric data analysis. We introduce a novel technique of automatic selection of nodes inside hidden layer of an autoencoder through pareto optimization. Moreover, linear regression, ϵ-SVR , and Gaussian process regressor are applied on the reduced size feature vector for the regression. We evaluated our technique on orange juice and wine dataset and results are compared against state-of-the-art methods. Quantitative results are shown on Normalized Mean Square Error (NMSE) and the results show considerable improvement in the state-of- the-art.