TomoBreast hypothesized that hypofractionated 15 fractions/3 weeks image-guided radiation therapy (H-IGRT) can reduce lung-heart toxicity, as compared with normofractionated 25-33 fractions/5-7 weeks conventional radiation therapy (CRT). 123 women with stage I-II operated breast cancer were randomized to receive CRT (N=64) or H-IGRT (N=59). The primary endpoint used a four-items measure of the time to 10% alteration in any of patient self-reported measure, physician clinical evaluation, echocardiography or lung function tests, analyzed by intention-to-treat without exclusion. Results found comparable survivals, but H-IGRT significantly reduced the toxicity measured by lung diffusion capacity and alveolar volume as compared with CRT, G1 in 53% (31/59) versus 72% (44/61) patients, P=0.006; G2, 29% versus 48%, P=0.020. H-IGRT significantly reduced the risk of composite cardio-pulmonary alteration at 5 years, 10.2% versus 26.7%, P=0.024. In conclusion, TomoBreast is a proof-of-concept that image-guided radiation-therapy can improve the overall balance of lung-heart outcomes in breast cancer adjuvant therapy. Furthermore, the significance of the findings supports the efficacy of a small trial size design, which can be critical when clinical research resources are limited.

In the context of intelligent and integrative lighting, in addition to the need for color quality and brightness, the non-visual effect is essential. This refers to the retinal ganglion cells (ipRGCs) and their function, first proposed in 1927. The melanopsin action spectrum has been published in CIE S 026/E: 2018 with the corresponding melanopic equivalent daylight (D65) illuminance (mEDI), melanopic daylight (D65) efficacy ratio (mDER) and 4 other parameters. Due to the importance of mEDI and mDER, this work synthesizes a simple computational model of mDER as the main research objective, based on a database of 4214 practical spectral power distributions (SPDs) of daylight, conventional, LED, and mixed light sources. In addition to the high correlation coefficient R2 of 0.96795 and the 97% confidence offset of 0.0067802, the feasibility of the mDER model in intelligent and integrated lighting applications has been extensively tested and validated. The uncertainty between the mEDI calculated directly from the spectra and that obtained by processing the RGB sensor and applying the mDER model has reached ± 3.3% after an appropriate matrixing process and proper illumination characterization combined with the successful mDER calculation model. This result opens the potential of low-cost RGB sensors for applications in intelligent and integrative lighting systems to optimize and compensate the non-visual effective parameter mEDI using daylight and artificial light in indoor spaces. The goal of the research on RGB sensors and the corresponding processing method are also presented and their feasibility is methodically demonstrated. A comprehensive investigation with a huge amount of color sensor sensitivities is necessary in a future work of other researches.

This paper presents a new high-efficiency-high-step-up based converter integrating two stype DC-DC Boost and Flyback coupled magnetic converter with recovery stage dedicated to smart HVDC distributed architecture in renewable energy production systems. Appropriate duty cycle ratio assumes that the recovery stage work with parallel charge and discharge to achieve high step-up voltage gain. Besides, the voltage stress on the main switch is reduced with a passive clamp circuit and thus, low on-state resistance Rdson of the main switch can be adopted to reduce conduction losses. The circuit is simple to control. As a final point of this research, the simulation and the prototype investigational results are presented to demonstrate the effectiveness of this proposed converter.

Limited knowledge is available about the association between autistic spectrum disorder (ASD) and precocious puberty. Our study examined the association between the two medical conditions, and the effect modification by sex and neuropsychiatric comorbidities in a nationwide population. To compare the risk of precocious puberty between ASD and non-ASD cases, we conducted Cox regression analysis, using ASD as exposure and time-to-precocious puberty as outcome. We adjusted for sex, attention-deficit/hyperactivity disorder (ADHD), tics disorder, obsessive–compulsive disorder (OCD), anxiety disorder, intellectual disability, and epilepsy. We performed a moderation analysis to examine the potential moderating effects by sex or comorbidities. Patients with ASD were prone to have precocious puberty, with an adjusted hazard ratio (aHR) of 1.80 (95% CI: 1.61-2.01). For the effect modification, sex, specifically females, moderated the association between ASD and precocious puberty, with relative excess risk due to interaction (RERI) 7.35 (95% CI 4.90-9.80). No significant effect modification was found for any of the comorbidities within the scope of additive effect modification. We found that patients with ASD were prone to precocious puberty, regardless of sex or comorbid neuropsychiatric disorders. Girls with ASD are at particularly higher risk to develop precocious puberty.

Modern agriculture demands for comprehensive information about the plant itself. Conventional chemistry-based analytical methods - due to their low throughput and high associated cost - are no longer capable of providing these data. In recent years, remote reflectance-based characterization has developed as one of the most promising solutions for rapid assessments for plant attributes. However, in many cases, expensive equipment is required because accurate quantifications need assessment of the full reflectance spectrum. We examined the versatility of visible colour sensors as reflectance measuring devices for biological / biochemical quantifications on sweet basil (Ocimum basilicum). Our results indicate for the wide potential of spectral colour sensors for quantitative determination of leaf phenolic compounds, flavonoids in particular, and non-invasive plant phenotyping in agricultural applications by low-cost sensors.

This paper introduces in general the conservation work of the Nara Palace Site in Japan carried out by Japanese conservation experts. Concerning conservation theory, this paper focused on the international conventions in which the 1994 Nara Document on Authenticity plays an important role in contributing to Japanese cultural heritage conservation achievements with the expansion of Cultural diversity and Heritage diversity, the Values and Authenticity conceptions which proposed a new orientation in terms of awareness and openness broader vision in identifying architectural heritage values. Regarding, based on the 1994 Nara Document on Authenticity, the 1964 Venice Charter, and the 1999 Bura Charter, we try to propose a new concept of the so-called “Architectural Heritage DNA” and its potential structure, providing a simple formula for the authentic values appraisal of the heritage conservation works. In addition, we also try to supplement the concept of Inheritance between Conservation and Development which is the lack of international conventions of cultural heritage conservation. The viewpoints expressed in this paper are based on the most common concepts of international conventions but interpreted in a new-understandable, practical, and highly applicable way possible to the current socio-political situation and in the future, as well.

It is unavoidable that the characteristics of the robot system alter inexactly or cannot be correctly determined during movement and are impacted by external disturbances. There are many adaptive control methods, such as sliding control or neural control, to improve the quality of trajectory tracking for robot motion systems. Still, those methods require a large amount of computation related to solving the problem of constrained nonlinear optimization problem. The article presents some techniques for determining an intelligent controller's online learning function parameters, including two circuits: the inner circuit is a state feedback linearized controller, and the outer course is an Interactive learning controller and does not use the robot's mathematical mode with function parameters online optimal learning and intelligent online learning closely following the joint model to stabilize the robot system. Finally, the 2-DOF planar robot simulation is performed on Matlab software R2022b. The findings indicate that outstanding tracking performance is achievable.

In table tennis competitions the rule violation judgment with the greatest controversy is the height of the ball serve. This is because inaccuracy in the ball height judgment, which results in erroneous judgment, is unavoidable. Thus, we designed an automatic image judgment auxiliary system for table tennis ball height during service in this study. We used a high-speed camera to record the ball toss in the table tennis service. The designed algorithm architecture can automatically search for the ball and the position of the hand action under low light source conditions. The algorithm is mainly divided into hue-saturation-value color space processing and morphology processing using Hough transform to search for the circular ball. Experiment result shows that color segmentation can successfully and accurately determine the ball position under low light conditions. The morphology method can find the position of the hand and help determine the moment when the ball leaves the hand during the service ball toss. Finally, the actual size of the target is used to estimate the actual distance unit represented by the image pixel.

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

In this article, the research team has systematically developed a method to model the kinematics and dynamics of a 3-wheeled robot subjected to external disturbances and sideways wheel sliding. These models will be used to design control laws that compensate for wheel slippage, model uncertainties, and external disturbances. These are control algorithms developed based on Dynamic Surface Control (DSC). Adaptive trajectory tracking DSC algorithm using fuzzy logic system (AFDSC) and radial neural network (RBFNN) with fuzzy logic system to overcome the disadvantages of DSC and expand the application domain for wheeled Mobile Robots (WMR). However, this Adaptive Fuzzy Neural Network Dynamic Surface Control (AFNNDSC) adaptive controller ensures the closed system is stable and follows the preset trajectory in the presence of wheel slippage model uncertainty and is affected by significant amplitude disturbances. The stability and convergence of the closed-loop system are guaranteed based on the Lyapunov analysis. The AFNNDSC adaptive controller is evaluated by simulation on MATLAB/Simulink software and in a steady state. The maximum position error on the right wheel and left wheel is 0.000572 (m) and 0.000523 (m), and the angular velocity tracking error in the right and left wheels of the control method is 0.000394 (rad/s). The experimental results show the correctness of the theoretical analysis, the effectiveness of the proposed controller, and the possibility of practical application.

While a raft of existing Chinese literature examines the associations between the outbreak of the pandemic and students’ mental health, rarely do Chinese studies assess the nuanced relationships between digital learning, parenting, and students’ mental health since the coronavirus (COVID-19) outbreak. Such a rarely discussed topic has substantial scholarly value as mismanagement of digital learning and parenting, such as the exposure to cyberbullying and negative parenting during the public health crisis, could add substantial, unforeseeable psychological burdens for Chinese students. In this article, the author applied a systematic review to find all relevant Chinese literature that contains the words “digital learning”, “children/adolescents”, “mental health”, and “parenting” published since January 2020. As such a complex topic has rarely been addressed in Chinese contexts, the author was only able to find four related scholarly articles. The author summarises the arguments and empirical findings to explore the nuanced relationships between a) digitalisation, isolation, parenting, and children’s mental health, b) parenting, teacher-student relationships, and students’ mental health, and c) maternal and paternal parenting styles.

This paper aims to build up a preference function to evaluate the public benefits of the type of agricultural farming, biodiversity, water provisions, land use type, ecotourism modes, and a monetary attribute (environmental trust fund and willingness to contribute) associated with an ecosystem service and land use program in a forest park. This study used the choice experiments to build a random utility model, analyze the average preference for the above land use attributes based on the conditional logit and used a latent class model to test the resident’s heterogeneous preferences for land use planning in the forest park. We also estimated the welfare derived from various land use programs. The empirical result had shown that: (1) increasing organic farming area, maintaining the status quo of species biodiversity, increasing the surface water provision, increasing the area of custom flora, increasing the wetland area, and setting up an integrated framework for ecotourism increase the public’s preference for the land use program; (2) we found that farmer and non-farmer haven’t the same land use preferences; (3) the ecotourism development program incorporating biodiversity, organic farming, ethnobotany, and wetland area with integrated ecotourism are more preferred than other land use program scenarios.

In this paper, we investigate the accuracy and robustness of three classes of methods for solving two-phase incompressible flows on a staggered grid. Here, the unsteady two-phase flow equations are simulated by finite volumes and penalty methods using implicit and monolithic approaches (such as the augmented Lagrangian and the fully coupled methods), where all velocity components and pressure variables are solved simultaneously (as opposed to segregated methods). The interface tracking is performed with a Volume-of-Fluid (VOF) method, using the Piecewise Linear Interface Construction (PLIC) technique. The home code Fugu is used for implementing the various methods. Our target application is the simulation of two-phase flows at high density and viscosity ratios, which are known to be challenging to simulate. The resulting strategies of monolithic approaches will be proven to be considerably better suited for these two-phase cases, they also allow to use larger time step than segregated methods.

Exisitng studies argue life chances are, in part, vertically reproduced. Such a statement is applicable to the Chinese contexts as, but not limited to, parental hukou status, to some extent, determines the life chances their children receive. In this essay, the author would like to introduce human capital theory (HCT) and educational signalling theory (EST), and assess how the applications of each of these two models can enrich the understanding of vertical reproduction of individuals’ social mobility opportunities. The author would also present the limitations of each of these two models when addressing relevant Chinese contexts.

This study is to explore the feasibility using neural network (NN) and deep learning to diagnose three common respiratory diseases with only few symptom words. These three diseases are nasopharyngitis, upper respiratory infection, and bronchitis/bronchiolitis. Through natural language processing, the symptom word vectors are encoded by GPT-2 and classified by the last linear layer of the NN. The experimental results are promising, showing that this model achieves a high performance in predicting all these three diseases. They reach 90% in accuracy, which suggests the implications of the developed model, highlighting its potential use in assisting patients understanding their conditions via a remote diagnosis. Unlike previous studies that focus on extracting various categories of information from medical records, this study directly extracts sequential features from unstructured text data, reducing the effort required for data pre-process.

Lymphangioleiomyomatosis (LAM) is a cystic pulmonary disorder that has been known to primarily affect women of childbearing age. The disorder can develop with mutations in the tuberous sclerosis (TSC) genes TSC1 & TSC2 but may arise by other irregular cases in the lack of these mutations. LAM can manifest in a wide variety of ways and generally compounded in symptoms of other lung disorders such as bronchitis, emphysema, & asthma. However, due to lack of effective diagnostic methods, biomarkers of LAM leading to a treatment for the disorder are currently unmet. Using integrative dating analytics and bioinformatics approaches; this study used publicly available microarray data to identify biomarkers in LAM. The results showed important differential expressed genes (DEGs) including: CDH2, CXCL6, ANXA10, MFAP5, RPS4Y1, DCBLD2, PAPPA, TFPI2, SERPINE1, LOX, MYH11, RGS1, SEPP1, TMOD1, ID1, SFTPB, CDH1, HTR2B, OGN, and IGLC1. Furthermore, gene – network and gene ontology (GO) analysis were conducted to show the importance of these genes from a molecular understanding. Taken together, the results revealed could serve as diagnostic and prognostic biomarkers with LAM patients.

Cerebral cavernous malformation (CCM) is a collection of irregular small blood vessels that may be present in the brain or spinal cord. These vessels contain slow – moving blood that commonly clot. These malformations are frequently caused by mutations in one of the CCM genes. The CCM1 (also known as KRIT1) gene is essential for vascular morphogenesis however, its interactions with transcriptional regulators are unknown. Inhibitor of DNA-Binding/Differentiation-3 (ID3) has been recognized to be involved in different vascular/blood vessel diseases such as peripheral arterial disease, stroke, arteriovenous malformations, and atherosclerosis. We show interactions between ID3 and additional key differential expressed genes (DEGs) in microarray data of overexpressed CCM1 in endothelial cells through bioinformatic and data analytic tools. Improved understanding of how ID3, CCM1, and DEGs interact will play an important role in adding to the increasing knowledge for creating therapeutic targets for cerebral cavernous malformations.

Gravitational data are the only ones able to provide otherwise hidden details of what happens below the surface. In this paper, several models are implemented to constrain them with the outer structure of Mars: firstly a Bouguer inversion to reduce gravitational residual, obtaining a crustal depth in the range 23.19-94.56 km. Successively, it was considered an Airy model providing for local buoyancy. Given that its configuration does not permit to evaluate properly the different flexural responses of the lithosphere, and that it overcompensates for the crust of the highest volcanic conformations, a flexural response function was used to filter this model. It uses the lithosphere elastic thickness T_e to scale especially the short wavelengths, correcting for local phenomena. Finally, an optimization was implemented to find the best-fitting values for T_e, using both an infinite plate and a thin shell approximation, and then obtain a global map for crustal density and thickness. Even if the Martian dichotomy is basically confirmed by all the models, along with the well-known anomalies localized at the major basin craters and volcanic Paterae, the Bouguer model supplies the best representation of the state-of-the-art knowledge about the outer layers of Mars and with further improvements can represent a future benchmark for the research on the Red Planet.

In this review, we trace the evolution of the quantum spin-wave theory treating non-collinear spin configurations. Non-collinear spin configurations are consequences of the frustration created by competing interactions. They include simple chiral magnets due to competing nearest-neighbor (NN) and next-NN interactions and systems with geometry frustration such as the triangular antiferromagnet and the Kagomé lattice. We review here spin-wave results of such systems and also systems with the Dzyaloshinskii-Moriya interaction. Accent is put on these non-collinear ground states which have to be calculated before applying any spin-wave theory to determine the spectrum of the elementary excitations from the ground states. We mostly show results from a self-consistent Green’function theory to calculate the spin-wave spectrum and the layer magnetizations at finite T in two and three dimensions as well as in thin films with surface effects. Some new unpublished results are also included. Analytical details and the validity of the method are shown and discussed.

Porcine circovirus type 2 (PCV2) is a small non-enveloped DNA virus that causes swine immunosuppression by inducing apoptosis in lymphocytes. The ORF3 protein plays a major role in PCV2-induced apoptosis in porcine kidney cells, but there is little information regarding this protein in PCV2-infected lymphocytes. In this study, hybridoma screening and epitope mapping were determined by using an indirect ELISA. The mAb 7D3 against ORF3 peptide (residues 35–65) of PCV2 were generated in this study. In vivo situation, the mAb 7D3 recognized ORF3 protein existed in PCV2-infected apoptotic porcine PBMCs. It is noteworthy that thimerosal interfered with the binding of mAb 7D3 to epitope and it was diminished by adding cysteine. Additionally, thimerosal interacting with cysteine-containing peptide was demonstrated by the PTI assay. Furthermore, thimerosal specifically interacted with the antigen-binding sites of mAb 7D3. This study suggested that thimerosal blockade the occlusion of the antigen-binding sites of mAb 7D3 to bind ORF3 peptide (residues 35–65) via thimerosal interacting with cysteine residues which should be located within the antigen-binding sites of mAb 7D3. Overall, the mAb 7D3 has been characterized and it will be a valuables tool in future studies of ORF3 function and the wider mechanism of cell apoptosis caused by PCV2 infection. Similarly, these techniques will be useful for applications in detecting thimerosal too.

In this study, we present a federated learning approach that combines a multi-branch network and the Oort client selection algorithm to improve the performance of federated learning systems. This method successfully addresses the significant issue of non-iid data, a challenge not adequately tackled by the commonly used MFedAvg method. Additionally, one of the key innovations of this research is the introduction of uniformity, a metric that quantifies the disparity in training time amongst participants in a federated learning setup. This novel concept not only aids in identifying stragglers but also provides valuable insights into assessing the fairness and efficiency of the system. Experimental results underscore the merits of the integrated multi-branch network with the Oort client selection algorithm and highlight the crucial role of uniformity in designing and evaluating federated learning systems.

Despite the existence of pasteurization and its advocacy worldwide, most marketed milk in a developing country like Nigeria are sold raw by the locals. It costs hundreds of millions of Naira to set up a conventional pasteurizing plant in Nigeria. Boiled milk ends up curdling which results in to a loss of nutrients, so it is not beneficial to treat milk by this method. This paper hence presents the re-designing and fabrication of the contact area of a solar milk pasteurizer

COVID-19 pandemic and its restrictive measures have present serious unprecedented challenges to human eating behavior. Given that Google Search has become a valuable information resource to examine, predict, and estimate human online interests and behavior, that somehow linked to real people concerns. This study aimed to investigate the features and evaluate the impacts of the COVID-19 pandemic and its lockdown on consumer worldwide interest in eating behavior and its related factors. Google Trends-Relative Search Volumes (RSV) of distinct keywords related to eating behavior, were obtained from a timeframe before and during the COVID-19 pandemic, from January 1, 2018, to December 13, 2020. During the global lockdown from March 11, 2020, to June 30, 2020, RSV curves exhibited a short-term fluctuation of interest in multiple keywords related to eating behavior and its related factors such as food purchasing, food security, food poisoning, panic buying, stocking up, health awareness and mental illness. Spearman’s correlation analysis showed a strong correlation between daily confirmed cases and examined keywords. Univariate repeated measures ANOVA following with Bonferroni Post-hoc test revealed that during the year with the presence of COVID-19 pandemic, people worldwide pay more concerns in each keyword (1) environmental and economic factors (unemployment: +269%, food shortage: +180%, food bank: +50%); (2) health- and food-safety concerns (immunity: +138%, vitamin C: +90%, vitamin D: +55%, zinc: +47%, food storage containers: +40%, food packaging: +31%); (3) food choices and interest (local meat: +84%, frozen food: +67%, CSA: +65%, flour: +66%, bread: +53%, soybean oil: +45%, local fruit: +43%, canned tomato: +42%, refrigerated food: +41%, canned meat: +39%, pancake: +37%, cookie: +29%, butter: +29%, canned fish: +29%, liquior: +20%); (4) social and individual factors (take-out: +128%, deliver: +53%), (6) lifestyle factors (stationary bicycle: +110%, dumbbell: +89%, yoga mat: +84%, treadmill: +65%, grocery store: +51%); (6) psychological factors (isolation: +113%). COVID-19 pandemic and its lockdown have had far-reaching effects on global concerns in many factors related to human eating behavior. Swift action is necessarily performed to strengthen the resilience of the food supply chain system, support and adapt to the new normal behavior, and mitigation the profound negative changes, especially targeting those in high-risks and vulnerable groups and food-insecure regions.

Our knowledge of physics and chemistry is relatively well defined. Results from that knowledge are predictable as, largely, are those of their technical offspring such as electrical, chemical, me-chanical and civil engineering. By contrast biology is relatively unconstrained and unpredictable. A factor common to all areas is the trade-off, which provides a means of defining and quantifying a problem and, ideally, its solution. In order to understand the anatomy of the trade-off and how to handle it, its development (as the dialectic) is tracked from Hegel and Marx to its implementa-tion as dialectical materialism in Russian philosophy and TRIZ, the Theory of Invention. With the ready availability of mathematical techniques, such as multi-objective analysis and the Pareto set, the trade-off is well-adapted to bridging the gaps between the quantified and the unquantifiable, allowing modelling and the transfer of concepts by analogy. It is thus an ideal tool for biomimet-ics. An intracranial endoscope can be derived with little change from the egg-laying tube of a wood wasp. More complex transfers become available as the technique is developed. Most im-portant, as more trade-offs are analyzed, their results are stored to be used again in the solution of problems. There is no other system in biomimetics which can do this.

A formula for the turbulent Prandtl number is derived in terms of local variables available from two-equations turbulence models. The derivation is a direct consequence of the expected square additivity of the molecular and turbulent parameters defining the effective viscosity and the effective conductivity. The formula does not degenerate and leads to a Kays like formulation if approximated. Its utility and its limitation are discussed.

Construction productivity entails a wide range of work combinations. When human resources are scarce, it is critical to replace manpower with machinery, and calculating machinery productivity is crucial. Traditional labor productivity methods, however, cannot address dredging complex multi-attribute decision-making (MADM) problems. Aiming to address the limitations of the traditional labor productivity method, this paper extends the data envelopment analysis (DEA) and proposes a new dredging productivity evaluation method. The proposed method can solve the problem of evaluating various combinations of factors (single-input, multiple-input, sin-gle-output, and multiple-output) and the problem suggesting that the efficiency of the DEA method'scalculation results is equal to 1. The effectiveness of the proposed method was verified using reservoir dredging examples. The simulation results show that the proposed method has broad applicability, can accurately evaluate the related dredging performance issues, and provide directions for construction managers to improve labor productivity.

The ongoing growth of international occurrence of depression and its ability to co-occur with other serious medical disorders such as heart disease, cancer, diabetes, and Parkinson’s disease is a current public health problem. Inhibitor of DNA-Binding/Differentiation (ID) proteins are part of a group of transcriptional factors that have been seen to be involved in neurocognitive disorders and therefore, may have influence on depressive disorders. Previously, it has been established that environmental estrogenic endocrine disruptors (EEDs) such as polychlorinated biphenyls (PCBs) & bisphenol A (BPA) have played an important role in the impact of depressive disorders. Hence, based on many studies, we consider the impact of these environmental pollutants on the group of ID proteins. Improved understanding of how the interaction of ID proteins by EED exposure can influence depressive disorders will contribute essential evidence that can further benefit our public health community with innovative knowledge to prevent these types of mental illnesses.

Curcuma longa is only a rich source of curcumin (1) and its major analogues demethoxycurcumin (2) and bisdemethoxycurcumin (3) among the Curcuma species. The content ratio of these three curcumins in turmeric is depended on the varieties and growing environment. Recently, curcumin (1) has been reported as potential inhibitor of hepatic and enzymatic protein tyrosine phosphatase 1B (PTP1B) and its related disorders such as hypertriglyceridemia, hyperlipidemia and liver steatosis. Thus, we further purified curcumins (1–3) from C. longa and investigated their inhibitory effects against PTP1B and α-glucosidase enzymes. As the result, curcumins (1–3) exhibited potential PTP1B inhibition with IC50 values of 37.8 ± 1.4, 45.3 ± 0.7, and 72.6 ± 1.1 μM, respectively, and α-glucosidase inhibition with similar manner (IC50 values of 78.2 ± 0.2, 82.4 ± 0.6, and 90.6 ± 1.0 μM, respectively). These results reveal a key role of methoxylation in the variation of PTP1B and α-glucosidase inhibitory activity for these curcumins. In addition, density functional theory (DFT) was used accompanying with molecular docking (MD) to analyze the ligand stability and the interaction of curcumins (1–3) with PTP1B and glucoside hydrolase proteins. Assay-based results and the MD data obtained are highly correlation suggesting that the deterioration of the enzyme activity caused by the distortion of structural conformation of PTP1B and glucoside hydrolase may be related to the arrangement of amino acids in protein structure. This reported for the first time that inhibitory effects of curcumins (1–3) against PTP1B and glucoside hydrolase have been examined in vitro and in silico as well.

Due to their excellent physicochemical properties, fluorinated greenhouse gases (F-gases), including hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), sulfur hexafluoride (SF6), and nitrogen trifluoride (NF3), are used in a variety of applications, but they are potent greenhouse gases. Therefore, they have been blanketed into the list of phase-out under the international protocols or treaties. In this work, the updated statistics of the Taiwan’s national inventory report (NIR) were used to analyze the trends of F-gases (i.e., HFCs, PFCs, SF6 and NF3) emissions during the period of 2000-2020. Furthermore, the regulatory strategies and measures for reducing the emissions of the four F-gases will be summarized to be in accordance with the national and international regulations. With the progressive efforts by the regulatory requirements and the industry’s voluntary reduction, the total F-gases emissions indicated a significant increase from 2,462 kilotons of carbon dioxide equivalents (CO2eq) in 2000 to the peak value (i.e., 12,643 kilotons) of CO2eq in 2004, but sharply decreased from 10,284 kilotons of CO2eq in 2005 to 3,906 kilotons of CO2eq in 2020. It was also found that the most commonly used method for controlling the emissions of F-gases from the semiconductor and optoelectronic industries was based on the thermal destruction-local scrubbing technology.

Embryologically, the left brachiocephalic vein (LBV) originates as an anastomotic channel between the right and left anterior cardinal veins 1 . This positions the LBV between the manubrium sterni anteriorly and the innominate artery posteriorly 2 . This pattern of adjacency of the aorta to the LBV is unique to mammals and results from a quirk of evolution 3 . With age, the ascending aorta unfolds, elongates and dilates 4 . Simultaneously, there is a change in the thoracic geometry that reduces the thoracic volume primarily from disc height loss and kyphosis 5 . These transitions progressively compress the LBV. Normally this compression is circumvented via collateral pathways and "Blood finds a way" 6 . However, traversing these circuitous pathways comes at a cost and can result in delayed transit times and venous congestion. While it is possible that compression of the LBV in the setting of adequate collateral channels may fail to provoke any pathologic sequelae, we propose a phenomenon in which such compression in the setting of inadequate collateral circulation may lead to a state of pathologic venous congestion. This anatomic anomaly and its associated clinical features, if identified, can offer a new avenue for treatment options for some of the hitherto unexplained neurologic disorders.

This paper presents a deep learning-based multi-guidance line detection approach, enabling an autonomous four-wheeled agricultural robot to navigate strip farmland. The integration of Proportional-Integral-Derivative (PID) and Fuzzy Logic Control (FLC) systems optimizes the velocity and heading angle of robot, facilitating smooth navigation along identified guidance line. Enhance cornering maneuverability with real-time kinematics (RTK)-assisted Global Navigation Satellite System (GNSS) (RTK-GNSS) positioning. Additionally, the spraying system combined with deep learning can effectively identify weeds and crop nutrient deficiencies to achieve precise spraying. The guidance system prioritizes irrigation lines for navigation, with additional guidance from crop and furrow lines. Trigonometric analysis determines the angular deviation between the identified guidance line and the vertical line of the top view image. Experiments under diverse weather conditions demonstrate the stable navigation of robot at 12.5 cm/s, achieving up to 90% accuracy in weed and nutrient deficiency identification. The spraying accuracy for weeds and deficiencies averages 87% and 73%, respectively, underscoring the system's contribution to sustainable and precision horticulture practices.

The betel nut chewing habit of taxi drivers has shown that can promote companionship, relaxation, and emotional stability at work. 19.3% of betel nut behaviors are instigated by an invitation among drivers to engage in the habit. The objective of this study was to explore the impact of social learning, subjective norm, and self-efficacy on betel nut chewing in taxi drivers in Taiwan. We conducted a cross-sectional study on taxis’ driver of betel nut behaviour and measured their evaluations of its consequences using a self-report questionnaire. Other variables include socio-demographic characteristics and chewing-related experience. The overall variance is 44.5% in socio-demographic variable, social learning, self-efficacy, and subjective norm. Results show that the variables of affected betel nut chewing behavior, driving years has the strongest influence, followed by learning about betel nut from the media, relatives, and friends, and education level, which positively affect betel nut chewing behavior. Findings indicated that education related to quitting betel nut chewing, significant others, and rules of the taxi fleet are adopted to design a plan for quitting betel chewing to reduce the positive expectations of taxi drivers for betel nut chewing and decrease their betel nut chewing behavior.

In all areas of medicine, especially in radiology, computers are increasing year by year. Filmless radiology, speech recognition software, electronic application forms, and teleradiology are recent developments that have greatly improved radiologists' performance. This research explores radiology software trends, predictions, and the challenges posed by informatics and historical trend analysis. The rationale behind this research is that information technology (IT) is overgrowing almost every day. We must continuously seek new ways to apply IT to make more use of resources. Consequently, IT becomes increasingly crucial to radiology organizations' innovative thinking, workflow, and business models. This study aimed to analyze all radiology software publications in the Science Citation Index (SCI). From 1991 to July 2021, SCI was used to search for publications systematically. We have also widely used this historical method in radiology software research. The findings and discussions are base on an assessment of trends, predictions, contributions, and challenges in radiology software, and we are exploring radiology software with six evolutionary stages. The gift of this research is that radiology managers realize that the use of new information technologies is closely related to survival in a competitive environment. Radiology companies can review these new technologies to develop more innovative business models and services to improve operational deficiencies.

The present study propose an innovative turn-boring operation method and focuses on finding optimal turn-boring process parameters for 15-5PH Stainless steel by considering multiple performance characteristics using Taguchi orthogonal array with the grey relational analysis, the effect of machining variables such as concentration of cutting fluid , temperature of cutting fluid , feed rate, depth of cut and cutting speed are optimized with considerations of multiple performance characteristics namely surface roughness, roundness error and material removal rate, the optimal values were found out from the Grey relational grade. The result of the Analysis of Variances (ANOVA) is shown that the most significant factor is cutting speed, followed by feed rate, concentration of cutting fluid, radial depth of cut and temperature of cutting fluid. Finally, confirmation tests were carried out to make a comparison between the experimental results and developed model. Experimental results have shown that machining performance in the turn-boring process can be improved effectively through this approach.

Surface-enhanced Raman spectroscopy (SERS) is commonly used for super-selective analysis through nanostructured silver layers in the environment, food quality, biomedicine, and materials science. To fabricate a high-sensitivity but more accessible device of SERS, dc magnetron sputtering technology was used to realize high sensitivity, low cost, stable deposition rate, and rapid mass production. This study investigated various thicknesses of a silver film ranging from 3.0 to 12.1 nm by field-emission-scanning-electron microscope, X-ray diffraction, and X-ray photoelectron spectroscopy. In the rhodamine 6G (R6G) testing irradiated by a He-Ne laser beam, the analytical enhancement factor (AEF) of 9.35x108, the limit of detection (LOD) of 10-8 M, and the relative standard deviation (RSD) of 1.61% were better than other SERS substrates fabricated by the same dc sputtering process because the results show that the 6 nm thickness silver layer has the highest sensitivity, stability, and lifetime. The paraquat and acetylcholine analytes were further investigated and high sensitivity was also achievable. The proposed SERS samples were evaluated and stored in a low humidity environment for up to forty weeks, and no spectrum attenuation could be detected. Soon, the proposed technology to fabricate high sensitivity, repeatability, and robust SERS substrate will be an optimized process technology in multiple applications.

Aluminum wire is a common material for wire bonding due to its resistance to oxidation and low price. It does not melt when becoming a free air ball (FAB) during the electronic flame-off (EFO) process with wettability, and is applied by wedge bonding. This study used 20μm Zn-Coated Al-0.5wt.%Si (ZAS) wires to improve the FAB shape after the EFO process, while maintaining stability of the mechanical properties, including the interface bonding strength and hardness. In order to test circuit stability after ball bonding, the current-tensile test was performed. During the experiment, it was found that 80nm ZAS with wire bonding has lower resistance and higher fusing current. For the bias tensile test, the thicker Zn film diffused into the Al-Si matrix easily, after which the strength was reduced. The ball-bond interfaces had no change in their condition before and after the bias. Accordingly, ZAS could be a promising candidate for ball bonding in the future.

EGFR mutations are heterogenous but all carry the same weighting in the Lung-molGPA. The aim of this study was to elucidate the different prognostic implications of molecular subtypes and frontline TKIs in EGFR-mutated lung adenocarcinoma with synchronous brain metastases (BM) using the Lung-molGPA. Medical records were searched in hospital databases from 2011 to 2015. Patients with EGFR-mutated adenocarcinoma and brain metastases who received TKIs were included. The Kaplan-Meier method was used to estimate survival, and multivariate Cox proportional hazard models were used to estimate adjusted hazard ratios (HRs) and 95% confidence intervals (CIs). A total of 256 patients were included with a median overall survival (OS) of 17.2 months. In multivariate analysis of OS, only age (70 versus <70 years, HR:1.71, 95% CI:1.25-2.35, p<0.001), KPS (<70 versus 70, HR:1.71, 95% CI:1.26-2.31, p<0.001), and rare mutations (other versus exon 19 deletions, HR:1.78, 95% CI:1.04-3.05, p=0.037) remained statistically significant. In patients with a Lung-molGPA score 2.5, EGFR molecular subtypes had different median OS (exon 19 deletions versus Leu858Arg versus other, 18.8 vs 12.4 vs 12.1 months, p=0.021). In conclusion, different molecular subtypes treated with frontline TKIs have different prognostic implications in the Lung-molGPA. Further prospective studies are warranted to validate these findings.

This study explores the implementation of an interdisciplinary curriculum in product and media design education and its impact on knowledge innovation and competency development. The curriculum is based on scaffolding theory, incorporating design proposals, workshops, digital design, marketing tests, and marketing activities guided by teachers and mentors from a branded ceramic workshop. The research findings demonstrate that the interdisciplinary curriculum facilitates knowledge innovation and fosters the development of students' professional skills, creativity, and problem-solving skills. The study also highlights the importance of stable scaffolding, including artefact, peer, and teacher support, which significantly contributes to cultivating transformational competencies, as outlined in the 2030 OECD Learning Compass. Additionally, the collaboration with Shanshing Four Seasons Celadon Studio on a patented ceramic product, "Funny Monkey," is a tangible example of the journey towards a triple-helix knowledge economy. This research underscores the importance of interdisciplinary curricula in promoting knowledge innovation and integrating transformational competencies in education. Scaffolding theory provides practical guidance for student learning and teaching strategies, presenting a sustainable roadmap for developing interdisciplinary curricula and offering a concrete and transferable pedagogical prototype for educational innovation.

Traffic accidents are among the most common causes of death worldwide. According to statistics from the World Health Organization (WHO), 50 million people are involved in traffic accidents every year. Canada, particularly Montreal, is not immune to this problem. Data from the Société de l’Assurance Automobile du Québec (SAAQ) shows that there were 392 deaths on Québec roads in 2022, 38 of them related to the city of Montreal. This value represents an increase of 29.3% for the city of Montreal compared to the average for the years 2017 to 2021. In this context, it is important to take concrete measures to improve traffic safety in the city of Montreal. In this article, we present a web-based solution based on machine learning that predicts the severity of traffic accidents in the city of Montreal. This solution uses dataset of traffic accidents that occurred in Montreal between 2012 and 2021. Classification algorithms such as eXtreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), Random Forest (RF) and Gradient Boosting (GB) were used to develop the prediction model. When evaluating the prediction model, performance metrics such as precision, recall, F1 score, and accuracy are taken into account. The performance analysis shows an excellent accuracy of 96% for the prediction model based on the XGBoost classifier. The other models (CatBoost, RF, GB) achieved 95%, 93% and 89% accuracy, respectively. The prediction model based on the XGBoost classifier was deployed using a client-server web application managed by Swagger-UI and the Flask Python framework.

In September 2021, the European Chemicals Agency evaluated a dossier for restricting polycyclic aromatic hydrocarbons (PAHs) in infant diapers and concluded that risks were not demonstrat-ed because of inconclusive exposure data. To fill this gap, we measured the 16 priority PAHs of the U.S. Environmental Protection Agency in the diaper core of four brands and in the sheets and fastening tapes of six brands of commercially available diapers. Health risks were conservatively assessed by assuming that dermally absorbed PAHs can cause both local (skin cancer) and sys-temic critical effects (neurobehavioral changes). Total concentrations of PAHs in diaper core and top sheet, the only significant contributors to skin exposure, averaged 34.5 μg/kg and 66.8 μg/kg, respectively. Excess skin cancer risks and hazard quotients for neurobehavioral effects calculated with the daily dose of total PAHs from combined diaper core and top sheet averaged 1.4 x 10-7 and 1.19 x 10-2, respectively. The median daily dose of total PAHs and of its ben-zo[a]pyrene-equivalent from breast milk estimated worldwide are 171 and 30 times greater than that from combined diaper core and top sheet, respectively. Altogether these findings indicate that trace levels of PAHs found in infant diapers are unlikely to pose health risks.

Sustainable agriculture is essential to provide food security for a growing world population without further sacrificing the integrity of the environment. To make progress towards agricultural sustainability we must consider ecological and socioeconomic processes within the agricultural socio-ecosystem and involve stakeholders in the research process. We propose an innovative experimental approach for examining how natural regulation of ecosystems may provide an alternative to increasing external inputs in agriculture while improving the socio-economic welfare of farmers. These “social-ecological experiments” go further to participatory action research by not only involving stakeholders in the research process but also by manipulating simultaneously socioeconomic and ecological processes under real field conditions to give a faster route to sustainability. Social-ecological experiments are undertaken in real field conditions, explicitly involving stakeholders, and help untangle the drivers of social-ecological dynamics under various land management and farming practices. Social-ecological experiments are distinct from adaptive management and scenario-planning approaches as they highlight the interactions between ecological and social processes, manipulate the social and ecological processes shaping the system and show causal links between patterns and processes. As an example, we describe a social-ecological experiment for reducing herbicide use. Social-ecological experiments offer great opportunities for increasing stakeholders’ acceptance of environmental policies implemented through adaptive management. These experiments may help to identify management practices that optimize multiple objectives, deliver a portfolio of ecosystem services and satisfy key stakeholders.

Cardiopulmonary lesions, which manifest from various types of diseases such as pulmonary arterial hypertension, atherosclerosis, pulmonary arteriovenous malformations, lymphangioleiomyomatosis, and peripheral arterial disease, pose a public health problem. Vascular remodeling, which refers to alternations to the structure of the vessel is an important pathophysiological feature of these diseases. The Inhibitor of DNA-binding/Differentiation-3 (ID3), which is part of the ID family of transcriptional regulators, has been demonstrated to contribute to an essential role in the vasculature and therefore may influence the alterations of these lesions. This review will cover the existing understanding of how ID3 may contribute to cardiopulmonary lesion perturbations via involvement in vascular remodeling. Furthermore, based on the accumulative quantity of reports that indicate oxidative stress plays a essential function in the pathophysiology of vascular remodeling, we will also consider the impact of exposure to estrogenic endocrine disruptors (EEDs) such as polychlorinated biphenyls (PCBs) and bisphenol A (BPA) on ID3 & cardiopulmonary disease. Improved understanding of how ID3 pathways contributes to these molecular mechanisms in the lesion will prospectively deliver beneficial information in the mediation of vascular remodeling associated with ID3 & EED exposure, which may play an essential role in cardiopulmonary disease prevalence.

Theoretical approaches were applied to study the effect of magnetic field and heat transfer on the flow of blood plasma through an asymmetric arterial segment. The plasma was considered to be unsteady, laminar and an incompressible fluid through non-uniform arterial segment in a two-dimensional flow. Axial velocity, pressure gradient, stream function and pressure shift per wavelength were evaluated in blood plasma flow in arteries by use of coupled linear partial differential equations, solved with the help of Finite difference method. The corresponding initial and boundary conditions were obtained by discretizing the flow channel. The effect of magnetic field on blood plasma in arteries was determined by creating a magnetic field gradient through the application of a varying strength of magnetic field on the flow. Heat transfer characteristics due to applied magnetic field and viscosity of blood was obtained by use of linear partial differential equations to determine varying temperature conditions in heat transfer characteristics. Numerical results for velocity profiles, magnetic profiles and temperature profiles were obtained to characterize blood plasma viscosity by use of various non-dimensional parameters. The results were graphically presented using MATLAB software. The results obtained helped in analyzing theoretically the effects of magnetic field and heat transfer in arterial plasma flow.

In recent years, nonalcoholic fatty liver disease/steatohepatitis (NAFLD/NASH) has become a leading worldwide disease, and its therapies are facing many complexities. Peroxisome proliferator-activated receptors (PPARs) are ligand-activated transcription factors that belong to the nuclear receptor (NR) superfamily and have three subtypes: PPARα (NR1C1), PPARβ/δ (NR1C2), and PPARγ (NR1C3). They have been identified to be essential in the regulation of lipid metabolism by controlling the transcription of genes related to fatty acids, bile acids, and cholesterol metabolism. Many PPAR agonists have been reported, such as natural agonists (fatty acids, eicosanoids, and phospholipids) and synthetic ligands (fibrates, thiazolidinediones, glitazars, and elafibranor). PPAR-based chemicals have a breakthrough in the innovation of therapy for fatty liver disease. This review will provide an overview of how PPARα and PPARγ play roles in lipid homeostasis, discussing the consideration of their agonists as potential therapeutic agents for NAFLD/NASH.

This study presents an instant preview and analysis system implementation of intelligent stereo cameras (ISCs). A parameter optimization prototype adopted the instant preview and analysis system of the ISCs has been achieved the automatic alignment function, and obtained optimal stereo films with the automatic alignment function by adjusting gap and angle between dual cameras. The instant preview and analysis system of the ISCs with parameter optimization can enhance the quality of stereo films effectively and reduce filmed errors and save retouched cost and time in harsh filmed environment.

In the face of pressing global challenges, coupling the synergy of dance arts, sustainability, and marketing communications emerges as a potent catalyst for advancing sustainable behavior within society. Although prior research has examined the emotional stimulations raised by dance performance, it has not systematically demonstrated the role of traditional dance as a vehicle for sustainability marketing communications. This study fills this gap by using the AIDA model (Attention, Interest, Desire, Action) as a foundation, conceptualizing dance performance elements into seven dimensions, and incorporating emotional engagement and environmental consciousness to foster sustainable behavior. Collaborating with Taiwanese dance troupe, the research produced a performance titled, ‘The New Dragon Palace Chronicle’, which focuses on the themes of destruction and preservation of underwater life, enriched with cultural and sustainability symbolism. The hypothesis were tested on a sample of 315 Taiwanese audience who watching the performance. The results confirmed that the seven dimensions of the dance elements have substantial validity and reliability, significantly drawing the attention of the audience as preliminary stage of awareness. This attention fosters emotional engagement and amplified environmental consciousness and significantly influences interest, desire, and ultimately, sustainable behavior action. The findings underscore the vital role of dance art performance in nurturing actions that contribute to social sustainability.

With the emergence of new VR/AR technologies, the question arises of how they can be used in human-swarm interaction. Where are the limitations and boundaries of conventional interfaces, and how can VR/AR overcome them? To find out, a user study was conducted. The subjects were able to interact with an autonomously acting swarm of drones using both a conventional desktop interface (Desktop-HSI) and a newly developed VR interface (VR-HSI).The resulting assessments are used as a basis for determining the extent to which this new technology can be applied in human-swarm interaction. The technical basis was an already tested simulation software that was expanded with a network interface to allow interacting with a drone swarm using connected HSIs (Human-Swarm Interfaces). Standardized communication was emphasized to enable the development of HSIs with different requirements. Specifically for this work, a VR-HSI was developed with Unity and a desktop-HSI with Java and their performance regarding human-swarm interaction was evaluated.

Rapid in vitro assessment of antimicrobial drug efficacy under clinically relevant pharmacokinetic conditions is an essential element of both drug development and clinical use. Here we present an overview of a novel integrated methodology for rapid assessment of such efficacy, particularly against emergence of resistant bacterial strains, as jointly researched by the authors in recent years. This methodology enables rapid in vitro assessment of antimicrobial efficacy of a single or multiple drugs in combination, following clinically relevant pharmacokinetics. The proposed methodology entails (a) automated collection of longitudinal time-kill data in an optical-density instrument; (b) processing of collected time-kill data with the aid of a mathematical model to de-termine optimal dosing regimens under clinically relevant pharmacokinetics for a single or mul-tiple drugs; and (c) in vitro validation of promising dosing regimens in a hollow fiber system. Proof-of-concept of this methodology through a number of in vitro studies is discussed. Future directions for refinement of optimal data collection and processing are discussed.

The southern extent of the boreal forest in North America has experienced intensive human disturbance in the past decades. Among these, forest harvesting leads to the substantial loss of late-successional stands that include key habitat attributes for several avian species. The American Three-toed Woodpecker, Picoides dorsalis, is associated with continuous old spruce forests in the eastern part of its range. In this study, we assess the influence of habitat characteristics at different scales on the occupancy of American Three-toed Woodpecker in a heavily managed boreal landscape of northeastern Canada, and we inferred species occupancy at the regional scale. We conducted 185 playback stations over two breeding seasons and modelled the occupancy of the species while taking into account the probability of detection. American Three-toed Woodpecker occupancy was lower in stands with large areas recently clear-cut, and higher in landscapes with large extents of old-growth forest dominated by black spruce. At the regional scale, areas with high probability of occupancy were scarce and mostly within protected areas. Habitat requirements of the American Three-toed Woodpecker during the breeding season, coupled with over-all low occupancy rate in our study area, challenge its long-term sustainability in such heavily managed landscapes. Additionally, the scarcity of areas of high probability of occupancy in the region suggest that the ecological role of old forest outside protected areas could be compromised.

Thermally-assisted-occupation density functional theory (TAO-DFT) has been an efficient electronic structure method for studying the ground-state properties of large electronic systems with multi-reference character over the past few years. To explore the time-dependent (TD) properties of electronic systems (e.g., subject to an intense laser pulse), in this work, we propose a real-time (RT) extension of TAO-DFT, denoted as RT-TAO-DFT. Besides, we employ RT-TAO-DFT to study the high-order harmonic generation (HHG) spectra and related TD properties of molecular hydrogen H2 at the equilibrium and stretched geometries, aligned along the polarization of an intense linearly polarized laser pulse. The TD properties obtained with RT-TAO-DFT are compared with those obtained with the widely used time-dependent Kohn-Sham (TDKS) method. In addition, issues related to the possible spin-symmetry breaking effects in the TD properties are discussed.

This article presents a study conducted within a wind tunnel to enhance understanding of the excitation mechanism of stay cable vibration under arid conditions. Numerous wind tunnel tests were meticulously analyzed. Initially, the vibration of the stay cable was measured under steady flow conditions at a flow angle of 45 degrees and an inclination of 25 degrees, while varying wind velocities were applied. Additionally, an investigation into the flow field surrounding the stay cable was conducted in both vertical and horizontal directions. By utilizing two hot wire anemometers in the cable wake, an extensive database of flow field measurements was obtained. The experimental results revealed that the vibration characteristics of the stay cable under the arid conditions considered in this study aligned with findings reported in existing literature. Notably, a deeper comprehension of the excitation mechanism of a stay cable in a dry state was attained. This mechanism is closely associated with the inhibition of Karman vortices and the development of low-frequency vortices. At low wind speeds, Karman vortices predominated, resulting in small-amplitude vibrations. However, as the wind speed increased, the influence of Karman vortices diminished progressively, while the low-frequency vortices grew stronger. These low-frequency vortices exhibited high energy and a significant correlation with shedding along the stay cable, thereby inducing cable vibration in a dry environment.

Aluminum alloy (Al6061) sheet micro-hole processing is extensively used in smartphones, tablet PC, and smart wearable devices. The micro-hole processing is commonly performed using laser, micro drilling, microstamping, micro discharge, and chemical etching technologies. Micro-stamping technology is characterized by precision and rapid processing, but the precision positioning of the punch head and lower die is one of the major difficulties in micro-hole stamping, especially in manufacturing array micro-holes. This study used stamping without die technology. This technology uses an array punch head to punch the lower die holes on the base, then performs array micro-hole stamping. The experimental results show that a micro-hole array with 37 micro-holes were successfully manufactured and can be reproduced many times. Tapered array micro-holes with a high aspect ratio can be manufactured using this stamping without die technology; the micro-hole depth can be 260μm, the inlet diameter is 116μm, and the outlet diameter is 25μm. This study has successfully developed the feasibility of array micro-hole stamping technology.

A road network represents road objects in a given geographic area and their interconnections, and is an essential component of intelligent transportation systems (ITS) enabling emerging new applications such as dynamic route guidance, driving assistance systems, and autonomous driving. As the digitization of geospatial information becomes prevalent, a number of road networks with a wide variety of characteristics coexist. In this paper, we present an area partitioning approach to the conflation of two road networks with a large difference in level of details. Our approach first partitions the geographic area by the Network Voronoi Area Diagram (NVAD) of low-detailed road network. Next, a subgraph of high-detailed road network corresponding to a complex intersection is extracted and then aggregated into a supernode so that a high matching precision can be achieved via 1:1 node matching. To improve the matching recall, we also present a few schemes that address the problem of missing corresponding object and representation dissimilarity between these road networks. Numerical results at Yeouido, Korea's autonomous vehicle testing site, show that our area partitioning approach can significantly improve the performance of road network matching.

In this paper, we present an extended version of the Hadi-Vencheh model for multiple criteria ABC inventory classification. The proposed model is a nonlinear weighted product model (WPM) which determines a common set of weights for all the items. Our proposed nonlinear WPM incorporates multiple criteria with different measure units, without converting the performance of each inventory item in terms of each criterion into a normalized attribute value, thereby providing an improvement over the model proposed by Hadi-Vencheh. Our study mainly includes various criteria for ABC classification, and demonstrates an efficient algorithm for solving nonlinear programming problems in which the feasible solution set does not have to be convex. The algorithm presented in this study improves the solution efficiency of the Canonical Coordinates Method (CCM) algorithm substantially when applied to large scale, nonlinear programming problems. The modified algorithm was tested to compare our proposed model results to the results derived using the Hadi-Vencheh model and demonstrate the algorithm's efficacy. The practical implications of the study are to develop an efficient nonlinear optimization solver by optimizing the quality of existing solutions, thus improving time and space efficiency.

Using smartphone especially android platform has already got eighty percent market shares, due to aforementioned report, it becomes attacker’s primary goal. There is a growing number of private data onto smart phones and low safety defense measure, attackers can use multiple way to launch and to attack user’s smartphones.(e.g. Using different coding style to confuse the software of detecting malware). Existing android malware detection methods use multiple features, like safety sensor API, system call, control flow structure and data information flow, then using machine learning to check whether its malware or not. These feature provide app’s unique property and limitation, that is to say, from some perspectives it might suit for some specific attack, but wouldn’t suit for others. Nowadays most malware detection methods use only one aforementioned feature, and these methods mostly analysis to detect code, but facing the influence of malware’s code confusion and zero-day attack, aforementioned feature extraction method may cause wrong judge. So, it’s necessary to design an effective technique analysis to prevent malware. In this paper, we use the importance of word from apk, because of code confusion, some malware attackers only rename variables, if using general static analysis wouldn’t judge correctly, then use these importance value to go through our proposed method to generate picture, finally using convolutional neural network to see whether the apk file is malware or not.

The signal processing technique is one of the principal tools for diagnosing power quality (PQ) issues in electrical power systems. The Discrete Fourier Transform (DFT) is a frequency analysis technique used to process power system signals and identify PQ problems. However, the DFT algorithm may lead to spectral leakage and picket-fence effect problems for asynchronously sampled signals that contain harmonic, inter-harmonic, and flicker components. To resolve this shortcoming, a hybrid method for frequency estimation based on a second-level DFT approach and a frequency-domain interpolation algorithm to obtain the accurate fundamental frequency of a power system is proposed in this paper. This method uses a second-level DFT to compute the cosine and sine parts for the fundamental frequency components of the acquired signals. Then, a frequency-domain interpolation approach is adopted to determine the amplitude ratio for the cosine and sine parts of the system's fundamental frequency. To demonstrate the performance of the proposed frequency estimation method, the observation window used by this paper to evaluate different estimation algorithms is 200 ms. According to the IEC standards, a 200 ms acquisition window is recommended for power system quality assessment. A set of mixed signals with harmonic, inter-harmonic, and flicker components with the fundamental frequency deviation is used. The evaluation results demonstrate the superiority of the new method over other approaches for assessing asynchronously sampled signals contaminated with noise, harmonic, inter-harmonic, and flicker components.

The present study propose an innovative turn-boring operation method and focuses on finding optimal turn-boring process parameters for AA7050-T7451 by considering multiple performance characteristics using Taguchi orthogonal array with the grey relational analysis, the effect of cutting variables such as, feed rate, depth of cut and cutting speed are optimized with considerations of multiple performance characteristics namely surface roughness, roundness error, material removal rate and power consumption the optimal values were found out from the Grey relational grade. The result of the Analysis of Variances (ANOVA) is proved that the most significant factor is cutting speed, followed by feed rate, radial depth of cut. Finally, confirmation tests were performed to make a comparison between the experimental results. Experimental results have shown that machining performance in precision turn-boring process can be improved effectively through this approach

Due to the demands of personal travels and entertainments, light airplanes and small business aircrafts are developing rapidly. Light airplane structure is simple; however, it lacks crashworthiness design, especially the considerations on the impact of energy absorption. Therefore, in an event of accident, significant damage to passengers will be usually incurred. Airplanes made of composite materials structurally have high specific strength and good aerodynamic configuration. These materials have become the primary choice for new airplane development. This study mainly explores the topology optimization analysis of the light aircraft’s cockpit made of carbon fiber reinforced composites. This paper compares the compression amounts in the original models of composite material and aluminum alloy fuselages with the models after optimization during the crash-landing, in order to investigate the safety of fuselages made of different materials after structural optimization under the dynamic crashing. This study found that the energy absorbed by the aluminum alloy fuselage during crash-landing is still higher than that by the carbon fiber reinforced composites fuselage. On the other hand, the aluminum alloy fuselage after topology optimization could have an energy absorption capability enhanced by 40%, as compared to the that of the original model of aluminum alloy fuselage.

This study compares the physico-chemical characteristics of three different types of biochar produced from biomass residues in Vietnam as a basis for optimising their application in water purification and soil fertilisation. Wood biochar (WBC), rice husk biochar (RBC), and bamboo biochar (BBC) were produced under limited oxygen conditions using equipment available locally in Vietnam, known as a Top-Lid Updraft Drum (TLUD). The resulting biochars were characterised using a suite of state-of-the-art methods to understand their morphology, surface chemistry and cation exchange capacity. Surface areas (measured by BET) for WBC and BBC were 479.34 m²/g and 434.53 m²/g, respectively, significantly higher than that of RBC which was only 3.29 m²/g. The morphology as shown in SEM images corresponds with the BET surface area, showing a smooth surface for RBC, a hollow surface for BBC, and a rough surface for WBC. All three biochars produced alkaline, with pH values around 10, and all have high carbon contents (47.95 - 82.1 %). Cation exchange capacity (CEC) was significantly different (p<0.05) among the biochars, being 26.70 cmol/kg for RBC, 20.7 cmol/kg for BBC, and 13.53 cmol/kg for WBC, which relates to the cations (Ca, Mg, K) and functional groups with negative charge (carboxyl, hydroxyl) present on the biochar surfaces. The highest contents of Ca, Mg and K in rice husk BC may explain its highest CEC values. Thus, although the biochars were produced by the same method, the various feedstocks lead to quite different physico-chemical properties. Ongoing work is linking these physico-chemical properties to the biochar efficiencies in terms of nitrate and ammonia capture capacities for use as fertilisers, and for adsorption of heavy metals (Zn, Cu) or water filtration, in order to design optimal biochar properties for specific applications.

Objective: This study aimed to demonstrate the availability of portable devices for nasal FeNO measurement, which could replace exhaled NO measurement based on the continuous aspiration of multiple flow measurement systems. Methods: A descriptive cross-sectional study. The healthy subjects without rhinosinusis disorders (control group) and those with diagnosed allergic rhinitis (AR group) were enrolled in the study. They underwent nasal and bronchial FeNO measurements by both fixed flow NObreath® connected to nasal mask and multiple flows HypAir FeNO®. Results: A total of 153 people were recruited including 63 healthy subjects and 90 subjects with AR. There were no statistically significant differences in anthropometric characteristics between the two groups (P > 0.05). There were a significant increase in FeNO values in the AR group compared to the control group; concentrations of nasal and bronchial FeNO mearsured by NObreath® were 76.0 ± 50.7 ppb (vs 36.0 ± 28.1 ppb, P < 0.01) and 32.3 ± 15.4 ppb (vs 9.2 ± 5.6 ppb, P < 0.001), and those mearsured by NObreath® Hypair FeNO® were 1796.9 ± 523.3 ppb (vs 400.2 ± 123.1 ppb, P < 0.001) and 37.3 ± 23.0 ppb (vs 17.4 ± 18.3 ppb, P < 0.01). A weak correlation between the values measured by NObreath® and Hypair FeNO® devices was observed (r = 0.023 and r = 0.089, respectively; p < 0.05). Conclusions: The handheld NObreath® device is effective in measuring FeNO concentrations in both normal subjects and AR patients. The advantages in price and size, creating a compact, low-cost device that is convenient for use and research.

Thermal infrared imagery provides temperature information on target objects, and has been widely applied in non-destructive testing. However, thermal infrared imagery is not always able to display detailed textures of inspected objects, which hampers the understanding of geometric entities consisting of temperature information. Although some commercial software has been developed for 3D thermal model displays, the software requires the use of expensive specific thermal infrared sensors. This study proposes a cost-effective method for 3D thermal model reconstruction based on image-based modeling. Two smart phones and a low-cost thermal infrared camera are employed to acquire visible images and thermal images, respectively, that are fused for 3D thermal model reconstruction. The experiment results demonstrate that the proposed method is able to effectively reconstruct a 3D thermal model which extremely approximates its corresponding entity. The total computation time for the 3D thermal model reconstruction is intensive while generating dense points required for the creation of a geometric entity. Future work will improve the efficiency of the proposed method in order to expand its potential applications to in-time monitoring.

We study in this paper the time evolution of stock markets using a statistical physics approach. We consider an ensemble of agents who sell or buy a good according to several factors acting on them: the majority of the neighbors, the market ambiance, the variation of the price and some specific measure applied at a given time. Each agent is represented by a spin having a number of discrete states q or continuous states, describing the tendency of the agent for buying or selling. The market ambiance is represented by a parameter T which plays the role of the temperature in physics: low T corresponds to a calm market, high T to a turbulent one. We show that there is a critical value of T, say T_c, where strong fluctuations between individual states lead to a disordered situation in which there is no majority: the numbers of sellers and buyers are equal, namely the market clearing. The specific measure, by the government or by economic organisms, is parameterized by $H$ applied on the market at the time t₁ and removed at the time t₂. We have used Monte Carlo simulations to study the time evolution of the price as functions of those parameters. In particular we show that the price strongly fluctuates near T_c and there exists a critical value H_c above which the boosting effect remains after H is removed. Our model replicates the stylized facts in finance (time-independent price variation), volatility clustering (time-dependent dynamics) and persistent effect of a temporary shock. The second party of the paper deals with the price variation using a time-dependent mean-field theory. By supposing that the sellers and the buyers belong to two distinct communities with their characteristics different in both intra-group and inter-group interactions, we find the price oscillation with time. Results are shown and discussed.

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

In manual maintenance inspections of large-scaled photovoltaic (PV) or rooftop PV systems, several days are required to survey the entire PV field. To improve reliability and shorten the amount of time involved, this study proposes an electrical examination based method for locating multiple faults in the PV array. The maximum power point tracking (MPPT) algorithm is used to estimate the maximum power of each PV panel; this is then compared with metering the output power of PV array. Power degradation indexes are parameterized to quantify the degradation between maximum power and metered output power. Bidirectional hetero-associative memory (BHAM) networks are then used to locate multiple faults within the entire PV field. For a rooftop PV system with two strings, as seen in Figure 1, experimental results demonstrate that the proposed model has computational efficiency for real-time applications and that its algorithm is easily implemented in a mobile intelligent vehicle.

Food fraud (often called fake food in South Africa), the deliberate misrepresentation or adulteration of food products for financial gain, is a growing problem in South Africa (SA) with severe public health and financial consequences for consumers and businesses. The recent public outcry against food fraud practices, especially in communities that have lost loved ones due to the consumption of allegedly unsafe foodstuffs, highlights the grave danger that food fraud poses to consumers and the potential for significant reputational damage to food manufacturers. Despite the risks, food fraud often goes undetected, as perpetrators are becoming increasingly sophisticated. The precise magnitude of food fraud remains obscure, as incidents that do not cause consumer illnesses are frequently unreported and, as a result, are not investigated. Food fraud costs the global economy billion annually. This cost is borne by consumers, businesses, and the government. Food fraud can occur at any stage of the food supply chain, from production to processing to retailing or distribution. This is due in part to the limitations of current analytical methods, which are not always able to detect food fraud. This systematic literature review of food fraud in SA looks at several factors that may be contributing to epidemic of food fraud (fake food), including lenient penalties, inadequate government commitment, a complex labelling regulations, emerging threats such as e-commerce, and a shortage of inspectors and laboratories. The review recommends establishing a single food control/safety authority, developing more food safety laboratories, and adopting innovative technologies to detect and prevent food fraud. SA faces a serious food fraud crisis unless decisive action is taken.

Before the adaptive immune response is established, retroviruses can be targeted by several cellular host factors at different stages of the viral replication cycle. This intrinsic immunity relies on a large diversity of antiviral processes. In the case of HTLV-1 infection, these active innate host defence mechanisms are debated. Among these mechanisms, we focused on a RNA decay pathway called nonsense-mediated mRNA decay (NMD), which can target multiple viral RNAs, including HTLV-1 unspliced RNA, as it has been recently demonstrated. NMD is a cotranslational process that depends on the RNA helicase UPF1 and regulates the expression of multiple types of host mRNAs. RNA sensitivity to NMD depends on mRNA organization and the ribonucleoprotein (mRNP) composition. HTLV-1 has evolved several means to evade the NMD threat, leading to NMD inhibition. In the early steps of infection, NMD inhibition favours the production of HTLV-1 infectious particles, which may contribute to the survival of the most fit clones despite genome instability; however, its direct long-term impact remains to be investigated.

An ongoing monkeypox outbreak in non-endemic countries has resulted in the declaration of a Public Health Emergency of International Concern by the World Health Organization (WHO). Though monkeypox has long been endemic in regions of Sub-Saharan Africa, relatively little is known about its ecology, epidemiology, and transmission. Here, we consider the relevant research on both monkeypox and smallpox, a close relative, to make inferences about the current outbreak. Undetected circulation, combined with atypical transmission and case presentation, including mild and asymptomatic disease, have led to the spread of monkeypox in non-endemic regions. Broader availability of diagnostics, enhanced surveillance, and targeted education, combined with a better understanding of the routes of transmission, are critical to identify at-risk populations and design science-based countermeasures to control the current outbreak.

In this paper, we introduce a compressible formulation for dealing with 2D/3D compressible interfacial flows. The formulation is combined with an original monolithic solver, according to which a strong velocity-pressure coupling is achieved. The results show that the scheme can ensure accuracy and stability for various fluid flow scenarios: from the standard incompressible conditions to compressible flows, spanning both single-phase and two-phase flows.

There has been a growing awareness of the need to implement environmentally friendly operations in the hotel industry, but most studies focus on guest behaviors. Only a few studies investigate employees’ willingness to comply with hotels’ green practices. This study seeks to further the investigation from the human resource perspective. Using alumni from a tourism and hospitality programs, this study collected 233 responses. The study hypothesizes that the employees’ green ability consisting of environmental awareness, environmental knowledge, and environmental skill creates a positive impact on hotels’ green ability. The results indicate that employees contribute approximately a fifth of hotels’ ability to implement greener practices.

Considering the current energy environment, both efficient and environmentally friendly solutions have to be developed for building construction. Bio-based building materials offer new perspectives through their insulating and natural humidity regulation capacities. Nevertheless, these materials are as complex as they are promising and grey areas still remain regarding their behavior. Their water sorption and desorption curves recorded in experimental work demonstrate a hysteresis phenomenon and, although plausible hypotheses have been formulated in the literature, there is currently no consensus on its causes. Furthermore, it is important to emphasize that no reference considers the hydrophilic nature of the resource. Yet this is a specificity of raw material coming from the plant world. In this context, this paper explores the microstructure and chemical composition of plant aggregates to propose a new explanation for the hysteresis. It is based on recent work demonstrating the existence of differentiated hydrogen bonds between the water sorption and desorption phase in cellulose. Obviously, hysteresis also has an origin at the molecular scale. Lastly, the hypothesis put forward here is supported by the swelling of bio-based materials that has been observed at high relative humidity and this study aims to identify a link between the mechanical (swelling/shrinking) of bio-based materials and their hygroscopic behavior. This leads to a better understanding of the hydro-mechanical coupling of these materials.

The devastating growth in the worldwide frequency of neurocognitive disorders and its allied difficulties such as decline in memory, spatial competency, and ability to focus poses a significant psychological public health problem. Inhibitor of Differentiation (ID) proteins are members of a family of helix-loop-helix (HLH) transcription factors. ID proteins have been demonstrated to be involved in neurodevelopmental & depressive diseases and thus may influence neurocognitive deficiencies due to environmental exposure. Previously, it has been demonstrated that environmental factors such as estrogenic endocrine disruptors (EEDs) have played an essential role in the influence of various neurocognitive disorders such as Alzheimer’s, Dementia, and Parkinson’s disease. Based on this increasing number of reports, we consider the impact of these environmental pollutants on ID proteins. Better understanding of how these ID proteins by which EED exposure can affect neurocognitive disorders in populations will prospectively deliver valuable information in the impediment and regulation of these diseases linked with environmental factor exposure.

Aptamers have a well-earned place in therapeutic, diagnostic, and sensor applications, and we now show that they provide an excellent foundation for education, as well. Within the context of the Freshman Research Initiative (FRI) at The University of Texas at Austin, students have used aptamer selection and development technologies in a teaching laboratory to build technical and 21st century skills appropriate for research scientists. One of the unique aspects of this course-based undergraduate research experience is that students develop their own projects, and take ownership of their own science in what would otherwise be a traditional teaching lab setting. Of the many successes, this work includes the isolation and characterization of novel calf intestinal alkaline phosphatase (anti-CIAP) RNA aptamers by an undergraduate researcher. Further, preliminary survey data suggest that students who participate in the aptamer research experience express significant gains in their self-efficacy to conduct research, and their perceived ability to communicate scientific results, as well as organize and interpret data. This work will describe the use of aptamers in an educational setting, highlight the positive student outcomes of the aptamer research experience, and more particularly present the research findings relative to the anti-CIAP aptamer.

Head and neck squamous cell carcinoma (HNSCC) is a type of cancer that arises from the epithelium lining of the oral cavity, hypopharynx, oropharynx, and larynx. Despite advancement of the current treatment to HNSCC, such as surgery, chemotherapy, and radiotherapy, the overall survival rate of HNSCC remains poor due to late diagnosis and acquired resistance to treatment. Natural products have been extensively explored as a safer and more acceptable alternative to current treatments, with numerous studies displaying their potential against HNSCC. This review highlights preclinical studies in the past 5 years involving natural products against HNSCC and explores the signalling pathways altered by these products. This review also addresses challenges and future directions of the use of natural products as chemotherapeutic and chemoprevention agents against HNSCC.

An autonomous concrete crack inspection system is necessary for preventing hazardous incidents arising from deteriorated concrete surfaces. In this paper, we represent a concrete crack detection framework to aid the process of automated inspection. The proposed approach employs a deep convolutional neural network architecture for crack segmentation from concrete image. The proposed network alleviates the effect of gradient vanishing problem present in deep neural network architectures. A feature silencing module is incorporated in the crack detection framework, for eliminating unnecessary feature maps from the network. The overall performance of the network significantly improves as a result. Experimental results support the benefit of incorporating feature silencing within a convolutional neural network architecture for improving the network’s robustness, sensitivity, and specificity. An added benefit of the proposed architecture is its ability to accommodate for the trade-off between specificity (positive class detection accuracy) and sensitivity (negative class detection accuracy) with respect to the target application. Furthermore, the proposed framework achieves a high precision rate and processing time than crack detection architectures present in literature.

This research presents a comprehensive computational analysis of the two-phase flow dynamics within a three-dimensional porous transport layer (PTL) in a proton exchange membrane (PEM) electrolyzer. Employing advanced computational fluid dynamics (CFD) and a volume of fluid (VOF) approach, the study utilizes the finite-volume method to model a time-dependent, isothermal process within a laminar flow regime. Notably, the study treats oxygen as the dispersed phase and water as the continuous phase within the system. Investigations on the anode side uncovered the formation of gas bubbles on the electrode's surface subsequent to the electrochemical reaction. The simulation, spanning 5 seconds with 0.25-second intervals, highlights the critical role of the time interval between the initiation and 2 seconds in achieving pressure and velocity equilibrium within the PTL. The initial 0.75 seconds witnessed a peak in oxygen concentration, followed by its movement across the PTL, exhibiting a transition from regions of lower to higher concentration, consistent with known physical behaviour. Remarkably, the model recorded the highest pressure of 3.5 Pa at 0.25 s. Furthermore, the study observed an incremental pressure drop from 0.25 s to 0.5 s and 1 s, approximately amounting to 28.5% and 20%, respectively, ultimately leading to a uniform pressure distribution within the model. Over time, the pressure drop intensified across the entire model due to the evolving nature of the oxygen gas. These findings provide valuable insights into the complex dynamics of two-phase flow processes within PEM electrolyzers, contributing to the advancement of sustainable energy technologies.

Noise reduction in medical images is a perplexing undertaking for the researchers in digital image processing. Noise generates maximum critical disturbances as well as touches the medical images quality, ultrasound images in the field of biomedical imaging. The image is normally considered as gathering of data and existence of noises degradation the image quality. It ought to be vital to reestablish the original image noises for accomplishing maximum data from images. Medical images are debased through noise through its transmission and procurement. Image with noise reduce the image contrast and resolution, thereby decreasing the diagnostic values of the medical image. This paper mainly focuses on Gaussian noise, Pepper noise, Uniform noise, Salt and Speckle noise. Different filtering techniques can be adapted for noise declining to improve the visual quality as well as reorganization of images. Here four types of noises have been undertaken and applied on medical images. Besides numerous filtering methods like Gaussian, median, mean and Weiner applied for noise reduction as well as estimate the performance of filter through the parameters like mean square error (MSE), peak signal to noise ratio (PSNR), Average difference value (AD) and Maximum difference value (MD) to diminish the noises without corrupting the medical image data.

This research develops the Osteoporosis Knowledge, Attitude, and Behaviors Questionnaire (OKABQ) with the intent to evaluate levels of osteoporosis knowledge, attitude and behavior change. The aims of the study were to develop and validate the OKABQ; and to establish a mediation model of the research. A quantitative, descriptive, and instrumental study was conducted in two phases: Phase I—development of the scale through Delphi Method by osteoporosis experts; Phase II—evaluation of the validity and reliability of the scale and construction of a mediation model using SmartPLS. In Phase I, the content validity index (CVI) of the questionnaire was higher than 0.96 and the inter-rater reliability (IRR) kappa was 1.00. In Phase II, exploratory factor analysis showed that two predominating factors of attitude as a mediation were addressed by the 26-item OKABQ. The indirect effect results from the estimated model indicates that attitude mediates the relationship between knowledge and behaviors (β = 0.114, t = 2.627, p < 0.001), which is positive and statistically significant. We concluded that the OKABQ is a valid measure of osteoporosis knowledge, attitude and behaviors for women with osteoporosis. These assessment results could assist healthcare providers in mitigating insufficiency on health education.

In this study, we aimed to develop a deep learning model for identifying bacterial keratitis (BK) and fungal keratitis (FK) by using slit-lamp images. We retrospectively collected slit-lamp images of patients with culture-proven microbial keratitis between January 1, 2010, and December 31, 2019, from two medical centers in Taiwan. We constructed a deep learning algorithm, consisting of a segmentation model for cropping cornea images and a classification model that applies convolutional neural networks to differentiate between FK and BK. The model performance was evaluated and presented as the area under the curve (AUC) of the receiver operating characteristic curves. A gradient-weighted class activation mapping technique was used to plot the heatmap of the model. By using 1330 images from 580 patients, the deep learning algorithm achieved an average diagnostic accuracy of 80.00%. The diagnostic accuracy for BK ranged from 79.59% to 95.91% and that for FK ranged from 26.31% to 63.15%. DenseNet169 showed the best model performance, with an AUC of 0.78 for both BK and FK. The heat maps revealed that the model was able to identify the corneal infiltrations. The model showed better diagnostic accuracy than the previously reported diagnostic performance of both general ophthalmologists and corneal specialists.

Despite the implementation of control strategies, the invasive parasitic mite Varroa destructor remains one of the principal causes of honey bee (Apis mellifera) colony losses in numerous countries. For this reason, the parasite represents a serious threat to beekeeping and to agro-ecosystems that benefit from the pollination services provided by honey bees. Numerous selection programmes have been initiated over the last three decades with the aim of promoting the establishment of balance in the host–parasite relationship and, thus, helping European honey bees to survive in the presence of the parasite without the need for acaricide treatments. Such programmes have focused on either selective breeding for putative resistance traits or natural selection. To date, no clear overview of these attempts has been available, which has prevented building on past successes or failures and, therefore, hindered the development of a sustainable strategy for solving the V. destructor problem. In the present study, we review past and current selection strategies, report on their outcomes and discuss their limitations. Based on this state-of-the-art knowledge, we propose a strategy for increasing response to selection and colony survival against V. destructor infestations. Developing in-depth knowledge regarding the selected traits, optimising selection programmes and communicating their outcomes are all crucial to our efforts to establish a balanced relationship between the invasive parasite and its new host.

The fluctuations in both time and space of the fish larvae community in relation to hydrographic characteristics in the waters surrounding the Taiwan Bank were studied in October 2021 (autumn) and March 2022 (spring). Throughout the study period, we identified a total of 149 taxa of fish larvae, encompassing 96 genera and 71 families. Engraulis japonicus, Diaphus slender type, unidentified Gobiidae, Apogon sp., unidentified Clupeidae, and Benthosema pterotum were the six most dominant taxa, together constituted 47.39% of the total catch. No significant temporal difference in abundance of fish larvae was found, but the species number of fish larvae was more diverse in spring than in autumn. There was a notable difference in species composition between the cruises, and the cluster analysis unveiled a distinct temporal structure in the assemblage of fish larvae. The dynamics of prevailing currents induced by seasonal monsoons play an important role in the transport of fish larvae. The distribution of fish larvae was closely linked to hydrographic features, where seawater temperature and salinity emerged as the primary explanatory factors influencing the composition of fish larvae assemblages in the waters surrounding the Taiwan Bank. While the increased influx of nutrients from upwelling ensures abundant food availability, the hydrographic conditions may not be suitable for every fish larva.

This study presents an innovative approach for the utilization of industrial by-products and municipal waste in the production of sustainable and environmentally friendly cement mortar. We explored stabilized stainless-steel reduced slag (SSRS) and polyethylene (PE) plastic waste as partial replacements for aggregates. Various engineering properties of the resulting cement mortar specimens, including slump, slump flow, compressive strength, flexural strength, tensile strength, water absorption, and ultrasonic pulse velocity (UPV), were investigated through comprehensive experimental tests. The influence of different water-cement (w/c) ratios and substitution amounts on the engineering properties of the cement mortar samples was thoroughly examined. The findings revealed that an increase in PE substitution adversely affected the overall workability of the cement mortar mixtures, whereas an increase in SSRS amount contributed to enhanced workability. As for the hardened properties, a consistent trend was observed for both cases, with higher w/c ratios and substitution amounts leading to reduced mechanical properties. Water absorption and UPV test results validated the increased formation of porosity with higher w/c ratios and substitution amounts. This study proposes a promising method to effectively repurpose industrial by-products and municipal wastes, transforming them into sustainable construction and building materials. Additionally, a comparative analysis of transportation costs and carbon footprint emissions between SSRS-cement mortar and PE-cement mortar was conducted to assess their environmental impact and sustainability. Generally, higher w/c ratios and replacement levels corresponded to reduced carbon footprint. The geographical location of the source of SSRS and PE remains a challenge and studies to overcome this challenge must be further explored.

This paper reviews several techniques, including analytical theories and real frequency techniques, to design ultra-wideband impedance matching networks for low-power applications. The comparison of solutions obtained using the simplified real frequency technique to the published solution using Chebyshev filter theory is demonstrated.

This article belongs in the emerging area of research seeking ways to depolarize societies in the short run (around events such elections) as well as in a sustainable fashion. We approach the depolarization process with a model of three homophilic groups (US Democrats, Republicans and Independents interacting in the context of upcoming federal elections). We expand a previous polarization model, which assumed that each individual interacts with all other individuals in its group with mean-field interactions. We add a depolarization field which is analogous to the Blume-Capel model’s crystal field. There are currently numerous depolarization efforts around the world, some of which act in ways similar to this depolarization field. We find that for low values of the depolarization field, the system continues to be polarized. When the depolarization field increases, the polarization decreases.

Every year, the Montreal police are confronted with countless crimes committed by criminals. These crimes affect the quality of life of city residents and impose a socio-economic burden on the city. In this study, we conduct a comparative analysis based on several machine learning algorithms to develop a model to predict the crime category in Montreal. The performance of algorithms such as eXtreme Gradient Boosting (XGBoost), Decision Trees (DT) and Random Forest (RF) were analyzed. The performance analysis takes into account the performance metrics such as precision, accuracy, recall and F1-score. This analysis was based on crime data in Montreal from 2015 to 2023. This data is characterized by a strong imbalance between crime categories. To address the data imbalance problem, a data balancing approach based on the SMOTE-ENN algorithm was adopted. In the exploratory data analysis phase, temporal trends by crime category were highlighted. The results of the analysis showed that the XGBoost algorithm outperformed the other two. Specifically, the XGBoost algorithm achieves an accuracy of 92%, while DT and RF achieve an accuracy of 86% and 84%, respectively. As a result, XGBoost was deployed via a web application using the Flask and Swagger UI Python frameworks. This study provides the Montreal police with an effective tool to better utilize their resources in fighting crime. In addition, policymakers in the city of Montreal can use this tool to identify high-risk areas and give them more attention.

The expansion of mankind in Space will eventually lead to the need for resource extraction on places different from Earth. For the few who would succeed, the monetary gains from space mining are promising but inevitably, such activities will bring about substantial consequences to the celestial bodies they are conducted on and to the political-economical scene of the parties involved. Thus, a new set of moral interrogatives needs to be dealt with. This paper reflects, from a utilitarian perspective, on whether it is ethical or not to exploit extra-terrestrial resources and tries to produce moral norms on the matter. The main research question is analyzed with respect to three crucial aspects: environmental consequences, interaction with possible forms of life and the political-economical setting around resource extraction in space. Reflections, argumentations, and claims are backed up or confronted with already existing theories and positions of other authors. Altogether, we concluded that the extraction and usage of the resources of another celestial body is ethical only when: 1) it is regulated enough to avoid the destruction of naturalistic, aesthetically or culturally valuable environments; 2) it does not interfere (or at least, it tries to deal) with existing or possible future forms of life, which ought to be protected with ad-hoc legislation; 3) it is regulated to prevent tensions that would likely arise between countries that want to access specific resources-rich sites, issuing laws able to avoid conflicts and a possible economic collapse. To summarize, we identified the concept of responsible innovation as the key director for future policies that will need to be established when space resource extraction becomes a reality.

The identification of biomarkers is crucial for cancer diagnosis, understanding the underlying biological mechanisms, and developing targeted therapies. In this study we propose a machine learning approach to predict the outcome and platinum resistance status of ovarian cancer patients using public available gene expression data. Six classical machine learning algorithms are compared on their predictive performance. Those with the highest score are analyzed by their feature importance using the SHAP algorithm. We were able to select multiple genes that were correlating with the outcome and platinum resistance status of the patients and validated those using Kaplan-Meier plots. In comparison to similar approaches the performance of the models were higher and different genes using feature importance analysis were identified. The most promising identified genes that could be used as biomarkers are: TMEFF2, ACSM3, SLC4A1 and ALDH4A1.

Optimization of Magnetohydrodynamics parameters on the velocity profile and temperature distribution of incompressible fluid flow on a porous channel was evaluated. The fluid flow was considered to be unsteady, incompressible and flowing in 2-D in porous channel. The effects of magnetic parameter, Darcy’s number and fluid pressure on velocity profiles, and the effect of variable viscosity and Eckert number on temperature distribution in incompressible fluid flow were determined. The flow was considered to be in a channel running along the x-axis on which the magnetic field exist and finally along the y-axis on the porous part of the channel. The resulting model equations were solved by Finite Difference Method (FDM) in MATLAB software. Analysis of results indicated that increasing Darcy’s number and fluid pressure leads to increase in fluid velocity profile, while increasing magnetic parameter decreases fluid velocity profile. Also, it was observed that increase in both Eckert number and fluid viscosity lead to increase in temperature distribution. Optimization in temperature was achieved by increasing the magnetic field while viscosity was optimized by increasing the length of the porous part of the channel. This study will helpful to contribute alternative equations and methodology to engineering and in factories where getting the MHD parameters optimally is the main objective, particularly on temperature, velocity and pressure.

Cancer diseases lead to the second-highest death rate all over the world. The dynamics of invasion of cancer cells into the human body tissues and metastasis are the main causes of death in patients with cancer. This study deals with theoretical investigation of the dynamics of invasion of cancer cells for tumour growths in human body tissues using discretized Cahn-Hilliard, concentration and reaction-diffusion equations which were solved by Finite Difference Method with the aid of MATLAB computer software. A Crank-Nicolson numerical scheme was developed for the discretized model equations. The numerical result obtained was used to describe the dynamics of cancer invasion of tissues with respect to cancer cells density on tumour growth, turbulence and mobility and equilibrium between charge and discharge of cancer cells. The results of the study provide new insights into combating cancer disease by providing mitigating and intervention measures to this major health problem.

Copernicus is Europe's space-based Earth monitoring asset, which consists of a complex set of systems that collect data from different sources: remote sensing satellites (RS) and in-situ sensors such as ground stations, airborne and marine sensors. This study was originally prepared for the needs of the Czech agricultural community, where we provided an in-depth analysis of articles related to Earth observation in precision agriculture. At a later stage, we extended this study by comparing the recommendations of the European EO4Agri project and scientific articles published in MDPI. We had two important objectives, one was to validate the results of the EO4Agri project and the other was to look for gaps in current research and community needs. To recognize the importance of using Sentinel 1 data, we also added a specific analysis of methods for data fusion of Sentinel 1 and Sentinel 2 data.

The PACS (Picture Archiving and Communication System) market is heterogenous with dozens of PACS providers having deployed installations in healthcare facilities. The DICOM query and retrieve interfaces provided by PACS have multiple variations, related to the implemented SOP (Service Object Pair) Class UID (Unique Identifier), transfer syntaxes, extended negotiations, matching attributes and matching types. These variations make any integration of a new DICOM consumer with a PACS complex and time consuming. As there is no collective information describing the various PACS query and retrieve capabilities, application developers and healthcare facilities lack a method to evaluate the PACS capabilities and classify its functionalities. GE designed a method to evaluate the PACS capabilities in terms of query retrieve functionalities. Our aim is to analyze several PACS in test and production environments, using our method to provide the DICOM object consumers a macroscopic knowledge of query/retrieve capabilities of PACS functionalities and its different variations. Our evaluation can also be used by PACS and VNA (Vendor Neutral Archive) developers to evaluate their query/retrieve capabilities, for quality improvement purpose.

This paper presents the development of a new system designed to measure the local temperature field in adiabatic shear band. Transient temperature field are simultaneously recorded by an array of 32 InSb infrared (IR) detectors and a streak camera working in visible-near infrared (VIS-NIR). Observations in IR offer a low temperature detection limit (350°C) but they are highly sensitive to uncertainty in the emissivity. Observations in VIS-NIR allow for measurement only at high temperatures (750°C) but they are less affected by uncertainty on emissivity and present a higher temperature sensitivity. By performing simultaneous measurements, it is possible to obtain data on a large temperature range with an improved accuracy at high temperature. The different sources of errors caused by uncertainty in the emissivity, spatial and temporal resolution of the detectors has been analyzed and an estimation of the total measurement uncertainty of the system is given.

Our study explores the role of cancer-derived extracellular exosomes (EXs), particularly focusing on collagen alpha-3 (VI; COL6A3), in facilitating tumor dissemination and metastasis in epithelial ovarian cancer (EOC). We found that COL6A3 is expressed in aggressive ES2 derivatives, SKOV3 overexpressing COL6A3 (SKOV3/COL6A3), and mesenchymal-type ovarian carcinoma stromal progenitor cells (MSC-OCSPCs), as well as their EXs, but not in less aggressive SKOV3 cells or ES2 cells with COL6A3 knockdown (ES2/shCOL6A3). High COL6A3 expression correlates with worse overall survival in EOC patients, as evidenced by TCGA and GEO data analysis. In vitro experiments showed that EXs from MSC-OCSPCs or SKOV3/COL6A3 cells significantly enhance invasion ability in ES2 or SKOV3/COL6A3 cells, respectively (both, P &lt;0.001). In contrast, ES2 cells with ES2/shCOL6A3 EXs exhibit reduced invasion ability (p&lt;0.001). In vivo, the average disseminated tumor numbers in the peritoneal cavity were significantly greater in mice receiving intraperitoneally injected SKOV3/COL6A3 cells than in SKOV3 cells (p&lt;0.001). Furthermore, mice intravenously (IV) injected with SKOV3/COL6A3 cells and SKOV3/COL6A3-EXs showed increased lung colonization compared to mice injected with SKOV3 cells and PBS (p=0.007) or SKOV3/COL6A3 cells and PBS (p=0.039). Knockdown of COL6A3 or treatment with EXs inhibitor GW4869 or rapamycin-abolished COL6A3-EXs may suppress the aggressiveness in EOC.

A methodology for selecting rational parameters of atmospheric aircrafts at the initial stages of design using an optimization algorithm of differential evolution and numerical mathematical modeling of aerodynamics problems is proposed. The technique involves the implementation of weight and aerodynamic balance in the main flight modes, has the ability to consider atmospheric aircrafts with one or two lifting surfaces, apply parallel calculations and automatically generate a three-dimensional geometric model of aircraft appearance based on the optimization results. A method for accelerating the process of optimizing the parameters of an aircraft in terms of take-off weight by more than three times by introducing a objective function into a set of design variables is proposed and demonstrated. The results of assessing the reliability of the used mathematical models of aerodynamics and the correctness of the calculation the objective function, taking into account various constraints, are presented. A comprehensive test of the performance and efficiency of the methodology is considered by solving demonstration problems to optimize more than ten main design parameters the appearance of two existing heavy-class unmanned aerial vehicles with known characteristics from open sources.

Course-based Undergraduate Research Experiences (CUREs) provide the same benefits as individual, mentored faculty research while expanding the availability of research opportunities. One important aspect of CUREs is student’s engagement in collaboration. We developed a partnership with the Caenorhabditis elegans (C. elegans) database, WormBase, in which students submitted annotations of published manuscripts to the website. This activity provided students with a collaborative research activity that benefitted the greater scientific community and enhanced students’ understanding of molecular genetics during the COVID-19 pandemic. WormBase relies on community annotators to read published articles and input phenotypic data. Students submitted a total of nine annotations directly to WormBase, which were curated by WormBase to ensure correctness and to reduce overlap from redundant annotations. Due to the stress on students during this time of crisis, qualitative data were collected in lieu of quantitative pre-post analyses. Students described their learning experiences in terms of interactions with the scientific community and the “real world”, content knowledge and competencies, and changes in perspectives and use of resources. Students also reported that this activity was helpful in their understanding of critical molecular genetics concepts. Most students reported on cognitive processes that represent mid-level Bloom’s categories. The shift to online learning during the COVID-19 pandemic created an immediate need for meaningful, collaborative experiences in CUREs. By partnering with WormBase, students gained insight into the scientific community and contributed as community members. We describe possible modifications for future courses, potential expansion of the WormBase collaboration, and future directions for quantitative analysis.

As precision medicine such as targeted therapy and immunotherapy often have limited accessibility, low response rate and evolved resistance, it is urgent to develop simple, low-cost, and quick-turnaround personalized diagnostic technologies for drug response prediction with high sensitivity, speed, and accuracy. Major challenges of drug response prediction strategies employing digital database modeling are the scarcity of labeled clinical data, applicable only to a few classes of drugs, and losing the resolution at the individual patient level. Although these challenges have been partially addressed by large-scale cancer cell line datasets and more patient-relevant cell-based systems, the integration of different data types and data translation from pre-clinical to clinical utilities are still far-fetched. To overcome current limitations of precision medicine with a clinically proven drug response prediction assay, we have developed an innovative and proprietary technology based on in vitro patient testing and in silico data analytics. First, a patient-derived gene expression signature was established via transcriptomic profiling of cell-free mRNA (cfmRNA) from the patient’s blood. Second, a gene-to-drug data fusion and overlaying mechanism to transfer data was performed. Finally, a semi-supervised method was used for database searching, matching, annotation and ranking of drug efficacies from a pool of ~700 approved, investigational or clinical trial drug candidates. A personalized drug response report can be delivered to inform clinical decision within a week. The PGA (Patient-derived Gene expression-informed Anticancer drug efficacy) test has significantly improved patient outcomes when compared to the treatment plans without PGA support. Implementation of PGA, which combines patient-unique cfmRNA fingerprints with drug mapping power, has the potential to identify treatment options when patients are no longer responding to therapy and when standard-of-care is exhausted.

LiFePO4 (LFP) has undergone extensive research and is a promising cathode material for Li-ion batteries. The high interest is due to its low raw material cost, good electrochemical stability, and high-capacity retention. However, poor electronic conductivity and a low Li+ diffusion rate decrease its electrochemical reactivity, especially at fast charge/discharge rates. In this work, the volumetric energy density of lithium-ion batteries is successfully increased by using different amounts of conductive carbon (Super P) in the active material content. The particle size and morphology of the electrode material samples are studied using field emission scanning electron microscopy and dynamic light scattering. Two-point-probe DC measurements and adhesive force tests are used to determine the conductivity and evaluate adhesion for the positive electrode. Cyclic voltammetry, electrochemical impedance spectroscopy (EIS), and charge/discharge tests are used to analyze the electrochemical properties of the battery. The samples containing 88% LFP, 5.5% Super P and 6.5% PVDF perform best, with discharge capacities reaching 169.8 mAhg-1 at 0.1C, and they can also manage charging/discharging of 5C. EIS indicates that this combination produces the lowest charge-transfer impedance (67 Ω) and the highest Li+ ion diffusion coefficient (5.76 x 10-14 cm2s-1).

The rapid and sensitive detection of pathogenic bacteria is becoming increasingly important for the timely prevention of contamination and treatment of infections. Biosensors based on nucleic acid aptamers integrat-ed with optical, electrochemical, and mass-sensitive analytical techniques have garnered intense interest for their versatility, cost-efficiency, and ability to exhibit high affinity and specificity in binding bacterial bi-omarkers, toxins, and whole cells. This review emphasizes the development of aptamers, their structural op-timization, and their use in bacterial sensing applications. Moreover, we explore barriers and develop per-spectives in applying aptamer-based bacterial detection.

In urban Vehicular Ad Hoc Network (VANET) environments, buildings play a crucial role as they can act as obstacles that attenuate the transmission signal between vehicles. Quantifying the impact of buildings on the transmission quality is essential, especially in critical scenarios involving emergency vehicles, where reliable communication is of utmost importance. In this research, we propose a supervised learning approach based on artificial neural networks (ANNs) to develop a predictive model capable of estimating the level of signal degradation, represented by the bit error rate (BER), based on the obstacles perceived by moving emergency vehicles. By establishing a relationship between the level of signal degradation and the encountered obstacles, our proposed mechanism enables efficient routing decisions to be made prior to the transmission process. Consequently, data packets are routed through paths that exhibit the lowest BER. To gather the necessary training data, we employed SUMO and NS-3 simulations. The simulation results demonstrate that our developed model successfully learns and accurately estimates the BER for new data instances. Overall, our research contributes to enhancing the performance and reliability of communication in urban VANET environments, especially in critical scenarios involving emergency vehicles, by leveraging supervised learning and artificial neural networks to predict signal degradation levels and optimize routing decisions accordingly.

The investigation aims to analyze the effect of homogenization heat treatment on the quantity, distribution, morphology, and chemical composition of δ ferrite and sigma phase in the 316L austenitic stainless steel cast billet. Microstructures of the cast billet at different locations, center, 0.5 R, R, and the subsequent 2/6-hour homogenization heat treatment at 1240 oC were examined. The microstructure at the radius R was much more uniform and expected a better performance in the following hot/cold forging processes due to the highest cooling rate compared with those at 0.5R and center. The δ ferrite often accompanies the generation of the sigma phase, and the transformation of δ ferrite into the sigma/(austenite) and retained δ ferrite is strongly related to the cooling rate after homogenization heat treatment. The sigma phase is hard and detrimental to the subsequent hot/cold forgings. It must be removed from the homogenization heat treatment before forging. Fast air cooling from 850 oC to room temperature after homogenization heat treatment cannot prohibit the sigma phase formation. Rapid cooling between 850 and 1240 oC after homogenization treatment is required to avoid the sigma phase formation in the 316L stainless steel cast billet.

Numerous cases and a shortage of resources usually limit wild animal rescue. New technology implemented might save these severely injured wild animals from the situation of euthanasia by easing the requirement of intensive medication. Three-dimensional (3D) technologies provide precise and accurate results that improve the quality of the medical application. These 3D tools have become relatively low-cost and accessible in the past years. In the medical field of exotic animal, turtle shell defect is highly challenging because of inevitable water immersion. This report is the first attempt to apply the combination of 3D scanning, computer-aid design (CAD), and 3D printing to make a protective device that frees the wound from exposure to water or infection sources. The presenting techniques successfully extricate a wild freshwater turtle from an extensive shell defect within a short period. Integration of multiple sciences to 3D technology can provide a facile model for veterinary medical applications.