Compressed gas storage of hydrogen has emerged as the preferred choice for fuel cell vehicle man- ufacturers, as well as for various applications like road transport, and aviation. However, designers face increasing challenges in designing safe and efficient composite overwrapped pressure vessels (COPVs) for hydrogen storage. One challenge lies in the development of precise software that con- sider a multitude of factors associated with the filament winding process. These factors include layer thickness, stacking sequence, and the development of particularly robust models for the dome region. Another challenge is the formulation of predictive behavior and failure models to ensure that COPVs have optimal structural integrity. The present study offers an exploration of numerical methods used in modeling COPVs, aiming to enhance our understanding of their performance characteristics. The methods examined include finite element analysis in Abaqus, involving conventional shell elements, continuum shell elements, three-dimensional solid elements, and specialized homogenization tech- niques for multilayered composite pressure vessels. Through rigorous comparisons with type-III pressure vessels from the literature, the research highlights the most suitable choice for simulating COPVs and their practicality. Finally, we propose a new design for type-IV hydrogen composite pressure vessels using one explored method, paving the way for future developments in this critical field.

Public health is an important aspect of community activities, making research on health necessary because it is a crucial field in maintaining and improving the quality of life in society as a whole. Research in public health allows for a deeper understanding of the health problems faced by a population, including disease prevalence, risk factors and other determinants of health. This work aims to explore the potential of hospital patient data analysis as a valuable tool for understanding community implications and deriving insights for effective community health interventions. The study recognises the significance of harnessing the vast amount of data generated within hospital settings to inform population-level health strategies. The methodology employed in this study involves the collection and analysis of deidentified patient data from a representative sample of a hospital in Indonesia. Various data analysis techniques, such as statistical modelling, data mining and machine learning algorithms, are utilised to identify patterns, trends and associations within the data. A program written in Python is used to analyse patient data in a hospital for five years from 2018 to 2022. These findings are then interpreted within the context of public health implications, considering factors such as disease prevalence, socioeconomic determinants and healthcare utilisation patterns. The results of the data analysis provide valuable insights into the public health implications of hospital patient data. The research also covers prediction for the patient data to the hospital based on disease, age and geographical residence. The research prediction shows that in 2023, the number of patients was not considerably affected, but in in March to April 2024, the number significantly increased to 10,000 patients. These recommendations encompass targeted prevention strategies, improved healthcare delivery models and community engagement initiatives. The research emphasises the importance of collaboration between healthcare providers, policymakers and community stakeholders in implementing and evaluating these interventions.

Over the last decade, highly pathogenic avian influenza (HPAI) has severely affected poultry production systems across the globe. In particular, massive pre-emptive depopulation of all poultry within the certain distance has raised concerns regarding animal welfare and food security. Thus, alternative approaches to reducing unnecessary depopulation, such as risk-based depopulation, are highly demanded. This paper proposes a data-driven method to generate a rule table and risk score for each farm to identify preventive measures against HPAI. To evaluate the proposed method, 105 cases of HPAI occurring in a total of 381 farms in Jeollanam-do from 2014 to 2023 were evaluated. The accuracy of preventive measure identification was assessed for each case using both the conventional culling method and the proposed data-driven method. The evaluation showed that the proposed method achieved an accuracy of 84.19%, significantly surpassing the previous 10.37%. The result was attributed to the proposed method reducing the false-positive rate by 83.61% compared with the conventional method, thereby enhancing the reliability of identification. The proposed method is expected to be utilized in selecting farms for monitoring and management of HPAI.

Objective: To compare the overall survival (OS) and surgical outcomes between open and minimally invasive surgery (MIS) in a large cohort of women with Type II endometrial cancer. Methods: Using the National Cancer Database, we identified a cohort of women who underwent hysterectomy for type II endometrial cancer (serous, clear cell, carcinosarcoma) between January 2010 and December 2014. Primary outcome was the comparison of OS between MIS and the open approach. Secondary outcomes included the length of hospital stay, readmission rate, 30- and 90-day mortality. Multivariable logistic regression and inverse propensity weighting analyses were performed. Results: A total of 12,905 patients with Type II, Stage I-III endometrial cancer who underwent hysterectomy were identified. Among these patients, 7,123 (55.2%) underwent MIS. The utilization of MIS increased from 39% to 64% over the four-year period. In propensity-score-weighted analysis, MIS was associated with superior OS (101.7 vs. 86.7 months, p=0.0003 by the log-rank test), with a hazard ratio of 0.9 (CI 0.857-0.954, p=0.0002). MIS was also associated with improved perioperative outcomes, including a shorter length of stay (1 vs. 4 days, p<0.001), lower 30-day readmission rate (2.5% vs. 5%), and lower 30- and 90-day postoperative mortality (0.5% vs. 1.3% and 1.5% vs. 3.6%, respectively; p<0.001). The time interval between surgery and initiation of chemotherapy was shorter in the MIS group (38 vs. 41 days, p<0.001).The increase in MIS adoption correlated with a decrease in 90-day postoperative mortality (2.8% to 2.2%, r=-0.89; p=0.003) and 12-month overall mortality (51% to 38%, r=-0.95, p=0.006). Conclusions: In an epidemiologic study, MIS is associated with improved overall survival and superior perioperative outcomes compared to open surgery in women with Type II endometrial cancer.

Accurately glacier mapping is crucial for understanding climate change impacts, but existing efforts may be biased due to overlooking spatial autocorrelation during map validation. To address this, we compared several widely used machine learning algorithms as gradient boosting machines (GBM), k-nearest neighbor (KNN) and random forest (RF) with parametric logistic regression (GLM) and an unsupervised remote sensing-based method (NDSI) for mapping Peru's glacier regions in a thoughtful experimental setup. Spatial and non-spatial cross-validation methods were used to evaluate model’s performance and compared with a fully independent test set. Performance differences of up to 18% were found between bias-reduced (spatial) and overoptimistic (non-spatial) cross-validation results when compared to independent test set, emphasizing the need to consider spatial autocorrelation when using machine learning for glacier mapping. K-nearest neighbors (KNN) was the overall best model across regions consistently demonstrating the highest performance followed by logistic regression (LR) and gradient boosting machines (GBM). Our novel validation approach, accounting for spatial characteristics, provides valuable insights for glacier mapping studies and future efforts on glacier retreat monitoring. Incorporating this approach improves the reliability of glacier mapping, guiding future national-level initiatives.

A significant replacement for numerous fossil fuels is solar energy as the main type of renewable energy resource. However, solar cells need battery energy storage units to handle the intermittent and uncontrollable nature of solar energy. In this light, there is a significant market for small, portable battery charge controllers that automate battery charging. However, the issue of slow charging and premature battery life loss plagues current industry-standard or innovative battery technology. This study proposes a quick pulse charging technique that stops battery deterioration and minimizes the overall charging period. The primary goal of this study is to develop, construct, and execution of a practical, versatile, and compact photovoltaic charge controller at cut rates. The suggested charge controller can disconnect and reconnect the battery during battery overcharging and deep discharging conditions using sensors with relays. The laboratory model is tested using a less expensive PV panel, battery, and DSP controller. The charging behavior of the solar-powered PWM charge controller is studied compared to that of the Constant Voltage - Constant Current (CV-CC) method. The proposed method is pertinent for minimizing energy issues in impoverished placesat a reasonable price.

The paper presents theoretical, experimental and numerical studies on the thermal behavior of mineral wool used in sandwich panels. The aim of this study is to investigate the thermal properties of mineral wool at elevated temperatures and provide a simple model that would allow to determine the heat propagation in sandwich panels during fire. The paper proposes a new method to experimentally evaluate thermal diffusivity, derived from theoretical premises. Experiments are conducted in a laboratory furnace, where specimens are placed and temperatures inside specimens are measured. Different methods are used to process the test results and calculate the thermal diffusivity of mineral wool. Finally, a numerical analysis of heat transfer using finite element method (FEM) is performed to validate the obtained thermal properties.

In Japan, bariatric surgical treatment was started in 1982. The Japanese Society for Treatment of Obesity (JSTO) was established in 2007 and then JSTO started national registry of bariatric surgery cases and multidisciplinary educational program. 44 facilities registered 4055 bariatric surgical cases until 2021. In this study, the purpose is to clarify the indication, the safety and the effectiveness of the sleeve gastrectomy using national registry data base compiled by JSTO. Preoperative BMI was ranged from 27.6 to 90.7kg/m², and the mean value was 42.7. As gender, men/women was 1/1.3. Age was 42.2 as mean.　As preoperative comorbidities, DM ratio was 54.4% of the patients, Hypertension 64.5%, dyslipidemia 65.1%, and sleep apnea syndrome 69.8%. As operation method, laparoscopic method were taken in 99.7% of the cases. The intra operative incidents rate was 0.9 %. Conversion rate to open method was 1.1%. Postoperative morbidity ratio was 5.6% and mortality was 0 %. Reoperations were performed in 1.5 % of the cases. Postoperative hospital stay was 5 days in Median value. Body weight loss was 27.6 kg in the mean value after follow up days of 279±245. As the effect on the preoperative metabolic comorbidities, DM has improved in 82.9 % of the cases, hypertension 67.9 % and Dyslipidemia 66.6%. In conclusion, using JSTO database, we evaluated the indication, postoperative complications and weight loss effect of sleeve gastrectomy in Japan. Regarding the evaluation of the effect on preoperative comorbidities, future follow-up based on more detailed criteria was considered to be necessary.

Valorization of excess sludge through the recovery of high-value products, such as biopolymers, could be a crucial step to implement circular economy principles in wastewater treatment plant (WWTP). In this frame, the present study was aimed at evaluating the simultaneous production of polyhydroxyalkanoates (PHA) and extracellular polymeric substances (EPS), obtainable from the treatment of an agro-industrial wastewater. Two biological systems, one implementing aerobic granular sludge (AGS) and the other a conventional activated sludge operating as a sequencing batch reactor (SBR) were operated for 204 and 186 days, respectively. Both the systems involved a three-stage process for mixed microbial culture enrichment and biopolymers accumulation. The maximum biopolymers accumulation capacity was close to 0.60 mg gVSS^-1 in the AGS when the enrichment reactor was operated at 3 kgCODm^-3d^-1, whereas in the SBR it was slightly more than half (0.35 mg gVSS^-1). Biopolymers extracted from the AGS were mainly constituted by EPS (>70%), which percentage increased with the organic loading rate applied in the enrichment reactor up to 95%. In contrast, SBR enabled to obtain a higher PHA production (50% of the biopolymers). Results suggested that organic carbon was mainly channeled toward metabolic pathways for extracellular storing in AGS, likely as a consequence of metabolic stressors (e.g., hydraulic selection pressure, shear forces) applied for promoting aerobic granulation.

Recent advancements in surgical and anti-cancer therapies have provided significant hope of long survival in patients with pancreatic cancer (PC). To realize this hope, routine medical checkups of asymptomatic people should be performed to identify operable PCs. In this study, we evaluated the efficacy of medical checkups using abdominal ultrasonography (US). We retrospectively analyzed 374 patients with PC at our institute between 2010 and 2021. We divided these patients into several groups according to the diagnostic approach and compared their background and prognosis. These groups comprised PCs diagnosed through (a) symptoms, 242 cases; (b) US during medical checkup for asymptomatic individuals, 17; and other means. Of the 375 patients, 192 were men (51.3%), and the median age was 74 years (34–105). Tumors were located in the pancreatic tail in 67 patients (17.9%). Excision ratio and 5-year survival rate were significantly better in group (b) than in (a) (58.8% vs. 23.1%, P<0.01 and 42.2% vs. 9.4%, P<0.001, respectively). The prognosis of patients diagnosed using US during medical checkup was better than that of patients identified through symptomatic presentation of PC. US for asymptomatic individuals with PC might be useful for promoting better prognosis of PCs.

Breakthrough cancer pain (BTcP) is a temporary exacerbation of pain that "breaks through" a phase of adequate pain control by an opioid-based therapy. The non-predictable BTcP (NP-BTcP) is a subtype of BTcP that occurs in the absence of any specific activity. Since NP-BTcP has an important clinical impact, this analysis is aimed at characterizing the NP-BTcP phenomenon through a multidimensional statistical technique. This is a secondary analysis based on the Italian Oncologic Pain multiSetting - Multicentric Survey (IOPS-MS) . A correlation analysis was performed to characterize NP-BTcP profile about its intensity, number of episodes per day, and type. The Multidimensional Correspondence Analysis (MCA) determined the identification of 4 groups (Phenotypes). A univariate analysis was performed to assess differences between the 4 Phenotypes and selected covariates. The four phenotypes represent the hierarchical classification according to the status of NP-BTcP: from the best (Phenotype 1) to the worst (Phenotype 4). The univariate analysis found a significant association between the onset time &gt;10 min in the Phenotype 1 (37.3%) vs. the onset ≤ 10 min in Phenotype 4 (74.2%) (p&lt;0.001). The Phenotype 1 was characterized by gastrointestinal type of cancer (26.4%) respect to Phenotype 4 where the most frequent cancer affected the lung (28.8%) (p&lt;0.001). Phenotype 4 was mainly managed with rapid onset opioids, while in Phenotype 1 many patients were treated with oral, subcutaneous, or intravenous morphine (56.4% and 44.4%, respectively; p=0.008). The ability to characterize NP-BTcP can offer enormous benefits for the management of this serious aspect of cancer pain. This strategy can provide many indications for identifying the diagnostic and therapeutic gaps on NP-BTcP management.

Novel cultivation technologies demand the adaptation of existing analytical concepts. Metabolic flux analysis (MFA) requires stable-isotope labeling of biomass-bound protein as the primary information source. Obtaining the required protein in cultivation set-ups where biomass is scarce or inaccessible due to low cell densities and cell immobilization is difficult to date. We developed a non-disruptive analytical concept for 13C-based metabolic flux analysis based on secreted protein as an information carrier for isotope mapping in the protein-bound amino acids. This "metabolic flux probe" (MFP) concept was investigated in different cultivation set-ups with a recombinant, protein-secreting yeast strain. The obtained results grant insight into intracellular protein turnover dynamics. Experiments under metabolic but isotopically nonstationary conditions in continuous glucose-limited chemostats at high dilution rates demonstrated faster incorporation of isotope information from labeled glucose into the recombinant reporter protein than in biomass-bound protein. Our results suggest that the reporter protein was polymerized from intracellular amino acid pools with higher turnover rates than biomass-bound protein. The latter aspect might be vital for 13C-flux analyses under isotopically nonstationary conditions for analyzing fast metabolic dynamics.

RaTG13 (a bat derived SARS-like CoV) is the closest relative sequence of SARS-CoV-2 reported till date. The sample from which RaTG13 was sequenced was a bat fecal swab collected in 2013 from Tongguan, Mojiang, Yunnan province, China. The Illumina based sequence of RaTG13, MN996532.1, was deposited on 27^th Jan 2020 and the raw data (Illumina), https://www.ncbi.nlm.nih.gov/sra/SRX7724752[accn]. There are discrepancies in dates about when the metagenome sequencing of RaTG13 sample was done (2018 or 2020), both stated by the same corresponding author. Comparison of the RNA Seq data of RaTG13 fecal swab to the corresponding data from the bat fecal swabs deposited by the same working group using the same methods indicated that the RaTG13 raw data seemed to be different in various aspects. The fecal swab sample showed abnormally less read of bacterial reads in the swab was exceptionally low, i.e. 0.7%, compared to the 20-90% abundance in other fecal swabs from bats processed by similar methods. Also, another raw data in the form of amplicon sequences was deposited in May 2020; however, the dates mentioned on the files of the sequenced amplicons were older (2017, 2018). The genome assembly of RaTG13 could not be done de-novo and the average coverage of the genome ~8%. Also, literature indicates that RaTG13 RBD cannot bind to Rhinolophus ACE-2 receptors. Collectively, the anomalies in the raw data of RaTG13 and other issues pose an important question about the overall authenticity of the RaTG13 genome sequence.

At the molecular level, response to an external factor or an internal condition causes reprogramming of temporal and spatial transcription. When an organism undergoes physiological and/or morphological changes, several signaling pathways are activated simultaneously. Examples of such complex reactions are the response to temperature changes, dehydration, various biologically active substances, and others. Synergistic action of multiple pathways greatly complicates the experimental study of the molecular genetic mechanisms of the organism's reactions. As a result, a significant part of the regulatory ensemble in such complex reactions remains unidentified. We developed metaRE, an R package for the systematic search for cis-regulatory elements enriched in the promoters of the genes significantly changed their transcription in a complex reaction. metaRE mines multiple expression profiling datasets generated to test the same organism's response and identifies simple and composite cis-regulatory elements systematically associated with differential expression of genes. Here we showed metaRE performance for identification of cold stress-responsive cis-regulatory code in Arabidopsis thaliana. MetaRE identified potential binding sites for known as well as unknown cold response regulators. Software with source files, documentation, and example data files are freely available online at the repository (https://github.com/cheburechko/MetaRE).

Introduction: The Pandemic of COVID-19, an infectious disease caused by SARS-CoV-2 motivated the scientific community to work together in order to gather, organize, process and distribute data on the novel biomedical hazard. Here, we analyzed how the scientific community responded to this challenge by quantifying distribution and availability patterns of the academic information related to COVID-19. The aim of our study was to assess the quality of the information flow and scientific collaboration, two factors we believe to be critical for finding new solutions for the ongoing pandemic. Materials and methods: The RISmed R package, and a custom Python script were used to fetch metadata on articles indexed in PubMed and published on Rxiv preprint server. Scopus was manually searched and the metadata was exported in BibTex file. Publication rate and publication status, affiliation and author count per article, and submission-to-publication time were analysed in R. Biblioshiny application was used to create a world collaboration map. Results: Our preliminary data suggest that COVID-19 pandemic resulted in generation of a large amount of scientific data, and demonstrates potential problems regarding the information velocity, availability, and scientific collaboration in the early stages of the pandemic. More specifically, our results indicate precarious overload of the standard publication systems, significant problems with data availability and apparent deficient collaboration. Conclusion: In conclusion, we believe the scientific community could have used the data more efficiently in order to create proper foundations for finding new solutions for the COVID-19 pandemic. Moreover, we believe we can learn from this on the go and adopt open science principles and a more mindful approach to COVID-19-related data to accelerate the discovery of more efficient solutions. We take this opportunity to invite our colleagues to contribute to this global scientific collaboration by publishing their findings with maximal transparency.

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.

The variability of meteorological parameters such as temperature and precipitation, and climatic conditions such as intense droughts, are known to impact vegetation health over southern Africa. Thus, understanding large-scale ocean–atmospheric phenomena like the El Niño/Southern Oscillation (ENSO) and Indian Ocean Dipole/Dipole Mode Index (DMI) is important as these factors drive the variability of temperature and precipitation. In this study, 16 years (2002–2017) of Moderate Resolution Imaging Spectroradiometer (MODIS) Terra/Aqua 16-day normalized difference vegetation index (NDVI), extracted and processed using JavaScript code editor in the Google Earth Engine (GEE) platform in order to analyze the response pattern of the oldest proclaimed nature reserve in Africa, the Hluhluwe-iMfolozi Park (HiP), during the study period. The MODIS-enhanced vegetation index and burned area index were also analyzed for this period. The area-averaged Modern Retrospective Analysis for Research Application (MERRA) model maximum temperature and precipitation were also extracted using the JavaScript code editor in the GEE platform. This procedure demonstrated a strong reversal of both the NDVI and Enhanced Vegetation Index (EVI), leading to signs of a sudden increase of burned areas (strong BAI) during the strongest El Niño period. Both the Theilsen method and the Mann–Kendall test showed no significant greening or browning trends over the whole time series, although the annual Mann–Kendall test, in 2003 and 2014–2015, indicated significant browning trends due to the most recent strongest El Niño. Moreover, a multi-linear regression model seems to indicate a significant influence of both ENSO activity and precipitation. Our results indicate that the recent 2014–2016 drought altered the vegetation condition in the HiP. We conclude that it is vital to exploit freely available GEE resources to develop drought monitoring vegetation systems, and to integrate climate information for analyzing its influence on protected areas, especially in data-poor counties.

Compound parabolic concentrators are relevant systems used in solar thermal technology. With adequate tailoring, they can be used as an efficient and low-cost alternative in residential water applications. This work presents a simulation study using a ray tracing methodology. With this technique we simulate the interaction between solar rays and solar concentrator to quantify the amount of energy that impinges on the receiver at a particular time. Energy availability is evaluated in a comparison of two configurations: stationary at 21° throughout the year and multi position setup; tilted with respect to the horizontal depending on three seasonal positions: 0° for summer, 16° for spring / autumn and 32° for winter, with the objective of increasing the amount of available energy in each season. The fact that a tracking system can be dispensed with also represents an economical option for the proposed application. The results showed that at 21°, the proposed system works satisfactorily; however, by carrying out the selected angular adjustments, the overall energy availability increased by 22%, resulting in a more efficient option. The methodology developed herein proved to be a valuable tool for prototype design and performance evaluation.

In the Vietnamese Mekong delta, soil quality and crop yield are steadily declining under rice monocultures with three crops per year. The objective of this study was to evaluate the medium-term effects of rotating rice with upland crops and adding organic amendments on rice yield and relate it to soil quality. A field trial with split-plot design including two factors and three replicates was carried out from 2017 to 2020, over the course of nine consecutive cropping seasons. Crop rotations and organic amendments were applied as main-plot and subplot factors, respectively. The rotations were: 1) rice–rice–rice (R–R–R), 2) soybean–rice–rice (So–R–R), and 3) sesame–rice–rice (Se–R–R), while organic amendment treatments included: i) no amendment (NO-AM); ii) compost of rice straw and cow manure (RS+CM), and iii) sugarcane compost (SGC); the composts were applied at a rate of 2.0 t ha-1. The rotation cycle started with the so-called spring–summer (SS), followed by summer–autumn (SA) and ended with the winter–spring (WS) season. Rice yield significantly (P &lt; 0.05) increased by organic amendments after nine growing seasons (2019–2020 WS), with an increment of 5.1% for RS+CM (7.07 ton/ha) and 6.1% for SGC (7.14 ton/ha). Contrary to our expectation, rotations with upland crops did not significantly increase rice yield. Rice yield significantly and positively correlated with an integrated soil quality index SQI (r = 0.86) for the topsoil (0–15 cm), but not for the subsoil (15–30 cm). The increased availability of soil nutrients (Si and marginally also P) and improved soil physical properties probably induced by organic amendments, along with other soil properties under study, cumulatively attributed to enhanced rice yield. Use of a SQI involving several soil quality indicators enables to overall quantify the importance of soil fertility for rice yield versus other factors and it provides an effective means of quantifying the integrated effect of improved management. Moreover, integrating a wide range of soil quality indicators in a SQI ensures its applicability across diverse settings, including different crop rotations and various soil types.

Spinal cord stimulation (SCS) is a therapeutic modality for the treatment of various chronic pain conditions that has rapidly evolved over the past 50 years. Unfortunately, over time, patients implanted with SCS undergo a habituation phenomenon leading to decreased pain relief. Consequently, discovery of new stimulation waveforms and SCS applications have shown to prolong efficacy and reduce explantation rates. This article explores various SCS waveforms, their applications and proposes a graded approach to habituation mitigation. We suspect the neural habituation phenomenon parallels that seen in pharmacology. Consequently we urge further exploration of early introduction of these stimulation strategies to abate spinal cord stimulation habituation.

Reliable and precise damage localization in mechanical structures is of high importance in the context of structural health monitoring (SHM). The acoustic emission (AE) method has already shown its excellent suitability for damage localization. However, low signal-to-noise ratios (SNRs) are often prevalent in SHM, thus an increase in localization accuracy and robustness is still in demand. The present study faces this task through the integration of various model knowledge for AE source localization. The basis of the presented algorithm is the consideration of the dispersive behavior of elastic waves in thin-walled structures. The continuous wavelet transform (CWT) is used to obtain time-frequency representations of the signals, where frequency-dependent values for time-of-arrival (TOA) are extracted. Furthermore, the algorithm incorporates the knowledge that all sensors receive signals with the same time and location of origin, as well as the slightly different sensitivity of the theoretically equal sensors. The final localization results are achieved by a two-parameter grid search optimization. The algorithm is experimentally tested on a large aluminum plate with four piezoelectric wafer active sensors (PWASs) arranged in an array of 100 mm × 100 mm. The mean localization error of pencil lead breaks at ten different positions within the sensor array is used as an accuracy measure. It is shown that as more of the described model knowledge is incorporated into the localization, the accuracy increases. With the final algorithm, the mean localization error is more than halved compared to AE localization based on classical TOA estimation. Although the experiment described is conducted under laboratory conditions, the remarkable increase in accuracy suggests that AE source localization may be successful even at low SNR as typical for operational conditions.

The drinking water temperature is expected to increase in the Netherlands due to climate change and the installation of district heating networks as part of the energy transition. To determine effective measures to prevent undesirable temperature increase of the drinking water a model is developed. This model describes the temperature in the drinking water distribution network as a result of the transfer of heat from the climate, above and underground heat sources through the soil. The model consists of two coupled applications. The extended soil temperature model (STM+) describes the soil temperatures, in a two-dimensional finite element method that includes a drinking water pipe and two hot water pipes, coupled to a micro meteorology model. The extended water temperature model (WTM+) describes the drinking water temperature as a function of the surrounding soil temperature (the boundary temperature, resulting from the STM+), thermal sphere of influence where the drinking water temperature influences the soil temperature, and the hydraulics in the drinking water network. Both models are validated with field measurements. This paper describes the WTM+. Previous models did not consider the cooling effect of the drinking water on the surrounding soil, which led to an overestimation of the boundary temperature, and to an overestimation of how quickly the drinking water temperature reaches this boundary temperature. The field measurements show the improved accuracy of the WTM+ when considering one to two times the radius of the drinking water pipe as the thermal sphere of influence around the pipe.

The aim of this technical note is to describe the Cycle 46 reference configuration of the HARMONIE-AROME convection-permitting numerical weather prediction (NWP) model. HARMONIE-AROME is a configuration of the shared ALADIN-HIRLAM system, which is developed, maintained, and validated in the ACCORD consortium, a collaboration of 26 countries in Europe and northern Africa on short-range mesoscale NWP. This technical note describes updates to the physical parametrizations, both upper-air and surface, configuration choices such as lateral boundary conditions, model levels, horizontal resolution, model time step and databases associated with the model, such as for physiography and aerosols. An outlook on upcoming developments is also included.

In this work, we propose a generalized partially functional linear regression model with an interaction term between functional predictors. The estimators of the regression coefficient of scalar predictors and regression coefficient functions of functional predictors and their interactions are obtained. The asymptotic property of the estimators is established. Extensive simulation studies illustrate the asymptotic results. Finally, the proposed model was applied to investigate the influence of air qualities, climate factors, medical and social indicators and the interactions on cancer incidence which is a binary response.

It has been known that dimensional constants such as ℏ, c, G, e, and k are merely human constructs whose values and units vary depending on the chosen system of measurement. Therefore, the time variation of dimensional constants lacks operational significance due to their dependence on them. It is well-structured and represents a valid discussion. However, this fact only becomes a meaningful debate within the context of a static or present universe. As well-established theoretically and observationally, the current universe is undergoing accelerated expansion, wherein dimensional quantities, like the wavelength of light, also experience redshift phenomena elongating over cosmic time. In other words, in an expanding universe, dimensional quantities of physical parameters vary with cosmic time. From this perspective, there exists the possibility that dimensional constants, such as the speed of light, could vary with the expansion of the universe. In this review paper, we contemplate under what circumstances the speed of light may change or remain constant over cosmic time, and discuss the potential for distinguishing these cases observationally.

Cosmography, as an integral branch of cosmology, strives to characterize the Universe without relying on pre-determined cosmological models. This model-independent approach utilizes Taylor series expansions around the current epoch, providing a direct correlation with cosmological observations and the potential to constrain theoretical models. Various observable quantities in cosmology can be described as different combinations of cosmographic parameters. Furthermore, one can apply cosmography to models with a varying speed of light. In this case, the Hubble parameter can be expressed by the same combination of cosmographic parameters for both the standard model and varying speed of light models. However, for the luminosity distance, the two models are represented by different combinations of cosmographic parameters. Hence, luminosity distance might provide a method to constrain the parameters in varying speed-of-light models.

Building energy demand impacts a myriad of interconnected economic, societal, and environmental aspects. As a result, Buildings Energy Models (BEM) play an important role in the process of urban design and planning. While previous studies have investigated the effects of building interventions on energy efficiency, their applicability may be limited due to the BEM’s high computational complexity. This limits their ability to systematically study important aspects of energy demand on a large scale. The development of Machine Learning Models (MLM) allows to design the required detailed analysis and solutions, while reducing the computational burden, making MLM attractive for urban designers. The capability of MLM to generalize well for multiple contexts (in our case, multiple buildings) is a crucial contributor to their applicability. However, the validation process in a wider context is often overlooked, therefore its generalization capabilities are not quantified. In this paper, we present a framework to train and validate a surrogate model derived from a physics-based BEM. Our method employs a Multiple Linear Regression model to predict Energy Use Intensity (EUI) for office buildings in Singapore using 36 input parameters (covariates), based on a training dataset of 23,000 samples. Model validation is performed by comparing the results of the Surrogate Model (SM) to a widely used BEM for a sample of 120 buildings. Our results indicate that the SM has an accuracy of NRMSE of 13%, NMBE of −3.56%, and R2 of 0.92, which suggests it can effectively and accurately predict building EUI. We also conduct a sensitivity analysis, which indicates that the parameters associated with internal loads and internal space usage are the most influential. Additionally, we present a reduced order model trained with only the 11 most influential parameters, which exhibits negligible loss in accuracy compared to the full SM while providing reduced complexity. Finally, we demonstrate an application of our SM to evaluate energy efficiency under uncertainty scenarios. The analytically derived results indicate a potential reduction of EUI of offices in Singapore from 227kWh/m2 to 99kWh/m2 by altering the building parameters that were identified as most influential.

The precise mathematical description of gaze patterns is still an unsettled matter of debate. This can, consequently, affect the analysis of this type of data in its practical applications. In this scope, we show evidence that a Lévy-flight description of eye-gaze trajectories is not only appropriate but its scale invariant properties are remarkably useful to assist in diagnosing children with Attention Deficit and Hyperactivity Disorder (ADHD), alongside with the usual cognitive tests. Using this picture, we find that the distribution of the characteristic exponent of Lévy-flights statistically is different in children with ADHD. Furthermore, we observe that these children deviate from a strategy that is considered optimal for searching processes, in contrast to non-ADHD children. We focus on the case where both eye-tracking data and data from a cognitive test are present and show that the study of gaze patterns in children with ADHD can help in identifying this condition. Since eye-tracking data can be gathered during cognitive tests without needing extra time-consuming specific tasks, we argue that it is in prime position to provide assistance in the arduous task of ADHD diagnosing.

This work presents a heat transfer and boiling model that computes the evolution of the temperature field in a representative steel workpiece quenched from 850 or 930°C by immersion in water flowing at average velocities of 0.2 or 0.6 m/s, respectively. Under these conditions, all three boiling regimes were present during cooling: stable vapor film, nucleate boiling, and single-phase convection. The model was based on the numerical solution of the heat conduction equation coupled to the solution of the energy and momentum equations for water. The mixture phase approach was adopted using the Lee model to compute the rates of water evaporation-condensation. Heat flux at the wall was calculated for all regimes using a single semi-mechanistic model. Therefore, evolution of boiling regimes at every position on the wall surface was automatically determined. Predictions were validated using laboratory results, namely: a) Videorecording the upward motion of the wetting front along the workpiece wall surface, and b) cooling curves obtained with embedded thermocouples in the steel probe. Wall heat flux calculations were used to determine the importance of the simultaneous presence of all three boiling regimes on the heat flux distribution. It was found that this simultaneous presence leads to high heat flux variations that should be avoided in production lines.

This study aims to explore the utilization of deep learning models, specifically the innovative Multi-Modal Contextual Fusion Convolutional Neural Network (MSHA), for the detection and diagnosis of Adrenocortical Carcinoma (ACC) using computed tomography (CT) images. The objective is to develop an accurate and reliable model that can assist in the effective detection and classification of ACC. The study utilizes a dataset comprising contrast-enhanced CT images from 53 confirmed ACC patients. The MSHA model is employed, which incorporates a combination of mixed-scale dense convolution, self-attention mechanism, hierarchical feature fusion, and attention-based contextual information techniques. Evaluation metrics are used to assess the performance of the MSHA model, and a comparison is made with other established models, including ResNet50, VGG16, VGG19, and InceptionV3. The evaluation of the MSHA model demonstrates high performance, with an accuracy of 96.65% and precision, sensitivity, specificity, and F1 score of 96.0%. These results highlight the MSHA model's capability in accurately detecting and classifying ACC. Furthermore, compared to other models, the MSHA model outperforms ResNet50, VGG16, VGG19, and InceptionV3, indicating its superior performance in ACC detection and diagnosis. The findings of this study suggest that the MSHA model holds significant potential in assisting healthcare professionals with the detection and diagnosis of ACC. With its advanced features and contextual fusion techniques, the MSHA model achieves high accuracy and performance. The results highlight the clinical significance of this novel model and its potential to improve patient management and outcomes in the detection and diagnosis of ACC using CT images.

Chronic Chagas Cardiomyopathy (CCC) results from infection with the protozoan parasite Trypanosoma cruzi and is a prevalent cause of heart disease in endemic countries. We previously found that cardiac fibrosis can vary widely in C3H/HeN mice chronically infected with T. cruzi JR strain, mirroring the spectrum of heart disease in humans. In this study, we examined functional cardiac abnormalities in this host:parasite combination to determine its potential as an experimental model for CCC. We utilized electrocardiography (ECG) to monitor T. cruzi-infected mice and determine whether ECG markers could be correlated with cardiac function abnormalities. We found that the C3H/HeN:JR combination frequently displayed early onset CCC indicators, such as sinus bradycardia and right bundle branch block, as well as prolonged PQ, PR, RR, ST and QT intervals in the acute stage. Our model exhibited high levels of cardiac inflammation and enhanced iNOS expression in the acute stage, but denervation did not appear to have a role in pathology. These results demonstrate the potential of the C3H/HeN:JR host:parasite combination as a model for CCC that could be used for screening new compounds targeted at cardiac remodeling and for examining the potential of anti-parasitic drugs to prevent or alleviate CCC development and progression.

Coxsackievirus B3 (CVB3) is one of the most common pathogens that cause myocarditis in humans. However, a sufficient therapeutic drug was not developed yet. Caboxamycin, a benzoxazole antibiotic, isolated from the culture broth of the marine strain Streptomyces sp., SC0774, displayed an antiviral effect in the CVB3-infected HeLa cells and CVB3-induced myocarditis mouse model. Caboxamycin significantly inhibited CVB3 VP1 production and cleavage of translation factor eIF4G1 from CVB3-infected HeLa cells at a 100 μg/ml concentration. Moreover, virus RNA amplification was dramatically reduced by caboxamycin treatment in HeLa cells. The caboxamycin cardiac protective effect was observed on the CVB3-mediated myocarditis mice model. The caboxamycin-treated mice survival was significantly increased compared to untreated mice (treated vs untreated, 100 vs 45 %). Heart histologic findings showed that myocardium damage and inflammation were significantly decreased by caboxamycin treatment. Caboxamycin directly inhibited caspase3 activity and cardiac myocytes apoptosis. These results suggested that caboxamycin strongly suppressed cardiac myocyte apoptosis and effectively inhibited the proliferation of CVB3 from in vitro and in vivo conditions. Furthermore, this study proposed that caboxamycin could potentially be applied in developing a new antiviral drug for CVB3-induced myocarditis.

In this work, we incorporate a thixotropic-viscoelastic model into the widely used Computational Fluid Dynamics (CFD) software OpenFOAM, along with the rheoTool library. The model we implement is known as the Modified-Bautista-Manero (MBM), and effectively describes the rheological behavior of worm-like micellar solutions in extensional flows. We provide a detailed explanation of the numerical implementation of the model, specifically using the log-conformation tensor approach. Unlike previous works focused on this kind of fluids, we simulate inertial flows while considering convective terms in the governing equations, thus obtaining a more realistic behavior on the calculated results. The MBM model implementation is validated through numerical simulations on two different industrial-relevant geometries: the planar 4:1 contraction and the 4:1:4 contraction-expansion configurations. Furthermore, we investigate the influence of inertial, viscoelastic, and thixotropic effects on various flow field variables. These variables include velocity, viscosity, normal stresses, and corner vortex size. Our analysis encompasses both transient and steady solutions of corner vortexes across a range of Deborah and Reynolds numbers. Our results are also directly compared with simulations obtained using the non-thixotropic rubber network-based exponential Phan-Thien-Tanner (EPTT) model. From our planar 4:1 contraction results, we found that vortex-enhancement is seen when high elasticity is coupled with quick structural reformation and very low inertial effects. From our planar 4:1:4 contraction-expansion simulations, we show that an increase in inertia leads both to vortex-inhibition in the upstream channel and slight vortex-enhancement in the downstream channel. Lastly, we show the strong effect of the convection of fluidity into the fluidity profiles and into the upstream/downstream corner vortex sizes.

The Chen circuit system, a three-dimensional autonomous system with intriguing dynamics, has gained considerable attention due to its potential applications in diverse scientific fields. In this article, a Chen circuit-like system is defined and a comprehensive dynamics has been studied. The system exhibits chaotic dynamics, limit cycles, and bifurcations, making it a captivating subject of study. By employing numerical simulations and analytical techniques, we explore the system's stability and identify critical parameter values leading to qualitative changes. Notably, we delve Hopf bifurcations, which give rise to stability changes and the emergence of limit cycles. Furthermore, we analyze the fractal dimension of the system's attractor, providing insights into its complexity and self-similarity. Through a systematic examination of the Chen circuit-like system, we deepen our understanding of its intricate dynamics and offer valuable insights into the underlying mechanisms. The findings contribute to the field of dynamical systems and hold potential implications in areas such as chaos-based secure communications, signal processing, and nonlinear control. This work serves as a valuable reference for researchers and practitioners interested in the dynamics and bifurcation analysis of nonlinear systems.

Currently, two-component integrable nonlinear equations from the hierarchies of the vector nonlinear Schrodinger equation and the vector derivative nonlinear Schrödinger equation are being actively investigated. Usually, such two-component integrable nonlinear evolutionary equations are considered, the form of which does not depend on the replacement of one component with another. In this paper, we propose for consideration and usage a new hierarchy of two-component integrable nonlinear equations, which have an important difference from the already known equations. Among the hierarchy equations there are analogues of the two-component nonlinear Schrödinger equation (second equation from hierarchy) and the two-component modified Korteweg-de Vries equation (fourth equation from hierarchy). The third equation of the hierarchy is a combination of the nonlinear Schrödinger equation for one component and the modified Korteweg-de Vries equation for the second component. The equations for the individual components are very different from each other, even if they have the same order. Let us note that even hierarchy equations can be reduced to well-known variants of the scalar derivative nonlinear Schrödinger equations, and odd equations can’t be reduced. To construct the hierarchy equations, we use the monodromy matrix method, first proposed by B.A. Dubrovin. Knowledge of the monodromy matrix makes it possible to construct spectral curves of multiphase solutions, as well as to find stationary equations that these solutions satisfy. The last section presents the simplest solutions in the form of solitons and periodic one-phase waves, as well as spectral curves corresponding to these solutions.

The thermal properties of a building envelope are key indicators of the energy performance of the building. Therefore, methods are needed to determine quantities like U-values or heat capacitance in a fast, reliable way and with as little impact on the use of the building as possible. In this paper a technique is proposed that relies on a simplified electrical analogical model of building envelope components which can cover their dynamic thermal behavior. The parameters of this model are optimized to produce the best fit between simulated and measured outside surface temperatures. As the temperatures can be measured remotely with an infrared camera this approach requires significantly less installation effort and intrusion in the building than other methods. At the same time, a single measurement provides data for a large range of locations on a facade or a roof. The paper describes the method and a first experimental implementation of it. The experiment indicates that this method has the potential to produce results which have an accuracy that is comparable to standardized reference methods.

A locally-rotationally-symmetric Bianchi-I model filled with strange quark matter (SQM) is explored in f(R, T) gravity. We take f(R, T) = R + 2 f(T) = R + 2λT, where R is the Ricci scalar, T is the trace of energy-momentum tensor and λ is an arbitrary constant. Exact solutions are obtained by assuming that the expansion scalar is proportional to the shear scalar. This yields a constant value for the deceleration parameter, q = 2, which corresponds to the decelerated phase of the universe. The model is found to be physically viable for λ < −1. SQM during early evolution mimics ultra-relativistic radiation whereas at late times, it behaves as dust, quintessence or even the cosmological constant depending on the value of λ. The effective matter acts as a stiff fluid irrespective of the matter content and of the f(R, T) terms. The model is shear free at late times but remains anisotropic throughout the evolution. In the absence of the f(R, T) terms, SQM accelerates the universe. In the specific case when SQM vanishes at late times, the effect of f(R, T) is to accelerate the universe. The model admits the possibility that SQM could be an alternative to dark energy.

In this paper, a Markov random field model is proposed to determine a site’s light pollution risk level. The specific data of 12 indicators of five typical cities in China is first collected to establish a hierarchical indicator system using an R-type clustering algorithm. Then, the entropy weight method is used to filter, determine 10-factor indicators, three potential impact indicators, and a light pollution risk index, and establish an undirected probability map model. A light pollution measurement based on Markov random field is obtained, and a location-based light pollution risk assessment index (LBLPRAI) is proposed. LBLPRAI of different types of sites is analyzed, and three possible intervention strategies are proposed to solve the light pollution problem: road lighting system planning, increasing vegetation coverage, and building system planning. Finally, the simulated annealing algorithm is used to determine the best intervention strategy, and it is concluded that the use of strategy 1 in the urban community 2 is the most effective measure, which can reduce the risk level of light pollution by 17.2%.

The way humans use natural resources and especially freshwater, reflects their relationship with nature. It also influences the conceptual Water Resources Management (WRM) model. A historical review shows that the interplay between Humans and Nature is diachronically in constant change between two opposites: conflict and cooperation. Lessons from the past 20.000 years indicate that the WRM model is a function of two main parameters: (1) socio-economics, and (2) climate conditions. Three different Eras of the Humans-Nature relationship have been distinguished: (1) Naturalistic: Nature dominating Humans during the Last Glacial Period (100-10) kyr BC, (2) Dualistic: Nature-Humans cooperation and competition from 10 kyr BC to 1800 AD, and (3) Anthropocentric: Humans dominating Nature from 1800 AD to now. Since 2000, the Integrated WRM (IWRM) model is promoted as state-of-the-art and remains anthropocentric producing huge externalities. Its assessment during the last 20 years has given mixed results and needs to be reformulated. The new model we suggest is based on the dialectical tool for conflict resolution. It unifies Humans and Nature and enhances the social dimension of WRM. After identifying conflicts between stakeholders and the natural laws (eristic part), opposite objectives are unified to harmonize Humans with Nature (dialectical resolution). A case study of flood mitigation illustrates the eristic-dialectical methodology.

Within this paper, a machine learning algorithm is used to investigate the importance of certain setpoints and parameters in the filtration processes of a large-scale water treatment facility. Previously, a model for the filtration process based on Run-to-Run Control was proposed and tested against sample data from the treatment plant, but it was quickly found that such a model was incompatible for successfully computing setpoints of operation which minimize the energy cost of running the filtration systems. The machine learning model described herein is an attempt to elucidate the importance of the available data on the filtration systems and to identify the most important variables that influence the filtration run time.

This paper proposes a multi-terminal adaptive collaborative operation method to solve the problem of unstable internal force tracking in clamping and handling unknown objects by multi-terminal robots. In the proposed method, the internal command force changes the complex internal force control problem into an internal force tracking problem from multi-slave to master. Moreover, we develop an algorithm for multi-slave setups to estimate object stiffness and motion uncertainty in the direction of the internal command force according to Lyapunov theory. Finally, the impedance control generates a reference trajectory for the multi-slave to maintain the desired internal force and track the master’s motion. Several experiments are conducted on a self-made robot equipped. The experimental results show that the oscillation amplitude of each slave end is less than 1 mm, the directional oscillation amplitude is less than 1 degree during the tracking of the desired commanded internal force. For objects with low stiffness, the error of the commanded internal force is less than 1 N (6%) per slave. The error in tracking the commanded internal force for objects with high stiffness is less than 2 N (8%). The results prove the feasibility and effectiveness of the proposed method.

Several theories have been proposed on the subject of fundamental psychological human needs. A brief overview was given for some of these major theories. A more recent theory on core emotional needs, derived from the vantage point of schema therapy, was also proposed and comparisons were drawn with the other models, each with different psychological boundaries. Implications and what these psychological needs mean to parents were also discussed covering cultural norms that may inadvertently inflict harm on young children and adolescents.

During the progression from ductal carcinoma in situ (DCIS) to invasive breast cancer (IBC), cells have to overcome the physically restraining basement membrane (BM) which compartmentalizes the epithelium from the stroma. Since the extracellular matrix (ECM) of the epithelial and stromal compartment is biochemically and physically distinct from one another, the progression demands a certain degree of cellular plasticity being essentially required for a primary tumor to become invasive. The Epithelial-to-Mesenchymal Transition depicts such a cell program equipping cancer cells with features allowing for dissemination from the epithelial entity and stromal invasion on the single-cell level. Here, we investigated the reciprocal interference between an altering tumor microenvironment and the EMT-phenotype in vitro. BM-typical collagen IV and stroma-typical collagen I coatings were applied as provisional 2-D matrices. Pro-inflammatory growth factors were introduced to improve tissue mimicry. Whereas the growth on coated surfaces did only slightly affect the EMT-phenotype, the combinatorial action of collagen with growth factor TGF-β1 induced prominent phenotypic changes. However, the EMT-induction was independent of the collagen type and cellular accessibility for EMT-like changes was strongly cell line dependent. Summarizing the entire body of data, we computed an EMT-phenotyping model that was used to decide on cellular EMT-status and estimate EMT-like changes. We confirmed that miR200c-mediated reversion of mesenchymal MDA-MB-231 cells is reflected by our EMT-phenotype model emphasizing its potential to predict the therapeutic efficacy of EMT-targeting drugs in the future.

Film therapy, the therapeutic use of movies in psychological therapies, is a growing area of interest to therapists and researchers. This paper presents the MOVIE model of film therapy, a widely applicable, trauma-informed framework for working with film imagery and themes in individual or group therapy, counsellor education and self-help. Film or cinema therapy is a highly accessible and relatable method for many diverse client and student groups with benefits that include, catharsis and emotional processing, greater ease of dialogue around issues, increased empathy, and interpersonal learning, engaging with new perspectives and learning skills. Following the model clients mindfully observe and voice their emotional and psychological responses to movie watching, identify how the imagery, characters, themes, and narrative symbolise personal experience and consider any new possibilities that emerged through re-authoring personal narratives. The model is informed by a practice focused review of literature.

The article considers a way to compare large bulks of experimental data with theoretical calculations, in which the quality of theoretical models is clearly demonstrated graphically. Published theoretical data of the three-fluid dynamic model (3FD) applied to the experimental data from heavy-ion collisions at the energy range \sqrt{s_{NN}} = 2.7 - 63 GeV are used as example of application of the developed methodology. When analyzing the results, the quantum nature of the fireball, created at heavy ion collisions, was taken into account.

Age estimation in dental radiographs Orthopantomography (OPG) is a medical imaging technique that physicians and pathologists utilise for disease identification and legal matters. For example, for estimating post-mortem interval, detecting child abuse, drug trafficking, and identifying an unknown body. Recent development in automated image processing models improved the age estimation's limited precision to an approximate range of +/- one year. While this estimation is often accepted as accurate measurement, age estimation should be as precise as possible in most serious matters, such as homicide. Current age estimation techniques are highly dependent on manual and time-consuming image processing. Age estimation is often a time-sensitive matter in which the image processing time is vital. Recent development in Machine learning-based data processing methods has decreased the imaging time processing; however, the accuracy of these techniques remains to be further improved. We proposed an ensemble method of image classifiers and transfer learning techniques to enhance the accuracy of age estimation using OPGs from one year to a couple of months (1-3-6). This hybrid model is based on convolutional neural networks (CNN) and K nearest neighbours (KNN). The hybrid (HCNN-KNN) model was used to investigate 1,922 panoramic dental radiographs of patients aged 15 to 23. These OPGs were obtained from the various teaching institutes and private dental clinics in Malaysia. To minimise the chance of overfitting in our model, we used the principal component analysis (PCA) algorithm and eliminated the features with high correlation. To further enhance the performance of our hybrid model, we performed systematic image pre-processing. We applied a series of classifications to train our model. We have successfully demonstrated that combining these innovative approaches has improved the classification and segmentation and thus the age-estimation outcome of the model. Our findings suggest that our innovative model, for the first time, to the best of our knowledge, successfully estimated the age in classified studies of one year old, six months, three months and one-month-old cases with accuracies of 99.98, 99.96, 99.87, and 98.78 respectively.

The ACC feature when activated augments the engine performance in real-time. This article presents a novel methodology to predict the optimal adaptive cruise control set speed profile (ACCSSP) by considering all the effecting parameters. This paper investigates engine operating conditions (EOC) criteria to develop a predictive model of ACCSSP in real-time. We developed a deep learning (DL) model using the NARX method to predict engine operating point (EOP) mapping the vehicle-level vectors (VLV). We used real-world field data obtained from Cadillac test vehicles driven by activating the ACC feature for developing the DL model. We used a realistic set of assumptions to estimate the VLV for the future time steps for the range of allowable speed values and applied them at the input of the developed DL model to generate multiple sets of EOP’s. We imposed the defined EOC criteria on these EOPs, and the top three modes of speeds satisfying all the requirements are derived for each second. Thus three eligible speed values are estimated for each second, and an additional criterion is defined to generate a unique ACCSSP for future time steps. Performance comparison between predicted and constant ACCSSPs indicates that the predictive model outperforms constant ACCSSP.

The primary purpose of this article is to present a maturity model dealing with environmental manufacturing processes in a company. First, a theoretical approach is presented where evolution from the first industrial revolution to the present (Industry 4.0.) is outlined with a need to implement environmental initiatives. Chapter two contains a detailed literature review, which resulted in the creation of our own maturity model presented in the next chapter. According to some authors, Industry 4.0 is based on characteristics that have already been the focus of “lean and green” concepts. The practical part of the article is a case study that shows which areas of the manufacturing process have “environmental” potential. The goal was to move from resource consumption, pollutant emissions and more extensive manufacturing towards environmentally responsible manufacturing (ERM). Using environmental materials and methods reduces energy consumption, which generates cost savings and higher profits. Here, VSM (Value Stream Mapping) was applied to identify core processes with environmental potential. The final part of the article summarizes the work and presents future possibilities. This paper provides an understanding of the role of environmental manufacturing in the era of the 4th industrial revolution.

In the book “Everyhing Is A Circle: A New Model For Orbits Of Bodies In The Universe”, and further in the Article “Distance Between Two Circles in Any Number of Dimensions is a Vector Ellipse”, it has been mathematically demonstrated that “distance between points on any two different circles in any number of multiple dimensions” is equivalent to “distance of points on a vector ellipse from another fixed or moving point”. Using this mathematical methodology, a method is provided in this Article as a measure for the amount of interaction between two international economies, which are two countries or economic zones, by modelling each economy in terms of a “circle in four dimensions”. Based on this method, the proximity of the two economies at the end of each fiscal period, which is generally a fiscal year, is then measured by the distance between points at the end of the given period on the two circles in four dimensions, associated with these two economies in our model.

Background Pregnancy is a process of maturity for women that requires adaptation to the changes in their new roles. Adjustment to a new role is not easy, especially if the pregnancy is accompanied by risks, so it is necessary to get treatment to minimize complications. Dyad models that optimize husband or family support are very important to prevent the risk of complications and adapt mothers to their new roles. The purpose of this study was to determine the effect of the dyad model on high-risk pregnancies. The method used with a literature review is through the collection of articles from Scopus, ebsco, ProQuest and PubMed. The results obtained were articles from Scopus 9, Ebsco 107, PubMed 214 and ProQuest 85 which carried out identification, screening and eligibility for 25 articles. Overall results mention. Journals are categorized into 4 groups, Dyad Model and social support, high-risk pregnancy, maternal role and Self Efficacy. Conclusion, there is an effect of the dyad model on care for high-risk pregnancies.

A given problem in physics can be solved if it is well formulated. Well formulated means that it has a bijective correspondence to physical reality. Mass Gap Problem has no bijective correspondence with the physical reality and is that’s why not solvable mathematically. It can be solved in the frame of quantum mechanics by the formulation of the photon’s mass accordingly to the Planck-Einstein relation.

Blockchain is the consensus-based technology to resolve the conflicts in Byzantine environments. Vehicles validate the messages received from the neighboring vehicles using Gradient Boosting Technique (GBT). Based on the validation results the message source vehicle generates the ratings that are to be uploaded to Road Side Units (RSUs) through that trust offset value can be calculated. All RSUs maintain the trust blockchain and each RSU tries to add their blocks to trust blockchain. We proposed a blockchain-based trust management model for VANET based on Tendermint. It eliminates the problem of malicious nodes entering the network and will also overcome the problem of power consumption. Simulation results also show that the proposed system is 7.8% and 15.6% effective and efficient in terms of Packet Delivery Ratio (PDR) and End-to-End Delay (EED) respectively to collect the trusted data between the vehicles.

In this paper we analyze and propose new method and algorithm of selecting the optimal labor time as a function of skills following our main references Mirrlees (1971), Saez (2001) and Stancheva (2014). The optimal labor time is a situation when the utility function of individual reaches a maximum. One of the main differences with Saez (2001) is our algorithm for creation the skill distribution.

While the use of crime data has been widely advocated in the literature, its availability is often limited to large urban cities and isolated databases tend not to allow for spatial comparisons. This paper presents an efficient machine learning framework capable of predicting spatial crime occurrences, without using past crime as a predictor, and at a relatively high resolution: the U.S. Census Block Group level. The proposed framework is based on an in-depth multidisciplinary literature review allowing the selection of 188 best-fit crime predictors from socio-economic, demographic, spatial, and environmental data. Such data are published periodically for the entire United States. The selection of the appropriate predictive model was made through a comparative study of different machine learning families of algorithms, including generalized linear models, deep learning, and ensemble learning. The gradient boosting model was found to yield the most accurate predictions for violent crimes, property crimes, motor vehicle thefts, vandalism, and the total count of crimes. Extensive experiments on real-world datasets of crimes reported in 11 U.S. cities demonstrated that the proposed framework achieves an accuracy of 73 and 77% when predicting property crimes and violent crimes, respectively.

Recently, the intensities of natural disasters have increased significantly owing to climate change and various other environmental factors, causing unprecedented damage. Measures must be established to reduce damage from large-scale natural disasters caused by the rapidly changing environment. The Japanese government published a hazard map manual in 2015 and obligates the creation of a hazard map as a measure to reduce high-scale storm surges. This manual presents a typhoon model based on a parametric model that is used to create a hazard map. The Myers model assuming concentric circles, which is primarily used in East Asia, is disadvantageous as it cannot consider geographic characteristics. Therefore, a new parametric model is necessary to calculate wind and pressure fields, which change according to geographic characteristics. To improve this limitation of the Myers model, we calculated the wind and pressure fields considering geographical effects by combining the Holland model, which can consider the size of a developing typhoon, and the Mascon model, which changes by geographic characteristics. To determine the gradient coefficient of the Holland model, the coefficient that changes every moment was calculated using grid point value data. The result indicated excellent reproducibility of storm surge height according to the geographic characteristics.

Cyber security has recently received enormous attention in today’s security concerns, due to the popularity of the Internet-of-Things (IoT), the tremendous growth of computer networks, and the huge number of relevant applications. Thus, detecting various cyber-attacks or anomalies in a network and building an effective intrusion detection system that performs an essential role in today’s security is becoming more important. Artificial intelligence, particularly machine learning techniques, can be used for building such a data-driven intelligent intrusion detection system. In order to achieve this goal, in this paper, we present an Intrusion Detection Tree (“IntruDTree”) machine-learning-based security model that first takes into account the ranking of security features according to their importance and then build a tree-based generalized intrusion detection model based on the selected important features. This model is not only effective in terms of prediction accuracy for unseen test cases but also minimizes the computational complexity of the model by reducing the feature dimensions. Finally, the effectiveness of our IntruDTree model was examined by conducting experiments on cybersecurity datasets and computing the precision, recall, fscore, accuracy, and ROC values to evaluate. We also compare the outcome results of IntruDTree model with several traditional popular machine learning methods such as the naive Bayes classifier, logistic regression, support vector machines, and k-nearest neighbor, to analyze the effectiveness of the resulting security model.

Over the years influenza B virus (IBV) contribute annual disease and can lead to serious respiratory disease among humans. More attention should be paid to the mammalian adaptive processes of B viruses and development of vaccines against current influenza. Because of preclinical trials of anti-influenza drugs are conducted mainly on mice, we developed adequate animal model using antigenically-relevant IBV strain for testing anti-influenza drugs and protective efficacy of flu vaccines. We serially passaged Victoria lineage (clade 1A) IBV 17 times in BALB/c mice. The adaptive amino acid substitutions were found in HA (T214I) and NA (D432N). By the electron microscopic examination, we showed spherical and elliptical shapes of IBV. Light microscopy showed that mouse-adapted B virus caused influenza pneumonia on day 6 post inoculation. We evaluated the illness pathogenicity, viral load and histopathological features of mouse-adapted IBV and estimated anti-influenza drugs and vaccine efficiency in vitro and in vivo. Assessment of investigational anti-influenza drug oseltamivir ethoxisuccinate and flu vaccine Ultrix® revealed effectivity against our mouse-adapted influenza B virus.

New modied Hayward metric of magnetically charged non-singular black hole spacetime in the framework of nonlinear electrodynamics is constructed. When the fundamental length introduced, characterising quantum gravity effects, vanishes one comes to the general relativity coupled with the Bronnikov model of nonlinear electrodynamics. The metric can have one (an extreme) horizon, two horizons of black holes, or no horizons corresponding to the particle-like solution. Corrections to the Reissner-Nordström solution are found as the radius approaches to innity. As r -> 0 the metric has a de Sitter core showing the absence of singularities. The asymptotic of the Ricci and Kretschmann scalars are obtained and they are nite everywhere. The thermodynamics of black holes, by calculating the Hawking temperature and the heat capacity, is studied. It is demonstrated that phase transitions take place when the Hawking temperature possesses the maximum. Black holes are thermodynamically stable at some range of parameters.

Globally, karst aquifers store large amount of precious water and create beautiful karst springs in many places. However, most of the karst springs flow declined, and some of the karst springs dried up with the effects of extensive groundwater development and climate variation. In order to obtain better understanding of factors contributing to the drying-up of karst springs, this study introduced grey system models to quantify spring flow taking Jinci Springs (China) as an example, which dried up in May 1994. Based on the characteristics of Jinci Springs flow, the spring flow was divided into two stages: the first stage (1954-1960), when the spring flow was affected only by climate variation; and the second stage (1961-1994), when the flow was impacted by both climate variation and anthropogenic activities. Results showed that the Jinci Springs flow had strong relations with precipitation occurring one year and three years earlier in the first stage. Subsequently, a grey system GM (1, 3) model with one-year and three-year lags was set up for the first stage. By using the GM (1, 3) model, we simulated the spring flow in the second stage under effects of climate variation only. Subtracting the observed spring flow from the simulated flow, we obtained the contribution of anthropogenic activities to Jinci Springs cessation. The contribution of anthropogenic activities and climate variation to Jinci Springs cessation was 1.46 m³/s and 0.62 m³/s, respectively. Finally, each human activity causing spring flow decline was estimated. The methods are useful to describe the karst hydrological processes under the effects of anthropogenic activities and climate variation.

Purpose: Localization and mapping with LiDAR data is a fundamental building block for autonomous vehicles. Though LiDAR point clouds can often encode the scene depth more accurate and steadier compared with visual information, laser-based Simultaneous Localization And Mapping (SLAM) remains challengeable as the data is usually sparse, density variable and less discriminative. The purpose of this paper is to propose an accurate and reliable laser-based SLAM solution. Design/methodology/approach: The method starts with constructing voxel grids based on the 3D input point cloud. These voxels are then classified into three types to indicate different physical objects according to the spatial distribution of the points contained in each voxel. A global environment model with Partition of Unity (POU) implicit surface is maintained along the process and located voxels are merged into it from stage to stage, through scan-to-model matching implemented by Levenberg-Marquardt method. Findings: We find a laser-based SLAM method. The method uses POU implicit surface representation to build the model and is evaluated on the KITTI odometry benchmark without loop closure. Experimental results indicate that the method achieves accuracy comparable to the state-of-the-art methods. Originality/value: We propose a novel, low-drift SLAM method, which falls into a scan-to-model matching paradigm, operates on point clouds obtained from Velodyne HDL64. The method is of value to researchers developing SLAM systems for autonomous vehicles.

This study have uniquely mad use of Box-Jenkins ARIMA models to address the core of the threes objectives set out in view of the focus to add meaningful value to knowledge exploration. The outcome of the research have testify the achievements of this through successful nine months out-of-sample forecasts produced from the program codes, with indicating best model choices from the empirical estimation. In addition, the results also provide description of risks produced from the uncertainty Fan Chart, which clearly outlined possible downside and upside risks to tourist visitations in the country. In the conclusion, it was suggested that downside risks to the low level tourist arrival can be managed through collaboration between authorities concerned with the management of tourist arrivals in the country.

An autonomous endogenous time-keeping is ubiquitous across many living organisms known as circadian clock when it has a period of about 24 hours. Interestingly, the fundamental design principle with a network of interconnected negative and positive feedback loops is conserved through evolution, although the molecular components differ. Filamentous fungus \textit{Neurospora crassa} is a well established chrono-genetics model organism to investigate the underlying mechanisms. The core negative feedback loop of the clock of \textit{Neurospora} is composed of the transcription activator White Collar Complex (WCC) (heterodimer of WC1 and WC2) and the inhibitory element called FFC complex which is made of FRQ (Frequency protein), FRH (Frequency interacting RNA Helicase) and CK1a (Casein kinase 1a). While exploring their temporal dynamics we investigate how limit cycle oscillations arise and how molecular switches support self-sustained rhythms. We develop a mathematical model of 10 variables with 26 parameters to understand the interactions and feedbacks among WC1 and FFC elements in nuclear and cytoplasmic compartments. We performed control and bifurcation analysis to show that our novel model produces robust oscillations with a wild-type period of 22.5 hrs. Our model reveals a switch between WC1 induced transcription and FFC assisted inactivation of WC1. Using the new model we also study possible mechanisms of glucose compensation. A fairly simple model with just 3 non-linearities helps to elucidate clock dynamics revealing a mechanism of rhythms production. The model can further be utilized to study entrainment and temperature compensation.

Runtime verification (RV) is a lightweight approach to detecting temporal errors of system at runtime. It confines the verification on observed trajectory which avoids state explosion problem.To predict the future violation, some work proposed the predictive RV which uses the information from models or static analysis. But for software whose models and codes cannot be obtained, or systems running under uncertain environment, these predictive methods cannot take effect. Meanwhile, RV in general takes multi-valued logic as the specification languages, for example the " true, false and inconclusive" in three-valued semantics. They cannot give accurate quantitative description of correctness when "inconclusive" is encountered. We in this paper present a RV method which learns probabilistic model of system and environment from history traces and then generates probabilistic runtime monitor to quantitatively predict the satisfaction of temporal property at each runtime state. In this approach, Hidden Markov Model (HMM) is firstly learned and then transformed to Discrete Time Markov Chain (DTMC). To construct incremental monitor, the monitored LTL property is translated into Deterministic Rabin Automaton (DRA). The final probabilistic monitor is obtained by generating the product of DTMC and DRA, and computing the probabilities for each state. With such method, one can give early warning once the probability of correctness is lower than pre-defined threshold, and have the chance to do adjustment in advance. The method has been implemented and experimented on real UAS (Unmanned Aerial Vehicle) simulation platform.

Timely processing of observations from hyper-spectral imagers, such as SEVIRI (Spinning Enhanced Visible and Infrared Imager), largely depends on fast radiative transfer calculations. This paper mostly concerns the development and implementation of a new forward model for SEVIRI to be applied to real time processing of infrared radiances for the physical retrieval of surface temperature and emissivity. The new radiative transfer model improves computational time by a factor of ≈ 7 compared to the previous versions and makes it possible to process SEVIRI data at nearly real time. The new forward model has been applied for the simultaneous retrieval of surface temperature and emissivity in three infrared channels (8.7, 10.8, 12 μm). The inverse scheme relies on a Kalman filter approach, which allows us to exploit a sequential processing of SEVIRI observations. Based on the new forward model, the paper also presents a validation retrieval performed with in situ observations acquired during a field experiment carried out in 2017 at Gobabeb (Namib desert) validation station. Furthermore, a comparison with IASI (Infrared Atmospheric Sounder Interferometer) emissivity retrievals has been performed as well. It has been found that the retrieved emissivities are in good agreement with each other and with in situ observations, i.e. average differences are generally well below 0.01.

The prevalence of obesity and eating disorders varies by sex, but the extent to which sex influences eating behaviors, especially in childhood, has received less attention. The purpose of this paper is to review the literature on sex differences in eating behavior in children and present new findings supporting the role of sex in child appetitive traits and neural responses to food cues. In children, the literature shows sex differences in basic taste response, food acceptance, eating self-regulation, and appetitive traits. New analyses demonstrate that sex interacts with child weight status to differentially influence appetitive traits and neural responses to food cues. Further, neuroimaging results suggest that obesity in female children is positively related to brain reactivity to higher-energy-dense food cues in regions involved with learning, memory, and object recognition, while the opposite was found in males. In addition to differences in how the brain processes information about food, other factors that may contribute to sex differences include parental feeding practices, societal emphasis on dieting, and peer influences. Future studies are needed to confirm these findings, as they may have implications for the development of effective intervention programs to improve dietary behaviors and prevent obesity.

Earth atmosphere is almost opaque in the infrared: about 374 W/m² is absorbed by the atmosphere out of 396 W/m² surface upward longwave radiation, and only about 22 W/m² leaves the system unabsorbed in the atmospheric window. This makes rise to the idea to approximate the annual global mean energy flow system from a simple idealized greenhouse model, where the surface is surrounded by a single-layer shortwave (SW) transparent, longwave (LW) opaque, non-turbulent atmosphere. The energy flows in this geometry can be described by elementary arithmetic relationships. Starting from this model, the realistic Earth’s atmosphere can be achieved by introducing partial atmospheric SW opacity, partial atmospheric LW transparency and turbulent fluxes during the course of the deduction. The resulted global mean energy flow system is then compared to several data sets such as satellite observations from the CERES mission; estimates using direct surface observations and climate models; global energy and water cycle assessments; and independent detailed clear-sky radiative transfer computations. We find that the deduction from this idealized model approximates the real values in Earth energy budget with reasonable accuracy: the deduced fluxes and the observed ones are consistent within the acknowledged error of observations; while fundamental features of the initial geometry like special ratios and definite relationships between the fluxes are preserved.

The optimum conditions for the fermentation of African locust bean (Parkia biglobosa) into a vegetable protein based condiment (Iru) were developed using Levenberg-Marquardt (or Powell) method (using PSI software) with three (3) variables namely; inoculum concentration (bacillus subtilis), temperature and the fermentation duration. African locust bean seeds were fermented at various temperature of 40 - 70 °С for five days (120 hours) with different concentrations of Inoculum. The proximate analysis shows that fermentation increased the percentage protein. Protein had the highest composition with about 51 % after 72 hours at the lowest fermentation temperature of 40°С.

This study presents a pre-processing approach adopted for the radar reflectivity data assimilation and results of simulations with the Harmonie numerical weather prediction model. The method shows an improvement of precipitation prediction within the radar location area in both the rain rates and spatial pattern presentation. With the assimilation of radar data, the model simulates larger water content in the middle troposphere within the layer from 1 to 6 km, with major variations at 2.5–3 km; it also reproduces better the mesoscale belt and cell patterns of precipitation fields.

The production of olives and olive oil in the Mediterranean region is one of the most important cultivation. The continuous changes of the European Common Agricultural Policy (CAP) towards strengthening the influence of market forces, has increased the necessity for assessing the efficiency of production protocols or patterns being implemented by the farmers. The case of olive trees cultivation, despite the fact that it is very important for both farmers and consumers, has not been in depth analyzed regarding the efficiency of inputs being used during the production process. This study evaluates the efficiency rates of 100 agricultural holding specialized on olive trees cultivation in Greece, by implementing a DEA input oriented model. The inputs being used are land, fertilizers, agrochemicals, labour, and energy. The output being used is the revenue of each holding. The results quantify the significant differentiation of efficiency scores, providing evidence that there is space for restructuring the production process, in order to improve efficiency and decrease by this way the production cost of inefficient farmers.

Synesthesia is a psychological phenomenon where sensory signals become mixed. Input to one sensory modality produces an experience in a second, unstimulated modality. In “grapheme-color synesthesia”, viewed letters and numbers evoke mental imagery of colors. The study of this condition has implications for increasing our understanding of brain architecture and function, language, memory and semantics, and the nature of consciousness. In this work, we propose a novel application deep learning to model perception in grapheme-color synesthesia. Achromatic letter images, taken from database of handwritten characters, are used to induce synesthesia. Results show the model learns to accurately create a colored version of the inducing stimulus, according to a statistical distribution from experiments on a sample population of grapheme-color synesthetes. The spontaneous, creative mental imagery characteristic of the synesthetic perceptual experience is reproduced by the model. A model of synesthesia that generates testable predictions on brain activity and behavior is needed to complement large scale data collection efforts in neuroscience, especially when articulating simple descriptions of cause (stimulus) and effect (behavior). The research and modeling approach reported here begins to address this need.

Wind turbine plants are complex dynamic and uncertain processes driven by stochastic inputs and disturbances, as well as different loads represented by gyroscopic, centrifugal, and gravitational forces. Moreover, as their aerodynamic models are nonlinear, both modelling and control become challenging problems. On one hand, high-fidelity simulators should contain different parameters and variables in order to accurately describe the main dynamic system behaviour. Therefore, the development of modelling and control for wind turbine systems should consider these complexity aspects. On the other hand, these control solutions have to include the main wind turbine dynamic characteristics without becoming too complicated. The main point of this paper is thus to provide two practical examples of development of robust control strategies when applied to a simulated wind turbine plant. Experiments with the wind turbine simulator and the Monte–Carlo tools represent the instruments for assessing the robustness and reliability aspects of the developed control methodologies when the model-reality mismatch and measurement errors are also considered. Advantages and drawbacks of these regulation methods are also highlighted with respect to different control strategies via proper performance metrics.

With the rapid development of satellite remote sensing technology, the volume of image datasets in many application areas is growing exponentially and the demand for Land-Cover and Land-Use change remote sensing data is growing rapidly. It is thus becoming hard to efficiently and intelligently retrieve the change information that users need from massive image databases. In this paper, content-based image retrieval is successfully applied to change detection and a content-based remote sensing image change information retrieval model is introduced. First, the construction of a new model framework for change information retrieval in a remote sensing database is described. Then, as the target content cannot be expressed by one kind of feature alone, a multiple-feature integrated retrieval model is proposed. Thirdly, an experimental prototype system that was set up to demonstrate the validity and practicability of the model is described. The proposed model is a new method of acquiring change detection information from remote sensing imagery and so can reduce the need for image pre-processing, deal with problems related toseasonal changes as well as other problems encountered in the field of change detection. Meanwhile, the new model has important implications for improving remote sensing image management and autonomous information retrieval.

The fault diagnosis of wind farms has been proven to be a challenging task and motivates the research activities carried out through this work. Therefore, this paper deals with the fault diagnosis of a wind park benchmark model, and it considers viable solutions to the problem of earlier fault detection and isolation. The design of the fault indicator involves data-driven approaches, as they can represent effective tools for coping with poor analytical knowledge of the system dynamics, noise, uncertainty and disturbances. In particular, the proposed data-driven solutions rely on fuzzy models and neural networks that are used to describe the strongly nonlinear relationships between measurement and faults. The chosen architectures rely on nonlinear autoregressive with exogenous input models, as they can represent the dynamic evolution of the system along time. The developed fault diagnosis schemes are tested by means of a high-fidelity benchmark model, that simulates the normal and the faulty behaviour of a wind farm installation. The achieved performances are also compared with those of a model-based approach relying on nonlinear differential geometry tools. Finally, a Monte-Carlo analysis validates the robustness and the reliability of the proposed solutions against typical parameter uncertainties and disturbances.

Background: Removable prostheses have seen a fundamental change recently because of advances in polymer materials, allowing improved durability and performance. Despite these advancements, notable differences still occur amongst various polymer materials and processing technologies, requiring a thorough grasp of their mechanical, physical, and therapeutic implications. Methods: Fours different processing techniques, all utilizing Polymethyl methacrylate (PMMA) as the main material, were used to construct complete dentures. The specimens underwent a mechanical test. Results: All the specimens broke under the influence of a certain force, rather than yielding through flow, as is characteristic for plastic materials. For each specimen, the maximum force (N) was recorded (figure 4), as well as the breaking energy, Conclusions: The best results were obtained in the case of the thermoplastic injection system and, surprisingly and contrary to those in the specialized literature, the worst results were recorded in the case of 3D printed samples.

A Pacific brittle star Ophiura sarsii has previously been shown to produce a chlorin (3S,4S)-14-Ethyl-9-(hydroxymethyl)-4,8,13,18-tetramethyl-20-oxo-3-phorbinepropanoic acid (ETPA) (1) with potent phototoxic activities making it applicable to photodynamic therapy. Using extensive LC-MS metabolite profiling, molecular network analysis and targeted isolation with de novo NMR structure elucidation, we here identify five additional chlorin compounds from O. sarsii and its deep-sea relative O. ooplax: 10S-Hydroxypheophorbide a (2), Pheophorbide a (3), Pyropheophorbide a (4), (3S,4S,21R)-14-Ethyl-9-(hydroxymethyl)-21-(methoxycarbonyl)-4,8,13,18-tetramethyl-20-oxo-3-phorbinepropanoic acid (5), and (3S,4S,21R)-14-Ethyl-21-hydroxy-9-(hydroxymethyl)-4,8,13,18-tetramethyl-20-oxo-3-phorbinepropanoic acid (6). Chlorins 5 and 6 have not been previously reported in natural sources. Interestingly, low amounts of chlorins 1-4 and 6 could also be identified in a distant species, the basket star Gorgonocephalus cf. eucnemis, demonstrating that chlorins are produced by a wide spectrum of marine invertebrates of the class Ophiuroidea. Following purification of these major Ophiura chlorin metabolites, we discovered the high singlet oxygen quantum yield upon their photoinduction and their strong phototoxicity against a panel of cancer cell lines. These studies identify an arsenal of brittle star chlorins as powerful natural photosensitizers with potential photodynamic therapy applications.

A novel five-surface phosphor-in-ceramic (FS-PiC) for high illumination and excellent color uniformity in larger-scale LEDs for sensor light source application is demonstrated. YAG phosphor (Y3Al5O12:Ce3+) was uniformly mixed with ceramic and sintered at 680°C to form a phosphor wafer. A sophisticated laser engraving was employed on the phosphor wafer to form a saddle-shaped large-scale FS-PiC LEDs. The performance of the FS-PiC LEDs exhibited the illumination of 401lm, average color temperature (CCT) of 5488K±110K, and color coordinates (CIE) of (0.3179±0.003, 0.3352±0.003). In contrast to convention single-surface phosphor-in-ceramic (SS-PiC) LEDs, the performance exhibited the illumination of 380lm, average CCT of 5830K±758K, and CIE of (0.3083±0.07, 0.3172±0.07). These indicated that the performance of the FS-PiC LEDs was higher than the SS-PiC LEDs for illumination, CCT, and CIE of 1.1, 7, and 23 times, respectively. Furthermore, the FS-PiC LEDs demonstrate lower lumen loss of 2% and less chromaticity shift of 5.4x10-3 under accelerated aging at 350°C for 1008 hours, owing to the high ceramic melting temperature of up to 510°C. In this study, the proposed FS-PiC larger-scale LEDs having excellent optical performance and high reliability may be the promising candidates to replace the convention phosphor-in-organic silicone material used in high-power LEDs for the next generation of sensor light sources, display, and headlight applications.

The issue of climate change represents a significant global risk that demands immediate attention. However, higher education frequently fails to incorporate global perspectives and intricate transdisciplinary matters. In order to integrate global and transdisciplinary aspects into the curriculum of a technical university with a focus on computer science, a Master's programme has been developed. The programme's objective is to situate global risk at the core of the curriculum, thereby encouraging scientists to work beyond the boundaries of their respective disciplines. The paper considers the challenges that science and higher education must address to facilitate pathways towards a peaceful and sustainable future. It argues for a shift in focus from short-term and localised issues to a more holistic approach, emphasising the importance of considering universal problems. The curriculum, as developed, may be regarded as a guide for future university efforts in the fields of global risk and climate change.

To date, the percentage composition of the lung microbiome in bronchopulmonary cancer has not been summarized. Existing studies on identifying the lung microbiome in bronchopulmonary cancer through 16S rRNA sequencing have shown variable results regarding the abundance of bacterial taxa. Objectives: To identify the predominant bacteria in the lung microbiome of bronchopulmonary cancer from samples collected through bronchoalveolar lavage and to assess their potential role in the diagnosis of bronchopulmonary cancers. Data Sources: A systematic review of English articles using MEDLINE, Embase, and Web of Science. Search terms included lung microbiome, lung cancer, bronchoalveolar lavage. Study Selection: Randomized clinical trials that investigated the lung microbiome in bronchopulmonary cancer with samples collected via bronchoalveolar lavage. Data Extraction: Independent extraction of articles using predefined data fields, including study quality indicators. Data Synthesis: Nine studies met the inclusion criteria, focusing on those that utilized a percentage expression of the microbiome at the phylum or genus level. There was noted heterogeneity between studies, both in terms of phylum and genus, with a relatively constant percentage of the Firmicutes phylum and the genera Streptococcus and Veillonella being mentioned. Significant differences were also observed regarding the inclusion criteria for study participants, the method of sample collection, and data processing. Conclusions: To date, there is no consistent percentage pattern at the phylum or genus level in bronchopulmonary cancer, with the predominance of a phylum or genus varying across different patient cohorts, resulting in non-overlapping outcomes.

Background: Patients with Inflammatory Bowel Disease (IBD) believe that diet is a key factor in the pathogenesis of their disease and in worsening their symptoms; they often follow restrictive diets that can lead to malnutrition, anxiety, and stress. Recent studies have found an association between eating disorders and IBD, particularly anorexia nervosa and ARFID (Avoidant Restrictive Food Intake Disorder). None of these studies report the association with orthorexia nervosa, that is the obsession with healthy and natural foods. Aim: to assess the risk of orthorexia nervosa in patients with IBD. Methods: 158 consecutive subjects were recruited, including 113 patients diagnosed with IBD and 45 controls. The Donini standardized questionnaire ORTO-15 was administered to assess the risk of orthorexia; clinical and demographic data were also collected. Results: patients with IBD have a prevalence of orthorexia of 77%, significantly higher than the 47% observed in the control group. In patients with IBD, the risk of orthorexia is associated with lower BMI and history of previous surgery. Conclusion: Many patients with IBD develop orthorexia nervosa that impacts the patient's psychological and social sphere, exposes them to a high risk of nutritional deficiencies, and affects quality of life. Further high quality studies are warranted to evaluate the clinical impact of orthorexia and its correlation with clinical features and frank eating disorders.

Blockchain is a shared, unchangeable, decentralized ledger that allows transactions to be recorded securely and transparently via a network of computers. In addition to its significant contributions to cryptocurrencies, blockchain technology has shown promise as a game-changing advancement in cyber security. This study offers a thorough analysis of how blockchain technology is strongly linked to defending against cyber security risks in the modern world. This research clarifies the primary mechanisms of decentralization, immutability, transparency, and cryptographic security by which blockchain improves cyber security through a review of the literature and case studies. Furthermore, the study addresses the difficulties and constraints associated with incorporating blockchain technology into cybersecurity frameworks and proposes potential directions for future research.

The inverse system identification toolbox named INVSID 1.0 for MATLAB, which is used to identify the inversion of single-input single-output systems, is developed. The complete process of developing the toolbox is demonstrated. Afterwards, a numerical example is illustrated to describe how the toolbox can be used to solve inverse identification problems. Simulation results demonstrate the effectiveness of the toolbox.

The healthcare system is crucial to the development, standard of living, and sustainability of any country. Amid the pandemic, most developed countries are experiencing varying levels of difficulty managing their healthcare systems. Overloading of healthcare services is common, leading to prolonged waiting times for medical services; thus, the wastage of hospital resources must be addressed seriously. In this paper, we examine the problem of no-shows in outpatient clinics. Through a review of the literature and practical industrial experience, we identify the processes of these clinics. We then propose a robust optimisation approach for overbooking using a traditional overbooking model and a robust system. This model aims to mitigate the significant parameter uncertainties encountered during the pandemic. Considering risk aversion, the optimal policy for overbooking can be established while accounting for the costs associated with overbooking. The main contribution of this paper is the introduction of an alternative method to address the uncertainty of no-shows using an overbooking technique.

Curcumin (CUR) is a phytochemical with potent anticancer activities as demonstrated by both preclinical and clinical studies. CUR bioformulations, including loading in biodegradable polymers, have been explored to increase CUR’s solubility and chemical stability, control its release, and improve its delivery to cancer cells. In this study, copolymers comprising poly (L-lactide)-poly (ethylene glycol)-poly (L-lactide) (PLA-PEG-PLA) and poly (ethylene glycol)-poly (L-lactide)-poly (ethylene glycol) (PEG-PLA-PEG) were designed and synthesized to assess and compare their curcumin delivery capacity and inhibitory potency on MCF-7 breast cancer cells. Molecular dynamics simulations indicated that PLA-PEG-PLA has a higher propensity to interact with the cell membrane and more negative free energy, suggesting it is the better carrier for cell membrane penetration. Our characterization indicated that the microsphere copolymers were synthesized successfully. Of the two formulations, PLA-PEG-PLA experimentally exhibited better results, with an initial burst release of 17.5%, followed by a slow, constant release of the encapsulated drug up to 80%. PLA-PEG-PLA-curcumin showed a significant increase in cell death in MCF-7 cancer cells (IC50 = 23.01 ± 0.85 µM) based on the MTT assay. These data were consistent with gene expression studies of Bax, Bcl2, and hTERT which showed that PLA-PEG-PLA-CUR induced apoptosis more efficiently in these cells. Our study integrates in vitro and in silico approaches to identify an optimal co-polymer for the delivery of CUR to cancer cells.

The cancer burden is increasing worldwide with contrasting national patterns. Therefore, providing national estimations is necessary to understand and control this health issue. Herein, we comparatively describe incidence, mortality, and disability-adjusted lived years (DALYs) due to 29 groups of cancer in Mexico from 1990 to 2019 by sex and age. Using public data from the Global Burden of Disease Study 2019, we assessed the national burden of cancer using counts and crude and age-standardized rates per 100,000 people with 95% uncertainty intervals for 2019 and their trends using the annual percentage of change from 1990 to 2019. In 2019, cancer caused 222,060 incident cases, 105,591 deaths, and 2,86,054 DALYs in the general population. Nonmelanoma skin cancer, prostate cancer, and breast cancer had the highest incidence, and six groups of cancer (lung, colorectal, stomach, prostate, breast, and pancreatic) caused 53% of the deaths in 2019. From 1990 to 2019, incidence and mortality showed an increasing trend that varied between 10% and 436% among the groups of cancer, whereas the crude and age-standardized rates showed cancer-specific sex disparities. Our results demonstrate a substantial growth of the national burden of cancer with sex-specific patterns of change. These results may guide national efforts toward reducing health loss due to cancer.

This study delves into Personal Narratives (PN), which encompass both oral and written recounting of individual experiences, encompassing facts, events, people, and thoughts. Traditional emotion recognition and sentiment analysis primarily focus on broader categories like utterances or documents. Our research, however, centers on identifying Emotion Carriers (EC), which are specific segments within speech or text that elucidate the narrator's emotional state (e.g.,"losing a parent", "decision-making moments"). Extracting these ECs enriches the representation of a user's emotional state, thereby enhancing natural language understanding and the sophistication of dialogue models. While previous studies have utilized lexical attributes to identify ECs, we argue that incorporating spoken narratives offers a more nuanced view of the context and emotional state. This paper explores the integration of speech and textual embeddings at the word level, alongside both early and late fusion techniques, for improved EC detection in spoken narratives. We employ Residual Neural Networks (ResNet), initially pre-trained on diverse speech emotion datasets and subsequently fine-tuned for EC detection. Our experimental findings demonstrate that late fusion, in particular, significantly enhances EC detection capabilities.

The digital era has significantly amplified the volume of online reviews, presenting both opportunities and challenges in harnessing insights from consumer feedback. Aspect-Based Sentiment Analysis (ABSA) has emerged as a crucial tool for distilling sentiments from these reviews, providing valuable data for enhancing product and service quality. This study introduces an advanced hybrid model, AdvSentiNet, which integrates adversarial training into the state-of-the-art framework to elevate the precision of sentiment detection at the aspect level. By employing an adversarial network, where a generative model competes against a classifier by crafting highly realistic synthetic samples, we aim to bolster the model's resilience against varied data samples. This innovative approach, unexplored in its entirety within the realm of ABSA, demonstrated remarkable performance improvements on benchmark datasets. For instance, accuracy on the SemEval 2015 dataset escalated from 81.7% to 82.5%, and for the SemEval 2016 dataset, it surged from 84.4% to 87.3%.

Recent advancements in machine learning have ushered in innovative techniques for augmenting datasets, particularly through contrastive learning in the computer vision domain. This study pioneers the application of contrastive learning for sentiment analysis, introducing a novel approach termed EmoConLearn. By fine-tuning contrastive learning embeddings, EmoConLearn significantly surpasses BERT-based embeddings in sentiment analysis accuracy, as evidenced by our evaluations on the DynaSent dataset. This research further delves into the efficacy of EmoConLearn across various domain-specific datasets, highlighting its versatility. Additionally, we investigate upsampling strategies to mitigate class imbalance, further enhancing EmoConLearn's performance in sentiment analysis benchmarks.

The purpose of the study is to analyse and to identify specific structural characteristics of melanocytic nevi, to youth pacients. Using the optical microscope, could be possible a proper describtion related melanocytic nevi. In this study play a significant role, different factors, such as genetic, epigenetic, microbiomic and proteomic factors. Future directions refers to preventive and prophylactic methods. Disease diagnostic and management directions are also important key points.

The Fractal Analysis of Medical Images is an investigation field in those that numerous results of important value have been obtained. This work describes the basic foundations of the Fractal Analysis and its application has more than enough medical images, leaning on in five scientific investigations.

Nanoparticles (NPs) are commonly used in healthcare and nano therapy, but their toxicity at high concentrations is well-known. Recent research has shown that NPs can also cause toxicity at low concentrations, disrupting various cellular functions and leading to altered mechanobiological behavior. While researchers have used different methods to investigate the effects of NPs on cells, including gene expression and cell adhesion assays, the use of mechanobiological tools in this context has been underutilized. This review emphasizes the importance of further exploring the mechanobiological effects of NPs, which could reveal valuable insights into the mechanisms behind NP toxicity. Such investigations could aid in developing new strategies to mitigate NP toxicity and improve their safety for biomedical applications. Moreover, understanding how NPs affect cell cytoskeletal functions through mechanobiology could have significant implications, including the development of innovative drug delivery systems and tissue engineering techniques. In summary, this review highlights the significance of incorporating mechanobiology into the study of NP toxicity and demonstrates the potential of this interdisciplinary field to advance our knowledge and practical use of NPs.

This study delves into hospital mobility, understood as an indicator of perceived service quality, across the Italian regions of Apulia and Emilia Romagna, utilizing logistic regression among machine learning techniques. The focus is on how structural, operational, and clinical variables impact patient perceptions of service quality, influencing their healthcare choices. Through the analysis of mobility trends with machine learning, significant differences between regions were uncovered, highlighting the influence of regional context on perceived quality. The integration of SHAP (SHapley Additive exPlanations) values into our analysis provided deeper insights into the logistic regression model, elucidating the specific contribution of each variable to perceived healthcare quality. This incorporation of SHAP values underscores the study's commitment to employing advanced, explainable AI techniques to enhance the interpretability and fairness of healthcare service evaluations. The choice of logistic regression elucidated the impact of specific variables on quality perception, offering essential insights for optimizing healthcare resource distribution and underscoring the importance of data-driven strategies to foster more equitable, efficient, and patient-centred healthcare systems. Contributing to the understanding of perceived quality dynamics within the healthcare context, the research paves the way for further investigations into enhancing accessibility and service quality, leveraging machine learning as a tool for improving healthcare services efficiency in diverse regional settings.

In Cameroon, Aedes mosquitoes transmit various arboviruses, posing significant health risks. We aimed to characterize the Aedes virome in southwestern Cameroon and identify potential core viruses which might be associated with vector competence. A total of 398 Aedes mosquitoes were collected from four locations (Bafoussam, Buea, Edea and Yaounde). Aedes albopictus dominated all sites except for Bafoussam where Aedes africanus prevailed. Metagenomic analyses of mosquitoes grouped per species into 54 pools revealed notable differences in eukaryotic viromes between Ae. africanus and Ae. albopictus, with the former exhibiting greater richness and diversity. Thirty-seven eukaryotic virus species from 16 families were identified, including six novel viruses with near complete genome sequences. Seven viruses were further quantified in individual mosquitoes via qRT-PCR. Although none of them could be identified as core viruses, Guangzhou sobemo-like virus and Bafoussam mosquito solemovirus virus were highly prevalent regionally in Ae. albopictus and Ae. africanus, respectively. This study highlights the diverse eukaryotic virome of Aedes species in southwestern Cameroon. Despite their shared Genus, Aedes species exhibit limited viral sharing, with varying viral abundance and prevalence across locations. Ae. africanus, an understudied vector, harbors a rich and diverse virome, suggesting potential implications for arbovirus vector competence.

Predators are dependent on the capture of prey to meet their energetic and nutritive requirements, imposing a regime of predation risk. Consequently, prey experience both consumptive and non-consumptive effects. Non-consumptive effects may manifest through altered growth and ontogenetic trajectories of prey species, a dynamic modulated by olfactory or other sensory cues from predators. Bactrocera dorsalis Hendel represents a great invasive threat to global horticulture. While earlier research has primarily centered on the consumptive interactions between B. dorsalis and its natural enemies, the potential consequences of non-consumptive interactions on the development of B. dorsalis have been overlooked. In this study, we investigated the impact of predation risk effects, induced by both visual exposure to the predatory mantis Hierodula patellifera Serville and its associated odor, on the life history traits of B. dorsalis. Female B. dorsalis demonstrated reduced development time in the presence of isolated H. patellifera or H. patellifera odor, yet evidenced significantly increased fecundity. Conversely, males displayed no significant change in development time. Additionally, neither female nor male body weight at death was significantly influenced by predation risk from isolated predators or predator odor. These results indicated adaptive developmental strategies of B. dorsalis when faced with risks from predators and highlight the important role of predator odor, which has the potential to lay the groundwork for improving biological control efforts and innovative approaches within pest control.

Members of the genus Armillaria are widespread forest pathogens against which effective protection has not yet been developed. Due to their longevity and the creation of large-scale cloning of Armillaria individuals, the use of mycoviruses as biocontrol agents (BCAs) against these pathogens could be an effective alternative. This work describes the screening of double-stranded RNA banding patterns as a first approach for potential virus discovery and the use of stranded total RNA-seq for viral detection and characterization in Armillaria spp. collected in the Czech Republic. A total of five single-stranded RNA viruses were detected in Armillaria ostoyae and A. cepistipes, including the first virus of the family Tymoviridae found infecting a member the genus Armillaria and four viruses belonging to the recently described virus group "ambiviruses" with circular ambisense genome organization. Both hammerhead (HHRz) and hairpin (HpRz) ribozymes were detected in all the ambiviricot sequences. Armillaria viruses were compared by phylogenetic analysis and confirmed their specific host by direct RT-PCR, which showed the occurrence of interspecies transmission.

The utilization of natural waxes to form oleogels has emerged as a new and efficient technique for structuring liquid edible oil into solid-like structures for diverse food applications. The objective of this study was to investigate the interaction between sunflower wax (SW) and monoglycerides (MGs) in olive oil oleogels and to assess their physical characteristics and storage stability. To achieve this, pure SW and a combination of SW with MGs in a 1:1 ratio were examined within a total concentration range of 6-12% w/w. The formed oleogels were characterized based on their microstructure, melting and crystallization properties, textural characteristics, and oxidative stability during storage. All the oleogels were shelf-stable, and as the concentration increased, the hardness of the oleogels also increased. The crystals of SW oleogels were long needle-like, while the combination of SW and MGs led to the formation of crystal aggregates and rosette-like crystals. The differential scanning calorimetry and FTIR showed that the addition of MGs led to different crystal structures. The oxidation results revealed that oleogels had low peroxide and TBARS values throughout the 28-day storage. These results provide useful insights about the utilization of SW and MGs oleogels for potential applications in the food industry.

In this paper, we first discuss the research status and application progress of the finite element method and the smoothed particle method. By analyzing the advantages of the smoothed particle method and the finite element method, a new coupling algorithm, namely FEM-SPH algorithm, is proposed. By the method of comparison, it shows that finite element method and SPH method in the simulation of large deformation problems each have advantages and disadvantages, the finite element method smoothed particle coupling algorithm is effective to achieve the performance of high computational efficiency and can naturally simulate large deformation problems across. In the process of calculation, the large deformation unit can be freely into an algorithm to facilitate the calculation accuracy and efficiency of three methods of numerical simulation. Through the study found, FEM-SPH algorithm not only overcome the defect of smooth particle tensile instability, but also overcomes the problem of low efficiency of finite element computation. To further test the FEM-SPH algorithm has advantages in the practical engineering, we have carried out the actual test to the example of the super high speed collision, concluded that, since the target of most of the computational domain is always finite element, smoothed particle focused only in contact with the projectile and target of local area, particle number is not much, the whole calculation process just ten minutes, computational efficiency has been greatly improved, at the same time in the simulation of large deformation, the advantage is very obvious .This provides a criterion for the actual project, depending on the specific material deformation mode and choose a more appropriate conversion algorithm.

Alzheimer's Disease (AD) presents a significant challenge in the wake of its multifaceted nature, characterized by cognitive decline, memory impairment, and neuroinflammation. Despite extensive research, effective pharmacological interventions remain elusive, prompting exploration into non-pharmacological approaches. Microcurrent therapy (MC), utilizing imperceptible currents, has emerged as a potent clinical protocol. While previous studies have focused on its therapeutic effects, this study investigates its impact on neuronal damage and neuroinflammation in an AD mouse model, specifically addressing potential side effects. Utilizing 5xFAD transgenic mice, we examined the effects of MC therapy on neuronal integrity and inflammation. Our findings suggest that MC therapy attenuates memory impairment and reduces neurodegeneration, as evidenced by improved performance in memory tests and preservation of neuronal structure. Additionally, MC therapy significantly decreases amyloid-beta (Aβ) plaque deposition and inhibits apoptosis, indicating its potential to mitigate AD pathology. Our study discovered that MC therapy effectively reduces glial activation by inhibiting the TLR4-MyD88-NFκB pathway, consequently leading to decreased levels of inflammatory factors TNF-α, IL-1β, and IL-6, thus implicating TLR4 in neurodegenerative disease-related neuroinflammation. Furthermore, while our study did not observe significant adverse effects, further clinical trial into potential side effects and neuroinflammatory responses associated with MC therapy is warranted.

Background: Uncontrolled hemorrhage is a major preventable cause of death in patients with trauma. However, the majority of large animal models of hemorrhage have utilized controlled hemorrhage rather than uncontrolled hemorrhage to investigate the impact of immunopathy and coagulopathy on multi-organ failure (MOF) and mortality. This study evaluates these alterations in a severe porcine-controlled and uncontrolled hemorrhagic shock (HS) model. Methods: Anesthetized female swine underwent controlled hemorrhage (22 ml/kg) and uncontrolled hemorrhage by partial splenic resection followed with or without lactated Ringer solution (LR) or Voluven® resuscitation. Blood chemistry, physiologic variables, systemic and tissue levels of complement proteins and cytokines, coagulation parameters, organ function, and damage were recorded and assessed. Results: HS resulted in systemic and local complement activation, cytokine release, metabolic acidosis, hyperkalemia, MOF, and no animal survival. Resuscitation with LR and Voluven® after HS improved hemodynamic parameters (MAP and SI), metabolic acidosis, hyperkalemia, and survival but resulted in increased complement activation and worse coagulopathy. Compared with the LR group, the HS animal treated with Voluven® had worse dilutional anemia, coagulopathy, renal and hepatic dysfunction, increased myocardial complement activation and renal damage, and decreased survival rate. Conclusions: Hemorrhagic shock triggers early immunopathy and coagulopathy that appear associated with MOF and death. This study indicates that immunopathy and coagulopathy are therapeutic targets that may be addressed with a high-impact adjunctive treatment to conventional resuscitation.

Chipless radio frequency identification (RFID) technology is expected to replace barcode technology due to its ability to read in non-line-of-sight (NLOS) situations, long reading range, and low cost. Currently, there is extensive research being conducted on frequency-coded (FC) co-polarized radar cross-section (RCS)-based tags, which are widely used. However, detecting co-polarized chipless RFID tags in cluttered environments is still a challenge, as confirmed by measuring two co-polarized tags in front of a perfect metal reflector (30.5cm×22.5cm). To address this challenge, a realistic mathematical model for a chipless RFID system has been developed that takes into account the characteristics of the reader and the tag, as well as reflections from cluttered objects. This model has been simulated and verified with measurement results by placing a single flat metal reflector behind two co-polarized one-bit designs: a dipole array tag and a square patch tag. The results showed that the interfering signal completely overlaps the ID of the co-polarized tag, severely limiting its detectability. To solve this issue, the proposed solution involves reading the tag in cross-polarization mode by etching a diagonal slot in the square patch tag. This proposed tag provides high immunity to the environment and can be detected in front of both dielectric and metallic objects.

Tightly regulated and robust mitochondrial activities are critical for normal hearing. Previously, we demonstrated that Fus1 KO mice with mitochondrial dysfunction exhibit premature hearing loss. Molecular analysis of the cochlea revealed hyperactivation of the mTOR pathway, oxidative stress, altered mitochondrial morphology and quantity, suggesting compromised energy sensing and production. Here, we investigated whether pharmacological modulation of metabolic pathways using rapamycin (RAPA) or 2-deoxy-D-glucose (2-DG) supplementation can protect against hearing loss in female Fus1 KO mice. Additionally, we aimed to identify mitochondria- and Fus1-dependent molecular pathways and processes critical for hearing. We found that inhibiting mTOR or activating alternative mitochondrial energetic pathways to glycolysis protected hearing in the mice. Comparative gene expression analysis revealed dysregulation of critical biological processes in the KO cochlea, including mitochondrial metabolism, neural and immune responses, and cochlear hypothalamic–pituitary–adrenal axis signaling system. RAPA and 2-DG mostly normalized these processes, although some genes showed a drug-specific response or no response at all. Interestingly, both drugs resulted in a pronounced upregulation of critical hearing-related genes not altered in the non-treated KO cochlea, including cytoskeletal and motor proteins, and Ca2+-linked transporters and voltage-gated channels. These findings suggest that pharmacological modulation of mitochondrial metabolism and bioenergetics may restore and activate processes critical for hearing, thereby protecting against hearing loss