This review article is aimed at exploring the possibility of developing an integrated approach in the management of the different needs of endometrial cancer (EC) patients seeking to become pregnant. Life preservation of the woman, health preservation of the baby, a precocious and – as much as possible – minimally invasive characterization of the health and fertility parameters of the patient, together with the concerns regarding the obstetric, neonatal, and adult health risks of the children conceived via assisted reproduction techniques (ART) are all essential aspects of the problem to be taken into consideration, yet the possibility to harmonize such needs through a concerted integrated approach is still highly challenging. This article aims to illustrate the main features of EC and how it affects the normal physiology of pre-menopausal women. Then, we focus on the prospect for a miR-based, molecular evaluation of patient health status, including both EC early diagnosis and staging and, similarly, the receptivity of the woman, discussing the possible evaluation of both aspects using a single specific panel of circulating miR in the patient, thus allowing a relatively fast, non-invasive way to perform such testing with a significantly reduced margin of error. Finally, the ethical and legal/regulatory complexities arising from such innovative techniques require not only a risk-benefit analysis; respect for patient autonomy and equitable health care access allocation are fundamental issues as well.

Geometrical assessments of human skull have been done based on the anatomical landmarks. Automatic detection of the landmarks, if developed, will be a great help not only medically, but also anthropologically. An automated system with multi-phased deep learning networks to predict three-dimensional coordinate values of cranio-facial landmarks, was developed. From a publicly available database, computed tomography images of craniofacial area were obtained. They were digitally reconstructed into three-dimensional models. Sixteen anatomical landmarks were plotted on each of the models and coordinate values of them were recorded. Three-phased regression deep learning networks were trained respectively with 90 training datasets. For evaluation, 30 testing datasets were employed. Three-dimensional error for the first phase, testing 30 data, was 11.60 pixels in average. (1 pixel = 500 / 512 mm) For the second phase, it was significantly improved to 4.66 pixels. For the third phase, it was significantly progressed to 2.88. This was comparable to the gaps between the landmarks, plotted by two experienced practitioners. Our proposing method of multi-phased prediction, coarse detection first and narrowing down the detection area, may be a possible solution, within the physical limitation of memory and computation.

Age-in-place can reduce the progress of dementia syndrome and improve the quality of life of the sufferers and their families. This work proposes an innovative information home support system to be established at the houses of people suffering from dementia. The main innovation of the system are its transparent character that overcomes the need for training of the sufferer, as well as the exploitation of a well-known technique for dementia, namely music therapy. It addresses the need of people suffering from dementia as well as their familiars to be recognized by them and have better interaction and collaboration. The system offers a ubiquitous recognition system, just by using smart devices like smart-phones or smart-wristbands. When a familiar person is detected in the house, then a sound file is reproduced on smart speakers placed in the house, in order to trigger the memory, based on the principles of music therapy. The system reproduced the appropriate time at 100% of the cases and the benefits of music therapy are evident daily. To the best of the authors' knowledge, this is the first system of its kind ever reported in the literature.

This study was designed for the first time to improve phenolic yield and antioxidant activity of ultrasonic-assisted extraction from Acer truncatum leaves (ATL) using response surface methodology, and phenolic composition in ATL extracted under the optimized condition were characterized by UPLC-QTOF-MS/MS. Solvent and extraction time were selected based on preliminary experiments, and a four-factors-three-levels central composite design was conducted to optimize solvent concentration (X₁), material-to-liquid ratio (X₂), ultrasonic temperature (X₃) and power (X₄) for an optimal total phenol yield (Y₁) and DPPH• antioxidant activity (Y₂). The results showed that the optimal combination was ethanol: water (v:v) 66.21%, material-to-liquid ratio 1:15.31 g/mL, ultrasonic temperature 60 °C, power 267.30 W, and time 30 min with three extractions, giving a maximal total phenol yield of 7593.62 mg gallic acid equivalent /100 g d.w. and a maximal DPPH• antioxidant activity of 74241.61 μmol Trolox equivalent/100 g d.w.. Furthermore, 22 phenolics were first identified in ATL extract obtained under the optimized conditions, indicating that gallates, gallotannins, quercetin, myricetin and chlorogenic acid derivatives were the main phenolic composition in ATL. What’s more, a gallotannins pathway existing in ATL from gallic acid to penta-O-galloyl-glucoside was interpreted. All these results provided practical information aiming at full utilization of phenolics in ATL, together with fundamental knowledge for further research.

Objective: This study aimed to assess the impact of artificial intelligence (AI)-driven noise reduction algorithms on metal artifacts and image quality parameters in cone-beam computed tomography (CBCT) images of the oral cavity. Materials and Methods: This retrospective study included 70 patients, 61 of whom were analyzed after excluding those with severe motion artifacts. CBCT scans, performed using a Hyperion X9 PRO 13 × 10 CBCT machine, included images with dental implants, amalgam fillings, orthodontic appliances, root canal fillings, and crowns. Images were processed with ClariCT. AI deep learning model (DLM) for noise reduction. Objective image quality was assessed using metrics such as the differentiation between voxel values (ΔVVs), the artifact index (AIx), and the contrast-to-noise ratio (CNR). Subjective assessments were performed by two experienced readers, who rated overall image quality and artifact intensity on predefined scales. Results: Compared with native images, DLM reconstructions significantly reduced the AIx and increased the CNR (p<0.001), indicating improved image clarity and artifact reduction. Subjective assessments also favored DLM images, with higher ratings for overall image quality and lower artifact intensity (p<0.001). However, the ΔVV values were similar between the native and DLM images, indicating that while the DLM reduced noise, it maintained the overall density distribution. Orthodontic appliances produced the most pronounced artifacts, while implants generated the least. Conclusion: AI-based noise reduction using ClariCT. AI significantly enhances CBCT image quality by reducing noise and metal artifacts, thereby improving diagnostic accuracy and treatment planning. Further research with larger, multicenter cohorts is recommended to validate these findings.

This study assessed the performance of low-dose CT lung scans across various acquisition parameters and examined the impact of iterative reconstruction (IR) algorithms on image quality for lung cancer screening. The performance of multi-slice CT scanners was assessed by using a Catphan 600 phantom, specifically within the CTP404 test module that contains materials like Teflon, Delrin, Acrylic, Polystyrene, LDPE, PMP, Air, and water. Three protocols were selected and categorized into standard, low dose, and ultra-low dose. The standard protocol featured a tube voltage of 100 kVp for single-detector CT, a pitch of 0.75, detector collimation of 0.60 mm, and a gantry rotation time of 0.33 seconds, with 5 mm slice thickness. Images were reconstructed using standard kernels like B80f and 170f, combined with either FBP or various IR algorithms. Findings indicated that the noise power spectrum (NPS) peak value (HU2mm2) increased with higher IR levels, with FBP showing the highest peak. Additionally, NPS spatial frequency diminished as IR levels rose. Optimizing contrast and spatial resolution for both background and regions of interest involved adjusting the target transfer function (TTF) and contrast-to-noise ratio (CNR), crucial for protocol optimization. Proper adjustment of IR levels is vital, as higher levels can alter image texture and affect NPS spatial frequency. The results of this study showed that the use of IR algorithms in low-dose CT lung scans significantly improved image quality, particularly in terms of noise reduction and spatial resolution. However, the choice of acquisition parameters also played a crucial

Purpose: Hearing assistive technology (HAT) has been shown to be a viable solution to the speech-in-noise perception (SPIN) issue in children with autism spectrum disorder (ASD); however, little is known about its efficacy in tonal language speakers. This study compared sentence-level SPIN performance between Chinese children with ASD and neurotypical (NT) children and evaluated HAT use in improving SPIN performance and easing SPIN difficulty. Methods: Children with ASD (n=26) and NT children (n=19) aged 6-12 performed two adaptive tests in steady-state noise and three fixed-level tests in quiet and steady-state noise with and without using HAT. Speech recognition thresholds (SRT) and accuracy rates were assessed using adaptive and fixed-level tests, respectively. Parents or teachers of the ASD group completed a questionnaire regarding children’s listening difficulty under six circumstances before and after a ten-day trial period of HAT use. Results: Although the two groups of children had comparable SRTs, the ASD group showed a significantly lower SPIN accuracy rate than the NT group. Also, a significant impact of noise was found in the ASD group’s accuracy rate, but not in the NT group’s. There was a general improvement in the ASD group’s SPIN performance with HAT and a decrease in their listening difficulty ratings across all conditions after the device trial. Conclusion: The findings indicated inadequate SPIN in the ASD group using a relatively sensitive measure to gauge SPIN performance among children. The markedly increased accuracy rate in noise during HAT-on sessions for the ASD group confirmed the feasibility of HAT for improving SPIN performance in controlled laboratory settings, and the reduced post-use ratings of listening difficulty further confirmed the benefits of HAT use in daily scenarios.

The task of determining the distance from one object to another is one of the important tasks solved in robotics systems. Conventional algorithms rely on an iterative process of predicting distance estimates, which results in an increased computational burden. Algorithms used in robotic systems should require minimal time costs, as well as be resistant to the presence of noise. To solve these problems, the paper proposes an algorithm for Kalman combination filtering with a Goldschmidt divisor and a median filter. Software simulation showed an increase in the accuracy of predicting the estimate of the developed algorithm in comparison with the traditional filtering algorithm, as well as an increase in the speed of the algorithm. The results obtained can be effectively applied in various computer vision systems.

The space gravitational wave detection and drag free control requires the micro-thruster to have very low thrust noise within 0.1mHz~100mHz, which poses a great challenge to the ground thrust test. The evaluation and decoupling of thermal noise are the difficulties in the application of torsion balance for most thrusters dissipate heat in the experiment. The research has adopted a calibration scheme of micro-Newton thrust torsion balance. On the basis of Lisa Pathfinder's former research and using ideas inspired from PID control and multi time scale (MTS) for reference, the paper proposes to expand the state space of temperature to be applied on thrust prediction based on fine tree regression (FTR), to subtract the thermal noise filtered by transfer function fitted with z-domain vector fitting (ZDVF). The results show that the thrust amplitude thrust density in diurnal temperature fluctuation is decoupled from 24μN/Hz1/2 to 4.9μN/Hz1/2 at 0.11mHz. And the 1μN square wave modulations of electrostatic fins (ESF) is extracted from the simultaneously ambiguous background of temperature for PTC's heating and cooling. The FTR method is well demonstrated in thermal noise decoupling and can guide the design of thermal control and be extended to other physical quantities for noise decoupling.

The inaccurate estimates of linear prediction coefficient (LPC) and noise variance introduce bias in Kalman filter (KF) gain and degrades speech enhancement performance. The existing methods proposed a tuning of the biased Kalman gain particularly in stationary noise condition. This paper introduces a tuning of the KF gain for speech enhancement in real-life noise conditions. First, we estimate noise from each noisy speech frame using a speech presence probability (SPP) method to compute the noise variance. Then construct a whitening filter (with its coefficients computed from the estimated noise) and employed to the noisy speech, yielding a pre-whitened speech, from where the speech LPC parameters are computed. Then construct KF with the estimated parameters, where the robustness metric offsets the bias in Kalman gain during speech absence to that of the sensitivity metric during speech presence to achieve better noise reduction. Where the noise variance and the speech model parameters are adopted as a speech activity detector. The reduced-biased Kalman gain enables the KF to minimize the noise effect significantly, yielding the enhanced speech. Objective and subjective scores on NOIZEUS corpus demonstrates that the enhanced speech produced by the proposed method exhibits higher quality and intelligibility than some benchmark methods.

In this paper, at first, a color image of a car is taken. Then the image is transformed into a grayscale image. After that, the motion blurring effect is applied to that image according to the image degradation model described in equation 3. The blurring effect can be controlled by a and b components of the model. Then random noise is added in the image via Matlab programming. Many methods can restore the noisy and motion blurred image; particularly in this paper Inverse filtering as well as Wiener filtering are implemented for the restoration purpose. Consequently, both motion blurred and noisy motion blurred images are restored via Inverse filtering as well as Wiener filtering techniques and the comparison is made among them.

Software fault prediction is the very consequent research topic for software quality assurance. Data driven approaches provide robust mechanisms to deal with software fault prediction. However, the prediction performance of the model highly depends on the quality of dataset. Many software datasets suffers from the problem of class imbalance. In this regard, under-sampling is a popular data pre-processing method in dealing with class imbalance problem, Easy Ensemble (EE) present a robust approach to achieve a high classification rate and address the biasness towards majority class samples. However, imbalance class is not the only issue that harms performance of classifiers. Some noisy examples and irrelevant features may additionally reduce the rate of predictive accuracy of the classifier. In this paper, we proposed two-stage data pre-processing which incorporates feature selection and a new Rough set Easy Ensemble scheme. In feature selection stage, we eliminate the irrelevant features by feature ranking algorithm. In the second stage of a new Rough set Easy Ensemble by incorporating Rough K nearest neighbor rule filter (RK) afore executing Easy Ensemble (EE), named RKEE for short. RK can remove noisy examples from both minority and majority class. Experimental evaluation on real-world software projects, such as NASA and Eclipse dataset, is performed in order to demonstrate the effectiveness of our proposed approach. Furthermore, this paper comprehensively investigates the influencing factor in our approach. Such as, the impact of Rough set theory on noise-filter, the relationship between model performance and imbalance ratio etc. comprehensive experiments indicate that the proposed approach shows outstanding performance with significance in terms of area-under-the-curve (AUC).

Speckle noise reduction is an important area of research in the field of ultrasound image processing. Several algorithms for speckle noise characterization and analysis have been recently proposed in the area. Synthetic ultrasound images can play a key role in noise evaluation methods as they can be used to generate a variety of speckle noise models under different interpolation and sampling schemes, and can also provide valuable ground truth data for estimating the accuracy of the chosen methods. However, not much work has been done in the area of modelling synthetic ultrasound images, and in simulating speckle noise generation to get images that are as close as possible to real ultrasound images. An important aspect of simulated synthetic ultrasound images is the requirement for extensive quality assessment for ensuring that they have the texture characteristics and gray-tone features of real images. This paper presents texture feature analysis of synthetic ultrasound images using local binary patterns (LBP) and demonstrates the usefulness of a set of LBP features for image quality assessment. Experimental results presented in the paper clearly show how these features could provide an accurate quality metric that correlates very well with subjective evaluations performed by clinical experts.

Foliar fungal diseases in tomatoes include early blight (Alternaria linariae), Septoria leaf spot (Septoria lycopersici), and late blight (Phytophthora infestans) which is oomycetes. These are one of the significant production constraints in tomatoes. We describe the etiology, host range, distribution, symptoms, and disease cycle to understand the biology followed by management practices emphasizing the resistance breeding approach for these diseases. In crop improvement efforts, we provide an analytical review, including conventional and molecular methods for improving this disease resistance. Modern breeding tools, including genomics, genetic transformation, and genome editing, can be used to improve these traits. There is a good possibility of using these tools in the future to improve these traits.

Our wellbeing is greatly influenced by our childhood and adolescence, and the relationships that we form during those phases of our development. The human-dog bond started thousands of years ago. The higher prevalence of dog ownership around the world, especially in households including children along with the growing number of people studying dogs most likely explain the growing literature focusing on child-dog interactions. We review the potential effects of child-dog interactions on the physical, mental, and social wellbeing of both species. A scoping search of the SCOPUS database found several hundred documents meeting selection criteria. It allowed us to define the numerous ways in which children and dogs can interact, be it neutral (e.g., sharing a common area), positive (e.g., petting), or negative (e.g., biting). Then, we found evidence for an association between interacting with dogs during childhood and an array of health and mental benefits like stress relief and the development of empathy. Walking a dog and playing with one are perfect physical activity opportunities. Additionally, interacting with a dog can help lower stress and may have a role in the development of empathy. Nonetheless, a number of detrimental outcomes have also been identified in both humans and dogs. Children are the most at-risk population regarding dog bites and dog-borne zoonoses, which may lead to a subsequent fear of dogs or even death. Moreover, pet bereavement is generally inevitable when living with a canine companion and should not be trivialized. In terms of dogs, children sometimes take part in caretaking behaviors toward them which include going on walks. They are opportunities for dogs to relieve themselves outside, but also to exercise and socialize. In contrast, a lack of physical activity can lead to the onset of obesity. Dogs may present greater levels of stress when in the presence of children. Finally, the welfare of assistance, therapy, and free-roaming dogs remains underexplored. Overall, the study of the effects, positive as well as negative, on both species still requires further development. We call for more longitudinal studies and hope for cross-cultural research in the future in order to better understand the impact child-dog interactions might have.

Eating disorders (EDs) are serious, life-threatening psychiatric conditions associated with physical and psychosocial impairments, as well as high morbidity and mortality. Given the chronic refractory nature of EDs and the paucity of evidence-based treatments, there is a pressing need to identify novel approaches for this population. The noncompetitive N-methyl-D-aspartate receptor (NMDAr) antagonist, ketamine, has recently been approved for treatment-resistant depression, exerting rapid and robust antidepressant effects. It is now being investigated for several new indications, including obsessive-compulsive, post-traumatic, and substance use disorder; and shows transdiagnostic potential for EDs, particularly among clinical non-responders. As such, the aim of this review is to examine contemporary findings on the treatment of EDs with ketamine, whether used as a primary, adjunctive, or combination psychopharmacotherapy. Avenues for future research are also discussed. Overall, results are encouraging and point to therapeutic value, yet are limited to case series and reports on anorexia nervosa. Further empirical work is thus needed to explore ketamine efficacy across ED subgroups; establish safety profiles and optimize dosing; and develop theory-driven, targeted treatment strategies at the individual patient level.

Combining Matrix-Assisted Laser Desorption/Ionization Time-of-Flight (MALDI-TOF) spectra data and artificial intelligence (AI) has been introduced for rapid prediction on antibiotic susceptibility test (AST) of S. aureus. Based on the AI predictive probability, the cases with probabilities between low and high cut-offs are defined as “grey zone”. We aimed to investigate the underlying reasons of unconfident (grey zone) or wrong predictive AST. A total 479 S. aureus isolates were collected, analyzed by MALDI-TOF, and AST prediction, standard AST were obtained in a tertiary medical center. The predictions were categorized into the correct prediction group, wrong prediction group, and grey zone group. We analyzed the association between the predictive results and the demographic data, spectral data, and strain types. For MRSA, larger cefoxitin zone size was found in the wrong prediction group. MLST of the MRSA isolates in the grey zone group revealed that uncommon strain types composed 80%. Amid MSSA isolates in the grey zone group, the majority (60%) was composed of over 10 different strain types. In predicting AST based on MALDI-TOF AI, uncommon strains and high diversity would contribute to suboptimal predictive performance.

Nanometer-curved surfaces are abundant in biological systems as well as in nano-sized technologies. Properly functionalized polymer-grafted nanoparticles (PGNs) adhere to surfaces with different geometries and curvatures. This work explores some of the energetic and mechanical characteristics of the adhesion of PGNs to surfaces with positive, negative and zero curvatures using Coarse-Grained Molecular Dynamics (CGMD) simulations. Our calculated free energies of binding of the PGN to the curved and flat surfaces as a function of separation distance show that curvature of the surfaces critically impacts the adhesion strength. We find that the flat surface is the most adhesive, and the concave surface is the least adhesive surface. This somewhat counterintuitive finding suggests that while a bare nanoparticle is more likely to adhere to a positively curved surface than a flat surface, grafting polymer chains to the nanoparticle surface inverts this behavior. Moreover, we studied the rheological behavior of PGN upon separation from the flat and curved surfaces under external pulling force. The results presented herein can be exploited in drug delivery and self-assembly applications.

Nymphaea lotus L. is the medicinal plant that has long been used as food, cosmetic and traditional medicines in Africa and Asia since the ancient time. Its flavonoids and other interesting phytochemical compounds from rhizome, leaf, and the whole flowers have been reported in the previous published researches. However, stamens, which are essential for reproductive functions, may also represent new alternative sources of potential antioxidant flavonoids as investigated in this study. The innovative green chemistry method i.e. ultrasound-assisted extraction (USAE) as well as macroporous resin (MPR) purification procedure were employed in this current research. The optimal ultrasound-assisted extraction condition is 90 % (v/v) aqEtOH with 34.65 khz ultrasonic frequency and 46 minutes of extraction time. Comparing with heat reflux extraction (HRE) conventional method, the significant gain of 1.35 total flavonoids content was obtained using optimized USAE conditions, jumping to 2.80 when this USAE associated with MPR purification. Not only in vitro cell free antioxidant activity of N. lotus stamen extracts, but also in cellulo antioxidant investigation using yeast model showed the same trend to indicate that the best antioxidant flavonoid can be found in USAE coupled with MPR purification. Moreover, the key antioxidant genes expression in yeast model such as SIR2 and SOD2 were also expressed at the highest level in yeast cell treated with the extract from USAE together with MPR purification. Consequently, it can be seen that the USAE combined with MPR purification can help to enhance the flavonoids antioxidant potential of the stamens extract from this medicinal species.

Background: Animal-assisted therapy has positive effects on cognitive function, depression, performance ability, and social functioning in elderly patients. The aim of this study was to evaluate the effects of rearing pet insects on the cognitive function of healthy elderly participants, with fMRI (functional magnetic resonance imaging) being used for this purpose. Methods: Community-dwelling elderly women (≥60 years) with normal cognitive function were enrolled during April 2015. They were randomized at a 1:1 ratio into two groups: insect-rearing and control (n=16) groups, with the insect-rearing group being further classified into two groups for analysis according to the subjects’ scores in the Wisconsin Card Sorting Test, WCST) at the first fMRI: insect-rearing group I with a relatively high score (n=13), and insect-rearing group II with a relatively low score (n=6). All subjects were educated on a healthy lifestyle for better cognitive function at every visit, and the insect-rearing groups received and reared crickets as pet insects. The fMRI was performed at baseline and after 8 weeks using the WCST as a stimulus. The WCST consisted of two variations, a high level baseline (HLB) and semi-WCST version. Results: There were no significant differences in the baseline characteristics among the three groups. There was a significant difference accuracy of the HLB–semi-WCST (p<0.05) in insect-rearing group II after 8 weeks from the baseline test. In the fMRI analysis involving the WCST reaction test, increased activation was observed in the right dorsal lateral prefrontal cortex and parietal cortex in insect-rearing group II when the semi-WCST, rather than the HLB, was performed. There were no significant differences in the other groups. Conclusion: The rearing of pet insects as an animal-assisted therapy is cost-effective, easy, and occupies little space. In this study, it showed positive effects on executive functions and performance improvement in elderly women. Further larger studies on the effects of pet insects on cognitive function are warranted.

Background: Video assisted thoracoscopic surgery (VATS) has become the recommended approach for treatment of resectable lung cancer. However, no large randomized clinical trial has been conducted formally comparing surgical resections completed by VATS to those done by open thoracotomy (OT) in low volume centers. The current study sought to assess differences in recurrence-free survival (RFS), overall survival (OS), positive margins and postoperative length of stay (LOS) between VATS and OT lobectomies in our center. Method: A single institution retrospective chart review from May 2005 through May 2015 was conducted. All patients diagnosed with stage I through III lung cancer who underwent surgical resection were selected. Patient and tumor characteristics recorded included age at diagnosis, sex, tobacco use, tumor location (side and lobe), stage, size and receipt of chemotherapy or radiotherapy. Chis-square and Wilcoxon-Mann-Whitney tests were used to compare demographics, tumor characteristics and LOS. Multiple logistic and Cox regression analyses were used to compute relative risk (RR) for positive margins and mortality hazard ratios along with 95 percent confidence intervals (95%CI), respectively. Results: Of the 235 patients, 101 subjects had VATS while OT was performed in 134 patients. Age at diagnosis, sex, tobacco use, tumor location, and size were comparable for VATS and OT. No significant difference was observed in the relative risk of positive margins for VATS versus OT, RR = 0.56 (95%CI = 0.26, 1.05). However, VATS had shorter median LOS compared to OT (4 vs. 6 days, respectively), p = 0.002. A comparison of VATS versus OT showed no significant difference in the risk of recurrence, HR = 1.21 (95%CI = 0.74, 2.00), or death, HR = 1.34 (95%CI = 0.88, 2.06), in the intent-to-treat population. Similarly, no significant differences in recurrence or mortality risk were observed between VATS versus OT for analyses conducted separately for each cancer stage group or those limited to patients with negative margins. Conclusion: Our study indicates that compared to OT, VATS leads to shorter LOS while achieving comparable margins status, recurrence-free and overall survival regardless of tumor stage at diagnosis.

The persistent issue of bacterial and fungal colonization of artificial implantable materials and decreasing efficacy of conventional systemic antibiotics used to treat implant-associated infections has led to the development of a wide range of antifouling and antibacterial strategies. This article reviews one such strategy where inherently biologically active renewable resources, i.e. secondary plant metabolites (SPMs) and their naturally occurring combinations (i.e. essential oils) are used for surface functionalization and synthesis of polymer thin films. With a distinct mode of antibacterial activity, broad spectrum of action and diversity of available chemistries, secondary plant metabolites present an attractive alternative to conventional antibiotics. However, their conversion from liquid to solid phase without significant loss of activity is not trivial. Using select examples, this article shows how plasma techniques provide a sufficiently flexible and chemically reactive environment to enable the synthesis of biologically-active polymer-coatings from volatile renewable resources.

Structural self-organizing and pattern formation are universal and key phenomena observed during growth and cluster-assembling of the carbyne-enriched nanostructured metamaterials at the ion-assisted pulse-plasma deposition. Fine tuning these universal phenomena opens access to designing the properties of the growing carbyne-enriched nano-matrix. The structure of bonds in the grown carbyne-enriched nano-matrices can be programmed by the processes of self-organization and auto-synchronization of nanostructures. We propose the innovative concept, connected with application of the universal Cymatics phenomena during the predictive growth of the carbyne-enriched nanostructured metamaterials. We also propose the self-organization approach for increase stability of the long linear carbon chains. The main idea of suggested concept is manipulating by the self-organized wave patterns excitation phenomenon and their distribution by the spatial structure and properties of the nanostructured metamaterial grows region through the new synergistic effect. Mentioned effect will be provided through the vibration-assisted self-organized wave patterns excitation along with simultaneous manipulating by their properties through the electric field. We propose to use acoustic activation of the plasma zone of nano-matrix growing. Interaction between the inhomogeneous electric field distribution generated on the vibrating layer and the plasma ions will serve as the additional energizing factor controlling the local pattern formation and self-organizing of the nano-structures. Suggested concept makes it possible to provide precise predictive designing the spatial structure and properties of the advanced carbyne-enriched nanostructured metamaterials.

Deploying onshore wind energy as a cornerstone of future global energy systems challenges societies and decision-makers worldwide. Expanding wind energy should contribute to a more sustainable electricity generation without harnessing humans and their environment. Opponents often highlight the negative environmental impacts of wind energy to impede its expansion. This study reviews 152 studies to synthesize, summarize, and discuss critically the current knowledge, research gaps, and mitigation strategies on the environmental impacts of onshore wind energy. The investigated effects comprise impacts on the abiotic and biotic environment, with birds and bats in particular, noise and visual impacts. Effects are discussed in the context of social acceptance, other energy technologies, and wind energy expansion in forests. The review illustrates that many effects are highly case-specific and must be more generalizable. Studies are biased regarding the research focus and areas, needing more standardized research methods and long-term measurements. Most studies focus on the direct mortality of birds and bats at wind farms and are concentrated in Europe and North America. Knowledge gaps persist for many impact categories, and the efficacy of mitigation strategies has yet to be proven. More targeted, unbiased research is required that allows for an objective evaluation of the environmental impacts of wind energy and strategies to mitigate them. Impacts, such as those on biodiversity, need to be addressed in the context of other anthropogenic influences and the benefits of wind energy. This forms the basis for a socially acceptable, efficient, and sustainable expansion of wind energy.

The classical Lotka-Volterra predator-prey model is globally stable and uniformly persistent. However, in real-life biosystems extinction of species due to stochastic effects is possible. In this paper, we consider the classical Lotka-Volterra predator-prey model under stochastic perturbations. For this model, using an analytical technique based on the direct Lyapunov method and a development of the ideas of R.Z. Khasminskii, we find the precise sufficient conditions for the stochastic extinction of one and both species and the precise necessary conditions for the stochastic system persistence. The stochastic extinction occurs via a process known as the stabilization by noise of the Khasminskii type, and, in order to establish the sufficient conditions for extinction, we found the conditions for the stabilization. The analytical results are illustrated by numerical simulations. {\bf Keywords:} stochastic perturbations, white noise, Ito's stochastic differential equation, the Lyapunov functions method, stability in probability, stabilization by noise, stochastic extinction, persistence.

Globally, anthropogenic sound and artificial light pollution have increased to alarming levels. Evidence suggests that these can disrupt critical processes that impact ecosystems and human health. However, limited focus has been given to the potential effects of sound and artificial light pollution on microbiomes. Microbial communities are the foundations of our ecosystems. They are essential for human health and provide myriad ecosystem services. Therefore, disruption to microbiomes by anthropogenic sound and artificial light could have important ecological and human health implications. In this mini-review, we provide a critical appraisal of available scientific literature on the effects of anthropogenic sound and light exposure on microorganisms and discuss the potential ecological and human health implications. Our mini-review shows that a limited number of studies have been carried out to investigate the effects of anthropogenic sound and light pollution on microbiomes. However, based on these studies, it is evident that anthropogenic sound and light pollution have the potential to significantly influence ecosystems and human health via microbial interactions. Many of the studies suffered from modest sample sizes, suboptimal experiments designs, and some of the bioinformatics approaches used are now outdated. These factors should be improved in future studies. This is an emerging and severely underexplored area of research that could have important implications for global ecosystems and public health. Finally, we also propose the photo-sonic restoration hypothesis: does restoring natural levels of light and sound help to restore microbiomes and ecosystem stability?

The Chang’e-4 (CE-4) lunar rover, equipped with The Visible and Near-IR Imaging Spectrometer(VNIS) which based on acousto-optic tunable filter spectroscopy, was launched to the far side of the moon on December 8, 2018. The detection band of VNIS ranges from 0.45 to 2.4μm. Because of the weak reflection of infrared radiation from the lunar surface, a static electronic phase-locked acquisition method is adopted in the infrared channel for signal amplification. In this paper, full-link simulations and modeling are conducted of the infrared channel information flow of the instrument. The signal/noise characteristics of VNIS are analyzed in depth, and the signal-to-noise(SNR) ratio prediction and laboratory verification are presented. On January 4, 2019, the VNIS started working successfully and acquired high-resolution spectrum data of the far side of the moon for the first time. Through analysis, the SNR ratio is in line with predictions, and the data obtained by VNIS in orbit are consistent with the information model proposed in this paper.

The Folla Redonda grape is indigenous to the region of Rias Bajas in Galicia, Spain. This grape is distinguished by its characteristic acid flavor and pulp of remarkable coloration. Despite the wine obtained from this grape is highly valued by consumers of this region for its surprising acidic notes and thickness, it is important to note that it is not a variety of Vitis vinifera and its production is not legally regulated. In the present study, an evaluation of the physicochemical properties of the Folla Redonda grape (pH, °Brix, °Bé, density, acidity, sugar content, etc.) as well as its content in bioactive compounds (total polyphenols and anthocyanins) was carried out. Additionally, the antioxidant potential of the extracts obtained from this grape was demonstrated. This study proved the interesting organoleptic characteristics of the grape, along with a high content of compounds of interest and an important antioxidant activity comparable to that of Vitis vinifera grapes. Therefore, the grape's natural consumption and its application in different industries such as nutraceutical or pharmacological would result in an important revaluation of the grape and significant health benefits. In case that the production of wine from this variety is legalized, consumers will not only be able to enjoy a wine with a characteristic dense reddish color and important acid tones, but also with interesting concentrations of bioactive compounds.

The root extracts of Symphytum officinale (comfrey) are commonly used in traditional medicine to treat muscle, joint, skin and bone disorders. However, little information is available on the biomedical functions of comfrey leaves. In this study, sequencing results of Internal Transcribed Spacer and trnL-trnF genes showed that plants purchased from the local market were comfrey and named as S. officinale WL (WL). The optimal extraction conditions of WL leave with the highest extract yield and contents of total phenols and flavonoids by microwave-assisted extraction were identified. The antioxidative and anti-inflammatory activities and possible molecular mechanism(s) of the WL leaf extract (WLE) were evaluated. Furthermore, the major component of WLE was identified as rosmarinic acid by HPLC. Results showed that the optimal extract condition was obtained with 750W microwave power, 50℃, 75% methanol, the solid-to-solvent ratio of 1:10, and 15 minutes. Results of all DPPH, ABTS and superoxide radical scavenging activities, reducing power, ferrous ion chelating activity, and ferric reducing antioxidant power showed high antioxidative capacities of WLE. Furthermore, WLE showed obvious DNA protecting activity. WLE attenuated lipopolysaccharides-stimulated inflammation by suppressing iNOS, COX-2, IL-1β, IL-6, and TNF-α expressions in RAW264.7 macrophages. These attenuations involved in inactivations of lipopolysaccharides-induced NF-κB and MAPK signaling pathways.

Alzheimer's Disease (AD) is a neurodegenerative disease that is common in the elderly. This paper introduces the overview of Alzheimer's disease and the application of relevant signal processing methods in its detection. Signal processing is a technique that converts raw data into meaningful information and is widely used in the medical field. This paper lists common signal processing techniques, including Fourier transform, time-frequency analysis and statistical signal processing, and discusses their applications in the detection of Alzheimer's disease. Fourier transform can convert time domain signals into frequency domain representations, providing an effective tool for the study of EEG in Alzheimer's disease. Time-frequency analysis can perform a combined time and frequency analysis of the signal to help detect the signal characteristics of Alzheimer's disease. Statistical signal processing methods can be used to identify the features of Alzheimer's disease by building mathematical models. Finally, the challenges of Alzheimer's disease detection are discussed, including signal noise, diversity, and insufficient data volume. Through in-depth research and development of signal processing methods, it is expected to improve the accuracy and efficiency of early detection of Alzheimer's disease.

The research is devoted to the problem of noise generation during flights of supersonic civil aircrafts. The effect of a thermally stratified energy source used to control the flow past a pointed cylinder aerodynamic models on the nearfield and ground pressure signatures, as well as on the perceived loudness in decibels (PLdB) on the ground is evaluated. The fields of parameters and the dynamics of the drag forces are studied for different values of temperature in the layers of the thermally stratified energy source and for different number of layers in it. It has been shown that when performing the flow control at freestream Mach numbers 1.5-2 using thermally stratified energy sources with the number of layers from 2.5 to 7.5 and rarefaction parameters in the layers from 0.15 to 0.3, no additional noise impact on the ground is introduced. Thus, the simulations showed that changing the surface pressure signature due to drag reduction does not necessarily imply a change in the PLdB on the ground.

Room temperature superconductivity has recently been reported for a carbonaceous sulfur hydride (CSH) under high pressure by Snider et al [1]. The paper reports sharp drops in magnetic susceptibility as a function of temperature for five different pressures, that are interpreted as signaling a superconducting transition. Here I question the validity and faithfulness of the magnetic susceptibility data presented in the paper by comparison with the measured raw data reported by two of the authors of ref. [2]. This invalidates the assertion of the paper [1] that the susceptibility measurements support the case for superconductivity in this compound.

Abstract: Laryngoscopy for tracheal intubation has been developed for many decades. Among various conventional laryngoscopes, videolaryngoscopes (VL) have been applied in different patient populations, including difficult airways. The safety and effectiveness of VL have been repeatedly studied in both normal and difficult airways. The superiority of VL then has been observed and is advocated as the standard of care. In contrast to laryngoscopy, the development of video-assisted intubating stylet (VS, also named as styletubation) has been noticed two dec-ades ago. Since then, sporadic clinical experiences of use have appeared in literature. In this re-view article, we presented our vast use experiences of the styletubation (more than 55,000 pa-tients since 2016). We found this technique is swift (the time to intubate: from 3 s to 10 s), smooth (first-attempt success rate: 100%), safe (no airway complications), and easy (high subjective satis-faction and fast learning curve for the novice trainees) in both normal and difficult airway sce-narios. We therefore propose styletubation technique can be feasibly applied as universal rou-tine use for tracheal intubation.

Background Robotic-assisted thoracic surgery (RATS) has become the standard procedure for treating lung malignancy, offering numerous advantages over traditional methods. However, the minimally invasive nature of RATS presents unique challenges, notably a limited field of view and reduced tactile feedback for the surgeon. These limitations can significantly impact the surgeon's ability to navigate complex anatomical structures during surgery. To address these challenges, we recognized the need for enhanced preoperative familiarization with each patient’s specific anatomy. In response, we developed an innovative virtual reality (VR) surgical navigation system, utilizing head-mounted displays (HMDs), designed to provide surgeons with a more comprehensive and interactive understanding of the patient's anatomy before the actual surgery. This VR system aims to extend the surgeon's capabilities in both preoperative sumilation and intraoperative navigation, potentially mitigating the inherent limitations of RATS. Methods Three-dimensional (3D) volumized data, meticulously extracted from preoperative computed tomography (CT) scans, were seamlessly integrated into the Perspectus VR Education software. These data were then displayed on an HMD, facilitating an interactive 3D reconstruction of pulmonary structures. This reconstruction allowed for each structure to be visualized individually, enabling detailed preoperative resection simulations tailored to each patient's unique anatomy. During RATS, these specific 3D reconstructed images were made accessible within the surgeon's console through the TilePro™ multi-display input, providing real-time, intraoperative guidance. Results During the preoperative simulations, the 3D anatomic structure of the pulmonary vessels, as well as the spatial relationship between the lesion and adjacent tissues, were successfully visualized. This enhanced visualization enabled surgeons to thoroughly evaluate a patient's anatomy, significantly improving the safety and accuracy of the subsequent procedure. The integration of VR software with the HMD provided surgeons with a true 3D interaction with real patient data. This setup not only offered a unique perspective through the surgical console but also allowed for easy manipulation of the data on a mobile laptop, thereby enhancing the overall surgical planning process. Conclusions VR simulation using HMDs offers a viewing experience akin to that of a robotic 3D viewer, potentially providing an innovative avenue for surgeons to develop specific robotic surgical skills without the need for practicing on live patients. This technology, when integrated with routine imaging modalities, can significantly enhance preoperative planning. Such integration not only contributes to the safety but also improves the accuracy of anatomical resections. Our findings suggest that VR simulation with HMDs is particularly effective in assisting surgeons to accurately identify lesion locations in RATS, thereby optimizing surgical outcomes.

Falls, highly common in the constantly increasing global aging population, can have a variety of negative effects on their health, well-being, and quality of life, including restricting their capabilities to conduct Activities of Daily Living (ADLs), which are crucial for one’s sustenance. Timely assistance during falls is highly necessary, which involves tracking the indoor location of the elderly during their diverse navigational patterns associated with ADLs to detect the precise location of a fall. With the decreasing caregiver population on a global scale, it is important that the future of intelligent living environments can detect falls during ADL.s while being able to track the indoor location of the elderly in the real world. Prior works in these fields have several limitations, such as – the lack of functionalities to detect both falls and indoor locations, high cost of implementation, complicated design, the requirement of multiple hardware components for deployment, and the necessity to develop new hardware for implementation, which make the wide-scale deployment of such technologies challenging. To address these challenges, this work proposes a cost-effective and simplistic design paradigm for an Ambient Assisted Living system that can capture multimodal components of user behaviors during ADLs that are necessary for performing fall detection and indoor localization in a simultaneous manner in the real world. Proof of concept results from real-world experiments are presented to uphold the effective working of the system. The findings from two comparison studies with prior works in this field are also presented to uphold the novelty of this work. The first comparison study shows how the proposed system outperforms prior works in the areas of indoor localization and fall detection in terms of the effectiveness of its software design and hardware design. The second comparison study shows that the cost for the development of this system is the least as compared to prior works in these fields, which involved real-world development of the underlining systems, thereby upholding its cost-effective nature.

Objective: This study assessed hearing aid benefits for people with a normal audiogram but hearing-in-noise problems in everyday listening situations. Design: Exploratory double-blinded case control study whereby participants completed retrospective questionnaires, ecological momentary assessments, speech-in-noise testing, and mental effort testing with and without hearing aids. Twenty-seven adults reporting speech-in-noise problems but normal air-conduction pure-tone audiometry took part in the study. They were randomly separated into an experimental group who trialled mild-gain hearing aids with advanced directional processing and a control group fitted with hearing aids with no gain or directionality. Results: Self-reports showed mild-gain hearing aids reduce hearing-in-noise difficulties and provide a better hearing experience (i.e., improved understanding, participation, and mood). Despite the self-reported benefits, the laboratory tests did not reveal a benefit from the mild-gain hearing aids, with no group differences on speech-in-noise tests or mental effort measures. Further, participants found the elevated cost of hearing aids to be a barrier for their adoption. Conclusions: Hearing aids benefit the listening experience in some listening situations for people with normal audiogram who report hearing difficulties in noise. Decreasing the price of hearing aids may lead to greater accessibility to those seeking remediation for their communication needs.

This article is a big picture of how electrical noise or conducted Electromagnetic Interference (EMI) is generated and mitigated in power converters. It gives an overview of what EMI in power converters is – from generation through to conduction and mitigation. It is meant to cover the complete subject as a summary so that the reader will have an outline of how to control conducted EMI by design (where possible) and how to mitigate by filtering. A clear distinction is made between Differential Mode (DM) and Common Mode (CM) EMI generation and mitigation. By using a boost converter as an example the trade-offs for DM noise control are discussed. It is shown how CM EMI is generated in a boost converter using the concept of the “Imbalance Difference Model” (IDM). Practical measurements for an in-line power filter is given showing the effect of the filter on the total EMI of a boost converter. Measurements for the CM current produced due to the imbalance difference for different values of the boost conductor are also shown. The main contribution of this study is linking CM noise generation to DM EMI. It is shown that CM noise is a direct consequence of DM noise (although circuit imbalance and coupling to a common ground also play a role). This paper will be useful to designers seeking the “bigger picture” of how EMI is generated in power converters and what can be done to mitigate the noise.

This paper presents an overview of the smart electro-clothing systems (SeCSs) targeted at health monitoring, sports benefits, fitness tracking, and social activities. Technical features of the available SeCSs, covering both textile and electronic components, are thoroughly discussed and their applications in the industry and research purposes have been highlighted. In addition, it also presents the developments in the associated areas of wearable sensor systems and textile-based dry sensors. As it became evident during the literature research, such a review on SeCSs covering all relevant issues has not been presented before. This paper will be particularly helpful for new generation researchers investigating the design, development, function and comforts of the sensor integrated clothing materials.

Radar transmit signal design is a critical factor for the radar performance. In this paper, we investigate the problem of radar signal waveform design under the small signal power conditions for detecting a doubly spread target, whose impulse response can be modeled as a random process, in a colored noise environment. The doubly spread target spans multiple range bins (range-spread) and its impulse response is time-varying due to fluctuation (hence also Doppler-spread), such that the target impulse response is both time-selective and frequency-selective. Instead of adopting the conventional assumption that the target is wide-sense stationary uncorrelated scattering,we assume that the target impulse response is both wide-sense stationary in range and in time to account for the possible correlation between the impulse responses corresponding to close range intervals. The locally most powerful detector, which is asymptotically optimal for small signal cases, is then derived for detecting such targets. The signal waveform is optimized to maximizing the detection performance of the detector or equivalently maximizing the Kullback-Leibler divergence. Numerical simulations validate the effectiveness of the proposed waveform design for the small signal power conditions and performance of optimum waveform design are shown in comparison to the frequency modulated waveform.

Candidiasis is one of the global public health problems. In fluconazole resistant Candida spp. (FRCS), mutations in ergosterol biosynthesis gene 11 (ERG 11) have been commonly found. This study aimed to determine the profile of FRCS isolates and to detect mutations in part of ERG 11 gene in fluconazole resistant Candida albicans (FRCA) isolates. Study was conducted on 150 isolates of FRCS obtained from patients admitted in a tertiary care hospital, South India from September 2019 to December 2021. Candida spp. was identified by MALDI-TOF MS and fluconazole resistance was detected by VITEK 2 system. Following which a part of the ERG 11 gene of the FRCA isolates were amplified, purified, sequenced and a phylogenetic tree was constructed. Out of the 150 FRCS, the most common FRCS was C. auris followed by C. glabrata, C. krusei, C. parapsilosis, C. albicans, C. tropicalis and C. guilliermondii. Sequencing of amplicons detected a single missense mutation and 44 silent mutations. We opined that fluconazole resistance in FRCA isolates may be due to mecha-nisms other than the studied one in our region. Phylogenetic analysis of amplified sequences of 12 FRCA isolates observed three clusters and all 12 sequences showed relatedness to Malaysian 2019 isolates.

Considering the controversial benefits of video-assisted thoracoscopic surgery (VATS), we intended to evaluate the impact of surgical approach on cardiac function after lung resection using myocardial work analysis. Echocardiographic data of 45 patients (25 thoracotomy vs. 20 VATS) were retrospectively analyzed. All patients underwent transthoracic echocardiography (TTE) 2 weeks before and after surgery, including two-dimensional speckle tracking and tissue doppler imaging. No notable changes in left ventricular (LV) function, assessed mainly using the LV global longitudinal strain (GLS), global myocardial work index (GMWI), and global work efficiency (GWE), were observed. Right ventricular (RV) TTE values, including tricuspid annular plane systolic excursion (TAPSE), tricuspid annular systolic velocity (TASV), and RV free-wall GLS (RVFWGLS), indicated greater RV function impairment in the thoracotomy group than in the VATS group [TAPSE (mm): 17.90 ± 3.80 vs. 20.60 ± 3.50, p &lt; 0.018; TASV (cm/s): 12.40 ± 2.90 vs. 14.60 ± 2.50, p &lt; 0.010; RVFWGLS (%): −11.50 ± 8.50 vs. −17.50 ± 9.70, p &lt; 0.033), respectively]. Unlike RV function, LV function remained preserved after lung resection. The thoracotomy group exhibited greater RV function impairment than did the VATS group. Further studies should evaluate the long-term impact of surgical approach on cardiac function.

Animal-assisted pedagogy (AAP) is well known in classroom practice, but scientific evidence is still lacking. At the same time, the biggest challenge in education systems worldwide is the social in-tegration of students. In a pre-post design, 30 heterogeneous students (16f/14m) from four different school classes (grades 5-7) were interviewed (problem-centred interview) about their social inclu-sion and their social climate in class before and after being taught selected subjects with a school dog (of different breeds) for one school term. At the second measurement point, they were also asked about their perception of AAP. The data was analysed both qualitatively (Kuckartz) and in terms of the number of quotes on the main topics using MAXQDA analysis software. The presence of a dog in the classroom leads to an improved social climate and more social integration. In ad-dition, the perception of the other students and the teacher changes to a more positive and friendly image. Through AAP, a new social role is added to the classroom, where caring and bonding are prioritised. Social interaction and norms are influenced and sterotypical and individual roles can be changed. Therefore, AAP can be a key to promoting social inclusion in the school environment.

Abstract Introduction: The mHealth active participant centred (MAPC) adverse events following immunisation (AEFI) surveillance is a promising area for early AEFI detection resulting in risk minimisation. Passive (spontaneous) AEFI surveillance is the backbone for vaccine pharmacovigilance, but has inherent drawbacks of under reporting, and requires strengthening with active surveillance methods. The Zimbabwe stimulated telephone assisted rapid safety surveillance (Zm-STARSS) randomized controlled trial (RCT) sought to evaluate the efficacy and feasibility of AEFI detection using a short message service (SMS) and computer assisted telephone interview (CATI) approach. Method: A multicentre Zm-STARSS RCT enrolled consented adult vaccinees or parents or guardians of children receiving vaccines, including COVID-19 vaccines, at study vaccination clinics. At enrolment study participants were randomised to either SMS-CATI group or control group. SMS prompts were sent on days 0-2 and 14 post-vaccination to SMS-CATI group to ascertain if a medical event following immunisation (MEFI) had occurred. However, no SMSs were sent to the control group. SMS-CATI group who responded “Yes” to SMS prompts were interviewed by research healthcare workers (RHCWs) who completed a CATI to determine if an MEFI/AEFI had occurred whilst an AEFI in control group was determined from passive AEFI reporting channels. The primary study outcome was the AEFI detection rate in the SMS-CATI group compared to the control group. Results: A total of 704 (31%) participants responded to the SMS prompts, with 75% (528/704) indicating “No” and 25% (176/704) "Yes” to experiencing a MEFI. However, 31% (55/176) who responded “Yes” did not complete a CATI survey, but 69% (121/176) did; and 64% (77/121) of these indicated negative to AEFI experience whilst 36% (44/121) were affirmative. There were no AEFI reported in control group participants. Zm-STARSS showed promising results in that the AEFI detection rate using SMS-CATI was 2% (44/2280) on an intention to treat cohort. Conclusion: Despite the low SMS response and CATI completion rate, we demonstrated that Zm-STARSS SMS system improves AEFI detection compared to passive AEFI surveillance. We recommend cost-effective multi-channel digital approaches for holistic pharmacovigilance to improve AEFI detection in LMICs for all vaccines.

(1) Background: Considering highly selected patients with ductal carcinoma in situ (DCIS), active surveillance is a valid alternative to surgery. Our study is aimed at showing the reliability of post-biopsy complete lesion removal, documented by mammogram, as additional criterion to select these patients. (2) Methods: 2173 Vacuum Assisted Breast Biopsies (VABB) documented as DCIS have been reviewed. Surgery has been performed in all cases. We retrospectively collected the reports of post-VABB complete lesion removal and the histological results of the biopsy and surgery. We calculated the rate of upgrade of DCIS identified on VABB upon excision for patients with post-biopsy complete lesion removal and for those showing residual lesion. (3) Results: We observed 2173 cases of DCIS: 408 classified as low grade; 1262 as intermediate grade; 503 as high grade. The overall upgrading rate to invasive carcinoma was 15.2% (330/2173). The upgrade rate was significantly lower (8.2%) when considering patients showing mammographically documented complete removal of the lesion. (4) Conclusion: The absence of mammographically documented residual lesion following VABB is associated to a lower upgrading rate of DCIS to invasive carcinoma on surgical excision and should be considered when deciding the proper management DCIS diagnosis.

Objectives: Studies evaluating machine learning (ML) algorithms on cross-lingual RadLex® mappings for developing context-sensitive radiological reporting tools are lacking. Therefore, we investigated whether ML-based approaches can be utilized to assist radiologists in providing key imaging biomarkers – such as The Alberta stroke programme early CT score (APECTS). Material and Methods: A stratified random sample (age, gender, year) of CT reports (n=206) with suspected ischemic stroke was generated out of 3997 reports signed off between 2015-2019. Three independent, blinded readers assessed these reports and manually annotated clinico-radiologically relevant key features. The primary outcome was whether ASPECTS should have been provided (yes/no: 154/52). For all reports, both the findings and impressions underwent cross-lingual (German to English) RadLex®-mappings using natural language processing. Well-established ML-algorithms including classification trees, random forests, elastic net, support vector machines (SVMs) and boosted trees were evaluated in a 5 x 5-fold nested cross-validation framework. Further, a linear classifier (fastText) was directly fitted on the German reports. Ensemble learning was used to provide robust importance rankings of these ML-algorithms. Performance was evaluated using derivates of the confusion matrix and metrics of calibration including AUC, brier score and log loss as well as visually by calibration plots. Results: On this imbalanced classification task SVMs showed the highest accuracies both on human-extracted- (87%) and fully automated RadLex® features (findings: 82.5%; impressions: 85.4%). FastText without pre-trained language model showed the highest accuracy (89.3%) and AUC (92%) on the impressions. Ensemble learner revealed that boosted trees, fastText and SVMs are the most important ML-classifiers. Boosted trees fitted on the findings showed the best overall calibration curve. Conclusions: Contextual ML-based assistance suggesting ASPECTS while reporting neuroradiological emergencies is feasible, even if ML-models are restricted to be developed on limited and highly imbalanced data sets.

People wore clothes for warmth, survival and necessity in modern life as well but in modern era, eco-friendliness, shorten production time, design, and intelligence also matters. The seamless bonding technology strengthen the bond between fabrics in textiles, while Grey relational analysis (GRA) can be applied to fabrics to measure the relationship between data series and determine the closeness of each data series. In this study, polyurethane prepolymer, 2-Hydroxyethyl acrylate (2-HEA) as an end-capping agent, and n-octyl acrylate (ODA) as a photo-initiator were used to synthesize dual-curing polyurethane hot-melt adhesive. The Taguchi quality engineering and grey relational analysis used to discuss the influence of different mole ratio of NCO:OH, and the effect of the molar ratio of addition of octyl decyl acrylate on the mechanical strength. The Fourier transform infrared spectroscopy (FTIR) results showed the termination of prepolymer's polymerization reaction and the C=O peak intensity at 1730 cm-1 indicating the efficient bonding to main chain. The high molecular weight polymer (20,000-30,000) described by advanced polymer chromatography (APC) confirmed the polyurethane polymer bonded with octyl decyl acrylate to achieve the photo-thermosetting effect. The thermogravimetric analysis (TGA) results showed the thermal decomposition temperature of polyurethane hot melt adhesive also increased and highest pyrolysis temperature (349.89℃) for the polyhydric alcohols. The high value of shear strength (34.94 kg/cm2) as well as peel strength (1.68 kg/cm) were also observed in dual-cure photo-thermosetting polyurethane hot melt adhesive. The signal-to-noise ratio also used to generate the grey relational degree. It was observed that the best parameter ration of NCO: OH was 4:1 with 5 moles of monomer. The Taguchi quality engineering method was used to find the parameters of single quality optimization, and then the grey relation calculation was used to obtain the parameter combination of multi-quality optimization for thermosetting polyurethane hot melt adhesive. The study aims to meet the requirements of seamless bonding in textile factories and optimized experimental parameter design by setting target values can effectively increase production speed, reduce processing time and costs as well.

This article aims to assess, discuss and analyze the disturbances caused by electromagnetic field (EMF) noise of medical devices used nearby living tissues, as well as the corresponding functional control via the electromagnetic compatibility (EMC) of these devices. These are minimally invasive and non-ionizing devices allowing various healthcare actions involving monitoring, assistance, diagnosis and image-guided medical interventions. Following to an introduction of the main items of the paper, the different imaging methodologies are conferred, accounting for their nature, functioning, employment condition and patient comfort and safety. Then the magnetic resonance imaging (MRI) components and their fields, the consequential MRI-compatibility concept and possible image artifacts are detailed and analyzed. Next, the MRI-assisted robotic treatments, the possible robotic external matter introductions in the imager, the features of MRI-compatible materials and the conformity control of MRI-compatibility, are analyzed and conferred. Afterward, the embedded, wearable and detachable medical devices, their EMF perturbation control and their necessary shielding protection technologies are presented and analyzed. Then, the EMC control procedure, the EMF governing equations, the body numerical virtual models, are conferred and reviewed. A qualitative methodology, case study, simple example illustrating the mentioned methodology is presented. The last Section of the paper discussed potential details and expansions of the different notions conferred in the paper, in the perspective of monitoring the disturbances due to EMF noise of medical devices working near living tissues.

The application of the sensors optical fiber in the areas of scientific instrumentation and industrial instrumentation is very attractive due to its numerous advantages. In the industry of civil engineering for example, quasi-distributed sensors made with optical fiber are used for reliable strain and temperature measurements. Here, a quasi-distributed sensor in the frequency domain is discussed. The sensor consists of a series of low-finesse Fabry-Perot interferometers where each Fabry-Perot interferometer acts as a local sensor. Fabry-Perot interferometers are formed by pairs of identical low reflective Bragg gratings imprinted in a single mode fiber. All interferometer sensors have different cavity length, provoking the frequency-domain multiplexing. The optical signal represents the superposition of all interference patterns which can be decomposed using the Fourier transform. The frequency spectrum is analyzed and sensor´s properties were defined. Following, a quasi-distributed sensor was numerically simulated. Our sensor simulation considers sensor properties, signal processing, noise system and instrumentation. The numerical results show the behavior of resolution vs. signal-to-noise ratio. From our results, the Fabry-Perot sensor has high resolution and low resolutions. Both resolutions are conceivable because the FDPA algorithm elaborates two evaluations of Bragg wavelength shift

Direct laryngoscope and videolaryngoscope are the dominant endotracheal intubation tools. Styletubation technique (using a video-assisted intubating stylet) has shown its advantages regarding in short intubation time, high success rate, less stimulation, and operator’s satisfaction. The learning curve can be steep but easy to overcome if the technical pitfalls could be avoided. Conditions make styletubation challenging include secretions/blood, short/stiff neck, restricted mouth opening and cervical spine mobility, anatomical abnormalities over head and neck regions, and obesity, etc. In this clinical report, we present the effectiveness and efficiency of routine use of the styletubation for tracheal intubation in a super-super obese patient (BMI 103 kg/m2) undergoing bariatric surgery with laparoscopic sleeve gastrectomy.

There is great importance and need of improving existing carbon materials fabrication methods. As such, this work proposes to discuss, interrogate, and propose viable hydrothermal, solvothermal, and other advanced carbon materials synthetic methods. The advanced carbon materials to be interrogated will include the synthesis of carbon dots, carbon nanotubes, nitrogen/titania-doped carbons, graphene quantum dots, and their nanocomposites with solid/polymeric/metal oxide supports. This will be done with special mind to microwave-assisted solvothermal and hydrothermal synthesis due to their favourable properties such as rapidity, low cost, and green/environmentally-friendliness. Thus, these methods are important during the current and future synthesis and modification of advanced carbon materials for application in energy, gas separation, sensing, and water treatment. Simultaneously, the work will pay special cognizance to methods reducing the fabrication costs and environmental impact while enhancing the properties as a direct result of the synthesis methods. As a direct result, the expectation is to impart a significant contribution to the scientific body of work regarding the improvement of the said fabrication methods.

The objective of this study was to develop an ultrasonic-assisted procedure for the extraction of total phenolics from Citrus aurantium L. blossoms (CAB) and evaluate the free radical scavenging activity, anti-HMG-CoA reductase activity of total phenolics. In this work, a Box-Behnken design based on the single-factor experiments was used to explore the optimum extraction process. Under the optimum conditions (extraction solvent 70.31% ethanol, extraction temperature 61.94 °C, extraction time 51.73 min and liquid-to-solid ratio 35.63 mL/g), the extraction yield of total phenolics was 95.84 mg gallic acid equivalents (GAE)/g dry matter (DM), which was highly consistent with the theoretical value (96.12 mg GAE/g DM). The total phenolic extract showed excellent free radical scavenging properties against DPPH·, ABTS+·, ·OH and ·O2-, with the IC50 values of 197.007, 83.878, 218.643 and 158.885 μg/mL, respectively, and the extracts also showed good inhibition of HMG-CoA reductase activity, with the IC50 value of 117.165 μg/mL. Total phenolics from CAB could be a potential source of natural free radical scavenger and HMG-CoA reductase inhibitor.

Video capsule endoscopy (VCE) is increasingly used to decrease the discomfort among patients owing to its small size. However, VCE has a major drawback of not having narrow band imaging (NBI) functionality. The current VCE has the traditional white light imaging (WLI) only, which has poor performance in the computer-aided detection (CAD) of different types of cancer compared with NBI. Specific cancers, such as esophageal cancer (EC), do not exhibit any early biomarkers, making their early detection difficult. In most cases, the symptoms are unnoticeable, and EC is diagnosed only in later stages, making its 5-year survival rate below 20% on an average. NBI filters provide particular wavelengths that increase the contrast and enhance certain features of the mucosa, thereby enabling early identification of EC. However, VCE does not have a slot for NBI functionality because its size cannot be increased. Hence, NBI image conversion from WLI can be achieved only in post-processing at present. In this study, a complete arithmetic assessment of the decorrelated color space was conducted to generate NBI images from WLI images for VCE of the esophagus. Three parameters, namely, structural similarity index metric (SSIM), entropy, and peak-signal-to-noise ratio (PSNR), were used to asses the simulated NBI images. Results show the good performance of the NBI image reproduction method, with SSIM, entropy difference, and PSNR values of 93.215%, 4.360, and 28.064 dB, respectively.

Deep Learning (DL) and Machine Learning (ML) are widely used in many fields, but rarely used in Frequency Estimation (FE) and Slope Estimation (SE) of signals. Frequency and slope estimation for Frequency-Modulated (FM) and single-tone sinusoidal signals are essential in various applications, such as wireless communications, sonar, and radar measurements. In this work, artificial neural network (ANN) and convolutional neural network (CNN) are used in frequency and slope estimation for FM signals under Additive White Gaussian Noise (AWGN) and Additive Symmetric alpha Stable Noise (SαSN). SαS distributions are impulsive noise disturbances found in many communication environments like marine systems; their distribution lacks a closed-form Probability Density Function (PDF), except for specific cases, and infinite second-order statistic, hence Geometric SNR (GSNR) is used in this work to determine the impulsiveness of noise in a mixture of Gaussian and SαS noise processes. ANN is a machine learning classifier, designed with few layers for reducing FE and SE complexity while getting higher accuracy as compared with classical techniques. CNN is a deep learning classifier, designed with many layers for FE and SE, and proved to be more accurate than ANN when dealing with big data and finding optimal features. Simulation results show that SαS noise can be much more harmful for FE and SE of FM signals than Gaussian noise. DL and ML can significantly reduce FE complexity, memory cost, and power consumption, which is important in many systems such as some Internet of Things (IoT) sensor applications. After training DCNN for frequency and slope estimation of LFM signals, the performance of DCNN (in terms of accuracy) can give acceptable results at very low signal-to-noise ratios where TFD fails, giving more than 20dB difference in the GSNR working range.

Signal-to-noise ratio (SNR) is an important index to evaluate radiation performance and image quality of optical imaging systems under low illumination background. Under the nighttime lighting condition, the illumination of remote sensing objects is low and varies greatly, usually ranging from several lux to tens of thousands of lux. Nighttime light remote sensing imaging requires high sensitivity and large dynamic range of detectors. Luojia 1-01 is the first professional nighttime light remote sensing satellite in the world. In this paper, we took the nighttime light remote sensing camera carried on the satellite as research object, proposed an in-orbit SNR test method based on time series images to overcome the problem of low spatial resolution. We first analyzed the process of luminous flux transmission between objects and satellite and established a radiative transfer model. By combining the parameters of large relative aperture optical system and high sensitivity CMOS device, we established SNR model and specially analyzed the effect of exposure time and quantization bits on SNR. Finally we used the proposed in-orbit test method to calculate SNR of lighting images acquired by satellite. And the measured result is in good agreement with the model predicted data. Under the condition of 10lx illumination, the SNR of typical objects can reach 27.02dB, which is much better than the requirement of 20dB for engineering application.

The present grievous situation of the tuberculosis disease can be improved by efficient case management and timely follow-up evaluations. With the advent of digital technology this can be achieved by quick summarization of the patient-centric data. The aim of our study was to assess the effectiveness of the ODK Scan paper-to-digital system during testing period of three months. A sequential, explanatory mixed-method research approach was employed to elucidate technology use. Training, smartphones, application and 3G enabled SIMs were provided to the four field workers. At the beginning, baseline measures of the data management aspects were recorded and compared with endline measures to see the impact of ODK Scan. Additionally, at the end, users’ feedback was collected regarding app usability, user interface design and workflow changes. 122 patients’ records were retrieved from the server and analysed for quality. It was found that ODK Scan recognized 99.2% of multiple choice bubble responses and 79.4% of numerical digit responses correctly. However, the overall quality of the digital data was decreased in comparison to manually entered data. Using ODK Scan, a significant time reduction is observed in data aggregation and data transfer activities, however, data verification and form filling activities took more time. Interviews revealed that field workers saw value in using ODK Scan, however, they were more concerned about the time consuming aspects of the use of ODK Scan. Therefore, it is concluded that minimal disturbance in the existing workflow, continuous feedback and value additions are the important considerations for the implementing organization to ensure technology adoption and workflow improvements.

Nowadays, the high demand for healthy foods and sustainable products, has led the food industry to explore innovative food technologies, such as fortification with bioactive compounds like antioxidants and polyphenols, that may be sourced from herbs or by-products. The aim of the present study was to explore the enrichment of refined olive oils with natural bioactive compounds such as the herbs rosemary (salvia rosmarinus), basil (ocimum basil), sage (salvia officinalis), lemon balm (melissa oficinallis), st. john’s wort (hypericum perforatum), pink savory (Thymbra satureja), dittany (origanum dictamnus), and by-products such as pomace from olives, olive leaves (olea europaea tree), orange peel (citrus sinensis), lemon peel (citrus limon), pomegranate peel (punica granatum) and mandarin peel (citrus reticulate). The fortification of the refined olive oils was performed with the use of different methodologies such as Conventional maceration (CM), Incubation shaking maceration (ISM), and Ultrasound assisted maceration (UAM). Their phenolic content and antioxidant capacity were measured with Folin–Ciocalteu assay and Ferric Reducing Antioxidant Power (FRAP) assay respectively. All methods demonstrated that different parameters such as time of maceration, temperature and sample concentration, play an important role in the fortification process of the refined olive oils. The predicyed bioavailability of the antioxidant and phenolic compounds in the fortified oils was determined with in vitro digestion and ranged from 4.84% to 53.11%. Furthermore, the refined olive oils fortified with pomace, basil, st. john's wort, and pomegranate peel presented the highest antioxidant and phenolic predicted bioavailability indices during the in vitro process compared to the control refined olive oil. Finally, fortification with natural herbs or by-products can be considered an innovative method for the improvement of the nutritional value of refined olive oils.

In this study, the degradation characteristics of radio-frequency (RF) low-noise amplifier (LNA) based on silicon-germanium (SiGe) heterojunction bipolar transistors (HBTs) due to total ionizing dose (TID) are investigated. The small-signal equivalent model of a SiGe HBT is utilized to analyze circuit-level performance including the input and the output matching and noise figure (NF). As a target circuit, an LNA with a cascode common-emitter stage with emitter degeneration is studied and the equation of each performance parameter is derived. The RF LNA fabricated using commercial 350 nm SiGe technology was exposed to X-ray irradiation with the total dose up to 3 Mrad (SiO2). The experimental results exhibit that modeled device parameters estimate the degraded circuit performance. In addition, the relative impact of each parameter on the circuit metrics is revealed, which is expected from the derived design equations. The key device parameters for modeling TID-induced circuit degradations include the base resistance, the transconductance, and the base-to-emitter capacitance.

The temporo-spatial theory of consciousness postulates that the brain implements its own inner time and space for conscious processing of outside world. In line with this hypothesis, I present a mechanistic model of mutually connected processes that encode the inner space and time. The model is used to elaborate the binding mechanism between two sets of processes representing the inner space and time, respectively. Further, a stochastic version of the model is developed to investigate the interplay between the binding strength and noise. The spectral entropy is used to characterize the noise effects on the systems of processes when the binding strength is varied. The stochastic modeling results reveal that the spectral entropy values for strongly bound systems have similar entropy values for weakly bound or even decoupled systems. Thus, the analysis performed in this study allows to conclude that the binding mechanism is noise-resilient.

This study proposes a machine-learning-based framework for detecting mechanical damage in pipelines, utilizing physics-informed datasets collected from simulations for mechanical damage. The framework provides an effective workflow from dataset generation to damage detection and identification for three types of pipeline events: welds, clamps, and corrosion defects. While the study initially focused on optimizing the CNN structure using various advanced optimizers, it also investigated the impact of sensing systems on data classification and the effect of noise on classification performance. The study's analysis highlights the importance of selecting the appropriate sensing system for the specific application. The authors also found that the proposed framework is robust to experimentally relevant levels of noise, suggesting its applicability in real-world scenarios where noise is present. Overall, this study contributes to the development of a more reliable and effective method for detecting mechanical damage in pipelines. The proposed framework provides an effective workflow for damage detection and identification, and the findings on the impact of sensing systems and noise on classification performance add to its robustness and reliability.

Tree species composition is an important key element for biodiversity and sustainable forest management, and hyperspectral data provide detailed spectral information, which can be used for tree species classification. There are two main challenges for using hyperspectral imagery: a) Hughes phenomena, meaning by increasing the number of bands in hyperspectral imagery, the number of required classification samples would increase exponentially, and b) in a more complex environment, such as riparian mixed forest, focusing on spectral variability per pixel may not be adequate for definability of tree species. Therefore, the focus of this study is to assess spectral-spatial dimensionality reduction of airborne hyperspectral imagery by using minim noise fraction (MNF) transformation, and object-based image analysis (OBIA). An airborne prism experiment (APEX) hyperspectral imagery was used. A study area was a riparian mixed forest located along the Salzach river, and six tree species including Picea abies, Populus (canadensis and balsamifera), Fraxinus excelsior, Alnus incana, and Salix alba were selected. A machine learning algorithm random forest (RF) was used to train and apply a prediction model for classification. Using a spectral dimensionality reduced APEX, a pixel-level classification was also done. According to a confusion matrix, the object-level classification of MNF-derived components achieved the overall accuracy of 85 %, and kappa coefficient of 0.805. The performance of classes according to producer’s accuracy varied between 80% for Fraxinus excelsior, Alnus incana, and Populus canadensis to 90% for Salix alba and Picea abies. Comparison the results to a pixel-level classification, showed a better performance of object-level classification (an overall accuracy of 63% and Kappa coefficient of 0.559 were achieved for pixel-level classification). The performance of classes using pixel-based classification varied 45 % for Alnus incana to 80% for Picea abies. In general, Spectral-spatial complexity reduction using MNF transformation and object-level classification yielded a statistically satisfactory results.

General–purpose automatic speech recognition (ASR) systems have improved their quality and are being used for pronunciation assessment. However, the assessment of isolated short utterances, as words in minimal pairs for segmental approaches, remains an important challenge, even more for non-native speakers. In this work, we compare the performance of our own tailored ASR system (kASR) with the one of Google ASR (gASR) for the assessment of Spanish minimal pair words produced by 33 native Japanese speakers in a computer-assisted pronunciation training (CAPT) scenario. Participants of a pre/post-test training experiment spanning four weeks were split into three groups: experimental, in-classroom, and placebo. Experimental group used the CAPT tool described in the paper, which we specially designed for autonomous pronunciation training. Statistically significant improvement for experimental and in-classroom groups is revealed, and moderate correlation values between gASR and kASR results were obtained, beside strong correlations between the post-test scores of both ASR systems with the CAPT application scores found at the final stages of application use. These results suggest that both ASR alternatives are valid for assessing minimal pairs in CAPT tools, in the current configuration. Discussion on possible ways to improve our system and possibilities for future research are included.

Understanding speech in noise is particularly difficult for individuals occupationally exposed to noise due to a to a mix between noise-induced auditory lesions and energetic masking of speech signal. For years, the monitoring of conventional audiometric thresholds has been the usual way to check and preserve the auditory function. But the highlighting of supra-threshold deficits, notably speech in noise understanding difficulties, has pointed out the need for new monitoring tools. The present study aims to identify the most important variables that predict speech in noise under-standing in order to suggest a new way of regular monitoring. Physiological (distortion products of otoacoustic emissions, electrocochleography) and behavioral (amplitude and frequency modula-tion detection thresholds, conventional and extended high frequency audiometric thresholds) variables were collected in a population of individuals presenting a relative homogeneous occu-pational noise exposure. Those variables were used as predictors in a statistical model (random forest) to predict scores of three different speech in noise tests and a self-report of speech in noise ability. The extended high frequency threshold appears to be the best predictor and therefore an interesting candidate for a new way of monitoring noise exposed professionals.

In the area of dental healthcare services, where patients and professionals deal with multiple auditory stimuli, this cross-sectional study reports on the intricate dynamics of sound preferences, noise obstacles, and the profound impact of music on anxiety reduction within dental office set-tings. The sample comprises of 134 dental patients, primarily females (56.7%), with varying age groups, education levels, and therapy durations in the university clinics of the department of Dentistry, National and Kapodistrian University of Athens and four private dental offices at the metropolitan area of Athens, Greece. Notably, 92.5% of the participants report no hearing loss, and 56.7% exhibit some level of noise sensitivity. The study reveals a moderate to good perceived health status (M=3.84, SD=0.95) among participants, with low perceived noise disturbance in university dental clinics. Preferences for instrumental music, foreign pop, and classical music are prominent during waiting times. Potential irritations include impersonal treatment (44%) and staff behavior (41.8%). Music player usage within the settings of the dental clinic involves radios (49.3%) and headphones (41.0%). Correlations and predictors indicate associations between health status, music preferences, and noise disturbances. More specifically, visiting the university dental clinics (β = .300, p = .005) as well as younger age (β = .300, p = .034) were significant predictors of better perceived health status. Clearer perception of external sounds while using the music player (β = .190, p = .039) was a significant predictor of better perceived health status, while poorer health was predicted by the preference of Greek folk songs (β = -.200, p = .030). Also, the use of smartphones for playing music was related to poorer health (β = -.370, p < .001). Following, younger age (β = -.270, p = .015), lower education (β = -.210, p = .035), higher noise sensitivity (β = .190, p = .027), as well as feelings of anxiety and nervousness due to ambient noise from people and machines (β = .510, p = .027) were significant predictors of noise disturbance from machines. Also, visiting private clinics (β = .240, p = .018), preferences of specific playlist (β = .310, p = .012), no music (β = .280, p = .016) and use of smartphones to play music (β = .230, p = .029) were significant predictors of noise disturbance from patients or staff. Finally, the preference of listening to a specific playlist was related to better health status (r = .190, p < .05). Findings aim to optimize eco-soundscapes in dental offices, con-tributing to enhanced patient well-being. The research underscores the potential of music inter-ventions to alleviate dental anxiety and improve the overall dental experience and quality care.

We study two fundamental linguistic channels ‒ the Sentences and the Interpunctions channels ‒ and show they can reveal deeper connections between texts. The theory applied does not follow the actual paradigm of linguistic studies. As study‒case, we consider the Greek New Testament, with the purpose of determining mathematical connections between its texts and possible differences in writing style (mathematically defined) of writers, and in reading skill required to their readers. The analysis is based on deep‒language parameters and communication/information theory. To set the New Testament texts in the larger Greek Classical Literature, we consider texts written by Aesop, Polybius, Flavius Josephus and Plutarch. The results largely confirm what scholars have found about the New Testament texts giving, therefore, credibility to the theory. The gospel according to John is very similar to Fables written by Aesop. Surprisingly, the Epistle to the Hebrews and Apocalypse, are each other “photocopy” in the two linguistic channels, and not linked to all other texts. These two texts deserve further study by historians of the early Christian Church Literature at the level of meaning, readers and possible Old Testament texts which might have influenced them. The theory can guide scholars to study any literary corpus.

In this paper, several two-dimensional clustering scenarios are given. In those scenarios, soft partitioning clustering algorithms (Fuzzy C-means (FCM) and Possibilistic c-means (PCM)) are applied. Afterward, VAT is used to investigate the clustering tendency visually, and then in order of checking cluster validation, three types of indices (e.g., PC, DI, and DBI) were used. After observing the clustering algorithms, it was evident that each of them has its limitations; however, PCM is more robust to noise than FCM as in case of FCM a noise point has to be considered as a member of any of the cluster.

Artificial Intelligence (AI) is revolutionizing decision-making procedures in healthcare information systems (HIS), increasing the efficacy, accuracy, and personalization of healthcare. Notwithstanding remarkable progresses, yet there remain gaps in the holistic comprehension and implication of AI-driven technologies across various HIS scenarios. These gaps, consisting limited integration of AI across a variety of HIS parts and inadequate emphasize on interpretability and privacy, motivate our study to conduct a structured evaluation of recent AI applications in this domain. In this study, we present a novel taxonomy for the AI implication in medical decision-making. We analyzed 30 articles and categorized them into six groups including: Clinical Decision Support Systems (CDSS), Predictive Analytics, Natural Language Processing (NLP), Computer-Aided Diagnostics (CAD), Robotic-Assisted Surgery, and Virtual Health Assistants (VHAs/chatbots). Our study aims to propose a holistic perspective of the fast evolving perspective of AI in HIS. The majority of the investigated studies were published from 2023 and 2022, with Springer as the leading publisher (33%). Python and MATLAB were the most well-known simulation languages, applied in 48% and 20% of the papers, respectively, reflecting the technical trends in the area. By emphasizing on pivotal criteria like interpretability, accuracy, and privacy, this research aims to elucidate the crucial issues and development driving AI-driven decision-making in HIS.

Surgical navigation has evolved as a vital tool in maxillofacial surgery, offering precise and patient-specific data. This study explores the clinical applications and accuracy of intraoperative tool tracking in maxillofacial surgery. The research includes 37 patients with various pathologies who underwent surgery assisted by a surgical navigation system using tracked instruments. The study showcases two representative cases: one involving coronoid hyperplasia with mouth opening deficit and another addressing nose-orbital-ethmoidal-frontal ossifying fibroma resection. The results indicate that surgical navigation with tracked instruments provides high precision (<1.5 mm error), reduced surgical time, and less invasive approaches. The study highlights the potential for reproducible outcomes and increased safety, especially in complex cases. Despite some limitations, the synergy between surgical navigation and tracked instruments offers a promising approach in maxillofacial surgery, expanding its applications beyond current practices.

The integration of magnetic observatories of many countries into the Intermagnet network has provided researchers with new opportunities to investigate many geophysical processes. The joint processing of measurement evidence from magnetic observatories located in various geolocations makes it possible to better understand the dynamics of the interaction of solar plasma fluxes with the Earth's magnetosphere. For the correct interpretation of the dynamics of physical processes, the accuracy of the time reference of measurement results is of great importance. The measurement results of local observatories were transformed to a unified ecliptic coordinate system oriented towards the Sun in order to extract small variations in the magnetic field occurring before the magnetic storm commencement. Before transforming the coordinates, a constant component equal to the average daily value of the magnetic field components was removed from the results of minute-by-minute measurements. Owing to the averaging evidence from 84 magnetic observatories of the Intermagnet network, variations in the magnetic field of cosmic origin, preceding the magnetic storm commencement were detected. The correlation analysis of these variations recorded by different observatories revealed the presence of time shifts between the data series of different observatories. Only 62% of the observatories had a relative data shift within the range of ± 1 minute. Before the joint data processing in order to efficient use of data recorded with a time shift a method for correcting time shifts has proposed.

Pavement friction plays a crucial role in ensuring the safety of road networks. Accurately assessing friction levels is vital for effective pavement maintenance and management strategies employed by state highway agencies. Traditionally, friction evaluations have been conducted on a case-by-case basis, focusing on specific road sections. However, this approach fails to provide a comprehensive assessment of friction conditions across the entire road network. This paper introduces a hybrid clustering algorithm, namely the combination of density-based spatial clustering of applications with noise (DBSCAN) and Gaussian mixture model (GMM), to perform pavement friction performance rating across a statewide road network. A large, safety-oriented dataset is first generated by integrating network friction and vehicle crash data based on the attributes contributing possibly to friction related crashes. One-, two-, and multi-dimensional clustering analyses, respectively, are then performed to rate pavement friction. The Chi-square test is further employed to validate and identify the practical ratings. It is shown that by effectively capturing the hidden, intricate patterns within the integrated, complex dataset and prioritizing friction-related safety attributes, the hybrid clustering algorithm can produce pavement friction ratings that align effectively with the current practices of the Indiana Department of Transportation (INDOT) in friction management.

In the face of the upcoming 30th anniversary of econophysics, we review our contributions and other related works on the modeling of the long–range memory phenomenon in physical, economic, and other social complex systems. Our group has shown that the long–range memory phenomenon can be reproduced using various Markov processes, such as point processes, stochastic differential equations and agent–based models. Reproduced well enough to match other statistical properties of the financial markets, such as return and trading activity distributions and first–passage time distributions. Research has lead us to question whether the observed long–range memory is a result of actual long–range memory process or just a consequence of non–linearity of Markov processes. As our most recent result we discuss the long–range memory of the order flow data in the financial markets and other social systems from the perspective of the fractional Lèvy stable motion. We test widely used long-range memory estimators on discrete fractional Lèvy stable motion represented by the ARFIMA sample series. Our newly obtained results seem indicate that new estimators of self–similarity and long–range memory for analyzing systems with non–Gaussian distributions have to be developed.

Fatty acids (FAs) represent an important class of metabolites, impacting on membrane building blocks and cellular regulatory networks. In nature, prokaryotes are characterized with the most impressing FA structural diversity and the highest relative contents of free fatty acids (FFAs). Thereby, nitrogen-fixing bacteria (order Rhizobiales), often found in symbiosis with legumes, attract a special interest. Indeed, FAs impact on the structure of rhizobial nodulation factors, required for successful infection of plant root. Although the FA patterns can be addressed by GC- and LC-MS, these methods are time-consuming and suffer from compromised sensitivity, low stability of derivatives and artifacts. In contrast, MALDI-TOF-MS represents an excellent platform for high-efficient metabolite fingerprinting, also applicable to FFAs. Therefore, here we propose a simple and straightforward protocol for high-throughput relative quantification of FFAs in rhizobia by the combination of Langmuir technology and MALDI-TOF-MS, which is featured with high sensitivity, accuracy and precision of quantification. Here we propose a step by step procedure comprising rhizobia culturing, pre-cleaning, extraction, sample preparation, mass spectrometric analysis, data processing and post-processing. To demonstrate the analytical potential of the protocol we illustrate it by a case study – comparison the FFA metabolomes of two rhizobia species - Rhizobium leguminosarum and Sinorhizobium meliloti.

Infertility” is a condition defined by the failure to establish a clinical pregnancy after 12 months of regular, unprotected sexual intercourse or due to an impairment of a person’s capacity to reproduce either as an individual or with his/her partner’. The authors have set out to succinctly investigate, explore and assess infertility treatments harnessing the potential of stem cells to effectively and safely treat infertility, in addition to the legal and regulatory complexities at the heart of stem cell research, with an overview of the legislative state of affairs in six major European countries. In couples who cannot benefit from assisted reproductive technologies (ART) to treat their infertility, stem cells-based approaches have been shown to be a highly promising approach. Nonetheless, lingering ethical and immunological uncertainties require more conclusive findings and data before such treatment avenues can become mainstream and applied large scale. The isolation of human embryonic stem cells (ESCs) is ethically controversial, since their collection involves the destruction of human embryonic tissue. Overall, stem cell research has resulted in important new breakthroughs in the treatment of infertility. The effort to untangle the complex web of ethical and legal issues associated with such therapeutic approaches will have to rely on evidence-based, broadly shared standards, guidelines and best practices to make sure that the procreative rights of patients can be effectively reconciled with the core values at the heart of medical ethics.

Smart Living, an increasingly prominent concept, entails incorporating sophisticated technologies in homes and urban environments to elevate the quality of life for citizens. A critical success factor for Smart Living services and applications, from energy management to healthcare and transportation, is the efficacy of human action recognition (HAR). HAR, rooted in computer vision, seeks to identify human actions and activities using visual data and various sensor modalities. This paper extensively reviews the literature on HAR in Smart Living services and applications, amalgamating key contributions and challenges while providing insights into future research directions. The review delves into the essential aspects of Smart Living, the state of the art in HAR, and the potential societal implications of this technology. Moreover, the paper meticulously examines the primary application sectors in Smart Living that stand to gain from HAR, such as smart homes, smart healthcare, and smart cities. By underscoring the significance of the four dimensions of Context Awareness, Data Availability, Personalization, and Privacy in HAR, this paper serves as a valuable resource for researchers and practitioners striving to advance Smart Living services and applications.

Fusarium wilt of banana is a devastating disease that has decimated banana production worldwide. Host resistance to Fusarium oxysporum f. sp. cubense (Foc), the causal agent of this disease, is genetically dissected in this study using two Musa acuminata ssp. malaccensis segregating populations segregating for Foc Tropical (TR4) and Subtropical (STR4) race 4 resistance. Marker loci and trait association using 11 SNP-based PCR markers allowed the candidate region to be delimited to a 12.9 cM genetic interval corresponding to a 959 kb region on Chromosome 3 of ‘DH-Pahang’ reference assembly v4. Within this region, there is a cluster of pattern recognition receptors, namely leucine rich repeat ectodomain containing receptor-like protein kinases, cysteine-rich cell wall associated protein kinases, and leaf rust 10 disease-resistance locus receptor-like proteins positioned in an interspersed arrangement. Their transcript levels were rapidly upregulated in the resistant but not in susceptible F2 progenies at the onset of infection. This suggests that one or several of these genes may control resistance at this locus. To confirm the segregation of single-gene resistance, we generated an inter-cross between the resistant parent ‘Ma850’ and a susceptible line ‘Ma848’, to show that the STR4 resistance co-segregated with marker ‘28820’ at this locus. Finally, an informative SNP marker 29730 allowed the locus specific resistance to be assessed in a collection of diploid and polyploid banana plants. Out of the 60 lines screened, 22 lines were predicted to carry resistance at this locus, including lines known to be TR4 resistant, such as ‘Pahang’, ‘SH-3362’, ‘SH-3217’, ‘Ma-ITC0250’, and ‘DH-Pahang/CIRAD 930’. Additional screening in the International Institute for Tropical Agriculture’s collection suggests that the dominant allele is common among in the elite ‘Matooke’ NARITA hybrids, as well as in other triploid or tetraploid hybrids derived from East African highland bananas. Fine-mapping and candidate gene identification will allow characterization of molecular mechanisms underlying TR4 resistance. The markers developed in this study can now aid the marker-assisted selection of TR4 resistance in breeding programs around the world.

Smart living, a concept that has gained increasing attention in recent years, revolves around integrating advanced technologies in homes and cities to enhance the quality of life for citizens. Sensing and human action recognition are crucial aspects of this concept. Smart living applications span various domains, such as energy consumption, healthcare, transportation, and education, which greatly benefit from effective human action recognition. This field, originating from computer vision, seeks to recognize human actions and activities using not only visual data but also many other sensor modalities. This paper comprehensively reviews the literature on human action recognition in smart living environments, synthesizing the main contributions, challenges, and future research directions. This review selects five key domains: Sensing Technology, Multimodality, Real-time Processing, Interoperability, and Resource-Constrained Processing, as they encompass the critical aspects required for successfully deploying human action recognition in smart living. These domains highlight the essential role that sensing and human action recognition play in successfully developing and implementing smart living solutions. This paper serves as a valuable resource for researchers and practitioners seeking to explore further and advance the field of human action recognition in smart living.

This paper introduces an innovative nanotechnology-based approach that provides a pathway to enhance the energy release efficiency of nanoenergetic materials (nEMs) by harnessing self-synchronized collective atomic vibrations and phonon wave resonance within the transition domains between nanocomponents, without altering the material composition. The key innovation involves incorporating finely-tuned 2D-ordered linear-chain carbon-based multilayer nano-enhanced interfaces as programmable nanodevices into the transition domains using advanced multistage processing and assembly techniques. These programmable nanodevices enable precise control over self-synchronized collective atomic vibrations and phonon wave propagation, leading to synergistic effects. To activate and optimize these effects, a combination of various methods is employed, including energy-driven initiation of allotropic phase transformations, surface acoustic wave-assisted micro/nano-manipulation, heteroatom doping, directed self-assembly using high-frequency electromagnetic fields, and data-driven inverse design approaches. By leveraging a data-driven inverse design strategy and uncovering hidden structure-property relationships, we maximize energy release efficiency using the carbon nano-materials genome approach derived from multifactorial neural network-based predictive models. This approach not only unlocks new functionalities in nEMs but also improves environmental performance and safety levels. By pioneering transformative pathways for nEMs through harnessing phonon wave resonance in low-dimensional nanocarbon transition interfaces, this research brings significant advancements in the field.

A new generation of nano-technologies is expanding solid propulsion capabilities and increasing their relevance for versatile and manoeuvrable micro-satellites with safe high-performance propulsion. We propose the innovative concept, connected with application of new synergistic effect of the energetic materials performance enhancement and reaction zones programming for the next generation small satellite multimode solid propulsion system. The main idea of suggested concept is manipulating by the self-organized wave patterns excitation phenomenon, by the properties of the energetic materials reaction zones and by localization of the energy release areas. This synergistic effect can be provided through application of the functionalized carbon-based nanostructured metamaterials as a nano-additives along with simultaneous manipulating by their properties through the electrostatic field. Mentioned effect will be controlled through predictive programming both by the spatial structure and physics-chemical properties of the functionalized carbon-based nano-additives and through the electromagnetic control of the self-organized wave pattern excitation and micro- and nano- scale oscillatory networks in the energetic material reaction zones. Suggested new concept makes it possible to increase the energetic material regression rate and increase the thrust of the solid propulsion system with minimal additional energy consumption.