Since December 2019, the world is fighting against coronavirus disease (COVID-19). This disease is caused by a novel coronavirus termed as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This work focuses on the applications of machine learning algorithms in the context of COVID-19. Firstly, regression analysis is performed to model the number of confirmed cases and death cases. Our experiments show that autoregressive integrated moving average (ARIMA) can reliably model the increase in the number of confirmed cases and can predict future cases. Secondly, a number of classifiers are used to predict whether a COVID-19 patient needs to be admitted to an intensive care unit (ICU) or semi-ICU. For this, classification algorithms are applied to a dataset having 5644 samples. Using this dataset, the most significant attributes are selected using features selection by ExtraTrees classifier, and Proteina C reativa (mg/dL) is found to be the highest-ranked feature. In our experiments, random forest, logistic regression, support vector machine, XGBoost, stacking and voting classifiers are applied to the top 10 selected attributes of the dataset. Results show that random forest and hard voting classifiers achieve the highest classification accuracy values near 98%, and the highest recall value of 98% in predicting the need for admission into ICU/semi ICU units.

The Coronavirus Disease 2019 (COVID-19) pandemic has had a major impact on the mental and physical health of hospitalized patients. In our study we focused on the onset of symptoms correlated with Post-traumatic stress disorder (PTSD), depression and physical disabilities in patients admitted to the Intensive Care Unit (ICU) because of a severe respiratory distress related to COVID-19 (COVID Group) compared with patients admitted to the same ICU for trauma and other medical conditions than COVID-19 (No-COVID Group). The physical symptoms and the level of disability were evaluated with the Glasgow Outcome Scale-Extended (GOS-E), the Quality of Life after Brain Injury (QOLIBRI) and the 3 levels version of EQ-5D (EQ-5D-3L) questionnaire; psychiatric symptoms were investigated using the Impact of Event Scale-Revised 22-item (IES-R), the Patient Health Questionnaire, 9-Item Version (PHQ-9) and the Generalized Anxiety Disorder Assessment, 7-items version (GAD-7). These questionnaires were administered 6 months after discharge. Patients in the No-COVID Group showed statistically significant more severe scores in all the physical assessments while similar relevant PTSD and depressive symptoms were reported in both groups. The results of the present study underline the psychopathological impact of being hospitalized in ICU because of COVID-19 even after 6 months from discharge ,suggesting the importance of assessing the psychiatric effects of COVID-19 in the long term in order to create supportive measures.

(1) Background: Ventilator-associated pneumonia (VAP) causes high mortality among patients with respiratory disease and imposes major burdens on healthcare infrastructure. Models that use electronic health record data to predict the onset of VAP may spur earlier treatment and improve patient outcomes. We developed and studied the performance of interpretable machine learning (ML) models that predict the onset of VAP from electronic health records (EHRs); (2) Methods: We trained Logistic Regression (LR), full feature Explainable Boosting Machine (fEBM), and eXtreme Gradient Boosting (XGBoost) ML models on data from the MIMIC- III (v1.3) database. Model performance was measured by area under the receiver operating characteristic curves (AUCs). We trained a minimal-feature EBM model (mEBM) with features derived from white blood cell (WBC) counts, duration of ventilation, and Glasgow Coma Scale (GCS). Finally, model robustness was evaluated on randomly sparsified EHR datasets; (3) Results: The fEBM model outperformed the XGBoost and LR models at 24 hours post-intubation. The mEBM model maintained an AUC of 0.893. The fEBM model performance remained robust on sparsified datasets; (4) Conclusions: Our novel interpretable ML algorithm reliably predicts the onset of VAP in intubated patients. Integration of this EBM-based model into clinical practice may enable clinicians to better anticipate and prevent VAP.

(1) Background: Until December 2021, French Guiana (FG), located in South America, faced four consecutive COVID-19 epidemic waves. This study sought to analyze the mortality trend of severe COVID-19 patients admitted to the referral ICU of FG. (2) Methods: We conducted a prospective, observational, and non-interventional study in ICU at Cayenne Hospital. We included 383 patients older than 18 admitted with SARS-CoV-2-related pneumonia hospitalized from May 2020 to December 2021. The study covers three periods. Period 1 (Waves 1 and 2, original variant), period 2 (Wave 3, gamma variant), and period 3 (Wave 4, delta variant). (3) Results: The median age was 63 years (52-70). Frailty was diagnosed in 36 patients over 70 (32.4%). Only 4.8% of patients were vaccinated. The median ICU LOS was 10 days (6-19). Hospital mortality was 37.3%. It was 30.9% in period 1, 36.6% in period 2 (p=0.329 vs. period 1), and 47.1% in period 3 (0.015 vs. period 1). In multivariate analysis, independent factors associated with hospital mortality were age more than 40 years (]40-60 years] OR=5.2, 95%CI: 1.4-19.5; (]60-70 years] OR=8.5 95%CI: 2.2-32; (]70+ years] OR=17.9, 95%CI: 4.5-70.9), frailty (OR=5.6, 95%CI: 2.2-17.2), immunosuppression (OR=2.6, 95%CI: 1.05-6.7), and MV use (OR=11, 95%CI: 6.1-19.9). This model had an overall sensitivity of 72%, a specificity of 80.4%, a positive predictive value of 68.7%, and a negative predictive value of 82.8%. (4) Conclusions: The mortality of severe COVID-19 patients in French Amazonia was higher during the delta variant wave. This over-death is explained by the virulence of the responsible SARS-CoV-2 variant and the under-vaccination coverage of the studied population.

There is evidence that kidney involvement is frequently observed in COVID-19 patients, and can manifest as mild proteinuria to advancing acute kidney injury (AKI). One of the mechanisms is microvascular and macrovascular thrombosis caused by the hypercoagulation phase of the disease. Thromboelastography (TEG) is a valuable examination to detect significant hemostatic abnormalities, including the hypercoagulable state. This study analyzed the coagulation profiles, including TEG parameters, in COVID-19 patients who developed AKI compared to those who did not during their intensive care unit (ICU) stay, to identify predictors of AKI. Our single-center cohort retrospective study involved adult patients of COVID-19 in the ICU of Sardjito Hospital in Yogyakarta, Indonesia between May and September 2021. Patients were divided into two groups of AKI and non-AKI based on 2012 KDIGO definition and the elaboration by Indonesian Society of Nephrology. Variables showing a significant difference in the two groups were then analyzed using univariate and multivariate binary logistic regression. A total of 60 COVID-19 patients were included in the study, and 35% of them developed AKI. Compared to non-AKI patients, those with AKI exhibited a greater prevalence of diabetes mellitus (66.7% versus 35.9%, P = 0.023), higher D-Dimer levels (970 versus 685 ng/mL, P = 0.045), and higher values in TEG parameters of maximum amplitude/MA (74.6 versus 65.9 mm, P = 0.001) and coagulation index/CI (2.3 versus 1.0, P = 0.033). TEG parameter of MA emerged as the sole significant predictor for the development of AKI (OR, 6.33; 95% CI, 1.56 to 25.64). Our study validated the kidney involvement of COVID-19 infection, and showed that diabetes mellitus, high D-Dimer levels, and hypercoagulability serve as prominent risk factors in the development of AKI. Furthermore, TEG parameter of MA exceeding 70 mm is the single independent significant predictor of AKI.

Background Acute kidney injury (AKI) is a common comorbidity in the intensive care unit (ICU), with higher mortality rate among those who receive continuous renal replacement therapy (CRRT). This study aimed to investigate the association of white blood cell (WBC) levels with all-cause mortality in AKI patients undergoing CRRT. Method We conducted a post hoc analysis of ICU patient data from multiple centers in Korea. First, the levels of WBCs were classified into four groups based on quartiles. Second, the relationship between different WBC levels and mortality was investigated with the Kaplan-Meier (K-M) survival curves, Cox proportional hazards model, and generalized additivity model (GAM). Finally, we used multiple imputation to avoid bias caused by non-randomized missing data. The endpoints were short-term (28 days) and long-term (90 days) mortality. Result In our analysis, a total of 1,100 ICU patients were included. Among them, 670 (60.90%) and 781 (71.00%) died within 28 and 90 days, respectively. K-M survival curves showed that patients with the lowest WBC levels had the highest risk of mortality for both outcomes. Adjusted multivariate Cox regression analysis confirmed that compared with the reference level (quartile 2), low WBC levels increased the risk of both 28-day mortality (quartile 1, HR = 1.43, 95% CI: 1.13–1.83, P = 0.004) and 90-day mortality (quartile 1: HR = 1.32; 95% CI: 1.05-1.66, P = 0.016). GAM analysis showed a non-linear, U-shaped relationship between pre-CRRT WBC levels and all-cause mortality (non-linear P < 0.05). Conclusion The baseline WBC level is an independent predictor of prognosis for ICU patients receiving CRRT. Patients in the lowest WBC quartile had the highest risk of all-cause mortality. These results provide further evidence that early observation and intervention may have a prognostic value for these high-risk patients.

The SARS-CoV-2 infection is a cause of hypoxemic acute respiratory failure, leading to frequent intensive care unit (ICU) admission. Due to invasive organ support and immunosuppressive therapies, these patients are prone to nosocomial infections. Our aim was to assess the value of daily measurements of C-reactive protein (CRP) and Procalcitonin (PCT) in the early identification of ICU-acquired infections in COVID-19 patients Methods: We undertook a prospective observational cohort study (12 months). All adult me-chanically-ventilated patients admitted for ≥ 72 hours to the ICU with COVID-19 pneumonia were divided into an infected group (n=35) and a non-infected group (n=83). Day 0 was considered as the day of the diagnosis of infection (infected group) and day 10 of ICU stay (non-infected group). The kinetics of CRP and PCT were assessed from day -10 to day 10 and evaluated using a general linear model, univariate, repeated-measures analysis. Results: 118 patients (mean age 63 years, 74% males) were eligible for the analysis. The groups did not differ in patients' age, gender, CRP, and PCT serum levels at ICU admission. However, the infected group encompassed patients with a higher severity (SOFA score at ICU admission, p=0.009) and a higher 28-day mortality (p<0.001). Before D0, CRP kinetics showed a significant increase in infected patients, whereas in noninfected it remained almost unchanged (p<0.001), while PCT kinetics did not appear to retain diagnostic value to predict superinfection in COVID-19 patients (p=0.593). Conclusion: COVID-19 patients who develop ICU-acquired infections exhibited different biomarker kinetics before the diagnosis of those infections. Daily CRP monitoring and the recognition of the CRP kinetics could be useful in the prediction of ICU-acquired infections.

Objective: To evaluate the effect of care bundles on treatment compliance and intestinal barrier function in patients with refractory septic shock in the intensive care units (ICUs). Methods: In this retrospective study, the clinical data of 94 patients with refractory septic shock admitted to our hospital between June 2020 and April 2022 were collected. Patients with routine nursing were included in the routine group, and those with care bundles were assigned to the care bundles group, with 47 cases in each group. Outcome measures included nursing efficiency, treatment compliance, sequential organ failure assessment (SOFA) score, acute physiology and chronic health evaluation (APACHE II) score, intestinal barrier function, inflammation factor level, treatment outcome and complications. Results: Care bundles resulted in significantly higher nursing efficiency and treatment compliance versus routine care (P<0.05). Patients receiving care bundles showed significantly higher SOFA scores and APACHE II scores than those receiving routine care (P<0.05). Intestinal fatty-acid binding protein (I-FABP), diamine oxidase (DAO), lactate, endotoxin, and intestinal dysfunction scores in the care bundles group were significantly lower than those in the conventional group after treatment (P<0.05). Care bundles were associated with significantly lower levels of procalcitonin (PCT) and hypersensitive C-reactive protein (hsCRP), shorter time to symptom relief, ICU treatment time, and duration of mechanical ventilation, and lower 28-d morbidity and mortality versus routine care (P<0.05). Patients in the care bundles group had a significantly lower incidence of complications than those in the routine group (P<0.05). Conclusion: Care bundles effectively enhance treatment compliance, improve intestinal barrier function and treatment outcomes, reduce the inflammatory response, and decrease the risk of SOFA score, APACHE II score, and complications in patients with refractory septic shock.

Objective: To evaluate the effect of care bundles on treatment compliance and intestinal barrier function in patients with refractory septic shock in the intensive care units (ICUs). Methods: In this retrospective study, the clinical data of 94 patients with refractory septic shock admitted to our hospital between June 2020 and April 2022 were collected. Patients with routine nursing were included in the routine group, and those with care bundles were assigned to the care bundles group, with 47 cases in each group. Outcome measures included nursing efficiency, treatment compliance, sequential organ failure assessment (SOFA) score, acute physiology and chronic health evaluation (APACHE II) score, intestinal barrier function, inflammation factor level, treatment outcome and complications. Results: Care bundles resulted in significantly higher nursing efficiency and treatment compliance versus routine care (P<0.05). Patients receiving care bundles showed significantly higher SOFA scores and APACHE II scores than those receiving routine care (P<0.05). Intestinal fatty-acid binding protein (I-FABP), diamine oxidase (DAO), lactate, endotoxin, and intestinal dysfunction scores in the care bundles group were significantly lower than those in the conventional group after treatment (P<0.05). Care bundles were associated with significantly lower levels of procalcitonin (PCT) and hypersensitive C-reactive protein (hsCRP), shorter time to symptom relief, ICU treatment time, and duration of mechanical ventilation, and lower 28-d morbidity and mortality versus routine care (P<0.05). Patients in the care bundles group had a significantly lower incidence of complications than those in the routine group (P<0.05). Conclusion: Care bundles effectively enhance treatment compliance, improve intestinal barrier function and treatment outcomes, reduce the inflammatory response, and decrease the risk of SOFA score, APACHE II score, and complications in patients with refractory septic shock.

Blood glucose (BG) monitoring is an important issue for critically ill patients. Previous studies reported that poor sugar control was associated with increased mortality in admitted patients. However, repeated blood glucose monitoring can be resource-consuming and cause a healthcare burden in clinical practice. In this study, we aimed to develop a personalized machine-learning model to predict dysglycemia based on electrocardiogram (ECG) findings. The study included patients with more than 20 ECG records during single hospital admission in the Medical Information Mart for Intensive Care III database, focusing on the lead II recordings, along with the corresponding blood sugar data. We processed the data and used ECG features from each heartbeat as inputs to develop a one-class support vector machine (SVM) algorithm to predict dysglycemia. The model prediction for dysglycemia using a single heartbeat had an AUC level of 0.92 ± 0.09, with a sensitivity of 0.92 ± 0.10 and specificity of 0.84 ± 0.04. Based on 10 s majority voting, the model prediction for dysglycemia improved to an AUC of 0.97 ± 0.06. In this study, we found that a personalized machine-learning algorithm could accurately detect dysglycemia using a single-lead ECG.

On the 9th March 2020, the first patient with COVID-19 was admitted to ICU in the Royal Gwent Hospital, Newport, Wales. We prospectively recorded the rate of ICU admissions of 52 patients with COVID-19 over 60 days, focusing on the epidemiology of ethnicity and deprivation. Patients were 65% (34 of 52) male and had a median (IQR) age of 55 (48-62) years. Prevalent comorbidities included obesity (52%); diabetes (33%), and asthma (23%). COVID-19 hospital and ICU inpatient numbers peaked on days 23 and 39, respectively – a lag of 16 days. The ICU mortality rate was 33% (17 of 52). Black, Asian and Minority Ethnics (BAME) population represented 35% of ICU COVID-19 admissions (18 of 52) and 35% of deaths (6 of 17). Within the BAME group, 72% (13 of 18) were found to reside in geographical areas representing the 20% most deprived in Wales, versus 27% of Caucasians (9 of 33). Less than 5% of the population within the hospital catchment area are of BAME descent, yet they represent a disproportionately high proportion of patients with ICU admission and mortality suffering from COVID-19. The interplay between ethnicity and deprivation, which is complex, may be a factor in our findings. This in turn could be related to an increased prevalence of co-morbidities; higher community exposure; or genetic polymorphisms.

Introduction: The objective of this study was: compare the continuous infusion of cefepime with the intermittent infusion in patients with sepsis caused by Gram negative bacilli (GNB). Methods: Randomized 1:1 multicenter double-blinded placebo-controlled study with allocation concealment. Multicenter study in Intensive Care Units of Colombia. Patients with sepsis, severe sepsis or septic shock and GNB suspected bacteraemia. Cefepime was administered for 7 to 14 days over 30 m intermittently every 8 h over 24 h plus continuous saline solution (0.9%) (G1) or 3 g administered continuously plus saline solution every 8 h (0.9%) (G2). Percentage of clinical response at 3, 7, 14 days, relapse at 28 days and mortality at discharge were measured. Results: The recruitment was stopped by suggestion of the Institutional Review Board (IRB), following an FDA alert about cefepime. Thirty-two patients were randomized, 25 received the intervention and GNB bacteremia was confirmed in 16 (9 G1 and 7 G 2). Favourable clinical response in days 3,7,14 was 88.8%, 88.8%, 77.8 % (G 1) and was similar for G 2 (85.7%). There were not relapses and deaths in G2 while in G1 one relapse and 2 deaths were observed. Conclusions: The results of this study support the use of cefepime for the treatment of Gram-negative infections in critically ill patients but we could not demonstrate differences between continuous or intermittent administration, because of the small sample size, given the early suspension of the study.

SARS-CoV-2 pandemic caused an increase in Intensive Care Unit (ICU) hospitalizations with rise in morbidity and mortality; nevertheless, there is still little evidence of pandemic impact on antibiotic resistance in ICU. Retrospective monocentric epidemiological study. All microorganisms isolated from all patients admitted to E.O. Galliera ICU from January 2018 to December 2022 were included. Antibiotic resistance (AR) profiles were evaluated. Aim of the study was to describe and analyze the impact of SARS-CoV-2 pandemic on ICU microorganisms resistance patterns. 1,771 microorganisms identified, 221 (12.47%) had resistant pattern (Resistant Organisms; ROs) isolated from 1,679 patients during 12,030 hospitalization days. The majority of ROs were Gram-negative (79.66% 2018, 77.29% 2019, 61.83% 2020, 62.56% 2021, 60.75% 2022), but increase of Gram-positive microorganisms was observed (20.34% to 39.25% between 2018-2022). Prevalence of AR was: 19.44% 2018, 11.54% 2019, 38.04% 2020, 34.15% 2021, 39.29% 2022 for Gram-positives; 19.86%, 13.56%, 18.12%, 12.41%, 12.31% for Gram-negatives. Incidence of Ros showed COVID-related rise in 2020-2021, followed by a lowering trend since 2021, with new rise in 2022. Possible explanations are antibiotic overtreatment and drop of containing measures. Interesting finding is the cumulative lowering trend of carbapenem resistant K. pneumoniae and P. aeruginosa probably due to different patient features.

Myocardial deformation imaging (strain imaging) is a technique to directly quantify the extent of myocardial contractility and overcomes several of the limitations of ejection fraction. The application of the most commonly used strain imaging method; speckle-tracking echocardiography to patients with sepsis cardiomyopathy heralds an exciting development to the field. However; the body of evidence and knowledge on the utility, feasibility and prognostic value of left ventricular global longitudinal strain in sepsis cardiomyopathy is still evolving. We conducted a review of literature on utility of left ventricular global longitudinal strain in sepsis cardiomyopathy. We discuss the role of left ventricular global longitudinal strain in mortality prediction, utility and limitations of the technique in the context of sepsis cardiomyopathy.

A 1-year prospective study was carried out on patients in the ICU unit at a tertiary care hospital, Hail, Kingdom of Saudi Arabia. A total of 163 bacterial isolates were obtained from different clinical specimens with a high proportion of bacteria found associated with ventilator-associated pneumoniae (70, 43%), followed by catheter-associated urinary tract infection (39, 24%), central line-associated bloodstream infection (25, 15%), and surgical site infection (14, 8.6%). Klebsiella pneumoniae was the most common isolate (39, 24%), followed by Acinetobacter baumannii (35, 21.47%), Pseudomonas aeruginosa (25, 15%), and Proteus spp (23, 14%). Among the highly prevalent bacterial isolates, extended-spectrum beta-lactamase was predominant (42, 42.4%). Proper use of antibiotics, continuous monitoring of drug sensitivity patterns, and taking all precautionary measures to prevent beta-lactamases-producing organisms in the clinical settings are crucial and significant factors to fend off life-threatening infections and for a better outcome.

This study investigated the impact of seven-strain probiotic supplementation on the frequency of T helper 17 (Th17) cells, regulatory T (T-reg) cells, Treg/Th17 ratio, and their gene expression in individuals with severe traumatic brain injury (STBI). Probiotics can influence the gut microbiota, modulate the immune response, and prevent or alleviate certain immune diseases. However, it is necessary to clarify whether probiotics can effectively impact Th cells in STBI patient. Despite the great attention to improving the immune system in most diseases, there is currently no accurate mechanism for the use of probiotics in diseases with damage to the head injury, which can be a basis for the use of probiotics. The primary objective of this study is to address this research gap. The present study was a randomized, double-blind, placebo-controlled trial that enrolled 48 STBI patients. They were treated with probiotics or placebo: 4 capsule/day for 14 days. The pre- and post-study evaluation included examining the frequency of Treg, Th17 cells, as well as the expression of associated immunological parameters (Foxp3 and RORγt) and Treg/Th17 ratio in both groups. No significant differences were found between the two groups in terms of subject characteristics (P>0.05). GCS scores at 14 days were significantly higher in the probiotic compared to the placebo group. The probiotic group showed a significant decrease in Th17 cell and RORγt gene expression (P = 0.022 and P< 0.001, respectively), a notable increase in T-reg and FoxP3 gene expression (P= 0.025 and P< 0.001, respectively), and a higher Treg/Th17 ratio compared to the placebo group. Our study provides promising evidence of the beneficial effects of seven-strain probiotic supplementation on Th cells in individuals with STBI.

Multidrug resistance (MDR) among gram-negative bacteria is a global public health threat because it has exhausted all standard therapeutic options. This has necessitated clinicians to reconsider colistin, an “old sort” antibiotic which was deemed toxic for clinical use. Unfortunately, swift resistance towards colistin by K. pneumoniae has been reported in different parts of the world. The study aimed at investigating the genetic determinants of colistin resistance in K. pneumoniae isolated from patients admitted in a tertiary care hospital in Kampala, Uganda. 31 archived multidrug resistant K. pneumoniae isolates obtained from rectal swabs of patients admitted to the Mulago Hospital ICU were retrieved. From this collection, we selected 7 colistin resistant K. pneumoniae isolates for whole genome sequencing and molecular profile analysis. In silico sequence data analyses were performed using established web-based bioinformatics tools. The sequence analysis of the genes involved in colistin resistance revealed several non-synonymous mutations in nucleotide sequences of pmrA, pmrB, phoP, phoQ and mgrB genes. Insertion transposase genes for the insertion sequences IS1 and IS5 which are highly implicated in colistin resistance via modification or inactivation of mgrB gene were identified in all isolates and were located in the chromosomal DNA at different genomic positions. No plasmid-borne mcr genes were detected. More so, the strains harbored numerous multidrug resistant genes. This study underscores the need of genomic surveillance of K. pneumoniae strains to limit further spread of colistin resistance genes to avert the potential risk of pandrug resistance. And high throughput sequencing is imperative.

Bi1-xSbx crystal is one of the best n-type thermoelectric materials below 200 K, but its weak mechanical strength hinders practical applications for deep refrigeration. Herein, we adopted the mechanical enhancement method of hot extrusion to investigate the comprehensive mechanical and thermoelectric properties of Bi0.905Sb0.095. It revealed that reducing the grain size of the matrix and increasing the extrusion ratio can improve the gain size uniformity and mechanical properties. While the thermoelectric performance depends on the texture, grain size, and local composition. The extruded sample prepared by ingot with the high extrusion ratio of 9:1 generated a high texture degree of (001) plane and small grain sizes between ~4-40 mm, which resulted in the high bending strength of Bi1-xSbx ~130 Mpa and a high-power factor of ~ 68 μW·cm-1·K-2@173 K, as well as the relative high figure of merit of 0.25@173K. This work highlights the importance of the uniform distribution of the grain size and the compositions for Bi1-xSbx, as well as the required universal key parameter for the hot-extrusion method.

In this work, an improved collocation method based on the Bernoulli polynomials is presented to solve the Volterra integral equation (VIE) of the second kind. The main idea of the proposed method is to improve the results of the classic Bernoulli collocation method (BCM) by dividing the interval into some sub-intervals and considering the collocation points on each of them. Here, the zeros of the shifted Chebyshev polynomials (SCPs) are considered as collocation points. Then, BCM is applied step by step from the first sub-interval to the last one. By this process, a system of algebraic equations is attained for each sub-interval that could be easily solved. Convergence of the scheme is analyzed. For the purpose of demonstrating the validity, applicability, and efficiency of the suggested scheme several numerical examples are provided. Numerical results illustrate that the accuracy of the improved Bernoulli collocation method (IBCM) is more than BCM.

The aim of this prospective, randomized, clinical study was to evaluate the use of the pleth variability index (PVi) to guide the rate of intra-operative fluid therapy as compared with a traditional fixed fluid rate approach in ASA 1-2 dogs undergoing surgery. Twenty-seven dogs meet the inclusion criteria and were randomly assigned to conventional fluid management group (CFM, n = 12) or PVi-guided group (PVi, n = 15). The CFM group received a fixed rate of 5 ml kg-1 h-1 of crystalloid solution, while in the PVi group the rate was continuously adjusted based on PVi: PVi &lt; 14% = 3 ml kg-1 h-1; 14% ≤ PVi ≥ 20% = ml kg-1 h-1; PVi &gt; 20% = 15 ml kg-1 h-1. Hypotension (MAP &lt; 65 mmHg) was managed in CFM with maximum two fluid boluses (5 ml kg-1 in 10 minutes) and in case of no response dobutamine (1-3 mcg kg-1 min-1) was administered. In the PVi group treatment of hypotension was similar except when PVi &gt; 14% when dobutamine was directly initiated. The total fluid amount was significantly lower in PVI group (0.056 ± 0.027 ml kg-1 min-1) vs. CFM group (0.132 ± 0.115 ml kg-1 min-1), and hypotension incidence was lower (p = 0.023) in PVi group (0%) vs. CFM group (41%). Mean arterial pressure (MAP) was significantly higher during surgery in the PVi group. Dobutamine was never administered in both groups. Preliminary data suggest that PVi may be considered a potential target to guide fluid therapy in dogs; larger studies are needed, especially in cases of cardiovascular instability.

The deaf-mute and individuals with hearing disabilities who communicate through sign language should not be overlooked or excluded. It is a social responsibility to strive for an inclusive world without inequality. Advances in technology offer various solutions to address the communication gap, one of which is the use of computer-based sign language recognition systems. This project proposes a Malaysian Sign Language recognition model utilizing MobileNetV1, a convolutional neural network-based algorithm, to interpret sign language in real-time settings through a website application using Streamlit. The outcome of the model test shows that it has achieved a recognition rate of 96 percent. Although the proposed model has demonstrated the ability to recognize sign language, it is suggested that further improvement can be achieved by enhancing the quality and diversity of the dataset. The implementation of a more comprehensive dataset would lead to improved performance and increased accuracy of the model. This project underscores the importance of considering and accommodating the needs of the deaf-mute and individuals with hearing disabilities and highlights the potential of technology to bridge communication barriers and achieve a more inclusive society.

This study presents an in-depth analysis of land use and land cover change on the Kerch Peninsula over a period spanning three decades. Utilizing convolutional neural networks alongside satellite imagery analysis, we have mapped and quantified the changes in land use and cover, revealing significant trends and transformations within the peninsula's landscape. The research aims to elucidate the interplay between anthropogenic activities, climatic variations, and policy interventions in shaping land cover dynamics, thereby providing insights into the environmental and socio-economic impacts of these changes. Our findings indicate a marked increase in urban expansion at the expense of natural ecosystems, including forests and wetlands, underscoring the urgent need for sustainable land management strategies. The study highlights the role of agricultural intensification and expansion in altering the peninsula's ecological balance and emphasizes the critical importance of integrating change analysis and prediction. By leveraging advanced remote sensing and GIS technologies, our analysis not only enhances the understanding of the complex dynamics driving land use and land cover change but also showcases the potential of predictive modeling in forecasting future land cover scenarios. The implications of this study extend beyond the Kerch Peninsula, offering valuable lessons for managing land use changes and conserving natural landscapes in similar regions globally.

Visible light communication (VLC) has attracted much attention in recent years among industrial and academic researchers. The large scale use of energy efficient light emitting diodes (LEDs) in lighting applications offers a unique opportunity to use license free visible light as the carrier in wireless communications. It has many potentials to complement radio frequency (RF) based communication systems. Researchers working on VLC have mainly concentrated on improving the communication speeds to match with Wi-Fi though the latest generation Wi-Fi standards offer significantly higher capacity at longer communication distances. Thus, market penetration is challenging for VLC as Wi-Fi is already quite mature and ubiquitous. Commercialization of VLC can only be successful if VLC can offer applications where VLC is more advantageous over the existing RF-based solutions. Once VLC enters the market through specific applications and becomes more mature in the process, the ecosystem may gradually adopt VLC in broader applications. In this article, the state of the art of the three promising VLC applications, their issues, and possible solutions have been discussed.

Hydrogen gas (H2) has attracted attention as a next-generation clean energy source. Its efficient and safe preparation and utilization are crucial in both industry and organic chemistry. In this study, a Pt/CB-catalyzed MW-mediated continuous-flow hydrogenation reaction was developed using methylcyclohexane (MCH) as the reducing agent (hydrogen carrier). Alkynes, alkenes, nitro groups, benzyl esters, and aromatic chlorides were chemoselectively hydrogenated using Pt/CB under MW-assisted continuous-flow conditions. This methodology represents a safe and energy-efficient hydrogenation process, as it eliminates the need for an external hydrogen gas supply or heating jackets as a heat medium. Further application of MW-mediated continuous-flow hydrogenation reactions is a viable method for the efficient generation and utilization of sustainable energy.

The principal function of science education is to introduce students to core scientific concepts and practices; nevertheless, it is now impossible, in such a complex world, to neglect the ethical issues involved. Placing school science in a broader context motivates students and equips them with valuable critical skills. A meaningful example was from palm oil production and pervasive use worldwide: plantations result in major deforestation occurring in species-rich tropical regions, making the palm oil issue global in scope and highly critical for several aspects related to sustainability. This paper reports a sequence of didactic activities starting from palm oil chemistry, gradually extended initially by a multidisciplinary approach, then by interdisciplinary-systemic maps and, ultimately, through a holistic vision - involving anthropological and cultural considerations about indigenous people of deforested territories. This progressive broadening corresponds to an ever-greater penetration into the personal sphere, a condition for a real ethical consciousness. The increased students’ awareness was tested by qualitative considerations, supporting the teaching dimension more than the learning one. Contrary to disciplinary contents, the emergence of moral feelings cannot be measured but only perceived. Therefore, chemistry provides key concepts essential for global competences development in order to deal with sustainability issues with an increased ethical awareness.

Oral squamous cell carcinoma (OSCC) is a highly prevalent and aggressive malignancy, with mortality rates that reach 60%, mainly due to an excessive diagnostic delay. MiRNAs, a class of crucial epigenetic gene-expression regulators, have emerged as potential diagnostic biomarkers, with >200 molecules reported to be dysregulated in OSCC. We had previously established an in silico methodology for the identification of the most disease-specific molecules by bridging genetics and epigenetics. Here, we identified the stage-specific miRNAs that govern the asymptomatic early stages of oral tumorigenesis by exploiting the reverse interplay between miRNA-levels and their target-genes’ expression. Incorporating gene expression data from our group’s experimental hamster model of sequential oral oncogenesis, we bioinformatically detected the miRNAs that simultaneously target/regulate >75% of the genes that are characteristically upregulated or downregulated in the consecutive stages of hyperplasia, dysplasia and early-invasion OSCC, while exhibiting the opposite expressional dysregulation in OSCC-derived tissue and/or saliva specimens. We found that all stages share the downregulation of miR-34a-5p, miR124-3p, and miR-125b-5p, while miR-1-3p is underexpressed in dysplasia and early-invasion. Malignant early-invasion stage is distinguished by the downregulation of miR-147a and the overexpression of miR-155-5p, miR-423-3p and miR-34a-5p. The identification of stage-specific miRNA molecules may facilitate their utilization as biomarkers for presymptomatic OSCC diagnosis.

Whey protein was pretreated with multi-frequency ultrasound in mono, dual, and tri-frequency modes. The effect of multi-frequency ultrasound pretreatment on the hardness, chemical forces, and microstructure of acid-induced whey protein gel was investigated. Whey protein gels pretreated with dual and tri-frequency ultrasound showed higher hardness (p3.35 mm) in those samples were resisted to gastric digestion. Moreover, the tri-frequency ultrasound pretreatment of whey protein gel released the least free amino group during gastric digestion. In contrast, whey protein gel with the mono-frequency ultrasound pretreatment released the highest amount of free amino acid group during intestinal digestion. Findings from this study suggested that gel hardness and network density could modulate the digestion behaviors of protein gels.

Yttria stabilized zirconia materials are frequently used in mechanical engineering and biomedical applications. Demanding loading conditions require materials combining a high level of strength and fracture toughness. A ready to press alumina doped 2 mol% yttria stabilized zirconia powder was shaped by axial pressing and sintering in air at 1250 - 1500 °C for 2 h. The best combination of strength and toughness value of 7.8 MPa√m. Materials sintered in the middle of the chosen temperature range combine fully density, high transformability and small grain size. Toughness measurements by direct crack length measurements delivered unrealistically high fracture toughness values.

The basic idea is to expand the completed zeta function $\xi(s)$ in MacLaurin series (infinite polynomial), which can be further expressed as infinite product (Hadamard product) by conjugate complex roots. Finally, the functional equation $\xi(s)=\xi(1-s)$ leads to $(s-\alpha_i)^2 = (1-s-\alpha_i)^2, i \in \mathbb{N}$ with solution $\alpha_i= \frac{1}{2}, i \in \mathbb{N}$, where $\alpha_i$ are the real parts of the zeros of $\xi(s)$, i.e., $s_i =\alpha_i\pm j\beta_i, i\in \mathbb{N}$. Therefore, a proof of the Riemann Hypothesis is achieved.

In this study, we utilized the UniProt database to extract breast cancer genes Total 2658 genes in Homo sapiens are obtain. Then 166 genes are taken and after delating duplicates 158 genes were remains. These genes were then analyzed for their biological pathways using Gene Ontology enrichment analysis through the Genecodis platform, resulting in the identification of 12 unique genes involved in disease pathways. Protein-protein interaction (PPI) network analysis was conducted using the STRING database and Cytoscape software, identifying ANLN,TREM1,LZTFL1,OXSM,PLA2G3,TAS2R13,ABCB10,ECT2,TRPC,BCAS3,PLA2G3,ZDHHC7 and ERBB2 as potential gene targets for breast cancer.

In this multicenter, retrospective study we evaluated the added value of magnetic resonance dispersion imaging (MRDI) to standard multiparametric MRI (mpMRI) for PCa detection. The study included 76 patients - 51 with clinically significant prostate cancer (csPCa), who underwent radical prostatectomy and had an mpMRI including dynamic contrast enhanced-MRI. Two radiologists performed three separate randomized scorings based on mpMRI, MRDI and mpMRI+MRDI. Radical prostatectomy histopathology was used as the reference standard. Imaging and histopathology were both scored according to the Prostate Imaging-Reporting and Data System V2.0 sector map. Sensitivity and specificity for PCa detection were evaluated for mpMRI, MRDI and mpMRI+MRDI. Inter- and intra-observer variability for both radiologists were evaluated using Cohen’s Kappa. On a per patient level, sensitivity for csPCa for radiologist 1 (R1) for mpMRI, MRDI and mpMRI+MRDI were 0.94, 0.82 and 0.94, respectively. For the second radiologist (R2) these were 0.78, 0.94 and 0.96. R1 detected 4% additional csPCa cases using MRDI compared to mpMRI and R2 detected 20% extra csPCa cases using MRDI. Inter-observer agreement was significant only for MRDI (Cohen’s kappa = 0.4250, p=0.004). The results of this study show the potential of MRDI to improve interobserver variability and the detection of csPCa.

Digitalization have force emerging markets (EMs) firms operating in resource-constrained environment to adopt market-driven strategies, particularly frugal innovation, to provide affordable, optimize processes and high-value solutions. However, understanding the mechanisms behind developing frugal innovation capability (FIC) at the firm level in diverse EMs remains limited. From the perspective of the Resource-based View, this study added to the existing body of knowledge by exploring how strategic orientation (entrepreneurial orientation (EO) and market orientation (MO)) and organizational ambidexterity (OA) impact the development of FIC in EMs. The study utilized a cross-sectional survey in data collection with 386 valid respondents from Tanzanian manufacturing firms to empirically validate our theoretical predictions. The results show that both EO and MO have strong and positive association with OA and the development of FIC in EMs. In addition, OA partially mediate the relationship of both EO and MO with the development of FIC. Furthermore, our results indicate that MO exerts a more significant impact on the development of FIC than EO in EMs. Managers of manufacturing firms in EMs can use these findings to review their strategic decisions and their exploitative and exploratory approaches to enhance supply chains, develop cost-effective technologies, and produce affordable offerings that cater to the preferences of price-conscious consumers in the digitalization age.

Drowsiness is a main factor for various costly defects, even fatal accidents in areas such as construction, transportation, industry and medicine, due to the lack of monitoring vigilance in the mentioned areas. The implementation of a drowsiness detection system can greatly help to reduce the defects and accident rates by alerting individuals when they enter a drowsy state. This research proposes an Electroencephalography (EEG) based approach for detecting drowsiness. EEG signals are passed through a preprocessing chain composed of artifact removal and segmentation to ensure accurate detection followed by different feature extraction methods to extract the different features related to drowsiness. This work explores the use of various machine learning algorithms such as Support Vector Machine (SVM) the K Nearest Neighbor (KNN) the Naive Bayes (NB) the Decision Tree (DT) and the Multilayer Perceptron (MLP) to analyze EEG signals sourced from the DROZY database, carefully labeled into two distinct states of alertness (awake, and drowsy). Segmentation into 10-second intervals ensures precise detection, while a relevant feature selection layer enhances accuracy and generalizability. The proposed approach achieves high accuracy rates of 99.84% and 96.4% for intra (subject by subject) and inter (cross-subject) modes, respectively. SVM emerges as the most effective model for drowsiness detection in the intra mode, while MLP demonstrates superior accuracy in the inter mode. This research offers a promising avenue for implementing proactive drowsiness detection systems to enhance occupational safety across various industries.

Rural landscapes are acknowledged for their potential to restore human health due to their natural characteristics. However, few studies have systematically analysed the impact of naturalness, landscape types, and combinations of landscape elements on restorativeness using both subjective and objective measurements. This study investigated the restorative effects of various rural landscapes in Guangzhou, employing electroencephalography and eye-tracking technologies to record physiological responses and using the Restorative Components Scale and the Perceived Restorativeness and Naturalness Scale to evaluate psychological responses. The results indicated the following: (1) There was a significant positive correlation between perceived naturalness and restorativeness, surpassing the impact of actual naturalness. (2) Different landscape types had a varying impact on mental health at the equal perceived naturalness. Natural forest landscapes and artificial forest landscapes exhibited the most substantial restorative effects among the natural and semi-natural landscapes examined, respectively, while settlement landscapes showed the highest restorative potential among the artificial landscapes. (3) Restorative properties varied across landscape elements, with identical elements yielding different benefits in different environments. Trees and water significantly enhanced restorativeness, whereas constructed elements such as roads and infrastructure reduced it. This study provides a scientific basis for restorative rural landscape design to enhance mental health and well-being.

Objective: To explore motivation to exercise, support, and self-management needs among people with Parkinson’s (PwP), family-members and physiotherapists. Methods: Purposeful and maximum difference sampling methods (age, sex, geographical setting, and disease severity) were employed. PwP and family-members were recruited through physiotherapy services and local support groups. Semi-structured interviews with 12 PwP, focus groups with four family-members, and five physiotherapists were conducted. Interview guides were informed by patient-public input, and a recent systematic review. Interviews were recorded, transcribed, and analysed using thematic analysis. Results: Four common themes emerged: 1) The value of an intrinsic connection with exercise: but there are challenges. A greater connection to exercise led to long-term compliance. 2) Tailoring exercise is essential. Preferred exercises and environments were mixed, with differences emerging between sexes. 3) Physiotherapists’ aim to only maintain physical function led to frustration. Limited self-management opportunities, stigma and dehumanisation were discussed. 4) Non-motor symptoms, stigma, fear, and determination. Apathy, pain, and low mood were discussed. Conclusion: Exercise provided physical, emotional, and social rewards. Supports are necessary; however, challenges arise when PwP’s motivation are mismatched to family-members’ and physiotherapists’ goals. Co-created goals, tailored to their preferences, and exercise plans with supported self-management are recommended.

The application of adaptive antennas in wireless communication systems is examined in this review of the literature, with particular attention to how well they can transmit and receive signals by dynamically adjusting radiation patterns. The review covers a range of artificial intelligence (AI) approaches that have been used to enhance adaptive antenna performance in beamforming, channel estimation, interference mitigation, and spectrum utilization. Although adaptive antennas have the potential to improve wireless communication systems, the paper also discusses their limitations, including computational complexity, interference sensitivity, and the significance of precise channel state information (CSI) estimates.

Personalized cancer vaccines have emerged as a promising avenue for cancer treatment or prevention strategies. This approach targets the specific genetic alterations in individual patient’s tumors, offering a more personalized and effective treatment option. Previous studies have shown that generalized peptide vaccines targeting a limited scope of gene mutations are ineffective, emphasizing the need for personalized approaches. While studies have explored personalized mRNA vaccines, personalized peptide vaccines have not yet been studied in this context. Pancreatic ductal adenocarcinoma (PDAC) remains challenging in oncology, necessitating innovative therapeutic strategies. In this study, we developed a personalized peptide vaccine design methodology, employing RNA sequencing (RNAseq) to identify prevalent gene mutations underlying PDAC development in a specific patient solid tumor tissue. We performed RNAseq analysis for trimming adapters, read alignment, and somatic variant calling. We also developed a Python program, called GeneFinder, which validates the alignment of the RNAseq analysis. The Python program is freely available to download. Using chromosome number and locus data, GeneFinder identifies the target gene along the UCSC hg38 reference set. Based on the gene mutation data, we developed a personalized PDAC cancer vaccine that targets 100 highly prevalent gene mutations in the individual patient. We predicted peptide-MHC binding affinity, immunogenicity, antigenicity, allergenicity, and toxicity for each epitope. Then, we selected the top 50 and 100 epitopes based on our previously published vaccine design methodology. Finally, we generated pMHC-TCR 3D molecular model complex structures which are freely available fo download. The designed personalized cancer vaccine contains epitopes commonly found in PDAC solid tumor tissue. Our personalized vaccine was composed of neoantigens, allowing for a more precise and targeted immune response against cancer cells. Additionally, we identified mutated genes which were also found in the reference study where we obtained the sequencing data, thus validating our vaccine design methodology. This is the first study designing a personalized peptide vaccine using human patient data to identify gene mutations associated with the specific tumor of interest.

In this work, a GaN-on-Si quasi-vertical Schottky diode was demonstrated on a locally grown n-GaN drift layer. The diode achieves a high current density of 2.5 kA/cm², a specific on-resistance RON,sp of 1.9 mΩ.cm² despite the crowding effect in quasi-vertical device and on/off current ratio (Ion/Ioff) of 1010. Temperature-dependent current-voltage characteristics have been measured in the range of 300-433 K to investigate the mechanisms of leakage conduction in the device. At near zero bias, thermionic emission (TE) was found to dominate. At voltage range from -1 to -10 V, electrons gain enough energy to excite into trap states, leading to the dominance of Frenkel-Poole emission (FPE). For a higher voltage range (-10V to -40V), the increased electric field promotes electron hopping along the threading dislocations in the “bulk” GaN layers, and thus, Variable Range Hopping becomes the main mechanism for the whole temperature range. This work provides an in-depth insight into the leakage conduction mechanisms on vertical GaN-on-Si Schottky barrier Diodes (SBDs) grown by localized epitaxy.

This paper is showing the results of a study focused on the occurrence of failures in heterogeneous structural materials, especially on the issue of modelling the formation and propagation of cracks. Attention is paid to the application of the finite element method to various types of materials in order to find critical parameters determining the behaviour of materials during the damage process. Application of damage mechanisms and approaches to modelling the origin of crack propagation by modifications in FEM systems are presented. Furthermore, some practical applications are tested. The main effort is devoted to composites and searching for procedures when using cohesion models for their more accurate modelling, especially near the stress concentrators, or before the crack tip. The use of the cohesive approach of elements that represent the physical nature of energy release in front of the crack front has proven to be promising not only in the direct use of cohesive elements, but also in combination with modified methods of standard finite elements. The authors of this article use their long-term experience in the given issue, which they consistently confront with developments in this area in recent years.

Biosynthesis of C-dots using black mulberry fruit as a precursor for C-dot synthesis is a novel approach. We successfully synthesized C-dots from the fruit extract by employing a hydrothermal autoclave. This green synthesis method offers a sustainable and environmentally friendly route for C-dot production. Our results demonstrate the efficient degradation of pollutants under visible light irradiation, indicating the strong photocatalytic activity of the C-dots. This finding has implications for environmental remediation and the elimination of harmful substances, showcasing the potential practical applications of C-dots in addressing water contamination issues. We made an effort to identify the optimum doping concentration of C-dots and found that 0.2 mol% was the sweet spot. At this concentration, the photodegradation of pollutants reached an impressive 95.8%. The enhanced photocatalytic activity can be attributed to increased electron trapping due to more surface sites and different absorption wavelengths. This work contributes to the synthesis of carbon dots using black mulberry fruits as a sustainable precursor, as well as the comprehensive analysis of their optical and photocatalytic properties.

Abstract: (1) Background: The occurrence of youth aggression has aroused widespread concern, and we would like to explore the reasons for it from both the family and individual sides of the youth. This study constructed a chain mediation model based on Bandura's Social Learning Theory to explore the mechanism of internet gaming disorder and avatar identification in it; (2) Methods: We conducted a randomized survey involving 973 adolescents from various middle schools in China. We used the Harsh Parenting Scale, the Online Gaming Disorder Scale, the Incarnate Identity Scale, and the Buss-Perry Aggression Scale to conduct a randomized survey. Empirical analyses were conducted using SPSS 25.0 and Amos 24.0 software to examine the mediating role of online gaming barriers and avatar identity; (3) Results: There was a two-by-two significant positive correlation between harsh parenting, internet gaming disorder, avatar identification and aggression. The me-diation model between harsh parenting, internet gaming disorder, avatar identification and ag-gression was well fitted ( χ2/df=7.268, GFI=0.961, AGFI=0.923, IFI=0.970, CFI=0.970, TLI=0.951, RMSEA=0.080).; (4) Conclusions: Parental harsh parenting affects adolescent aggression not only through the separate mediating roles of internet gaming disorder and avatar identification, but also through the chain mediating roles of internet gaming disorder and avatar identification.

Background: Pulmonary embolism (PE) is a common multifactorial disease, but high-risk PE s a life-threatening condition associated with high mortality and morbidity and a heavy prognostic burden. Systemic thrombolysis is the first-line treatment approach for severe high-risk PE, but only few agents are indicated for use by international guidelines. Tenecteplase, a thrombolytic agent, is proven to reduce mortality and right ventricle dysfunction in high-risk PE, but is not approved yet by major recommendations. Aim: We present a series of cases of high-risk PE treated with Tenecteplase, with satisfying hemodynamic and clinical outcomes. Our goal is to encourage the usage of Tenecteplase with secure safety and efficacy in situations of unavailability of guidelines-approved fibrinolytic agents. Cases : Patients were included from January 2022 to January 2024. We report 3 cases of patients with high-risk PE treated with Tenecteplase where rtPA and other second-generations agents were not available. Clinical, biological and imaging data were detailed for each case. Conclusion: Tenecteplase was effective in our patients, without bleeding complications, and should be suggested in settings of unavailability of first-line recommended agents.

This study investigates the transformative impact of ChatGPT, a cutting-edge AI language model developed by OpenAI, on the financial sector. We explore ChatGPT's applications in financial analysis, focusing on its ability to perform tasks traditionally handled by human analysts. By creating a set of multi-step and advanced reasoning financial tasks, and establishing several task-specific evaluation metrics, our experimental results demonstrate that ChatGPT-4o can effectively perform both basic and some complex financial analysis tasks comparable to those executed by humans. However, our empirical evaluation reveals limitations in its performance on complex tasks that require comprehensive understanding and critical thinking. The study highlights the necessity for meticulous task formulation and robust evaluation to achieve consistent functionality. While ChatGPT enhances efficiency in financial analysis, its integration with human expertise remains crucial for effective decision-making. This study contributes to the understanding of AI's role in finance and provides insights into its potential and constraints, guiding future integration of AI technologies in financial practices.

Developing countries, including Small Island Developing States (SIDS) and Least Developed Countries (LDCs), are exceptionally vulnerable to climate change due to their distinct geographical and environmental characteristics. The impacts of climate change extend across their ecosystems, water resources, and vital economic sectors, such as food systems, health, education, and energy. Escalating sea levels and heightened salinity levels imperil freshwater reserves, while warmer ocean temperatures and acidification disrupt water demand, tourism, health services, and fisheries. Furthermore, developing countries bear the brunt of water shortages, flooding, and deteriorating water quality. Alarmingly, nearly two-thirds of the global population experience severe water scarcity for at least one month annually, with the majority residing in developing countries. By 2025, half of the world's population could inhabit areas grappling with water scarcity, predominantly concentrated in developing nations. In this context, innovative adaptation strategies, notably transformative wastewater projects, are imperative for fostering sustainability and resilience in these regions. However, significant barriers impede progress, creating a stark disparity between current financing capabilities and the imperative to fund water security initiatives. Compounding these challenges, developing countries find themselves ensnared in a burgeoning debt crisis, marked by escalating interest rates and inflation, which threatens to impede developmental progress and curtail investments in adaptation and mitigation endeavors. Consequently, there arises a critical necessity to harness innovative financial mechanisms to transform these debts into assets that facilitate climate action. This paper meticulously explores the potential of debt-for-climate swaps as a catalyst for advancing transformative wastewater projects, with a focused lens on their strategic deployment to underpin critical initiatives. Through rigorous examination of case studies and empirical evidence, the paper elucidates how debt-for-climate swaps can effectively bolster sustainable wastewater management systems in developing countries. Furthermore, it delineates best practices for leveraging these mechanisms to fortify environmental sustainability and resilience. Additionally, the paper delineates the roles and responsibilities of key stakeholders, including governments, policymakers, the private sector, communities, and climate financial institutions. By amalgamating theoretical insights with tangible examples, this paper furnishes a comprehensive framework for harnessing debt-for-climate swaps to enhance water security and resilience in developing countries. It endeavors to furnish actionable strategies for policymakers, practitioners, and stakeholders to navigate the complex terrain of climate change and engender sustainable development.

MATERIAL AND METHOD: This is an observational analytical cohort study carried out on a total of 160 patients, divided into 4 groups: healthy, with myopia, with primary open-angle glaucoma (POAG), and with POAG and myopia together. The study variables were Bruch's membrane opening area (BMO), the thickness of the RNFL and the RGC layer using the Glaucoma Premium program of the OCT Spectralis of Heidelberg. RESULTS: Regarding the analysis between age and the thickness of the RNFL, significant results were found in the healthy and glaucoma groups with myopia, for the rest of the groups, although there are no significant results, the correlation is negative, the older the younger is the thickness. Regarding intraocular pressure (IOP), significant results have been found in the nasal sector in the glaucoma group. In the glaucoma with myopia group, statistically significant results have been obtained when analyzing the thickness of the RNFL with refraction. In the analysis between the thickness of the RNFL and the RGC layer, significant results were obtained in the myopia and glaucoma groups; in the group of glaucoma with myopia, it occurs in the superior temporal sector. CONCLUSION: In the case of myopic patients with glaucoma, damage at the functional, both the thickness of the RNFL, the RCG and the BMO-MRW are greater than other groups. All these alterations can be evaluated and monitored with the OCT Spectralis thanks to the programs it includes, making this instrument one of the best for an early diagnosis of glaucoma and the damage produced in myopic patients.

In the realm of digital transformation, effective leadership and motivation are pivotal for organisations navigating the complexities of today’s systems. This study explores the intersection of intrinsic motivation and mental well-being among mature students—an analogy that sheds light on strategies applicable to organisational contexts. In developed nations like the UK, mental health for mature students is increasingly recognised as a crucial component of their educational journey. Mature students, who typically enrol in higher education after an educational gap and upon turning 21, often face specific challenges that can impact their mental well-being while pursuing academic goals. The primary objective of our study was to assess the relationship between intrinsic motivation and the mental well-being of mature students. Our research revealed a significant positive correlation between these two variables. Confirmatory factor analysis (CFA) results supported a one-factor solution for the sample data from mature students. Additionally, we found that intrinsic motivation is a significant predictor of the mental well-being of mature students. Recognising and addressing the unique difficulties individuals encounter and providing appropriate support can enhance their well-being and contribute to the overall success of the higher education community.

The object of study in the article is the auxiliary variable-frequency electric drive onboard of an AC electric locomotive, and more precisely, the structure and parameters of the control system of the active corrector of power factor (ACPF), which is part of the electric drive, as a unit that ensures electromagnetic compatibility of the electric drive with other equipment on board the electric locomotive and catenary and its energy efficiency. The topic of the article's research is the substantiation of the structure of the ACPF control system, the choice of types (transfer functions) of controllers and the calculation of their parameters. The research method chosen was the calculation of the transfer functions of the controllers of the cascaded loops control system, followed by computer simulation using the OrCAD software. The results of simulating a cascaded two loops control system of an ACPF with different types of controllers are presented and analytically compared. It is concluded that it is advisable to use a cascaded two loops control system, containing a voltage control loop and a current control loop. The best results show the simulation when using an aperiodic current controller (A-controller) and a proportional-integral voltage controller (PI-controller). The data obtained serve as the basis for further improvement of the operation of the ACPF by enhancing and optimizing the settings, structure and algorithms of the control system, which, with the selected hardware of power converter unit, will allow the ACPF to perform its functions with a high degree of reliability in an entire range of disturbing and control influences.

The monosodium glutamate (MSG) that elicits umami taste, one of the five primary tastes, has been shown to be a helpful tool in reducing salt intake and improving eating patterns. This study aimed to investigate how MSG taste is perceived and evaluated in a sample of adult participants living in Austria, taking into account their sex, education, and travel habits. A Text Semantic-mining Analysis (TSA) was conducted using data from 300 adults (152 males and 148 females, mean age = 26.71 years). They also performed an MSG and NaCl (salt) discrimination test and were further interviewed about taste preferences, travel habits, and type of residence. The results indicated that the taste sensation elicited by the MSG solution was complex, unfamiliar and had negative valence. The descriptor most commonly used for MSG was "sour," followed by "bitter" and "salty" for the whole group, but these differed by gender. Both males and females preferred the salt solution over MSG, with no significant difference between the two groups. Additionally, there were apparent differences in primary/spicy taste preferences between genders, as males tended to prefer spicy flavors while females favored sweet and salty tastes. This finding underscores the unexplored significance of umami taste, suggesting the importance of establishing an accurate notion in European culture and promoting its usage in food choices for better health.

To investigate the repair mechanism of umbilical cord mesenchymal stem cells (UC-MSCs) in hydrogel on pancreatic β-cells in diabetes, MIN-6 damaged by streptozotocin (STZ) in vitro was used in co-culture with UC-MSCs in hydrogel (UC-MSCs + hydrogel). It was found that UC-MSCs + hydrogel had a significant repair effect on injured MIN-6 cells, which was better than the use of UC-MSCs alone (without hydrogel). After repair, the expression of superoxide dismutase (SOD) and catalase (CAT) as well as the total antioxidant capacity (T-AOC) of the repaired MIN-6 cells were increased, effectively reducing the oxidative stress caused by STZ. In addition, UC-MSCs + hydrogel were able to curb the inflammatory response by promoting the expression of anti-inflammatory factor IL-10 and reducing inflammatory factor IL-1β. In addition, the expression of both nuclear antigen Ki67 for cell proliferation and insulin-related genes such as Pdx1 and MafA was increased in the repaired MIN-6 cells by UC-MSCs + hydrogel, suggesting that the repair effect promotes the proliferation of the injured MIN-6 cells. Compared with the use of UC-MSCs alone, UC-MSCs + hydrogel exhibited longer survival time of UC-MSCs, because the porous structure and hydrophilic properties of the hydrogel could affect the growth of cells and slow down their metabolic activities, resulting in a better repair effect on the injured MIN-6 cells.

Despite the availability of COVID-19 vaccines, the global pandemic remains a significant health challenge. Timely detection of the virus is crucial for effective containment efforts, prompting the exploration of machine learning (ML) models for COVID-19 detection. highlights the ongoing significance of COVID-19 as a global health crisis despite the availability of vaccines. To address the need for timely virus detection, the study explores the efficacy of machine learning (ML) models, including DNN, Voting, Bagging, SVC_rbf, SVC_linear, SVC_polynomial, and SVC_sigmoid, in predicting COVID-19 infection probability. With a dataset comprising 4000 samples split into 3200 for training and 800 for testing, the analysis reveals that DNN and Voting models demonstrate the highest performance, achieving an impressive accuracy of 89%. These results underscore the potential of ML techniques, particularly DNN and ensemble methods like Voting, in facilitating early COVID-19 detection and contributing to effective pandemic management. The study emphasizes the importance of continued research to further refine and optimize ML-based COVID-19 detection systems.

In the ground-based infrared optical remote sensing system, the target signal is very weak due to the dynamic strong light background and the movement of small and small targets. In order to improve the limit detection capability, the background suppression and target enhancement methods are required to be more suitable for this scene. To solve this problem, we first analyze the image features in the current scene, and propose a more perfect point target and noise model. Then, we propose a new WY distribution function based on the Fermi Dirac distribution function, and propose an anisotropic filtering method based on this function, and further suppress the back-ground through the difference results of two steps. Then, based on the distribution function, we further designed an energy concentration accumulation strategy in 9 scale directions, through which the SNR of the target is effectively improved and the suppression ability of the background is enhanced. Through quantitative and qualitative experimental analysis, compared with the same type of algorithms, the proposed method has better robustness against extremely weak targets and dynamic backgrounds.

This study focuses on the impact of collaboration on reducing price inflation in renewable energy supply chains. The study utilises in-depth interviews and theme analysis to reveal the complex dynamics of collaboration among stakeholders and the resulting implications for addressing difficulties in the renewable energy sector. Trust is a key factor in successful collaboration, whereas legal frameworks and policy incentives are important in influencing collaborative activities. Collaborative procurement, cooperative research and development, strategic alliances, and collaborative supply chain management are recognised as crucial measures for decreasing expenses, promoting innovation, and improving resilience in renewable energy supply chains. Nevertheless, in order to achieve successful collaboration, it is necessary to overcome obstacles such as lack of trust, uncertainty in regulations, and competitive dynamics. Creating a cooperative environment that prioritises common objectives, openness, and reciprocal advantages is crucial for fully harnessing the potential of renewable energy supply chains in promoting sustainable energy transitions. The report provides practical insights and recommendations for policymakers, industry professionals, and academics who aim to encourage collaboration and reduce price inflation in renewable energy supply chains.

Resistance to the existing drugs and increasing numbers of diseases result in identifying new drug candidates with new forms of activity. Marine sources of alkylglycerols such as the liver oil Shark (SLO) mixture of certain species or rat fish (elasmobranch fishes) contain high levels of these compounds as a mixture of few species varying by length and unsaturation or saturation of the alkyl chain. They have multiple biological activities such as hematopoiesis stimulation, lowering radiotherapy-induced injuries, reducing tumor growth and improving vaccination efficiency. The synthesis of two Alkylglycerols (AKGs) containing at least one alkyne moiety in the alkyl chain has been reported. In this work, we describe the synthesis of others unknown AKGs based alkyne 1-O-(hexadec-11’-ynyl)-sn-glycerol 4 and 1-O-(octadec-9’-ynyl)-sn-glycerol 5 as analogues of bioactive ether lipids found in the SLO mixture.

Over the last decade, a lot of research has been conducted on memristors. Most of this research focusses on using memristors for Artificial Intelligence (AI) applications and to fabricate non-volatile memory, but also the security aspects of memristors have been examined. The current study summarises and compares five reviews on the security aspects of memristors. These reviews cover two different perspectives: (1) security applications of memristors such as Physical Unclonable Functions (PUFs) or True Random Number Generators (TRNGs), and (2) potential threats when using memristors to train and store neural networks. The comparison of the reviews reveals that different sets of studies are included in the reviews and different characterisations of the studies are provided. This shows that different perspectives are necessary to get a comprehensive overview of the security aspects of memristors. By synthesising the perspectives of different reviews, this study helps to get such an overview.

The global steel industry is a major contributor to greenhouse gas emissions, and the adoption of green hydrogen in Direct Reduced Iron (DRI) production has the potential to significantly reduce CO2 emissions and support climate targets. This review explores the environmental, economic, technological, and social implications of using green hydrogen in DRI processes. The findings suggest that green hydrogen can reduce emissions by up to 90% compared to traditional methods, enhance energy security, and stimulate local economic development. However, high initial costs, energy efficiency concerns, and social acceptance issues remain significant barriers. The review also highlights the importance of integrating carbon capture and storage technologies, addressing logistical challenges, and considering the geopolitical implications of a shift towards green hydrogen. Green hydrogen presents a promising solution for decarbonising the steel industry but continued research, investment in infrastructure, and strategic planning are essential to overcome the challenges and drive widespread adoption.

Pepper leaf segmentation plays a crucial role in monitoring pepper leaf diseases in various backgrounds and ensuring the healthy growth of peppers. However, existing transformer-based segmentation methods suffer from computational inefficiency, excessive parameterization, and limited utilization of edge information. To tackle these challenges, we propose an adaptive multi-scale MLP framework, named AMS-MLP, which combines the multi-path aggregation module (MPAM) and the multi-scale context relation mask module (MCRD) to refine the object boundaries in pepper leaf segmentation. AMS-MLP consists of an encoder-based network, an adaptive multi-scale MLP (AM-MLP) module, and a decoder network. In the encoder network, the MPAM module effectively fuses five-scale features to generate a single-channel mask, improving the accuracy of pepper leaf boundary extraction. The AM-MLP module divides the input features into two branches: the global multi-scale MLP branch captures long-range dependencies between image information, while the local multi-scale MLP branch focuses on extracting local feature maps. Adaptive attention mechanism is designed to dynamically adjust the weights of global and local features. The decoder network incorporates the MCRD module into the convolutional layer, enhancing the extraction of boundary features. To verify the performance of the proposed method, we conducted extensive experiments on three pepper leaf datasets with different backgrounds. The results demonstrate mIoU scores of 97.39%, 96.91%, and 97.91%, as well as F1 scores of 98.29%, 97.86%, and 98.51%, respectively. Comparative analysis with U-Net and state-of-the-art models reveals that the proposed method dramatically improves the accuracy and efficiency of pepper leaf image segmentation.

The migration of gallstones from the gallbladder into the gastrointestinal tract has been recognized for over 100 years. The most common way gallstone reaches the digestive tract is through a fistula between the gallbladder and the duodenum. Large stones become impacted in the duodenum and result in gastric outlet obstruction (Bouveret's syndrome). In this report, we present a 81-years old patient with the cholecystoduodenal fistula, which was diagnosed by ultrasound and computed tomography and effectively surgically managed. Even Bouveret syndrome is rare, with a timely diagnosis followed with an appropriate treatment maybe we could decrease morbidity and mortality rates of this complication.

Hamman’s syndrome is a rare condition that mostly affects young males, often with a predisposition of asthma. It includes the presence of free air in the mediastinum and subcutaneous emfysema with no other underlying cause such as trauma or infection. It occurs spontanously and often in association with prolonged valsalva manouvre. This might explain why there are some cases of Hamman syndrome among young females giving birth. Here we present a case report of a 24-year- old primigravida with Hamman’s syndrome. She presented with symptoms a few hours after an uncomplicated vaginal delivery at 40+1 pregnancy week. The symptoms resolved spontanously after a few days of observation. We also give a systemic review of reported cases since 2000 to provide overview on pathomechanism, symptoms, diagnostics, treatment and management of this condition. Hamman’s syndrome is a rare, usually benign, but potentially serious complication that can occur during the second stage of labour. Diagnostics include inquiring about typical symptoms, clinical examination and chest x-ray or CT scan. Treatment is usually conservative with oxygen, bronchodilators and pain relief. The reccurence rate is low and there is no contraindication to vaginal delivery in future pregnancies. However, it is suggested that physicians and midwives be cautious and consider low threshold for instrumental delivery or caesarean section to avoid excessive valsalva manoeuvre.

The Shule River Basin (SRB) is a typical arid area in northwest China with a fragile ecology. Un-derstanding vegetation dynamics and its response to climate change and human activities provides essential information for ecological and environmental resource management. This study extracted fractional vegetation coverage (FVC) data from 2000 to 2019 using the Google Earth Engine plat-form and Landsat satellite images, employing trend analysis and other methods to examine spati-otemporal changes in vegetation in the SRB. Additionally, partial correlation and residual analysis were utilized to explore the response of FVC to climate change and human activities. The main results were as follows: (1) The regional average FVC in the SRB showed a significant upward trend from 2000 to 2019, increasing by 1.3 × 10−3 y–1. The area within 1 km of roads experienced a higher increase of 3 × 10−3 y–1, while the roadless areas experienced a lower increase of 1.1 × 10−3 y–1. Road construction from 2004 to 2006 significantly reduced the FVC within 200 meters of the roads. FVC spatial heterogeneity in the SRB is significant. (2) Partial correlation analysis shows that FVC has a strong positive correlation with precipitation and surface water area, with correlation coefficients of 0.575 and 0.744, respectively. There is a weak negative correlation between FVC and land surface temperature (LST). FVC changes are more influenced by precipitation than by LST. (3) Compared to climate factors, human activities have a greater impact on vegetation dynamics in the SRB. The contributions of climate change to vegetation recovery are increasing. (4) The area changes of dif-ferent land types are closely related to climate factors and human activities, reflecting their impact on vegetation. These results provide valuable information for ecosystem restoration and environ-mental protection in the SRB.

Teledermatology, the dermatological consultation and diagnosis via digital communication technologies, has become increasingly important in modern healthcare. The integration of teledermatology into clinical practice has gained significant interest, especially during the COVID-19 pandemic. Numerous studies have shown the effectiveness and reliability of teledermatology, with high levels of patient satisfaction and diagnostic accuracy. Pathpoint® eDerma is a cloud-based dermatology platform that aims to streamline the documentation and management of dermatological conditions. It offers several clinical benefits, including faster diagnosis, reduced unnecessary procedures, improved access to care, and cost savings. The platform follows a comprehensive risk management plan and complies with safety regulations. It undergoes regular recertification and reviews to ensure its safety and effectiveness. The clinical evaluation of eDerma confirms its ability to deliver significant clinical benefits while effectively mitigating risks. The benefit-risk ratio of eDerma is highly acceptable, supporting its use in clinical settings to enhance dermatological care.

The Interdisciplinary Canadian Collaborative ME Research Network (ICanCME), established in 2019, aims to foster research in Canada and contribute to finding the causes and possible treatments for myalgic encephalomyelitis /chronic fatigue syndrome (ME/CFS), thereby reducing the impact of ME/CFS on the health of Canadians. The main objectives of this paper are to suggest standards for ME/CFS research for the collection of data from participants (Recommended Data Elements) and to consider other factors, such as design, language, cultural issues, equity, and diversity. Consensus of the relevant contents of this research guideline was reached during the ICanCME working group meetings and were based on existing guidelines. Members of the working group contributed to guideline development based on their respective expertise. The proposed research guidelines could improve research quality and advance knowledge in the field of ME/CFS, and ultimately benefit ME/CFS patients.

Almost contact complex Riemannian manifolds, known also as almost contact B-metric manifolds, are equipped with a pair of pseudo-Riemannian metrics that are mutually associated with each other using the tensor structure. Here we consider a special class of these manifolds, those of the Sasaki-like type. They have an interesting geometric interpretation: the complex cone of such a manifold is a holomorphic complex Riemannian manifold (also called a Kähler–Norden manifold). The basic metric on the considered manifold is specialized here as a soliton, i.e. has an additional curvature property such that the metric is a self-similar solution of an intrinsic geometric flow. Almost solitons are more general objects than solitons because they use functions rather than constants as coefficients in the defining condition. A β -Ricci-Bourguignon-like almost soliton ( β is a real constant) is defined using the pair of metrics. The introduced soliton is a generalization of some well-known (almost) solitons (such as those of Ricci, Schouten, Einstein), which in principle arise from a single metric rather than a pair of metrics. The soliton potential is chosen to be pointwise collinear to the Reeb vector field, or the Lie derivative of any B-metric along the potential to be the same metric multiplied by a function. The resulting manifolds equipped with the introduced almost solitons are characterized geometrically. Appropriate examples for two types of almost solitons are constructed and the properties obtained in the theoretical part are confirmed.

It is challenging to precisely measure the slow interseismic crustal deformation rate from SAR data. The long-wavelength orbital errors, owing to the uncertainties in satellite orbit vectors, commonly exist in SAR interferograms, which degrade the precision of the InSAR products and become the main barrier of extracting interseismic tectonic deformation. In this study, we propose a novel temporal network orbital correction method that is able to isolate the far-fault tectonic deformation from the mixed long-wavelength signals based on its spatio-temporal characteristic. The proposed approach is straightforward in methodology but could effectively separate the subtle tectonic deformation from glaring orbital errors without ancillary data. Both synthetic data and real Sentinel-1 SAR images are used to validate the reliability and effectiveness of this method. The derived InSAR velocity fields clearly present the predominant left-lateral strike-slip motions of the Tuosuo Lake segment of the Kunlun fault in western China. The fault-parallel velocity differences of 5-6 mm/yr across the fault between areas ~50 km away from the fault trace are addressed. The proposed method presents significantly different performance from traditional quadratic approximate method in the far-field. Through the implementation of the proposed method, the RMSE between the LOSGPS and our derived descending InSAR LOS is reduced to less than one-third of the previous study, suggesting its potential to enhance the availability of InSAR technology for interseismic crustal deformation measurement.

New advances in creating effective and affordable copper-based catalysts for water splitting represent a promising strategy for driving sustainable energy technologies. However, ma- terials containing copper are susceptible to corrosion and agglomeration. In this study, we describe a synthesis route to produce olamine-functionalized CuO nanoparticles using the Hot Injection method. Nanoparticles were obtained and subsequently deposited on FTO substrates, undergoing thermal treatment at 500°C and 600°C in atmospheres containing O2 and N2, aiming to enhance the adhesion of the active material to the substrate and modify its oxidation state. Microstructural properties were analyzed using XRD, SEM, and TEM, while electrochemical properties were studied using CV, LSV, and EIS. Electrodes treated thermally in an N2 atmosphere at 600°C exhibited higher ESCA, indicating a greater area of active sites exposed to surface reactions and, therefore, superior catalytic activity in Hy- drogen Evolution Reaction (HER). These findings show an interesting strategy for avoiding rapid electron-hole recombination and, consequently, CuO corrosion. For Oxygen Evolution Reaction (OER), samples treated thermally in O2 atmosphere at 500°C and 600°C appear to be more effective.

Background Neisseria gonorrhoeae can acquire antimicrobial resistance (AMR) through horizontal gene transfer (HGT) from other Neisseria spp. such as commensals like Neisseria subflava. The prevalence of Neisseria subflava in the oropharynx is close to 100%. Low doses of antimicrobials in food could select for AMR in N. subflava, which could then be transferred to N. gonorrhoeae. In this study, we aimed to determine the lowest concentration of ciprofloxacin that can induce ciprofloxacin resistance (minimum selection concentration – MSC) in N. subflava. Methods Neisseria subflava Co000790/2 was serially passaged on GC agar plates containing ciprofloxacin concentrations ranging from 1:100- to 1:10,000-below its ciprofloxacin MIC (0.006 µg/ml) for six days. Results After 6 days of serial passaging at ciprofloxacin concentrations 1/100th of the MIC, 24 colonies emerged on the 0.06 µg/ml ciprofloxacin plate. Their ciprofloxacin MICs were between 0.19 to 0.25 µg/ml, and whole genome sequencing revealed a missense mutation T91I in the gyrA gene, which has previously been found to cause reduced susceptibility to fluoroquinolones. Conclusion The N. subflava MSCde novo was determined to be 0.06 ng/mL or 1:100 below the MIC. The implications of this finding are that the low concentrations of antibiotics found in certain environmental samples and even the food we eat may be able to select for ciprofloxacin resistance in N. subflava.

Vinyltriethoxysilane (VTES) was covalently bonded onto graphene oxide. Several methods, including X-ray photoelectron spectroscopy, Fourier transform infrared spectroscopy, and X-ray diffraction, were used to demonstrate the presence of VTES on the surface of graphene oxide and the creation of this covalent link. Following that, different quantities of the resultant vinyltriethoxysilane functionalized graphene oxide (VTESFGO) were mixed with polyvinyl alcohol (PVA) to form VTESFGO/PVA nanocomposite films (NCFs) with a 3-dimensional network structure. The creation of these NCFs and the presence of a three-dimensional network were confirmed by Fourier transform infrared spectroscopy. The three-dimensional network structure provided these NCFs extraordinary characteristics. The mechanical and thermal stability, oxygen transfer rate (OTR), and water resistance pressure (WRP) of the NCFs were investigated. The nanocomposite film (NCF) with the highest weight percentage of VTESFGO (3.0 wt.%) was 47.5% more thermally stable than pure PVA, with an oxygen transmission rate of 0.3 cm3/m2 per day and a water resistance pressure of 780 mm/cm2. However, as the weight percentage of VTESFGO increased, the mechanical strength of the NCFs decreased as a result of silanol self-condensation, resulting in particulate formation, as confirmed by scanning electron microscopy.

Infill well location optimization poses significant challenges due to its complexity and time-consuming nature. Currently, determining the scope of infill wells relies heavily on field engineers' experience, often using single indices such as remaining oil saturation or abundance of remaining oil reserves to evaluate the potential of remaining oil. However, this approach lacks effectiveness in guiding the precise tapping of remaining oil in ultra-high water cut reservoirs. To address this, our study comprehensively considers factors influencing the recoverable potential of remaining oil in such reservoirs. We characterize differences in reservoir heterogeneity, the scale of recoverable remaining oil reserves, water flooding conditions, and oil-water flow capacity to construct a quantitative evaluation index system for the recoverable potential of remaining oil. Recognizing the varying degrees of influence of different indices on the recoverable potential of remaining oil, we determine the objective weight of each evaluation index by combining an accelerated genetic algorithm with the projection pursuit model. This approach enables the construction of a recoverable potential index for remaining oil and forms a quantitative evaluation method for the recoverable potential of remaining oil in ultra-high water cut reservoirs. Subsequently, we establish a mathematical model for infill well location optimization, integrating and optimizing infill well location coordinates, well length, well inclination angle, and azimuth angle. Using the main layer sand body of an oilfield in Bohai as a case study, we conducted evaluations of remaining oil potential and infill well location optimization. Results demonstrate that the assessment of remaining oil potential comprehensively characterizes the influence of reservoir physical properties and oil-water diversion capacity on remaining oil potential across different regional positions. This evaluation can effectively guide the determination of infill well location ranges based on the evaluation results. Furthermore, further optimization of infill well locations can effectively enhance reservoir development outcomes.

Aiming at the problem of small size of some spots on apple leaves and the difficulty of accurate detection of spot targets brought by the complex background of the orchard, this paper takes the alternaria leaf spot, rust, brown spot, grey spot and frog eye leaf spot on apple leaves as the research object, and proposes a high-accuracy detection model YOLOv5-Res and lightweight detection model YOLOv5-Res4 are proposed. Firstly, a multiscale feature extraction module ResBlock is designed by combining the Inception multi-branch structure and ResNet residual idea. Secondly, a lightweight feature fusion module C4 is designed to reduce the number of model parameters while improving the detection ability of small targets. Finally, a parameter streamlining strategy based on optimized model architecture is proposed. The experimental results show that the performance of YOLOv5-Res model and YOLOv5-Res4 model is significantly improved, the [email protected] value is improved by 2.8% and 2.2% compared with YOLOv5s model and YOLOv5n model, respectively, and the size of YOLOv5-Res model and YOLOv5-Res4 model is only 10.8MB and 2.4MB, and the number of model parameters is reduced compared with YOLOv5s model and YOLOv5n model. counts are reduced by 22% and 38.3% compared to the YOLOv5s model and YOLOv5n model.

This paper addresses the cosmological constant problem, a significant discrepancy highlighted by \cite{Weinberg:1988cp} that underlines a fundamental inconsistency between quantum field theory (QFT) and general relativity (GR). QFT examines matter dynamics within Lorentzian spacetime under standard model symmetry, while GR uses Riemannian geometry but adapts it to ensure local Lorentzian behavior in small regions. Despite the experimental successes of both theories, a major challenge exists in theoretical understanding. This letter seeks to clarify this critical misunderstanding. As the universe cools from its hot beginning, the \(SU(3) \times SU(2) \times U(1)\) standard model gauge symmetry evolves. This symmetry breaks to \(SU(3) \times U(1)\) upon cooling at the electroweak scale and ultimately to \(SU(3)\) alone as temperatures approach near-absolute zero kelvin, facilitated by the experimental Meissner effect. This suggests that \(SU(3)\) symmetry forms the foundational "atoms" of vacuum energy. Calculating the number of \(SU(3)\) vacuum atoms across the universe results in a value that perfectly aligns the theoretical predictions with the observed vacuum energy densities, thereby resolving the cosmological constant problem. Since \(SU(3)\) atoms account for the cosmological constant, they must be unbreakable. The third law of thermodynamics, which states that it is impossible to reach absolute zero Kelvin, provides the protection that prevents SU(3) atoms from breaking at zero Kelvin. This reveals the connection between the third law of thermodynamics and the quark confinement. This leads to the \(SU(3)\) atom creating a mass gap within the universe. This solution serves as a symmetric evidence of gauge-gravity duality as well as gravity-superconductor duality.

The current 2019-2021 marine pharmacology literature review provides a continuation of previous reviews covering the period 1998 to 2018. Preclinical marine pharmacology research during 2019-2021 was published by researchers in 42 countries and contributed novel mechanism of action pharmacology for 171 structurally characterized marine compounds. The peer-reviewed marine natural products pharmacology literature reported antibacterial, antifungal, antiprotozoal, antituberculosis,and antiviral mechanism of action studies for 49 compounds, 87 compounds with antidiabetic and anti-inflammatory activities as well as affecting the immune and nervous system, while another group of 51 compounds demonstrated novel miscellaneous mechanisms of action, which upon further investigation, may contribute to several pharmacological classes. Thus, in 2019-2021, a very active preclinical marine natural product pharmacology pipeline provided novel mechanism of actions as well as new lead chemistry for the clinical marine pharmaceutical pipeline targeting the therapy of several disease categories.

As cardiovascular diseases continue to be a leading cause of mortality, recent-ly, wearable devices for monitoring cardiac activity have gained much interest among medical community. This paper introduces an innovative ECG monitoring system based on a single – lead ECG machine enhanced with machine learning methods. The system only processes and analyses the ECG data, but also predict potential heart disease at an early stage. The wearable device was built on the ADS1298 and a microcontroller STM32L151xD. A server module based on REST API architecture style was designed to fa-cilitate interaction with web-based segment of the system. The module is responsible for receiving data in real time from microcontroller and their deliver to web – based segment. Algorithms for analyzing ECG signals have been developed, including band filter artifact removal, K-means clustering for signal segmentation, and PQRST analysis. Machine leaning methods as Isolation Forest have been employed for ECG anomaly detection. Moreover, a comparative analysis with various machine learning methods, including lo-gistic regression, random forest, SVM, XGBoost, decision forest and, CNNs was conducted to predict cardiovascular diseases. Convoluted Neural Networks (CNN) showed an accu-racy of 0.926, proving high effectiveness in ECG data process.

Tryptophan, an essential amino acid in mammals obtained from the diet, is influenced by maternal diet, which impacts early life and development of offspring. This is studied under the Developmental Origins of Diseases and Health (DOHaD) concept, so factors from conception to early childhood affect health and disease susceptibility. Tryptophan is metabolized mainly through two pathways: serotonin (5-HT) and kynurenine. The kynurenine pathway, active in the brain, gut, liver, and placenta, breaks down over 95% of tryptophan and plays roles in inflammation, neurotransmission, immune responses, and immune modulation during pregnancy. The serotonin pathway uses up to 5% of Tryptophan, mainly in the gut, adipose tissues, pancreatic cells, and central nervous system, and regulates responses to environmental changes, including sleep, cognition, and feeding behavior. Key enzymes in these pathways include Trp-2,3-dioxygenase (TDO), indoleamine-2,3-dioxygenase (IDO) type1 (IDO1), and type 2 (IDO2) (kynurenine pathway), and tryptophan hydroxylase type 1 (TPH1) and type 2 (TPH2), serotonin pathway. The fetus-placental unit manages tryptophan metabolism. Serotonin and kynurenine are crucial for placental health and fetal development. Serotonin adjusts placental blood volume and aids neurodevelopment. Kynurenine metabolites protect the fetus from maternal immunity and offer initial neuroprotection. At birth, infants switch from placental nutrients to breast milk, rich in tryptophan and protective bioactive molecules. Tryptophan, solely from breast milk, is crucial for infants. Its levels are high in newborns (first three weeks, 2-4 times higher than in adults), gradually declining to adult levels by the fourth week. The highlights the importance of tryptophan for the serotonin and kynurenine pathways in fetuses, newborns and babies applied to the DOHaD concept.

Grey mullets comprise different species which represent the most ubiquitous teleost families in the planet coastal waters. They represent an important proportion of the Mediterranean lagoons production and have been recently considered a cultivated marine fish. This study aimed to explore the intestinal bacterial communities of 34 wild-caught grey mullets in order to study their composition and the variation during different seasons. Thirty four healthy wild-caught fish were sampled and the V3-V4 hypervariable regions of 16S rRNA were sequenced. Sixty prokaryotic phyla were identified and the most abundant ones were: Proteobacteria (mean relative abundance 35.4%±17.9), Actinobacteriota (mean relative abundance 16.4%±9.9) and Firmicutes (mean relative abundance 10.1%±10.9). Bacteria belonging to the phylum Chloroflexi were rele-vant in autumn, Spirochaetota, Verrucomicrobiota, Fusobacteriota and Cyanobacteria were particularly abundant in winter while Bacteroidota characterized summer fish. A total of three hundred thirty-two prokaryotic families were identified and the twenty-six most abundant ones were: Rhodociclaceae (Proteobacteria), Brevinemataceae (Spirocheaetota) and Fusobacteriaceae (Fusobacteriota) and the Staphylococcaceae (Firmicutes). This study sheds light on the ecology and high variation in the complex gut microbial community structure of Mediterranean grey mullets, the potential biotechnological role for fish nutrition and health and for preserving the aquatic habitat, a prerequisite for a sustainable aquaculture.

Fluidized beds are pivotal in the process industry and chemical engineering, with Computational Fluid Dynamics (CFD) playing a crucial role in their design and optimization. Challenges in CFD modeling stem from the scarcity or inconsistency of experimental data for validation, along with the uncertainties introduced by numerous parameters and assumptions across different CFD codes. This study navigates these complexities by comparing simulation results from the open-source MFIX and OpenFOAM, and the commercial ANSYS FLUENT, against experimental data. Utilizing a Eulerian-Eulerian framework and the kinetic theory of granular flow (KTGF), the investigation focuses on solid-phase properties through the classical drag laws of Gidaspow and Syamlal-O’Brien across varied parameters. Findings indicate that ANSYS Fluent, MFiX, and OpenFOAM can achieve reasonable agreement with experimental benchmarks, each showcasing distinct strengths and weaknesses. The study also emphasizes that both the Syamlal-O’Brien and Gidaspow drag models exhibit reasonable agreement with experimental benchmarks across the examined CFD codes, suggesting a moderated sensitivity to the choice of drag model. Moreover, the analysis were also carried out for 2D and 3D simulations, revealing that the dimensional approach impacts the predictive accuracy to a certain extent, with both models adapting well to the complexities of each simulation environment. The study highlights the significant influence of restitution coefficients on bed expansion due to their effect on particle-particle collisions, with a value of 0.9 deemed optimal for balancing simulation accuracy and computational efficiency. Conversely, the specularity coefficient, impacting particle-wall interactions, exhibits a more subtle effect on bed dynamics. This finding emphasizes the critical role of carefully choosing these coefficients to effectively simulate the nuanced behaviors of fluidized beds.

Objective: Daratumumab (Dara) is the first-in-class human-specific anti-CD38 mAb approved for treating multiple myeloma (MM). However, DARA-induced depletion of CD38high natural killer (NK) cells results in crippled antibody-dependent cellular cytotoxicity (ADCC), which limits its clinical detection and prediction for Dara efficacy. Therefore, we aimed to explore new NK subsets that could predict Dara efficacy and the underlying mechanism in Dara development. Methods: Multicolour spectral flow cytometry monitored changes in the immune cells of 13 paired RRMM patients. We used machine learning-driven clustering, FlowSOM (Flow Self-Organizing Maps), and dimensional reduction with tSNE (t-distributed Stochastic Neighbour Embedding) to identify unique cell populations pre- and posttreatment. Then, in vitro, fratricide and proliferation assays were performed to explore the function of new NK subsets against MM under Dara treatment. Results: The NK cells were divided into 25 subsets, among which we found that the percentages of KIR-NKP46+ NK cells decreased in paired RRMM patients compared to healthy controls but increased significantly post-Dara treatment. The fold change in KIR-NKP46+ after one dose of Dara treatment can predict whether the best response could be achieved within one month (ROC=0.86, p=0.011). Further in vitro experiments showed that KIR-NKP46+ NK cells are superior to KIR+ NK cells, cooperatively acting with daratumumab to sustainably eradicate MM cells. Meanwhile, apoptosis was significantly decreased, and the proliferation ability was relatively increased post-Dara treatment. Conclusion: In conclusion, the fold change in KIR-NKP46+ NK cell subsets after one dose of Dara treatment can predict the outcome of the curative effect, indicating a putative role of KIR-NKP46+ subsets against MM cells. The mechanism attributed to a relative expansion of the subset due to decreased apoptosis and increased proliferation ability.

Coronavirus disease 2019 (COVID-19) continues to cause morbidity and mortality worldwide; therefore, effective treatments remain crucial to controlling it. As interferon-alpha (IFN-α) and -beta (β) have been proposed as COVID-19 treatments, we sought to assess their effectiveness on respiratory, cardiovascular, neurological, and psychiatric signs and symptoms, as well as PASC and death, in hospitalized COVID-19 patients without multiple sclerosis (MS). Using a federated data research network (TriNetX), we performed a retrospective cohort study of hospitalized COVID-19 patients without MS who received IFN-α or -β treatment, comparing them to a similar cohort who did not receive treatment. Following propensity score-matched analyses, we demonstrate that hospitalized COVID-19 patients who were treated with IFN-α or -β had significantly higher odds of death. In contrast, there was no significant difference in any other outcomes between 1-30 days or 1 day to anytime afterward. Overall, hospitalized COVID-19 patients without MS who were treated with IFN-α or -β had similar short- and long-term sequelae (except for mortality) as those who did not receive treatment. The potential benefits of utilizing IFN-α or -β treatment as therapeutics remain to be realized, and our research highlights the need to explore repurposing drugs for COVID-19 using real-world evidence.

This study investigates the transformative impact of ChatGPT, a cutting-edge AI language model developed by OpenAI, on the financial sector. We explore ChatGPT's applications in financial analysis, focusing on its ability to perform tasks traditionally handled by human analysts. By creating a set of multi-step and advanced reasoning financial tasks, and establishing several task-specific evaluation metrics, our experimental results demonstrate that ChatGPT-4o can effectively perform both basic and some complex financial analysis tasks comparable to those executed by humans. However, our empirical evaluation reveals limitations in its performance on complex tasks that require comprehensive understanding and critical thinking. The study highlights the necessity for meticulous task formulation and robust evaluation to achieve consistent functionality. While ChatGPT enhances efficiency in financial analysis, its integration with human expertise remains crucial for effective decision-making. This study contributes to the understanding of AI's role in finance and provides insights into its potential and constraints, guiding future integration of AI technologies in financial practices.

We have investigated the influence of the relative proportions of glass formers in a series of lithium alumino-borosilicate glasses with respect to electrical conductivity () and glass transition temperature (Tg) as functions of glass structure, as determined by Raman spectroscopy. The ternary lithium alumino-borate glass exhibits the highest  and lowest Tg among all the compositions of the glass series, 30Li2O•3Al2O3• (67-x) B2O3•xSiO2. However, as B2O3 is replaced by SiO2, a shallow minimum in  as well as a shallow maximum in Tg are observed near x = 27, where the Raman spectra indicate isolated diborate/tetraborate/orthoborate groups are being progressively replaced by danburite/reedmergnerite-like borosilicate network units. Overall, as the glasses become silica-rich,  is minimized, while Tg is maximized. In general, these findings show correlations among Tg, (sensitive to network polymerization),  (proportional to ionic mobility), and the different borate and silicate glass structural units as determined by Raman spectroscopy. X-ray diffraction analyses demonstrate the absence of appreciable crystallinity in the glasses investigated. However, scanning electron microscopy (SEM) images of HF-etched samples showed that glasses, and especially the borate-rich compositions comprise two distinct glassy phases. The isolated phase is 25-100 nm in diameter, dispersed evenly in a glassy matrix.

The electronic properties of calcium intercalated graphite (CaC6) as a function of pressure are revisited using density functional theory (DFT). The electronic band structures of CaC6, like many other layered superconducting materials, display cosine-shaped bands at or near the Fermi level (FL). Such bands encompass bonding/antibonding information with a strong connection to superconducting properties. Using an hexagonal cell representation for CaC6, construction of a double supercell in the c-direction effects six-folding in reciprocal space of the full cosine function, explicitly revealing the bonding/antibonding relationship divide at the cosine midpoint. Similarly, folding of the Fermi surface (FS) reveals physical phenomena relevant to electronic topological transitions (ETT) with application of pressure. For CaC6, the peak value for the superconducting transition temperature, Tc, occurs at about 7.5 GPa, near to the observed pressure of the calculated ETT. At this pressure, the radius of the nearly spherical Ca 4s-orbital FS coincides with three times the distance from the G centre point to the Brillouin zone (BZ) boundary of the 2c supercell (which is equal to half the identically defined distance for the primitive rhombohedral BZ). In addition, the ETT coincides with alignment of the non-bonding (inflection) point of the cosine band (the point at which the sign of the cosine amplitude modulation is reversed) with the FL. At other calculated pressure conditions, the Ca 4s-orbital FS undergoes topological changes that correspond and can be correlated to experimentally determined changes in Tc. The ETT is a key mechanism that circumscribes the known significant drop in Tc for CaC6 as a function of increasing pressure.

Accurate forecasting of crop yields holds paramount importance in guiding decision-making processes related to breeding efforts. This study focused on the application of multi-sensor data fusion and machine learning algorithms based on unmanned aerial vehicles (UAVs) in wheat yield prediction. Five machine learning (ML) algorithms namely random forest (RF), partial least squares (PLS), ridge regression (RR), K-Nearest Neighbor (KNN) and eXtreme Gradient Boosting Decision Tree (XGboost) were utilized for multi-sensor data fusion, and three ensemble methods including the second-level ensemble methods (stacking and feature-weighted) and the third-level ensemble method (simple average) for wheat yield prediction. The 270 wheat hybrids were used as planting materials under full and limited irrigation treatments. A cost-effective multi-sensor UAV platform, equipped with red–green–blue (RGB), multispectral (MS), and thermal infrared (TIR) sensors, was utilized to gather remote sensing data. The results revealed that the XGboost algorithm exhibited outstanding performance in multi-sensor data fusion, with the RGB+MS+Texture+TIR combination demonstrating the highest fusion performance (R2=0.660, RMSE= 0.754). Compared with the single ML model, the employment of three ensemble methods significantly enhanced the prediction accuracy of wheat yield. Notably, the third-layer simple average ensemble method demonstrated superior performance (R2 = 0.733, RMSE= 0.668 t ha-1). It significantly outperformed both the second-layer ensemble methods of Stacking (R2= 0.668, RMSE= 0.673 t ha-1) and feature-weighted (R2= 0.667, RMSE= 0.674 t ha-1), thereby exhibiting superior predictive capabilities. This finding demonstrated that the third-layer ensemble method not only augments the predictive ability of the model but also fine-tuned the accuracy of wheat yield prediction through the employment of simple average ensemble learning. Consequently, it offers a novel perspective for crop yield prediction and breeding selection.

The present article describes the synthesis of an isonicotinate-derived meso-arylporphyrin, that has been fully characterized by spectroscopic methods (including fluorescence spectroscopy), as well as by elemental analysis and by HR-MS. The structure of a n-hexane monosolvate has been determined by single crystal X-ray diffraction analysis. The radical scavenging activity of this new porphyrin against the 2,2-diphenyl-1-picrylhydrazyl (DDPH) radical has been measured. Its antifungal activity against three yeast strains (C. albicans ATCC 90028, C. glabrata ATCC 64677 and C. tropicalis ATCC 64677) has been tested, using the disk diffusion and the microdilution methods. Whereas the measured antioxidant activity was low, the porphyrin showed moderate but encouraging antifungal activity. Finally, a study of its effect on the germination of lentil seeds revealed interesting allelopathic properties.

Entanglement is one of the most striking features in quantum systems. Today, quantum entanglement plays a vital role in quantum technology, the non-classical correlation of quantum entanglement is an important resource for quantum communication and quantum computing. Entanglement can be divided into two categories: between multi-particle systems and between various degrees of freedom in a single-particle system (intraparticle entangled states). This work first analogized the distinctions and connections between two kinds of entangled states. Then it designed three types of entanglement-swapping methods based on Bell state joint measurement, cross-Kerr medium, and linear optical elements for intraparticle entangled states by analogizing the entanglement swapping between multiple particles. Intraparticle entanglement swapping can be applied in super quantum dense encoding, quantum information transmission, and quantum teleportation. This work is beneficial for promoting the development of quantum information technologies.

Citrus fruits are extensively cultivated worldwide, with Italy and Spain being major producers. Southern Italy, particularly in Reggio Calabria, a typical citrus fruit is produced, namely, bergamot (Citrus bergamia Risso et Poiteau), known for its mysterious origins and exceptional quality essential oil protected by the EU's PDO designation. Despite historical challenges, bergamot has regained prominence for its nutraceutical potential, especially its flavonoid-rich juice, offering significant health benefits. However, little attention has been paid to understanding the qualitative and quantitative differences of bergamot juice in Calabrian production areas. For this reason, this work aims to investigate the quality characteristics of bergamot juice produced in different areas of Ca-labria sites. The results showed the best quality attributes of bergamot fruits harvested in the DPO area. In particular, higher levels of total soluble solids, stable acidity and higher juice were found. In addition, higher contents of ascorbic and citric acids, which are nutritionally valuable and tasteful, were found. The phenolic profile, characterized by the key compounds of bergamot, highlighted the better nutraceutical potential of the fruit grown in the DPO area.

Aluminum (Al), making up a third of the Earth's crust, is a widespread toxic contaminant, particularly in acidic soils. It impacts crops at multiple levels, from the cellular to the whole plant. This review delves into aluminum's reactivity, including its cellular transport, involvement in oxidative redox reactions, development of specific metabolites, and the influence of genes on the production of membrane channels and transporters, as well as its role in triggering apoptosis leading to senescence. It discusses the involvement of channel proteins in calcium influx, vacuolar proton pumping, the suppression of mitochondrial respiration, and the initiation of programmed cell death. At the cellular nucleus level, the effects of Al on gene regulation through alterations in nucleic acid modifications, such as methylation and histone acetylation, are examined. The review in addition outlines the pathways of Al induced metabolic disruption specifically citric acid metabolism, regulation of proton excretion, induction of specific transcription factors, modulation of Al-responsive proteins, changes of citrate and nucleotide glucose transporters and overall metal detoxification pathways in tolerant genotypes. It also considers the expression of phenolic oxidases in response to oxidative stress, their regulatory feedback on mitochondrial cytochrome proteins, and their consequences on root development. Ultimately, the review focuses on the selective metabolic pathways that facilitate Al exclusion and tolerance, emphasizing compartmentalization, antioxidative defense mechanisms, and the control of programmed cell death to manage metal toxicity.

Many studies on flexible strain and pressure sensors have been reported due to growing interest in wearable devices for healthcare. However, most are fabricated with multiple ingredients and complicated methods even though commercial textiles, such as cotton, nylon, polyester, and silk were used. Here we present flexible pressure and strain (motion) sensors prepared with only graphene oxide (GO) and commercial silk fabrics and yarns. The pressure sensors were fabricated by simply dipping the silk fabric into GO solution followed by thermal treatment at 400 °C for reduction of GO (rGO). The pressure sensors were made from rGO-coated fabrics, which were stacked in 3-, 5-, and 7-layers. The super-sensitivity of 2.58 ×103 kPa-1 at low pressure was observed in the 7-layer pressure sensor. The strain sensors were obtained from rGO-coated twisted silk yarns whose gauge factor was 0.307. Although this value is small or comparable to other sensors, it is appropriate for motion sensing. The results of this study show a cost-effective and simple method for the fabrication of pressure and motion sensors with commercial silk and GO.

In the intricate landscape of software development, the distinction between average and exceptional outcomes often hinges on the approach taken by the engineers involved. This paper explores the core aspects that characterize responsible and professional software engineering, emphasizing the importance of stakeholder engagement, innovative problem-solving, and adherence to standards and best practices. Through detailed real-life examples, we illustrate how professional engineers ensure the creation of robust, efficient, and adaptable software solutions. Additionally, the paper delves into advanced strategies for enhancing database security, including the implementation of Sealed Secrets in Kubernetes environments, continuous improvement, and future enhancements. The goal is to provide a comprehensive understanding of professional software engineering practices and their impact on delivering superior value to projects and organizations.

This study is the first to investigate the asymmetric effects of oil price volatility on stock returns for the Ho Chi Minh Stock Exchange (HOSE). We utilized weekly series of VN30-Index, WTI crude oil prices, geopolitical risks (GPR) index and gold prices spanning from 6 February 2012 to 31 December 2023 as data sources. Using a nonlinear Autoregressive Distributed Lag (NARDL) bounds testing approach, we found that in the short-term, oil price volatility has negative asymmetric effects on the market returns. Specifically, in the short-term, a one percent increase in oil price volatility immediately leads to a 2,6868 percent decrease in the market returns while a similar magnitude decrease in oil price volatility is associated with 6.3180 percent increase in the market returns. In addition, the results obtained from the NARDL model indicated that, in the long-term, the negative and positive changes of oil price volatility have significantly negative effects on the market returns. Finally, the findings derived from the error correction model (ECM) show that 98.21 percent of the disequilibria from the previous week are converged and corrected back to the long-term equilibrium in the current week.

Studies and opinion articles present unequal conclusions concerning empathy and morality, mainly because a somewhat heterogeneous and foggy conceptual approach prevails. This scoping review aims to analyse the relationship between empathy and morality and to present a novel conceptual model of empathy applied to moral decisions.A search in PsycINFO, Scopus and PubMed identified articles addressing the relationship between empathy and morality.Thirty-two articles were included. We find an absence of a consensual definition of empathy. It is commonly defined as a predominant emotional process and a factor of bias and prejudice in moral decisions.According to our conceptual model, empathy is a distinct, complex human neuropsychological capacity that mobilises and is dependent on the proper functioning of several brain areas and balances the scales on which are Emotion and Reason, enabling the most thoughtful moral decisions possible in diverse circumstances and contexts.

The collection of photo shoots during the systematic monitoring activities is useful to witness the ecological role of marine phanerogams as hosts for a rich variety of organisms in coastal and transitional waters. Cymodocea nodosa is present in the Taranto seas. In the Mar Piccolo, it reached high covers in short times, up to 100%, due to the bettering of the environmental conditions. The most recent observations showed that it offers welcoming towards several vertebrates and invertebrates, native and non-indigenous, as well as to micro and macroalgae. The NPPR funded activities will make these observations more robust and structural.

Candida auris is considered an emerging Candida species that has rapidly spread all over the world. The evidence of its origin and emerging resistance is still unclear. The severe infection by this species causes significant mortality and morbidity among the elderly and immune compressed individuals. Development of drug resistance is the major factor associated with therapeutic failure of existing antifungal agents. Previous studies addressed the antifungal resistance profile and drug discovery for C. auris. However, comprehensive coverage of these information in a single investigation yet to be available. In this review, we have mainly focused on recent development in therapeutic strategy against C. auris. Based on the available information, several differential approaches were discussed, including existing antifungal drugs, chemical compounds, essential oils, natural products, antifungal peptides, immunotherapy, antimicrobial photodynamic therapy, drug repurposing, and drug delivery systems. Among them, chemical medications, natural products, and antifungal peptides are the prime contributors. However, a limited number of resources are available to prove the efficiency of these potential therapies in clinical usage. Hence, we hope that the data gathered in this review can encourage in vivo studies and clinical trials.

In this research work the study of Buongiorno Model Darcy Forchheimer flow of Hybrid nanofluid flow on isothermal permeable stretch/shrink sheet with homogeneous and heterogeneous reaction is investigated. The effects of MHD, thermal radiation, viscous dissipation, Joule heating, and heat generation/absorption are taken into account. Hybrid nanofluids can act as a medium for heterogeneous-homogeneous reactions. The nanoparticles dispersed in the fluid can influence reaction kinetics and species transport, potentially, altering reaction rates and pathways. The applications of HNF in reactions, both homogeneous and heterogeneous, lie in leveraging their enhanced thermal properties, catalytic potential, and improved mixing capabilities to optimize reaction efficiency and control reaction conditions. Similarity transformation is used to convert a set of PDEs into a set of ODEs. HAM is used for the solution of the obtained ODEs system. In a heterogeneous reaction, the concentration gradient is amplified; in a homogeneous reaction, the impact is opposite. Moreover, at higher compositions of HNPs, the concentration and velocity rise while the temperature falls. Furthermore, as the values of homogeneous and heterogeneous reactions increase, the concentration falls. It is reliable with the idea that a greater reaction rate lowers the diffusion rate. Nevertheless, these parameters do not affect temperature or velocity.

Molecular transport and quantum tunneling of H2 and H2O molecules through nanoporous gra-phene is studied using computational modeling and first principles density functional theory. It is demonstrated that molecules with sufficiently high kinetic energies can tunnel through the na-nopores. It is also demonstrated that molecules can be trapped in front of the nanopore or behind it. These investigations help us learn the behavior of molecules in and around nanopores of gra-phene. They also help us learn fundamentals of molecular tunneling. We believe, nanoporous gra-phene can play important roles for gas separation and nanofiltration.

Low-frequency electromagnetic induction (EMI) is a non-invasive geophysical method that is based on the induction of electromagnetic (EM) waves into the subsurface to quantify changes in electrical conductivity. In this study we present an open (design details and software are accessible) and modular system for the collection of EMI data. The instrument proposed allows to adjust the separations between the transmitter and up to four receiving antennas as well as the acquisition frequency (in the range between 3 and 50 kHz) to permit measurements with variable depth of investigation. The sensor provides access to raw data and the software described in this study allows control of the signal processing chain. The design specifications permit apparent conductivity measurements in the range of between 1 mS/m and 1000 mS/m, with a resolution of 1.0 mS/m and with a sampling rate of up to 10 samples per second. The sensor allows for a synchronous acquisition of a time stamp and a location stamp for each data sample. The sensor has a mass of less than 5 kg, is portable and suitable for 1-person operation, provides 4 hours of operation time on one battery charge, and provides sufficient rigidity for practical field operations.

The limited understanding of how digital platforms, especially the Metaverse, influence public perception and attitude towards sustainable fashion in large scaled markets such as China highlights a critical gap. This study aims to examine the perspectives and perception of the Chinese public regarding sustainable fashion in the Metaverse. Through content analysis, this study evaluated 3,889 Weibo posts from Shanghai Fashion Week 2024 to identify key sentiments and discussions on sustainable and digital fashion. Results indicate that 76.08% of the conversations centered on sustainable fashion, reflecting strong public commitment to environmental issues. The findings highlight characteristics of public perception towards the utilization of immersive technologies and underscore the Metaverse's potential as a platform for fostering environmental consciousness in the fashion industry. This research provides crucial insights for fashion brands looking to integrate sustainability into their digital strategies, addressing a significant gap in current literature and offering practical guidance for leveraging digital platforms to promote sustainable consumption.

Alkaloid-based urea derivatives were produced with high yield through the reaction of anabasine and cytisine with isoxazolylphenylcarbamates in boiling benzene. Their antitumor ac-tivity in combination with the commonly used six anticancer drugs namely cyclophosphane, fluorouracil, etoposide, cysplatine, ribomustine with different mechanisms of action was investi-gated. Based on the quantum chemical calculations data and molecular docking, hypotheses have been put forward to explain their mutual influence when affecting on the С6 rat glioma model cells.

Urinary tract infections in kidney transplant patients are a real challenge. We conducted a retrospective observational study, enrolling all kidney transplant patients hospitalized for urinary tract infection (UTI), with the objective to evaluate the epidemiology, clinical sta-tus, therapeutic management, and clinical outcome of kidney transplant patients hospi-talized for urinary tract infection in a university hospital. From our real-life experience, infection with multidrug-resistant germs was confirmed as a risk factor for the severe evolution of the infection and at the same time the re-evaluation of immunosuppressive therapy could be an important therapeutic strategy in the course of infection. Prompt ini-tiation of empiric antibiotic therapy upon initiation of microbiological investigations could potentially reduce the risk of severe infection progression.

In modern warfare, the diversified tasks can sometimes be challenging to fulfill with a single projectile, necessitating the incorporation of cooperation constraints in trajectory planning. Limited by the stringent requirements of the launch platform and lower costs, the maneuverability and ability to adjust the flight time range of projectiles are weak. Therefore, the rationality of performance indicators and flight time coordination strategies is especially critical. To fully exploit the control capabilities of projectiles, a battlefield area segmentation method is proposed, differentiated performance indicators and a cooperative trajectory planning model that considers the entire range of projectiles are established. Considering the limitations of existing flight time coordination strategies in applications, targeted improvements have been made in different cooperation scenarios, and a bi-level adaptive cooperation strategy(BACS) with both optimality and flexibility is proposed. To rapidly determine feasible conditions, a simple and universal method for obtaining the capability ranges of cooperation is introduced. The multi-phase optimal control problems are transformed into nonlinear programming problems using the Radau pseudo-spectral method, and simulation results show that the proposed methods are capable of handling trajectory planning tasks in different cooperation scenarios. The improved strategies effectively enhance the planning success rate under relatively harsh conditions and BACS demonstrates good compatibility with the problems studied.

Purpose: This study aims to investigate the role of blockchain technology in enhancing supply chain transparency in Italy.Design/Methodology/Approach: A quantitative research approach was employed, with data collected through a structured questionnaire distributed to 240 companies across various industries in Italy. The questionnaire focused on assessing blockchain adoption rates, interoperability standards, stakeholder collaboration, regulatory support, cost considerations, and perceived impacts on supply chain transparency.Findings: The findings reveal high levels of blockchain adoption among surveyed companies, with strong positive correlations observed between blockchain adoption and interoperability standards, stakeholder collaboration, and regulatory support. However, challenges such as cost considerations and potential trade-offs between interoperability and transparency were also identified. Overall, blockchain technology shows promise in improving supply chain transparency in Italy.Research Limitations/Implications: This study is limited by its focus on a specific geographical context (Italy) and the use of self-reported data. Future research could explore the dynamics of blockchain adoption in other regions and employ objective measures to assess supply chain transparency.Practical Implications: The findings offer practical insights for businesses and policymakers aiming to leverage blockchain technology to enhance supply chain transparency and efficiency.Social Implications: Blockchain adoption has the potential to foster greater trust and accountability within supply chains, benefiting various stakeholders including consumers, producers, and regulators.Originality/Value: This study contributes to the growing body of literature on blockchain technology in supply chain management, particularly within the Italian context, by providing empirical evidence on its adoption and impact on transparency.Limitations: Despite the robust methodology employed, limitations include the reliance on self-reported data and the exclusion of qualitative insights.

Abstract Background: Atrazine, a widely used herbicide, is recognized for its potential endocrine-disrupting effects, though its direct link to diabetes has not been extensively studied. Objectives: This study investigates the relationship between atrazine exposure and diabetes incidence, especially in agricultural settings, and examines how socioeconomic factors like income and education levels influence diabetes risk. Methods: A retrospective cohort study was conducted using data from 67 counties in Pennsylvania from 2011 to 2019. Atrazine exposure was measured in local water sources and correlated with diabetes data from the Pennsylvania Behavioral Risk Factor Surveillance System (BRFSS). Socioeconomic data were sourced from U.S. Census Bureau records, with logistic regression used to adjust for confounders such as age, race, and lifestyle. Results: Findings showed a significant association between high atrazine exposure and increased diabetes incidence, even after adjusting for confounders. Discussion and Conclusion: The study highlights a significant health risk posed by atrazine, particularly for socioeconomically disadvantaged groups. It underscores the need for stricter atrazine regulation and public health interventions to mitigate its impact. Further research is crucial to understand how atrazine affects glucose metabolism and diabetes development. Implementing stricter environmental controls and educational initiatives could reduce the diabetes burden in impacted communities.

Recently, microfluidics deformability cytometry has emerged as a powerful tool for high throughput mechanical phenotyping of large populations of cells. These methods characterize cells by their mechanical fingerprints through exerting hydrodynamic forces and monitoring the resulting deformation. These devices have shown great promise for label free cytometry, yet there is a critical need to improve their accuracy and reconcile any discrepancies with other methods, such as atomic force microscopy. In this study, we employ computational fluid dynamics simulations and uncover how the elasticity of frequently used carrier fluids such as methyl-cellulose dissolved in phosphate-buffered saline is significantly influential to the resulting cellular deformation. We conducted CFD simulations that are conventionally used within the deformability cytometry field, which neglect fluid elasticity. Subsequently, we incorporated a more comprehensive model that simulates the viscoelastic nature of the carrier fluid. A comparison of the predicted stresses between these two approaches underscores the significance of the emerging elastic stresses in addition to the well-recognized viscous stresses along the channel. Furthermore, we utilize a two-phase flow model to predict the deformation of a promyelocyte (i.e. HL-60 cell type) within a hydrodynamic constriction channel. The obtained results highlight a substantial impact of the elasticity of carrier fluid on cellular deformation and raise questions about the accuracy of mechanical property estimates derived by neglecting elastic stresses.