Often, in biology, we are faced with the problem of exploring relevant unknown biological hypotheses in the form of myriads of combination of factors that might be affecting the pathway under certain conditions. For example, Brancati et al.1 observe that mutations in poliovirus receptor related protein 4 (PVRL4), encoding cell adhesion molecule nectin-4, causes Ectodermal dysplasia-syndactyly syndrome. Interaction with cad- herins also implies an influence of nectin-4 on Wnt signaling, which plays a relevant role in limb development (Brancati et al.1). However, not much work has been done to explore the relation of Wnts and PVR family. In CRC cells treated with ETC-1922159, both were found up regulated. In a recent unpublished work in Open Science Framework, Sinha2, we had the opportunity to rank these unknown biological hypotheses for both up and down regulated genes at 2nd order level after drug administration. The search engine alloted high nu- merical valued rankings to some combinations of PVR-WNT, thus indicating a possibility of high combinatorial synergy also. The in-silico derived influences can be represented graphically as - PVR w.r.t WNT with PVR <- WNT9A; and WNT w.r.t PVR with WNT-7B/9A <- PVR and WNT4 <- PVRL2; In the light of the recent findings of PVR with IFN (interferon) and the known interactions between IFN and Wnts, there might be a possibilty to explore the bridge of PVR, IFN and WNTs. The 3 fold (PVR - IFN; IFN - WNT; WNT - PVR), 2 way cross family analysis might shed light on the possible combinations that might be of import. Here, we present a 2-way cross family analysis of multiple, such in-silico ranked 2nd order synergistic combinations, after ETC-1922159 treatment of CRC cells. Via this 2-way cross family analysis, we are able to discover through majority voting, the combinations that might of interest to biologists and also derive plausible influences of components of combinations among themselves. Note that these form biological hypotheses which indicate whether a particular combination and the direction of influence within the combination, exist synergistically in CRC cells. Wet lab tests will indicate the veracity of these combinations and if proven true, will lead to further study of mechanism between the components.

Often, in biology, we are faced with the problem of exploring relevant unknown biological hypotheses in the form of myriads of combinations of factors/genes/proteins that might be affecting the pathway under certain conditions. In colorectal cancer (CRC) cells treated with ETC-1922159, many genes were found up and down regu- lated, individually. A recently developed search engine ranked combinations of Inter- leukin (IL)-X (X, a particular gene/protein) at 2nd order level after drug administration. These rankings reveal which IL-X combinations might be working synergistically in CRC. If found true, oncologists can further test the combination of interest in wet lab and determine the mechanism of functioning between the IL and X. In this research work, we cover combinations of IL with nuclear factor κ B (NFκB), Potassium ion channel sub- family members (KCN), mucin (MUC), TP53, STAT, TNF receptor as- sociated factor (TRAF), STEAP4 metalloreductase, STEAP3 metalloreductase, ATP- binding cassette (ABC) transporters and tumor necrosis factor (TNF).

Often, in biology, we are faced with the problem of exploring relevant unknown biological hypotheses in the form of myriads of combinations of factors/genes/proteins that might be affecting the pathway under certain conditions. In colorectal cancer (CRC) cells treated with ETC-1922159, many genes were found up and down regu- lated, individually. A recently developed search engine ranked combinations of DNA repair gene-X (X, a particular gene/protein) at 2nd order level after drug administration. These rankings reveal which Wnt-X combinations might be working synergistically in CRC. If found true, oncologists can further test the combination of interest in wet lab and determine the mechanism of functioning between the Wnt and X. In this re- search work, we cover combinations of RAD with X-ray repair cross complementing (XRCC) family, 5’-3’ exoribonuclease 2 (XRN2), NFKB repressing factor (NKRF), B cell CLL/lymphoma (BCL) family, exosome component (EXOSC) family, FA comple- mentation group (FANC) family and XRCC with EXOSC.

The development of straddle-type monorail tour-transit systems (MTTSs) has received keen attention, raising the need for revisions on related design codes. This work presents a comprehensive assessment of the lateral stiffness limit of steel substructures in MTTSs. Firstly, a wind‒vehicle‒bridge coupling model was established, integrating multibody dynamics and finite element methods. This model was then validated against field test results, considering measured track irregularities as the excitation. Afterwards, a trend analysis and a variance-based sensitivity analysis was performed to investigate the effect of various factors on the dynamic response of MTTS. Referring to existing specifications, an evaluation index for the lateral stiffness of pier columns and track beams was proposed. Results indicate that the pier height significantly impacts the lateral displacement of the pier top, accounting for 87% of the first-order sensitivity index and 75% of the total sensitivity index. In comparison, the lateral stiffness of track beams contributes over 70% to the maximum lateral acceleration and displacement at the mid-span. Finally, the lateral limited values for the displacement at the pier top and the deflection–span ratio of the track beam were determined as 8.04 mm and L/4200, respectively. These findings can serve as valuable references for future research and designs in this field.

The rapid advancements in satellite technology have led to a significant increase in high-resolution remote sensing (RS) images, necessitating advanced processing methods. Additionally, a patent analysis revealed a significant increase in deep learning and machine learning applications in remote sensing, highlighting the growing importance of these technologies. Therefore, this paper introduces the Kolmogorov-Arnold Network (KAN) model to remote sensing, aiming to enhance efficiency and performance in RS applications. We conducted several experiments to validate KAN's applicability, starting with the EuroSAT dataset, where we combined the KAN layer with multiple pretrained CNN models. The optimal performance was achieved with ConvNeXt, leading to the development of the KonvNeXt model. KonvNeXt was evaluated on the Optimal-31, AID, and Merced datasets for validation, and it achieved accuracies of 90.59%, 94.1%, and 98.1%, respectively. The model also showed fast processing speed, with the Optimal-31 and Merced datasets completed in 107.63 seconds each, while the bigger and more complicated AID dataset took 545.91 seconds. This result is meaningful since it achieved faster speeds and comparable accuracy compared to the existing study which utilized VIT and proved KonvNeXt's applicability for remote sensing classification tasks. Furthermore, we investigated the model's interpretability by utilizing Occlusion Sensitivity and by displaying the influential regions, it validated its potential use in a variety of domains including medical imaging and weather forecasting. This paper is meaningful in that it is the first use of KAN in remote sensing classification, proving its adaptability and efficiency.

The aim of this study was to synthetize a hydroxyapatite-capsaicin composite (HAp-CAP) and to evaluate its physicochemical data for further use in hydrogen peroxide in-office dental bleaching gel. The HAp-CAP was prepared by the precipitation method, performed in a 4:1 ratio using 3.2 g of nano-hydroxyapatite (nano-HAp) and 0.8 g of capsaicin (CAP) in 99.5% ethyl alcohol (q.s.p) and zirconia beads at SpeedMixer®. The HAp-CAP was characterized by morphological, spectroscopic, and thermal analyses. In the following, the HAp-CAP was added to an in-office dental bleaching to test its effect on reducing hydrogen peroxide diffusion into the pulp chamber, as well as the effect in the color alteration during dental bleaching. The HAp-CAP were suitably obtained by the precipitation method, and the results demonstrates that the thermal stability of the CAP is greatly improved after reaction of HAp and CAP. The HAp-CAP into a bleaching gel slightly decreased the amount of hydrogen peroxide inside the pulp chamber and didn’t interfering in the bleaching effectiveness of dental bleaching. The HAp-CAP synthesis and characterization were successfully carried out, and its use in dental materials is a challenging task that needs further study since its addition.

Zambia recently experienced extended hours of load shedding due to reduced hydroelectricity generation attributed to poor rainfall patterns. The inability to meet energy demand has influenced the government to consider alternative energy sources. This study aims to assess the economic feasibility of the eight sites using a 25-turbine layout for a 4 MW generic wind turbine at 130m hub height. An economic evaluation mathematical model was developed to analyze the economic feasibility using net present value, simple payback period, internal rate of return, and levelized cost of electricity (LCOE). The study shows that the Lusaka wind farm was the most economical with 386 GWh energy yield analysis (EYA), wind speed of 8 m/s, NPV of 316 million dollars, SPP of 2.9 years, IRR of 82%, and LCOE of $0.182/ kWh. The results also show that the other seven sites are economically feasible, with Petauke being the lowest. The study's economic sensitivity analysis conducted by varying the average electricity tariff shows that Lusaka was the most financially viable site. Therefore, policymakers should develop cost-reflective feed-in tariff (FiT) schemes and power purchase agreements for the country to have cost-reflective electricity tariffs that will motivate independent power producers to invest in Zambia.

Flood inundation causes socioeconomic losses for coastal tourism under climate extremes, progressively attracting global attention. Mapping, evaluating, and predicting the flood inundation risk (FIR) is significant for coastal tourism. The study develops a spatial tourism–aimed framework integrating a weighted k-Nearest Neighbors (WkNN), Geographic Information Systems, and flood-related spatially environmental criteria such as precipitation, elevation, soil, and drainage systems. These model inputs were standardized and weighted using distance, and integrated into WkNN to infer regional probability and distribution of FIR. Zhejiang province, China, was selected as a case study. The resulting map was pictured to denote the likelihood of the criteria at various risk categories, which was validated by historical Maximum Inundation Extent (MIE) extracted from World Environment Situation Room. The result indicates 80.59% of WkNN results reasonably confirm the MIE. Precipitation and elevation make a negative contribution to high-medium risk, and drainage systems positively alleviate the regional stress of FIR. The results can help stakeholders make suitable strategies to protect coastal tourism, and also weigh WkNN is superior to kNN in FIR assessment. The framework provides a productive way to yield a reliable assessment of FIR and can also be extended to other risk-related environmental studies under climate change.

Research background – In the conditions of a modern developed market economy, a rather important characteristic of the activities of a public company is its activity in the stock market (market activity), which involves the development of a dividend policy, which, on the one hand, should contribute to the achievement of the main goal of financial management - maximizing the material well-being of shareholders, and, on the other hand, take into account the interests of all other stakeholders interested in the activities of a public company (including potential investors) to ensure its sustainable development in the long term. Fulfillment of this requirement is possible only within the framework of a neutral approach to the implementation of the dividend policy of a public company.Purpose – The article is devoted to the consideration of the conceptual foundations for analyzing the sensitivity of key indicators of the activity of a public company in the stock market (market activity) to the main determining factors in the context of a neutral approach to the implementation of its dividend policy.Design/methodology/approach – The methodological basis of the study was the concept of a neutral approach to the dividend policy of a public company and the concept of sensitivity analysis, developed earlier by the author of this article.Findings – It is shown that the key indicators of the market activity of a public company include the dividend yield ratio, dividend coverage ratio, the expected price of an ordinary share, the dividend yield of an ordinary share and the quote ratio of an ordinary share (price and earnings ratio per ordinary share). An analysis of the sensitivity of these indicators to the main determining factors in the context of a neutral approach to the implementation of the dividend policy of a public company involves the construction of appropriate elasticity models that allow determining the change in key indicators of market activity depending on changes in the factors that determine them. The constructed elasticity models of the key indicators of market activity listed above can be used in predictive and analytical assessments of their changes, and also make it possible to identify the causes of these changes by calculating the impact on the specified elasticity of the determinants contained in their models, performed by the appropriate methods of factor analysis, under the conditions of a neutral approach to the implementation of the dividend policy of a public company.Originality/value – The author comes to the conclusion that the elasticity models developed by him for key indicators of the activity of a public company in the stock market in terms of the main factors determining them are a fairly effective tool for managing the market activity of a public company in the context of a neutral approach to the implementation of its dividend policy.

Pseudomonas aeruginosa is an antibiotic-resistant priority pathogen as listed by the World Health Organization. P. aeruginosa, a gram-negative, rod-shaped bacterium, is frequently encountered in hospital settings as an opportunistic pathogen in nosocomial infections. P. aeruginosa has shown high antibiotic resistance, which has been attributed to several mechanisms, such as intrinsic, acquired, and adaptive resistance. This study investigates several genes which may be attributed to the acquisition of antibiotic resistance in the bacteria P. aeruginosa, as it transitions from its planktonic form to the more threatening and resistant biofilm form. Subsequently, the study assesses the comparative efficacy of three antibiotics, Ofloxacin (OFX), Tobramycin (TOB), and Ceftazidime (CAZ), in altering the expression levels of the identified multidrug efflux pump genes associated with the ability to extrude antibiotics from the cell when in the biofilm form. Efflux transporter gene expression in P. aeruginosa was conducted via extraction of total RNA in planktonic and biofilm- both untreated and treated - with Tobramycin (TOB), Ofloxacin (OFX), and Ceftazidime (CAZ). Real-time quantitative reverse transcriptase PCR was employed to analyze alterations in expression levels of the Mex A, Mex B, Mex X, Mex Y, OprM, and RPSL genes in the collected samples. In samples where no antibiotics were administered, an increase in expression of the Mex B efflux pump gene was observed compared to all other efflux pump genes in the biofilm form, providing multidrug resistance when active. The gene of interest, Mex B, was assessed for antibiotic resistance by P. aeruginosa culturing in the planktonic and biofilm forms with simultaneous treatment with TOB, OFX, and CAZ. Of the three antibiotics used, OFX showed more effectiveness in preventing biofilm growth by reducing the expression level of the Mex B efflux pump gene in the biofilm form, making P. Aeruginosa more antibiotic sensitive to OFX. TOB has similar results compared to OFX but was slightly less effective in reducing the expression of Mex B. Conversely, CAZ was not effective in reducing expression of the Mex B gene in the biofilm form or the planktonic form and was determined to be ineffective at eradicating the organism.

Metformin, the treatment of first choice in type 2 diabetes (T2D), is known to mainly act by decreasing endogenous glucose production (EGP) in the liver. Paradoxically, in the last decade several reports documented increased EGP after metformin treatment. This increase, was often attributed to pronounced rises in glucagon, consistent with counter-regulatory response to the glucose lowering effect of metformin. However, considering that hyperglucagonemia, but not hypoinsulinemia, is a main driver of EGP in T2D, increased EGP should have been a common finding. This observation, together with the finding that metformin antagonizes glucagon effects on energy expenditure and protein synthesis, concurrently to its effects on EGP and the emerging evidences demonstrating increased branched chain and gluconeogenic amino acids in response to metformin treatment may points to a liver alpha-cell skeletal muscle cross talk similar to that observed in the liver-alpha cell axis. Here, we provide a mechanistic perspective to this latter possibility, based on mechanistic studies of metformin’s transcriptional targets; retaining thus its glucagon antagonistic and presumably anti-gluconeogenic effects on liver and attributing the increase in EGP to renal gluconeogenesis. We finally discuss how increased EGP might reflect an adverse response to metformin treatment, providing support from clinical and epidemiological data.

The effects of transcranial magnetic stimulations (TMS) show that the human brain is impacted by some magnetic fields (EMFs). Moreover, after a delay, it produces potentials that reveal a subsequent processing of this impact. The human brain might also be sensitive to very weak magnetic fields of extremely low frequencies (vwEMFelf). Namely, to the vwEMelf produced by the brain of other persons when they process visual stimuli. In effect, two studies report that the event-related brain potentials (ERPs) that are evoked by presenting a picture to a participant can be modulated by simultaneously presenting a picture to a partner. To confirm it here, we followed most of the methods of these studies. We recorded the ERPs evoked by presenting, at each trial, the photograph of a face. Simultaneously and, most importantly, privately, we presented a partner with the same or with a different face photograph. ERPs of participants were found to depend on that sameness (p0.001), unbeknownst to them. These joint processing effects (JPEs), confirm a sensitivity of the human brain to the vwEMFelf produced by other brains.

Objectives: This paper presents a statistical review of modelling simulations for frequency and sensitivity of COVID-19 testing paradigms. Methods: We performed a review of preprints and published articles on PubMed from January 1, 2020 – March 1, 2021 using the search terms “COVID screening testing”, “COVID testing frequency”, “COVID testing frequency screening” and “SARS-CoV-2 testing frequency”.Results: Several authors’ conclusions support the claim that test frequency and test sensitivity both play a role in reducing SARS-CoV-2 transmission. We highlight the interplay between frequency of testing, test sensitivity and the speed at which test results are available in our review. Conclusions: Evidence suggests that sensitivity and frequency of testing both play a part in decreasing transmission of disease. We conclude that, overall, test sensitivity plays less of a role in reducing disease transmission in a population compared to the frequency of testing and how quickly test results are available.

While the link between Sensory-processing sensitivity (SPS) and internalizing symptoms has been well-established, a link to externalizing problems is still to be explored. This study aimed to further examine the relation between SPS and behavioral problems by testing the potential mediating roles of trait emotional intelligence (TEI) and decision-making styles. Pathway analyses were conducted on data from 268 community sample participants (Mage= 25.81, SD=2.41, 61.2% females). Results indicated gender differences in the pathway level outcomes of SPS, as well as potential partial mediators in men and women. SPS both directly and via the mediating effects of well-being factor (TEI) and avoidant decision-making influenced depression, regardless of gender. Direct effects on aggression were, however, obtained only in the male sample. Indirect effects of SPS on aggression were found in spontaneous decision-making for men, and in self-control and sociability factors of TEI for women. Directions for future research were discussed. sensory-processing sensitivity, depression, aggression, emotional intelligence, decision-making style, gender differences

Chemotherapy treatment based on Cisplatin (CDDP) is established as the drug of choice for head and neck squamous cell carcinoma (HNSCC). Malignant tumors respond to microenvironment alteration through a dynamic balance of mitochondrial fission and fusion. HNSCC is known to have hypoxic conditions, yet the effects and underlying mechanisms of hypoxia on chemosensitivity and mitochondrial dynamics remain unclear. We found that hypoxia promoted mitochondrial fission and CDDP sensitivity in HNSCC cells. Importantly, Mff was shown to be correlated with chemosensitivity in clinical samples of HNSCC that underwent a hypoxic condition. Hypoxia-inducible factor 1 α-subunit (HIF-1α) dramatically increased Mff transcriptional expression and directly bound to Mff. Hypoxia enhanced the release of reactive oxygen species (ROS) and upregulated the expression of Mff via HIF-1α in HNSCC cells. ROS depletion in HNSCC cells attenuated HIF-1α, Mff expression, and mitochondrial fission. Moreover, a knockdown of Mff suppressed hypoxia-induced mitochondrial fission and decreased CDDP chemosensitivity in vivo and in vitro. Our findings revealed that the hypoxia-induced release of ROS promoted mitochondrial fission and CDDP chemosensitivity via the regulation of HIF-1α/Mff in HNSCC cells, indicating that Mff may serve as a new biomarker to predict neoadjuvant chemosensitivity in HNSCC patients

Since the beginning of the year 2020, the global healthcare system has been challenged by the threat of the SARS-COV 2 virus. Molecular, antigen, and antibody testing are the mainstay to identify infected patients and fight the virus. Molecular and antigen tests that detect the presence of the virus are relevant in the acute phase only. Serological assays detect antibodies to the Sars-CoV-2 virus in the recovering and recovered phase. Each testing methodology has its advantages and disadvantages. To evaluate the test methods, sensitivity (percent positive agreement - PPA) and specificity (percent negative agreement – PNA) are the most common metrics utilized, followed by the positive and negative predictive value (PPV and NPV), the probability that a positive or negative test result represents a true positive or negative patient. In this paper, we illustrate how patient risk and clinical costs are driven by false-positive and false-negative results. We demonstrate the value of reporting PFP (probability of false positive results), PFN (probability of false negative results), and costs to patients and healthcare. These risk metrics can be calculated from the risk drivers of PPA and PNA combined with estimates of prevalence, cost, and Reff number (people infected by one positive SARS COV-2).

In this paper, a solenoid based linearly movable armature system is designed using robust control theory in order to improve the performance of the system. Reference track method is the best performance analysis for position control systems. Among the robust controllers, H infinity mixed-sensitivity and Mixed H 2 /H∞ with Regional Pole Placement Controllers are used to improve the performance of the system. Comparison of the proposed controllers for tracking a reference displacement signals (step and sine wave) and a promising simulation result have been obtained.

The present novel corona virus (2019-nCoV) infection has created a global emergency situation by spreading all over the world in a large scale within very short time period. The infection induced death rate is also very high. There is no vaccine or anti-viral medicine for such infection. So at this moment a major worldwide problem is that how we can control this pandemic. On the other hand, India is a high population density country, where the corona virus disease (COVID-19) has started to spread from $1^{st}$ week of March, 2020 in a significant number of COVID-19 positive cases. Due to this high population density human to human social contact rate is very high in India. So control of the pandemic COVID-19 in early stage is very urgent and challenging problem. Mathematical models are employed in this paper to study the COVID-19 dynamics, to identify the influential parameters and to find the proper prevention strategies to reduce the outbreak size. In this work, we have formulated a deterministic compartmental model to study the spreading of COVID-19 and estimated the model parameters by fitting the model with reported data of ongoing pandemic in India. Sensitivity analysis has been done to identify the key model parameters. The basic reproduction number has been estimated from actual data and the effective basic reproduction number has been studied on the basis of reported cases. Some effective preventive measures and their impacts on the disease dynamics have also been studied. Future trends of the disease transmission has been Predicted from our model with some control measures. Finally, the positive measures to control the disease have been summarized.

The effect of sensitivity points (sequence order and position of every element) of sequences following comparative optinalysis under two proposed mechanisms, the paranodic and synodic skewization, was studied to develop a modeling approach to comparative optinalysis of sequences. The results show that the outcomes of comparative optinalysis (similarity measurement) in a set of paranodically skewed sequences can be modeled deterministically by suitable line regression functions. The sensitivity points (nodes) of a sequence display two important and distinct zones, the K-zones and the B-zones. Continues paranodic skewization within these zones (KB zones) operates within probability space, but at hyperskewization level and at the K-zones only, the outcomes of comparative optinalysis operate outside the probability space. Moreover, the outcomes of comparative optinalysis by synodic skewization can be modeled deterministically by some regression line functions, but a general regression function cannot be identified. At certain limit of skewization value space, following paranodic and synodic skewization, the outcomes of comparative optinalysis at the left-sided sequence form a similar pattern with the right-sided sequence.

The cardioprotective effects of volatile anesthetics versus total intravenous anesthesia (TIVA) are controversial, especially in patients undergoing non-cardiac surgery. Using current generation high-sensitivity cardiac troponin (hs-cTn), we aimed to evaluate the effect of anesthetics on the occurrence of myocardial injury after non-cardiac surgery (MINS). From February 2010 to December 2016, 3555 patients without preoperative hs-cTn elevation underwent non-cardiac surgery under general anesthesia. Patients were grouped according to anesthetic agent; 659 patients were classified into a propofol-remifentanil total intravenous anesthesia (TIVA) group, and 2896 patients were classified into into a volatile group. To balance the use of remifentanil between groups, a balanced group (n=1622) was generated with patients who received remifentanil infusion in the volatile group, and two separate comparisons were performed (TIVA vs. volatile and TIVA vs. balanced). The primary outcome was occurrence of MINS, defined as rise of hs-cTn I ≥ 0.04 ng/mL within postoperative 48 hours. The secondary outcomes were 30-day mortality, postoperative acute kidney injury (AKI), and adverse events during hospital stay (mortality, type I myocardial infarction (MI), and new-onset arrhythmia). In propensity-matched analyses, the occurrence of MINS was lower in the TIVA group compared to the volatile group (OR 0.642; 95% CI 0.450-0.914; p = 0.014). However, after balancing the use of remifentanil, there was no difference between groups in the risk of MINS (OR 0.832; 95% CI 0.554-1.251; p-value = 0.377). There were no significant associations between the two groups in type 1 MI, new-onset atrial fibrillation, in-hospital and 30-day mortality before and after balancing the use of remifentanil. However, the incidence of postoperative AKI was lower in the TIVA group (OR 0.362; 95% CI 0.194-0.675; p-value = 0.001). After balancing the use of remifentanil, volatile anesthesia and TIVA showed comparable effects on MINS in patients undergoing non-cardiac surgery without preoperative myocardial injury. Further studies are needed on the benefit of remifentanil infusion.

Analysis of 49 strains of F. langsethiae originating from northern Europe (Russia, Finland, Sweden, UK, Norway, and Latvia) revealed the presence of spontaneous auxotrophic mutants that reflect natural intraspecific diversity. Our investigations detected that 49.0% of F. langsethiae strains were auxotrophic mutants for biotin, and 8.2% of the strains required thiamine as a growth factor. They failed to grow on vitamin-free media. For both prototrophic and auxotrophic strains, no growth defect was observed in rich organic media. Without essential vitamins, a significant reduction in the growth of the auxotrophic strains results in a decrease of the formation of T-2 toxin and diacetoxyscirpenol. In addition, all analysed F. langsethiae strains were distinguished into two subgroups based on PCR product sizes. According to our results, 26 and 23 strains of F. langsethiae belong to subgroups I and II respectively. We determined that the deletion in the IGS region of the rDNA of F. langsethiae belonging to subgroup II is linked with temperature sensitivity and causes a decrease in strain growth at 30 °C. Four thiamine auxotrophic strains were found in subgroup I, while 21 biotin auxotrophic strains were detected in subgroups II. To the best of our knowledge, the spontaneous mutations in F. langsethiae observed in the present work have not been previously reported.

The demonstration of compliance of rolling stock against disturbance limits for railway signaling, and in particular track circuits, is subject to a large deal of variability, caused by the diverse values of the electrical parameters of the railway line and resulting transfer functions, as well as operating conditions of rolling stock during tests. Instrumental uncertainty is evaluated with a Type B approach and shown to be much less than the experimental variability. Repeated test runs in acceleration, coasting, cruising, braking conditions are considered, deriving both max-hold (spread) and sample dispersion curves compared to the respective mean values (Type A approach to the evaluation of uncertainty). The major source of variability affecting a significant portion of the spectrum is caused by the superposed oscillations of the onboard LC filter, for which different choices of the transformation window duration are discussed. The test runs and the acquired data covered overall 1 day of tests along about 300 km of the Italian 3 kV DC railway network.

Deterministic simulations using the SWAN model were conducted along a stretch of the Tietê-Paraná waterway in the Ilha Solteira dam reservoir, São Paulo, Brazil, to assess the water waves generated by winds. Simultaneously employs a probabilistic metamodeling approach (PCE) and Sobol indices, to quantify uncertainty of the five involved physical parameters (wind velocity and direction, bottom friction (JONSWAP), whitecapping and depth-induced breaking index). In the deterministic part, Janssen’s formulation yielded the most successful and accurate estimations of wave heights. Probabilistic analysis underscored the importance of wind data and the whitecapping parameter in model calibration, with a significant influence of wind speed indicated by Sobol indices. The results not only refined the model calibration process, but also allowed the creation of contour maps for wave height and period that offer practical pre-project applications. This work advances understanding of wind wave hydrodynamics deterministically and probabilistically, with good concordance and performance indices compared to field data, despite being scarce.

In this paper, a continuous power management for a decentralized DC microgrid (DCMG) is proposed to achieve voltage regulation and power balance even under system uncertainty and voltage sensor failure. The DCMG system achieves a continuous power management through only the primary controller to reduce the computational burden of each power agent. To enhance the reliability and resilience of the DCMG system under voltage sensor failure, a DC bus voltage (DCV) sensor fault detection algorithm is proposed. In this algorithm, the DCV sensor failure is detected by comparing the measured DCV with the estimated DCV. When the power agent identifies the DCV sensor failure, it changes the operation properly according to the proposed control mode decision algorithm to maintain the stability of the DCMG system. When uncertain conditions such as the power variation of the distributed generations, sudden grid disconnection, DCV sensor failure, electricity price change, and critical battery status occur, the DCMG system is changed to transitional operation modes. These transitional operation modes are employed to transmit the power agent information to other agents without digital communication links (DCLs) and to accomplish power sharing even under such uncertain conditions. In the transitional operation modes of the DCMG system, the DCV levels are temporarily shifted to an appropriate level, enabling each power agent to detect the uncertainty conditions, and subsequently to determine its operation modes based on the DCV levels. Simulation and experimental results under different test conditions confirm the reliability and effectiveness of the proposed control scheme.

The conformity assessment of products or a measured value with the given standards is carried out based on the global risk of producers and consumers calculation. A product may conform to specifications but be falsely rejected as non-conforming. This is about the producer's risk. If a product doesn't meet the requirements but is falsely accepted as conforming, that poses a risk to the consumer. The conventional approach to risk assessment, which yields only a single numerical value for the global risk of producers and consumers, is naturally extended, and utilized for assessing risk in measurement models with linear regression. The outcomes of the two-dimensional extension, along a moderate scale, are the parabolas with an opening upwards. Risk surfaces were obtained through three-dimensional extension over the area limited by the moderate scale and guard band axes. Four models with different ranges of tolerance intervals were used to test this innovative method of risk assessment in linear regression. The corresponding standard measurement uncertainties are determined by applying a simplified measurement model with the use of comprehensive data on the measurement performance and by determining measurement uncertainty derived from consideration of the functional relationship obtained by linear regression analysis. Models that utilize information from linear regression analysis to determine measurement uncertainty are biased towards risks at the edges of the moderate scale. Testing the model's performances with metrics related to the confusion matrix, such as the F1 score, further substantiated this assertion. The diagnostic odds ratio has been proven to be extremely effective in identifying the curve along the guard band axis, along which the global risks of producers and consumers are at their lowest.

The uncertainty in the secondary path of an Active Noise Control (ANC) headphone affects the waterbed effect and stability of the feedback system. This study focuses on the uncertainty of the secondary path when real users wear headphones, and proposes a new uncertainty constraint based on the measured results of the secondary path transfer function under different wearing conditions of dummy head and limited subjects. This constraint and a cascaded second-order infinite impulse response filter with fixed coefficients are used to formulate a control strategic function, which is optimized by the Improved Grey Wolf Optimizer (IGWO) algorithm to obtain the optimal controller with better noise reduction performance. The proposed method and simulation model are validated based on the experimental test results. The results demonstrate that the safety factor and waterbed suppressing factor contained in the proposed uncertainty constraint ensures more stable noise reduction and effective suppression of the waterbed effect for new subjects without a priori data.

This study rigorously investigates the impact of COVID-19 on the Tunisian stock market volatility. The investigation spans from January 2020 to December 2022, employing a GJR-GARCH model, bias-corrected wavelet analysis, and an ARDL approach. Specific variables related to health measures and government interventions are incorporated. The findings highlight that confirmed and death cases contribute significantly to the escalation in TUNINDEX volatility. Interestingly, certain indices related to government interventions exhibit no substantial impact on volatility, indicating a weak resilience of the Tunisian stock market amidst the challenges posed by COVID-19. However, the application of the bias-corrected wavelet analysis yields more nuanced outcomes in terms of correlations of volatility to the same metrics. Notably, the study recognizes the potential influence of uncertainties linked to the duration of lockdowns on financial market volatility. The positive and long-term impact on volatility of the US Equity Market uncertainty and VIX is evident through the Autoregressive Distributed Lag Model (ARDL), whereas economic policy uncertainty shows no significant impact. This indicates a potential vulnerability of the Tunisian stock market to future shocks, emphasizing the need for government efforts to instill investor confidence. This comprehensive exploration contributes to our understanding of the dynamics between the pandemic’ metrics, government interventions, and financial market stability. Our findings provide valuable insights for implementing risk management strategies amid the COVID‐19 crisis.

We show that one of the two important uncertainty principles derived by Maccone and Pati \textit{[Phys. Rev. Lett., 2014]} can be derived for arbitrary maps defined on subsets of $\mathcal{L}^p$ spaces for $1< p<\infty$. Our main tool is the Clarkson inequalities. We also derive a nonlinear uncertainty principle for weak parallelogram spaces and Type-p Banach spaces.

The introduction of ChatGPT in academia has raised significant concerns, particularly regarding issues like plagiarism and the ethical use of generative text for academic purposes. Existing literature has predominantly focused on ChatGPT adoption, leaving a notable gap in addressing the strategies users employ to mitigate these emerging challenges. To bridge this gap, this research utilizes the Uncertainty Reduction Theory (URT) to formulate user strategies for reducing uncertainty. These strategies include both interactive and passive approaches. Concurrently, the study identifies key sources of uncertainty, which include concerns related to transparency, information accuracy, and privacy. Additionally, it introduces the concepts of seeking clarification and consulting peer feedback as mediating roles to facilitate reduced uncertainty. We tested these hypotheses with a sample of Indonesian users (N = 566) using structural equation modeling via Smart-PLS 4.0 software. The results confirm that interactive Uncertainty Reduction Strategies (URS) are more effective in reducing uncertainty when using ChatGPT compared to passive URS. Transparency concerns, information accuracy, and privacy concerns are identified as factors that increase the level of uncertainty. In contrast, consulting peer feedback is the most effective strategy for reducing uncertainty compared to seeking clarification at the individual-system level. Insights from the mediating effects confirm that consulting peer feedback significantly mediates uncertainty sources to reduce uncertainty. The study discusses various strategies for the ethical use of ChatGPT by users in the educational context and contributes significantly to theoretical development.

We study the work fluctuations in ergotropic heat engines, namely two-strokes quantum Otto engines where the work stroke is designed to extract the ergotropy (the maximum amount of work by a cyclic unitary evolution) from a couple of quantum systems at canonical equilibrium at two different temperatures, whereas the heat stroke thermalizes back the systems to their respective reservoirs. We provide an exaustive study for the case of two qutrits whose energy levels are equally spaced at two different frequencies by deriving the complete work statistics. By varying the values of temperatures and frequencies, only three kinds of optimal unitary strokes are found: the swap operator U1, an idle swap U2 (where one of the qutrits is regarded as an effective qubit), and a non trivial permutation of energy eigenstates U3, which indeed corresponds to the composition of the two previous unitaries, namely U3 = U2U1. While U1 and U2 are Hermitian (and hence involutions), U3 is not. This point has an impact on the thermodynamic uncertainty relations (TURs) which bound the signal-to-noise ratio of the extracted work in terms of the entropy production. In fact, we show that all TURs derived from a strong detailed fluctuation theorem are violated by the transformation U3.

This article provides an overview of multi-fidelity modeling trends. Fidelity in modeling refers to the level of detail and accuracy provided by a predictive model or simulation. Generally, models with higher fidelity deliver more precise results but demand greater computational resources. Multi-fidelity models integrate high-fidelity and low-fidelity models to obtain fast yet accurate predictions. Their growing popularity is due to their ability to approximate high-fidelity models with high accuracy and low computational cost. This work classifies publications in multi-fidelity modeling based on various factors, including application, surrogate selection, fidelity difference, fidelity combination method, field of application, and year of publication. The study also examines the techniques used to combine fidelities, focusing on multi-fidelity surrogate models. To accurately evaluate the advantages of utilizing multi-fidelity models, it is necessary to report the achieved time savings. This paper includes guidelines for authors to present their multi-fidelity-related savings in a standard, succinct, yet thorough way to guide future users. According to a select group of publications that provided sufficient information, multi-fidelity models achieved savings of up to 90% while maintaining the desired level of accuracy. However, the savings achieved through multi-fidelity models depend highly on the problem.

The battery energy storage systems (BESS) are most promising solution for increasing efficiency and flexibility of distribution networks (DNs) with significant penetration level of photovoltaic (PV) systems. There are various issues related to the optimal operation of DNs with integrated PV sys-tems and BESS that need to be addressed to maximize DN performance. This paper deals with the day-ahead optimal active-reactive power dispatching in unbalanced DNs with integrated sin-gle-phase PV generation and BESSs. The objectives are the minimization of cost for electricity, en-ergy losses in the DN and voltage unbalance at three-phase load buses by optimal managing of active and reactive power flows. To solve this highly constrained nonlinear optimization problem, a hybrid particle swarm optimization with sigmoid-based acceleration coefficients (PSOS) and cha-otic gravitational search algorithm (CGSA), called PSOS-CGSA algorithm, is proposed. A scenario based approach encompassing the Monte Carlo simulation (MCS) method with the simultaneous backward reduction algorithm is used for the probabilistic assessment of the uncertainty of PV generation and power of loads. The effectiveness of the proposed procedure is evaluated through the series test cases in a modified IEEE 13-bus feeder.

We consider a class of density-driven flow problems. We are particularly interested in the problem of the salinization of coastal aquifers. We consider the Henry saltwater intrusion problem with uncertain porosity, permeability, and recharge parameters as a test case. The reason for the presence of uncertainties is the lack of knowledge, inaccurate measurements, and inability to measure parameters at each spatial or time location. This problem is nonlinear and time-dependent. The solution is the salt mass fraction, which is uncertain and changes in time. Uncertainties in porosity, permeability, recharge, and mass fraction are modeled using random fields. This work investigates the applicability of the well-known multilevel Monte Carlo (MLMC) method for such problems. The MLMC method can reduce the total computational and storage costs. Moreover, the MLMC method runs multiple scenarios on different spatial and time meshes and then estimates the mean value of the mass fraction. The parallelization is performed in both the physical space and stochastic space. To solve every deterministic scenario, we run the parallel multigrid solver ug4 in a black-box fashion. We use the solution obtained from the quasi-Monte Carlo method as a reference solution.

The Impact Chain framework for risk assessment has proven to be a robust and effective It is very useful to set up the conceptual framework associated to a given risk and allows accommodating naturally the different components that shape that However, the operationalization of the impact chain may not be straightforward, in particular due to the inherent uncertainties associated to the selected indicators and the assigned In this paper, we introduce an extension to the Impact Chain framework that allows to consider uncertainties in the different components of the risk In the framework of the UNCHAIN project, a web-based tool has been developed to ease the task of implementing that The tool has been applied to a case study on the loss of tourist attractiveness due to heat stress conditions on the Balearic island, Spain, to illustrate how uncertainties in different components of the impact chain can affect the robustness of the final risk Also, the tool provides an estimate of the sensitivity of the final risk to each component, which can be used to guide risk mitigation Finally, a proposal for the validation of the risk assessment is presented.

Media coverage of Lyme disease (LD) has led to an increase in consultations for presumed LD in Europe. However, LD is confirmed in only 10-20% of patients, with a significant number remaining in a diagnostic dead-end. The objective of our study was to understand the genesis of the LD hypothesis in care pathways. In 2019, 30 patients from a prospective cohort consulting in the infectious diseases department at University Hospital in Marseille for presumed LD were recruited for semi-structured interviews. The inclusion criteria were: suffering from subjective symptoms for 6 months, no clinical or paraclinical argument suggesting current LD. The patients’ medical trajectories were collected using a biographical approach. A majority of participants were convinced they had LD despite the lack of medical evidence and the scepticism of their referring GP. The diagnosis of Lyme disease was primarily triggered by identification with clinical stories circulating in the media. Most of participants had conducted the diagnostic investigation themselves. Diagnostic wandering in the diagnostic process suggests a failure of modern medicine to propose solutions for medically unexplained symptoms. Clinicians should systematically explore patients’ etiologic representations in a patient-centred care approach.

Most quantum gravity theories endow space-time with a discreet nature by space quantization on the order of Planck length (lp ). This discreetness could be demonstrated by confirmation of Lorentz invariance violations (LIV) manifested at length scales proportional to lp. In this paper, space-time line elements compatible with the uncertainty principle are calculated for a homogeneous, isotropic expanding Universe represented by the Friedmann-Lemaitre-Robertson-Walker solution to General Relativity (FLRW or FRW metric). To achieve this, the covariant geometric uncertainty principle (GeUP) is applied as a constraint over geodesics in FRW geometries. A generic expression for the quadratic proper space-time line element is derived, proportional to Planck length-squared and dependent on two contributions. The first is associated to the energy-time uncertainty, and the second depends on the Hubble function. The results are in agreement with space-time quantization on the expected length orders, according to quantum gravity theories and experimental constraints on LIV.

Knowledge-based approaches are useful alternatives to predict the Failure Probability (FP) coping with the insufficient data, process integrity, and complexity issue in manufacturing systems. This study proposes a Fault Tree Analysis (FTA) approach as proactive knowledge-based technique to estimate the FP based maintenance planning with subjective information from domain experts. However, the classical-FTA still suffers from uncertainty and static structure limitations which poses a substantial dilemma in predicting FP. To deal with the uncertainty issues, a Fuzzy-FTA (FFTA) model was developed by statistical analysing the effective attributes such as experts' trait impacts, scales variation and, assorted membership and defuzzification functions. Besides, a Bayesian Network (BN) theory was conducted to overcome the static limitation of classical-FTA. The results of FFTA model revealed that the changes in decision attributes were not statistically significant on FP variation while BN model considering conditional rules to reflect the dynamic relationship between events had more impact on predicting FP. After all, the integrated FFTA-BN model was used in the optimization model to find the optimal maintenance intervals according to estimated FP and the total expected cost. As a practical example, the proposed model was implemented in a semi-automatic filling system in an automotive production line. The outcomes could be useful for upgrading the availability and safety of complex equipment in manufacturing systems.

What is the function of cognition? On one influential account, cognition evolved to co-ordinate behaviour with environmental change or complexity (Godfrey-Smith 1996). Liberal interpretations of this view ascribe cognition to an extraordinarily broad set of biological systems – even bacteria, which modulate their activity in response to salient external cues, would seem to qualify as cognitive agents. However, equating cognition with adaptive flexibility per se glosses over important distinctions in the way biological organisms deal with environmental complexity. Drawing on contemporary advances in theoretical biology and computational neuroscience, we cash these distinctions out in terms of different kinds of generative models, and the representational and uncertainty-resolving capacities they afford. This analysis leads us to propose a formal criterion for delineating cognition from other, more pervasive forms of adaptive plasticity. On this view, biological cognition is rooted in a particular kind of functional organisation; namely, that which enables the agent to detach from the present and engage in counterfactual (active) inference.

We propose a methodology for the quantification of model risk in the context of credit derivatives pricing and CVA, where the uncertain or unmodelled parameter is often the correlation between rates and credit. We take the rates model to be Hull-White (normal) and the credit model to be Black-Karasinski (lognormal). We show how highly accurate analytic pricing formulae, hitherto unpublished, can be derived for CDS and extended to address instruments with defaultable Libor flows which may in addition be capped and/or floored. We also consider the pricing of a contingent CDS with an interest rate swap underlying. We derive explicit expressions showing how to good accuracy the dependence of model prices on the uncertain parameter(s) can be captured in analytic formulae which are readily amenable to computation without recourse to Monte Carlo or lattice-based computation. In so doing, we take into account the impact on model calibration of the uncertain (or unmodelled) parameter.

We present a generalized state-space model formulation particularly motivated by an online scheduling perspective. Through these proposed generalizations, we enable a natural way to handle routinely encountered disturbances and a rich set of corresponding counter-decisions. Thereby, greatly simplifying and extending the possible application of mathematical programming based online scheduling solutions to diverse application settings.

Often, in biology, we are faced with the problem of exploring relevant unknown biological hypotheses in the form of myriads of combinations of factors/genes/proteins that might be affecting the pathway under certain conditions. In colorectal cancer (CRC) cells treated with ETC-1922159, many genes were found up and down regu- lated, individually. A recently developed search engine ranked combinations of ATP- binding cassette (ABC) transporters-X (X, a particular gene/protein) at 2nd order level after drug administration. These rankings reveal which ABC-X combinations might be working synergistically in CRC. If found true, oncologists can further test the com- bination of interest in wet lab and determine the mechanism of functioning between the ABC and X. In this research work, we cover combinations of ABC with ubiquitin- conjugating enzymes (UBE2), Interleukin (IL), BCL and Caspase (CASP).

Often, in biology, we are faced with the problem of exploring relevant unknown biological hypotheses in the form of myriads of combinations of factors/genes/proteins that might be affecting the pathway under certain conditions. In colorectal cancer (CRC) cells treated with ETC-1922159, many genes were found up and down regulated, individually. A recently developed search engine ranked combinations of Nuclear factor kappa-light-chain-enhancer of activated B cells (NFκB)-X (X, a particular gene/protein) at 2^nd order level after drug administration. These rankings reveal which NFκB-X combinations might be working synergistically in CRC. If found true, oncologists can further test the combination of interest in wet lab and determine the mechanism of functioning between the NFκB and X. In this research work, we cover combinations of caspase (CASP) with receptor interacting serine/threonine kinase (RIPK) family, mucin (MUC) family with RIPK, tumor necrosis factor (TNF) with NF-κB family and NF-κB-Inhibitor (NF-κB-I), NFκB-2/I with STAT family, IκB kinase ε (IKBKE) with STAT family, IKBKE with conjugal transfer protein (TRAF), ATP-binding cassette (ABC) domain transporters with NFκB, IKBKE with ubiquitination modifier enzyme and ubiquitination conjugating enzymes (UBA/UBE) and REL-A/B with NFκB .

Traumatic brain injury (TBI) affects millions of people each year. Previous studies using the central fluid percussion injury (CFPI) model in adult male rats indicated that elevated intracranial pressure (ICP) was associated with long-term effects, including neuronal cell loss and increased sensory sensitivity post-injury and secondary ICP elevation, that were not seen following injury alone. Investigations also indicated that Cathepsin B (Cath B), a lysosomal cysteine protease, may play a role in the pathological progression of neuronal membrane disruption; however, the specific impact of Cath B inhibition following CFPI remains unknown. Thus, the focus of this study was to evaluate the effects of Cath B inhibition via intracerebroventricular infusion of CA-074Me 2w following injury with or without secondary elevation of ICP. This was accomplished using adult male rats continuously infused with CA-074Me or 10% DMSO as a vehicle control for 2w following either sham injury, a CFPI only, or a CFPI with subsequent ICP elevation to 20mmHg. We assessed Cath B activity in the lateral neocortices and liver, evaluated protein levels of Cath B and Cath B binding partners AIF, Bcl-XL, and Bak. We also conducted histological analyses of total cell counts to assess for cell loss, membrane disruption, and Cath B localization. Finally, we investigated somatosensory changes with the whisker nuisance task. Overall, this study demonstrated that Cath B is not a direct driver of membrane disruption, however, administration of CA-074Me alters Cath B localization and reduced hypersensitivity, emphasizing Cath B being an important component in late secondary pathologies.

Background and Objectives: High job strain is worsening the outcome in coronary artery disease, CAD. Severe dissatisfaction on workplace, imbalance between high demands , and low rewards, are affecting the quality of life, and diminishing the duration of life, especially in CAD patients. Our study included two samples : CAD patients (experimental group), and without CAD patients, but with traditional risk factors (control group). The main objective of the study was the role played by high sensitivity C reactive protein, hs CRP, and microalbuminuria, as mediators of job strain and CAD relationship. Secondary objectives were the following: identification of the most vulnerable patients( with significant job strain) and intervention towards this category, for cardiovascular events prevention (primary or secondary). Materials and Methods: The utilized methods were the following : clinical exam; electrocardiogram; echocardiogram; non contrast enhanced computed chest tomography; laboratory findings ( lipid profile, glycemia, hs CRP, microalbuminuria); type D scale, DS 14; depression questionnaire, PHQ-9; and satisfaction with work scale, SWWS. Results: The highest values for hs CRP and microalbuminuria were noticed in patients with the lowest Likert score, for both samples CAD, and without CAD patients. The highest job strain was noticed in females. More than half of CAD patients ,with low Likert scores, revealed myocardial infarction. Conclusions: Microalbuminuria and hs CRP were statistical significant in the study as intercessors, in job strain and CAD relationship. The most vulnerable patients for vascular events were females, in both groups : CAD and “CAD waiting desk”. In CAD sample, the vascular vulnerability was also increased by high job strain.

The relationship between the structure characteristics and performances of coal based hydrogenation isomeric base oil (CTL) and metallocene catalyzed coal based polyalphaolefin (mPAO) base oil was clarified here. CTL and mPAO were compared with typical petroleum based and natural gas based commercial API Class III and IV base oils. Pressurized differential scanning calorimetry (PDSC), Rotary Bomb Oxidation Test (RBOT) and four-balls friction tester were used to evaluate the oxidation stability and lubrication performance of base oils under different working conditions. The sensitivity of different base oils to typical antioxidants and extreme pressure antiwear agents was compared. Specially, the composition and structure of CTL base oil are obviously different from GTL and mineral base oil. The coal-based CTL and mPAO base oils exhibit commendable viscosity-temperature properties, characteristics, coupled with low-temperature fluidity, fire safety, and minimal evaporation loss. The lubricating properties, oxidation stability and the sensitivity to extreme pressure antiwear agents of CTL are close to those of similar base oils. But the sensitivity of CTL to typical antioxidants is relatively poor. Besides, compared with commercial PAO base oil, mPAO has lower isomerization degree and fewer isomerization types. The oxidation stability and sensitivity to typical antioxidants of mPAO base oil are comparable with those of commercial PAO base oil, while its lubrication performance and sensitivity to typical extreme pressure antiwear agents are significantly better than those of commercial PAO base oil.

Two-dimensional (2D) materials show advantages as surface enhanced Raman scattering (SERS) substrate over traditional noble metals, such as chemically inert flat surface and uniform SERS signals. However, due to the low density of free electrons of 2D materials, the enhancement mechanism is mainly restricted to chemical enhancement instead of electromagnetic enhancement, resulting a relatively low enhancement factor. In this work, we reported a sensitive 2D SERS substrate based on chemical vapor deposited MoN nanosheets with electromagnetic enhanced mechanism. The maximum enhancement factor can be 3×105 with a limit of detected concentration of 4×10-8 M, which is comparable to those noble metal SERS substrates without hot spots. The MoN nanosheets show strong surface plasmon resonance in the visible range as evidenced by the UV-vis absorption spectroscopy and first principle calculation. The MoN nanosheets SERS substrates exhibit excellent Raman signal uniformity within the nanosheets. Meanwhile, Distinct thickness dependent sensitivity was observed in the MoN nanosheets, higher sensitivity can be achieved by decreasing the thickness of the nanosheets. Furthermore, the MoN nanosheets SERS substrate also show high thermal stability, which can be annealed in air for more than 300℃ and maintain its SERS activity. The high stability of MoN nanosheets allows it to be reused for 20 cycles without obvious signal decay. The high sensitivity, stability, reusability and Raman signal uniformity, makes MoN nanosheets an ideal 2D SERS substrate for practical applications.

Numerous studies were published suggesting that troponin level is related to adverse outcomes in chronic cardiac and non-cardiac conditions. Our study investigated whether troponin levels gathered from an unselected blood sample taken during outpatient care are associated with adverse outcomes in a population with stable coronary artery disease. In a cohort of 949 patients with stable coronary artery disease, average age of 67.5 ± 9.5 years, 69.5% male, 52.1% diabetics, 51.6% with previous myocardial infarction, 57.9% with triple-vessel disease, 21.7% of patients encountered new events during an average period of monitoring of 2.07 ± 0.81 years. Troponin I/99th percentile categorized into tertiles emerged as an independent predictor of death and combined events risk (hazard ratio: 2.02 (1.13-3.60), p=0.017; 2.30 (1.37-3.88, p=0.002, respectively). A troponin ratio > 0.24 was able to identify 53.3% of patients at risk of death and heart failure hospitalization. In patients with stable coronary artery disease and adherent to treatment, troponin level is independently associated with death and heart failure hospitalization in a medium-term follow up.

The mechanical behavior, microstructures as well as the crystallographic textures of the Ti57-Nb43 alloy were investigated on cylindrical specimens compressed at high temperatures, in the range of 700-1000 °C, and strain rates between 0.001 and 1.0 s-1. Hardening, followed by softening behaviours were observed as a function of strain, due to the occurrence of dynamic recrystallization/recovery in hot deformation. The modified five-parameter Voce type equation described well the stress-strain curves, but for the present alloy, it was also possible with only four parameters. A new two-variables polynomial function was employed on the four parameters that described well the flow curves as a direct function of temperature and strain rate. It permitted to reduce the number of parameters and had the predictive capacity for the flow stress at any temperature, strain, and strain rate in the investigated range. The crystallographic textures were similar at all temperatures with an increase of intensity from 900 °C. The textures could be characterized by a double &lt;100&gt; and &lt;111&gt; fiber and a unique component of (001)&lt;110&gt;, the latter inherited from the initial hot rolling texture. Viscoplastic polycrystal self-consistent deformation modeling reproduced the measured textures showing that dynamic recrystallization did not alter the development of the deformation texture, only increased it intensity.

The nature of the fracture and fragmentation process of concrete medium under blast loading is the transformation process of the medium from continuum to discontinuity, coupled with the significant rate correlation of concrete medium, its mechanical behavior presents a high degree of complexity. Problems such as grid distortion and even negative volume are often encountered in the finite element method and others when solving this problem, while the material point method can effectively avoid these problems. In addition, the widely used HJC concrete constitutive model does not take into account the segmented characteristics of the calculation function for dynamic increasing factor. Therefore, in this paper, firstly, the calculation function for dynamic increasing factor in the HJC concrete model is modified by SHPB experiment, and the improved HJC concrete model is proposed; secondly, the material point method simulation program is developed, and the improved HJC concrete model is embedded into the simulation program; finally, the simulation program is verified by numerical examples, and the results show that the developed simulation program can better simulate the fracture and fragmentation process of concrete medium under blast loading, especially the pulverization characteristics of the medium in the near zone of the load.

University course scheduling (UCS) is one of the most important and time-consuming issues that all educational institutions face yearly. Most of the existing techniques to model and solve UCS problems have applied approximate methods, which are different in terms of efficiency, performance, and optimization speed. Accordingly, this research aims to apply an exact optimization method to provide an optimal solution to the course scheduling problem. In other words, in this research, an integer programming model is presented to solve the USC problem. In this model, hard and soft constraints include the facilities of classrooms, courses of different levels and compression of students' curriculum, courses outside the faculty and planning for them, and the limited time allocated to the professors. The objective is to maximize the weighted sum of allocating available times to professors based on their preferences in all periods. To evaluate the presented model's feasibility, it is implemented using the GAMS software. Finally, the presented model is solved in a larger dimension using a real data set from a college in China and compared with the current program in the same college. The obtained results show that considering the mathematical model's constraints and objective function, the faculty courses' timetable is reduced from 4 days a week to 3 working days. Moreover, master courses are planned in two days, and the courses in the educational groups do not interfere with each other. Furthermore, by implementing the proposed model for the real case study, the maximum teaching hours of the professors are significantly reduced. The results demonstrate the efficiency of the proposed model and solution method in terms of optimization speed and solution accuracy.

African swine fever (ASF) is one of the major threats to pig production, and real-time PCR (qPCR) protocols are integral part of ASF laboratory diagnosis. With the pandemic spread of ASF, commercial kits have risen on the market. In Germany, the kits have to go through an approval process and thus, general validation can be assumed. However, they were never compared to each other. In this study, 12 commercial PCR kits were compared to an OIE recommended method. Samples representing different matrices, genome loads, and genotypes were included in a panel that was tested under diagnostic conditions. The comparison included user-friendliness, internal controls, and the time required. All qPCRs were able to detect ASFV genome in different matrices across all genotypes and disease courses. With one exception, there were no significant differences when comparing the overall mean. The overall specificity was 100 % [95 % CI 87.66 - 100], and the sensitivity was between 95 % and 100 % [95 % CI 91.11 - 100]. As can be expected, variability concerned samples with low genome load. Concluding, all tests were fit for purpose. The test system can therefore be chosen based on compatibility and prioritization of the internal control system.

Background: Patients with severe chronic kidney disease (CKD G4-G5) often have chronically elevated high-sensitivity cardiac troponin T (hs-cTnT) values above the 99th percentile of the upper reference limit. In these patients, optimal cutoff levels for diagnosing non-ST-elevation acute cor-onary syndrome (NSTE-ACS) requiring revascularization remain undefined. Methods: Of 11,912 patients undergoing coronary angiography from 2012 to 2017 for suspected NSTE-ACS, 325 (3%) had severe CKD. Of these, 290 with available serial hs-cTnT measurements were included and 300 matched patients with normal renal function were selected as a control cohort. Results: Diagnostic performance for patients with severe CKD was high at presentation and similar to that of the control population (AUC, 95% CI: 0.81, 0.75-0.87 versus 0.85, 0.80-0.89, p=0.68) and the ROC-derived cutoff value at presentation was 4 times higher compared to the conventional 99th percentile. Combining the ROC-derived cutoff levels for hs-cTnT at presentation and absolute 3-hour changes, sensitivity increased to 98%, PPV and NPV improved up to 93% and 86%, re-spectively. (4) Conclusions: In patients with severe CKD and suspected ACS the diagnostic accu-racy of hs-cTnT for the diagnosis of NSTE-ACS requiring revascularization is improved by using higher assay specific cutoff levels combined with early absolute changes.

Although wheat is used worldwide as a staple food, it can give rise to adverse reactions, for which the triggering factors have not been identified yet. These reactions can be caused mainly by kernel proteins, both gluten and non-gluten proteins. Among these latter, -amylase/trypsin inhibitors (ATI) are involved in baker’s asthma and realistically in Non Celiac Wheat Sensitivity (NCWS). In this paper, we report characterization of three transgenic lines obtained from the bread wheat cultivar Bobwhite silenced by RNAi in three ATI genes CM3, CM16 and 0.28. We have obtained transgenic lines showing an effective decrease of the activity of target genes that, although showing a higher trypsin inhibition as a pleiotropic effect, generate a lower reaction when tested with sera of patients allergic to wheat, accounting for the important role of the three target proteins in wheat allergies. Finally, these lines show unintended changes differences in high molecular weight glutenin subunits (HMW-GS) accumulation, involved in technological performances, but do not show differences in terms of yield. The development of new genotypes accumulating a lower amount of proteins potentially or effectively involved in such pathologies, not only offers the possibility to use them as a basis for the production of varieties with a lower impact on adverse reaction, but also to test if these proteins are actually implicated in those pathologies for which the triggering factor has not been established yet.

G protein-coupled receptors (GPCRs) play a key role in many cellular signaling mechanisms, and must select among multiple coupling possibilities in a ligand-specific manner in order to carry out a myriad of functions in diverse cellular contexts. Much has been learned about the molecular mechanisms of ligand-GPCR complexes from Molecular Dynamics (MD) simulations. However, to explore ligand-specific differences in the response of a GPCR to diverse ligands, as is required to understand ligand bias and functional selectivity, necessitates creating very large amounts of data from the needed large-scale simulations. This becomes a Big Data problem for the high dimensionality analysis of the accumulated trajectories. Here we describe a new machine learning (ML) approach to the problem that is based on transforming the analysis of GPCR function-related, ligand-specific differences encoded in the MD simulation trajectories into a representation recognizable by state-of-the-art deep learning object recognition technology. We illustrate this method by applying it to recognize the pharmacological classification of ligands bound to the 5-HT2A and D2 subtypes of class A GPCRs from the serotonin and dopamine families. The ML-based approach is shown to perform the classification task with high accuracy, and we identify the molecular determinants of the classifications in the context of GPCR structure and function. This study builds a framework for the efficient computational analysis of MD Big Data collected for the purpose of understanding ligand-specific GPCR activity.

Typhoon Durian forming over the Western North Pacific Ocean and entering into the South China Sea (SCS), caused extreme and widespread damages in 2006. In this research, sensitivity analyses on different physical parameterization schemes of the Weather Research and Forecasting Atmospheric Model (WRF-ATM) have been utilized to study typhoon Durian. Model accuracy and performance testing were investigated with different initial conditions during the tropical cyclone simulation in the SCS. The initial and boundary conditions (IBCs) for all experiments were derived from the European Centre for Medium Range Weather Forecasts (ECMWF), Re-Analysis Interim (ERAI), and the National Centers for Environmental Prediction (NCEP) with Final (FNL) analysis data compiled through the WRF-ATM model. The sensitivity analysis results indicated a major improvement for the cumulus scheme by using the Grell-Devenyi scheme along with the PBL scheme of Yonsei University, mixed-phase microphysics scheme of the WRF Single Moment 5-class and IBCs for ECMWF-ERAI of TC simulation under the context of Wind-Pressure Relationships. This predicted better track and intensity comparing with these of the Joint Typhoon Warning Center. The results revealed that the TC track and intensity were well simulated by the WSM5-GD combination for the WRF-ATM model with an intensity error of 1.69 hPa for minimum surface level pressure, maximum wind speed of 1.83 knots and average track error of 25 km in 72 hours. The simulations showed that the potential track and intensity error decreased with the delayed IBCs, suggesting that the model simulation is more dependable when the coast is approached by the TC.

Green economy has emerged as a significant pillar of sustainable development in the current global context, and the unpredictability of economic policy has progressively garnered the interest of businesses and government officials as one of the obstacles they must confront. This paper examines how uncertainty in the green economy impacts the green economy's efficacy. The research concentrates on how changes in a particular region can result in geospatial diffusion (spatial spillover), which is a form of spatial spillover wherein the uncertainty of the green economy can impact its efficacy. This paper examines the policy formulation and current state of the green economy across various regions, as well as the diffusion of economic policy uncertainty across geographical areas and the subsequent impact on the green economy's status quo. There is a negative correlation between economic policy uncertainty and green economy efficacy, according to research. There is a spatial spillover effect between economic policy uncertainty and the efficacy of the green economy. Regional variations exist in the impact of economic policy uncertainty on the efficacy of the green economy. The findings indicate that the implementation of AI has a more substantial beneficial effect on carbon emissions. An increase in carbon dioxide emissions poses a growing challenge for the industrial sector in its pursuit of carbon neutrality.

This article addresses the challenge of simulating rainstorm waterlogging in urban-scale areas where reliable drainage pipe network data is often lacking. Despite existing methods attempting to tackle this issue, there remains a gap in effectiveness. We present a novel approach, the Modified Equivalent Infiltration (MEI) method, building upon the foundation of the Equivalent Infiltration (EI) method. The study focuses on the outer ring area of Shanghai, utilizing data from the "In-Fa" typhoon period for simulation and comparison. Our findings reveal that the MEI method, requiring the same data inputs as the EI method, surpasses its predecessor in both principle and simulation results. Additionally, the MEI method demonstrates robustness in handling rainstorm waterlogging scenarios.

The issue of equipment layout in multi-floor satellites consists of two primary parts: allocating the equipment to the satellite's layers and placing the equipment in each layer individually. In reviewing the past literature in this field, firstly, the issue of assigning equipment to layers is observed in a few articles and next in the layout part, the non-overlapping constraint has always been a challenge, particularly for components that do not have a circular cross-section. In addition to presenting a heuristic method for allocating equipment to different layers of the satellite, this article presents a robust flexible programming model (RFPM) for the placement of equipment at different layers, taking into account the inherent flexibility of the equipment in placement and the subject of uncertainty. This model is based on the existing uncertainty between the distances of cuboid equipment which has not been addressed in any of the previous research, and by comparing its outputs with cases from past studies, it demonstrates a significantly higher efficiency in placing the equipment and meeting the limit of non-overlapping constraints between the equipment. Finally, it would be possible to reduce the design time in the conceptual and preparatory stages, as well as the satellite's overall size, while still satisfying other constraints like stability and thermal limitations, moments of inertia and centre of gravity.

In the paper we suggest an implementation of the non-commutative logic and apply its operators for decision-making in the group of autonomous agents. The suggested operators extend the uninorm and absorbing norm aggregators and use an additional asymmetry parameter that defines the “level of non-commutativity”. The value of this parameter is specified using the perception bias of humans measured in the experiments. The suggested operators and decision-making method are illustrated by simulated behavior of mobile robots in the group.

Public trust in science was tested and relied on during the SARS-CoV-2 pandemic, which has shaped global events since the WHO declaration in March 11, 2020. Public trust has been impacted through the government recommendations and mandates informed by public health guidance, including non-pharmaceutical and pharmaceutical interventions. The free-flow of ideas and in-formation so essential to the functioning of science has faced unprecedented challenge from widespread censorship in both the media and in scientific journals. This has created a poisoned environment for the building of trust between science and society. Scientific norms and ac-countability must be restored in order to rebuild the vital relationship between scientists and the public they serve.

In this paper we perform numerous numerical studies for the problem of low-rank matrix completion. We compare the Bayesian approaches and a recently introduced de-biased estimator which provides a useful way to build confidence intervals of interest. From a theoretical viewpoint, the de-biased estimator comes with a sharp minimax-optimal rate of estimation error whereas the Bayesian approach reaches this rate with an additional logarithmic factor. Our simulation studies show originally interesting results that the de-biased estimator is just as good as the Bayesian estimators. Moreover, Bayesian approaches are much more stable and can outperform the de-biased estimator in the case of small samples. However, we also find that the length of the confidence intervals revealed by the de-biased estimator for an entry is absolutely shorter than the length of the considered credible interval. These suggest further theoretical studies on the estimation error and the concentration for Bayesian methods as they are being quite limited up to present.

Uncertainty relations in hydrodynamics are numerically studied. We first give a review for the formulation of the generalized uncertainty relations in the stochastic variational method (SVM), following the work by two of the present authors [Phys.\ Lett.\ A\textbf{382}, 1472 (2018)]. In this approach, the origin of the finite minimum value of uncertainty is attributed to the non-differentiable (virtual) trajectory of a quantum particle and then both of the Kennard and Robertson-Schr\"{o}dinger inequalities in quantum mechanics are reproduced. The same non-differentiable trajectory is applied to the motion of fluid elements in the Navier-Stokes-Fourier equation or the Navier-Stokes-Korteweg equation. By introducing the standard deviations of position and momentum for fluid elements, the uncertainty relations in hydrodynamics are derived. These are applicable even to the Gross-Pitaevskii equation and then the field-theoretical uncertainty relation is reproduced. We further investigate numerically the derived relations and find that the behaviors of the uncertainty relations for liquid and gas are qualitatively different. This suggests that the uncertainty relations in hydrodynamics are used as a criterion to classify liquid and gas in fluid.

A micro decision-making utility model under uncertainty is presented as a complementary foundation for macro coronavirus models. The micro model consists of two functions, a risk averse utility function depending on wellness and a wellness random output which is a function of the input variable called “treatment” consisting of such elements as social distance, washing hands, wearing a face mask, and others. The decision maker selects a level of treatment that maximizes her/his expected utility, given the probabilities of the respective outputs. The focus is on how changes in a person’s psychological attitude towards the macro determined (announced) probabilities affects the optimum results of the model. Such changes create a micro-macro dynamic interaction which is briefly outlined. A short discussion of the model’s behavioral implications for health policy is also given.

In the field of pavement engineering, the determination of the mechanical characteristics is one of the essential process for reliable material design and highway sustainability. Early determination of mechanical characteristics of pavement is highly essential for road and highway construction and maintenance. Tensile strength (TS), compressive strength (CS) and flexural strength (FS) of roller compacted concrete pavement (RCCP) are very crucial characteristics as they are necessitated for many data from mixture proportions as input variables. In this research, the classification-based regression models named Random Forest (RF), M5rule model tree (M5rule), M5prime model tree (M5p) and Chi-square Automatic Interaction Detection (CHAID) are developed for simulation of the mechanical characteristics of RCCP. A comprehensive and reliable dataset comprising 621, 326 and 290 data records for CS, TS and FS experimental cases extracted from several open sources over the literature. The mechanical properties are developed based on influential inputs combination that processed using Principle Component Analysis (PCA). The applied PCA method as feature selection is specified that volumetric/weighted content forms of experimental variables (e.g., coarse aggregate, fine aggregate, supplementary cementitious materials, water and binder) and specimens’ age are the most effective inputs to generate the better performances. Several statistical metrics are measured to evaluate proposed classification-based regression models. RF model revealed an optimistic classification capacity of the CS, TS and FS prediction of the RCCP in comparison with the CHAID, M5rule, and M5p models. The research is extended for the results verification using Monte-carlo model for the uncertainty and sensitivity of variables importance analysis. Overall, the proposed methodology indicated a reliable soft computing model that can be implemented for the material engineering construction and design.

Background: Systemic lupus erythematosus (SLE) is characterized by inflammation and cardiovascular complications. Our study aimed to investigate subclinical and early indicators of systolic myocardial dysfunction in SLE patients using advanced echocardiographic methods and biomarkers. Methods: In this cross-sectional study, we enrolled 102 SLE patients without known cardiac impairment and 51 healthy controls. Demographics, disease characteristics, laboratory results, disease activity (SLEDAI) and organ damage (SDI) indices were recorded. Left ventricular global longitudinal strain (GLS) and myocardial work indices were assessed by utilizing speckle tracking echocardiography. In addition, high sensitivity C-reactive protein (hsCRP), high sensitivity troponin (hsTn) and N-terminal-pro B-type natriuretic peptide (NT-proBNP) levels were measured in blood samples. Results: In comparison to controls, SLE patients had significantly higher GLS (-19.94±2.71%vs-21.15±1.55%, p&lt;0.001) and global wasted work (GWW) (94±71mmHg%vs71±49 mmHg%, p=0.025). Notably, NT-proBNP and hsTn were threefold and twofold higher in SLE group compared to control group, respectively (p&lt;0.001). Within SLE cohort, patients with at least moderate disease activity (SLEDAI≥4) both biomarkers were significantly elevated than those with low disease activity (SLEDAI&lt;4). Conclusion: Advanced echocardiographic parameters combined with specific biomarkers have a promising role in detecting systolic dysfunction at an early phase in SLE patients, potentially enabling timely interventions to mitigate cardiovascular risk.

Tularemia is a zoonotic disease caused by {\it Francisella tularensis} bacteria, a gram-negative coccobacillus-shaped bacterium. There are multiple transmission routes of the infection to humans such as consumption of contaminated food or water, handling of infected animals or bites from {\it haematophagous} arthropods (such as ticks, deer flies, or mosquitoes). In this study we focus on transmission via the bites of ticks and developed a deterministic model of ordinary and impulsive differential equations to gain insight in the differential effect of prescribed fire on {\it Demacenta variablis} and {\it Amblyomma americanum} ticks and the prevalence of tularemina. We found that prescribed fire differentially reduce the number of the two ticks with {\it A. americanum} reduced the least compare to {\it D. variablis} subsequently leading to differential increase of tularemia new infected cases in humans and rodents. Our result further indicates that the spatial arrangement of burn against unburn areas may not matter as either arrangement led to fewer ticks and reduction in tularemia transmission when prescribed fire was implemented. The results of this study provide important new understandings of the intricate effect of prescribed fire on tick species, and the dynamics of the tick-borne disease, tularemia disease in this study.

: Intracellular tripeptide glutathione is an important agent of cell survival in hypoxia. Glutathione covalently binds to SH-groups of hemoglobin cysteine residues, protecting them from irreversible oxidation and changes its affinity to oxygen. Reduced glutathione (GSH) can also form a non-covalent complex with hemoglobin. Previously, we showed that hemoglobin tetramer has four noncovalent binding sites of glutathione GSH molecules inside, two of which are released during hemoglobin transition to deoxy form. In this study, we characterized the conserved cysteine residues and residues of non-covalent glutathione binding sites in the sequences of a number of hypoxia tolerant and hypoxia-sensitive mammals. The alpha subunit of all species considered was found to have no conserved cysteines, whereas the beta subunit contains Cys94 residue, which is conserved across species and whose glutathionylation changes the affinity of hemoglobin for oxygen 5-6-fold. In addition, the key residues of noncovalent glutathione binding sites in both alpha and beta subunits are absolutely conserved in all species considered, suggesting a common mechanism of hemoglobin redox regulation for both hypoxia-sensitive and hypoxia-tolerant mammals. The solvent accessibility of all HbA and HbB residues in oxy- and deoxy- forms was analyzed. The key residue betaCys94 is solvent accessible only in the deoxy form.

In this work, we demonstrate the preparation of easy-to-fabricate nanogranular, strontium ferromolybdate/strontium molybdate core-shell ceramics and examine their properties including tunnel magnetoresistance, magnetic field sensitivity and temperature coefficient of the tunnel magnetoresistance. The tunnel magnetoresistance of nanogranular, strontium ferromolybdate/strontium molybdate core-shell ceramics was modeled yielding values suitable for magnetoresistive sensor application. Such structures possess a narrow peak of magnetic flux sensibility located at about 125 mT. For magnetic flux measurement, single domain granules with superparamagnetic behavior should be applied. The predicted TMR magnetic flux sensitivities for granules with superparamagnetic behavior amount to about 7.7 % per T and 1.5 % per T for granules sizes of 6.8 nm and 4 nm, respectively. A drawback of the tunnel magnetoresistance of such nanogranular core-shell ceramics is the unacceptable large value of the temperature coefficient. Acceptable values, lower than 2 % per K, are obtained only at low temperatures (less than 100 K) or large magnetic flux densities (exceeding 6 T). Therefore, a Wheatstone bridge configuration should be adopted for magnetoresistive sensors design to compensate the effect of temperature.

The subtropical forest soil plays a pivotal role in terrestrial carbon (C) cycling. Although global warming is expected to influence subtropical soil C cycling, a consensus on its impact on soil C dynamics remains elusive. Given the significant response of soil microbial respiration to temperature in subtropical soils, understanding its behavior under varying temperature regimes is critical for predicting soil organic C (SOC) responses to climate warming. We conducted a short-term laboratory soil warming incubation experiment, sampling both warmed and un-warmed soils from a subtropical plantation in southeastern China, incubating them at 20 °C, 30 °C, and 40 °C. Our aim was to study the SOC mineralization response to increasing temperatures. Our findings revealed that the temperature sensitivity (Q10) of SOC mineralization to short-term experimental warming varied between the warmed soil and the un-warmed soil. The Q10 of the un-warmed soil escalated with the temperature treatment (20-30 ℃: 1.31, 30-40℃: 1.63). Conversely, the Q10 of the warmed soil decreased (20-30 ℃:1.57, 30-40 ℃:1.41). Increasing temperature treatments enhanced hydrolytic enzyme activity but decreased soil substrate availability in both un-warmed and warmed soil. The Q10 of un-warmed soil was positively correlated with the response ratio of DOC. Additionally, soil microbial biomass exhibited a significant decline, leading to a reduced total amount of PLFAs and a decreased abundance of bacteria, fungi, and Gram-negative bacteria (GN). The changes in temperature also considerably altered the composition of the warmed soil microbial community. A drop in the microbial quotient coupled with a rise in the metabolic quotient indicated that warming amplified microbial respiration over microbial growth. The differential Q10 of SOC mineralization, in response to temperature across varying soil, can largely be attributed to shifts in soil nutrients, C-degrading enzyme activities, and microbial communities (the ratio of fungal to bacterial PLFAs).

This review article explores the convergence of virtual reality (VR) and architectural design from an inclusive lens. It underscores the crucial role of virtual reality (VR) in serving as a catalyst for the incorporation of social and cultural sustainability principles in architectural projects. The exploration is guided by inclusive design principles, which encompass several aspects such as accessibility, cultural sensitivity, diversity and inclusivity, user-centered design, and empathy. These concepts serve as the basis for further investigation into the potential of virtual reality (VR) technology to enhance and heighten these features within architectural design, therefore promoting the progress of social and cultural sustainability. The review article highlights the profound impact that virtual reality (VR) may have, going beyond mere visualisation to generate architectural experiences that are immersive, interactive, and empathetic. These experiences are designed to take into account diverse perspectives and needs.

Proposed fundamental laws of biology and a model of health and disease underscore the importance of the lymphatic system. The lymphatics are responsible for two of the laws of biology and the fulcrum of health and disease balancing proliferation with regeneration through the immune system. It is responsible for protection from the environment and repair of senile and damaged tissue. Life is constantly bombarded by forces that increase entropy. Lymphatics provide negative entropy to maintain health. Lymphatics help maintain cellular homeostasis removing products of metabolism. Using these principles, the role of lymphatics is investigated in salt sensitivity hypertension, cardiorenal system, the new pillar of heart failure and kidney disease - Sodium-Glucose Transport Protein 2 (SGLT2) Inhibitors, and brain diseases. The realization of organ lymphatics in maintenance of health and disease opens the avenue to new therapeutics. This is the unrealized potential of lymphatic study.

Blind image quality assessment (BIQA) aims to evaluate image quality in a way that closely matches human perception. To achieve this goal, the strengths of deep learning and the characteristics of human visual system (HVS) can be combined. In this paper, inspired by the ventral pathway and dorsal pathway of HVS, a dual-pathway convolutional neural network is proposed for BIQA task. The proposed method consists of two pathways: “what” pathway, which mimics the ventral pathway of HVS to extract the content features of distorted images, and the “where” pathway, which mimics the dorsal pathway of HVS to extract the global shape features of distorted images. Then, the features from the two pathways are fused and mapped to an image quality score. Additionally, the gradient images weighted by the contrast sensitivity are used as the input to the “where” pathway, allowing it to extract global shape features that are more sensitive to human perception. Moreover, a dual-pathway multi-scale feature fusion module is designed to fuse the multi-scale features of the two pathways, enabling the model to capture both global features and local details, thus improving the overall performance of the model. The experiments conducted on six databases show that the proposed method achieves the state-of-the-art performance.

Traumatic brain injury (TBI) is a major leading cause of death and disability. While previous studies regarding focal pathologies following TBI have been done, there is a lack of information concerning the role of analgesics and their influences on injury pathology. Buprenorphine (Bup), an opioid analgesic, is a commonly used analgesic in experimental TBI models. Our previous studies investigated the acute effects of Buprenorphine-sustained release-Lab (Bup-SR-Lab) on diffuse neuronal/glial pathology, neuroinflammation, cell damage, and systemic physiology. The current study investigated the longer-term chronic outcomes of Bup-SR-Lab treatment at 4 weeks following TBI utilizing a central fluid percussion injury (cFPI) model in adult male rats. Histological assessments of physiological changes, neuronal damage, cortical and thalamic cytokine expression, microglial and astrocyte morphological changes, and myelin alterations were done, as we had done in our acute study. In the current study the Whisker Nuisance Task (WNT) was also performed pre- and 4w post-injury to assess changes in somatosensory sensitivity following saline or Bup-SR-Lab treatment. Bup-SR-Lab treatment had no impact on overall physiology or neuronal damage at 4w post-injury regardless of region or injury, nor did it have any significant effects on somatosensory sensitivity. However, greater IL-4 cytokine expression with Bup-SR-Lab treatment was observed compared to saline treated animals. Microglia and astrocytes also demonstrated region-specific morphological alterations associated with Bup-SR-Lab treatment, in which cortical microglia and thalamic astrocytes were particularly vulnerable to Bup-mediated changes. There were discernable injury-specific and region-specific differences regarding myelin integrity and changes in specific myelin basic protein (MBP) isoform expression following Bup-SR-Lab treatment. This study indicates that use of Bup-SR-Lab could impact TBI-induced glial alterations in a region-specific manor 4w following diffuse brain injury.

The process of revitalising quiescent cells in order for them to proliferate plays a pivotal role in the repair of warn out tissues as well as for tissue homeostasis. This process is also crucial in the growth, development and well-being of higher multi-cellular organisms such as mammals. Deregulation of quiescent-proliferation transition is related to many diseases, in particular cancer. Recent studies have revealed that this process is regulated tightly by the Rb−E2F bistable switch mechanism. Based on experimental observations, in this study, we formulate a mathematical model to examine the effect of the growth factor concentration on the proliferation-quiescence transition in human cells. Working with a non-dimensionalised model, we prove positivity, boundedness and uniqueness of solutions. To understand model solution behaviour close to bifurcation points, we carry out bifurcation analysis, which is further verified by use of numerical bifurcation analysis, sensitivity analysis and numerical simulations. Indeed, bifurcation and numerical analysis of the model predicted a transition between stable, bistable and stable states, which are dependent on the growth factor concentration parameter (GF). The derived predictions confirm experimental observations.

While nursing is an ethical profession, unethical behavior among nurses is increasing worldwide. This study examined the effects of an ethics seminar on nurses' moral sensitivity and ethical behavior. A total of 37 nurses (17 experimental, 20 control) were recruited. The ethics seminar was held over a 6-month period from May to October, 2018, and comprised six sessions held once a month for two hours. Moral sensitivity and unethical behavior were measured at the start and end of the seminar. Moral sensitivity and unethical behavior showed a negative correlation (r= -.455, p&lt;0.01). After the ethics seminar, the experimental group's moral sensitivity was significantly increased (t = -1.039, p = 0.314). The mean scores of unethical behavior at pre and post-test in the experimental group were 12.59 and 9.47, respectively. This was a statistically significant difference (t = 3.118, p = 0.004). There was no statistically significant difference in the mean score in both moral sensitivity and unethical behavior in the control group. We conclude that ethics seminars can enhance moral sensitivity and reduce the risk of unethical behavior among nurses. Regular ethics seminars and training must be provided to nurses as a matter of policy.

Flow and transport processes in soil and rock play a critical role in agricultural non-point source pollution (ANSP) loads. In this study, we investigated the ANPS load discharged into rivers from an irrigation district in the Tibetan Plateau, and simulated ANPS load using a distributed model involving detailed descriptions of flow and ANPS transport and transformation processes in the soil and rock. Experiments were conducted for two years to measure soil water content and nitrogen concentrations and the quality and quantity of lateral flow in the rock and at the drainage canal outlet during the highland barley growing period. A distributed model, in which the subsurface lateral flow was described using a step-wise method, was developed to simulate flow and ammonium nitrogen and nitrate nitrogen transport. Sobol’s method was used to evaluate the sensitivity of simulated flow and transport processes to model inputs. The results showed that, with a 21.2% increase of rainfall and irrigation in the highland barley growing period, the average NH4+-N and NO3--N concentrations in the soil layer decreased by 10.8% and 14.3%, respectively, due to increased deep seepage. Deep seepage of rainfall water accounted for 0–52.4% of total rainfall, whereas deep seepage of irrigation water accounted for 36.6–45.3% of total irrigation. NH4+-N and NO3--N discharged into the drainage channel represented 19.9–30.4% and 19.4–26.7% of the deep seepage, respectively. The mean Nash-Sutcliffe coefficients, root mean square errors, and cumulative deviations between the measured and simulated flow rates and NH4+-N and NO3--N concentrations at the surface drainage canal outlet were 0.694, 0.081, and 0.242, respectively, indicating that the proposed method can effectively describe the hydrological and ANPS pollution migration in the plateau irrigation zone. The Sobol’ sensitivity analysis results demonstrated that subsurface lateral flow had the most important first order and total effect on the simulated flow and NH4+-N and NO3--N concentrations at the surface drainage outlet.

Most of the research of mental fatigue evaluation mainly concentrated on some indexes that require sophisticate and large instruments which make the detection of mental fatigue cumbersome, time-consuming, and difficult to apply on a large scale. A quick and sensitive mental fatigue detection index is necessary so that mental workers can be alerted in time and take corresponding countermeasures. But to date, no studies have compared the sensitivity of common objective evaluation indexes. To solve these problems this study recruited 56 human subjects. These subjects were evaluated using six fatigue indexes: the Stanford sleepiness scale, digital span, digital decoding, short-term memory, critical flicker fusion frequency (CFF), and speed perception deviation. The results of fatigue tests before and after mental fatigue were compared, and a one-way analysis of variance (ANOVA) was performed on the speed perception deviation. The result indicated the significance of this index. Considering individual differences, the relative fatigue index (RFI) was proposed to compare the sensitivity of the indexes. The results showed that when the self-rated fatigue grade changed from non-fatigue to mild fatigue, the ranges of RFI values for digital span, digital decoding, short-term memory and CFF were 0.175–0.258, 0.194–0.316, 0.068–0.139, and 0.055–0.075, respectively. Correspondingly, when the self-rated fatigue grade changed from non-fatigue to severe fatigue, the ranges of RFI values for the above indexes were 0.175–0.258, 0.194–0.316, 0.068–0.139, and 0.055–0.075, respectively. These results suggest that the sensitivity of the digital decoding, digital span, short-term memory, and CFF decreased sequentially when the self-evaluated fatigue grade changed from no fatigue to mild or severe fatigue. The RFI individuality of the speed perception deviation is highly variable and is not suitable as an evaluation index. In mental fatigue testing, digital decoding testing can provide faster, more convenient, and more accurate results.

Studies performed at single-cell resolution have demonstrated the physiological significance of cell-to-cell variability. Various types of mathematical models and systems analyses of biological networks have further been used to gain a better understanding of the sources and regulatory mechanisms of such variability. In this work, we present a novel sensitivity analysis method, called molecular density function perturbation (MDFP), for the dynamical analysis of cellular heterogeneity. The proposed analysis is based on introducing perturbations to the density or distribution function of the cellular state variables at specific time points, and quantifying how such perturbations affect the state distribution at later time points. We applied the MDFP analysis to a model of signal transduction pathway involved in TRAIL (tumor necrosis factor-related apoptosis-inducing ligand)-induced apoptosis in HeLa cells. The MDFP analysis showed that caspase-8 activation regulates the timing of the switch-like increase of cPARP (cleaved poly(ADP-ribose) polymerase), an indicator of apoptosis. Meanwhile, the cell-to-cell variability in the commitment to apoptosis depended on mitochondrial outer membrane permeabilization (MOMP) and events following MOMP, including the release of Smac (second mitochondria-derived activator of caspases) and cytochrome-C from mitochondria, the inhibition of XIAP (X-linked inhibitor of apoptosis) by Smac and the formation of apoptosome.

Based on the geologic conditions of natural gas hydrate (NGH) accumulations in the Shenhu area, northern slope of the South China Sea, this paper used a method of combining reservoir stimulation technique (RST) with depressurization to investigate the conversion efficiency of marine NGH reservoirs in different intrinsic permeability and initial NGH saturation conditions, and analyze the influence of variably-stimulation effect on marine NGH conversion efficiency in different accumulation conditions, provided a reference scheme for improving the NGH conversion efficiency in the Shenhu area. In this work, we performed calculations for the variations in CH4 production rate and cumulative volume of CH4 in different initial NGH saturation, intrinsic permeability and stimulation effect conditions, variance analysis and range analysis methods were used to analyze the significance of these key factors and their interaction, and investigated the sensitivity of stimulation effect on NGH conversion efficiency, respectively. The simulation results showed that stimulation effect have a significant influence on NGH conversion efficiency, and the influence of interaction between these three factors were not obvious. Possibly most important, we clarified an optimum stimulation effect for higher NGH conversion efficiency under different accumulation conditions, especially in low-permeability and high-saturation, which corresponding stimulation effect were different.

Highly flexible manufacturing systems require continuous run-time (self-) optimization of processes with respect to various parameters, e.g. efficiency, availability, energy consumption etc. A promising approach for achieving (self-) optimization in manufacturing systems is the usage of the context sensitivity approach. Thereby the Cyber-Physical Systems play an important role as sources of information to achieve context sensitivity. In this paper it is demonstrated how context sensitivity can be used to realize a holistic solution for (self-) optimization of discrete flexible manufacturing systems, by making use of Cyber-Physical System integrated in manufacturing systems/processes. A generic approach for context sensitivity, based on self-learning algorithms, is proposed aiming at a various manufacturing systems. The new solution is propos encompassing run-time context extractor and optimizer. Based on the self-learning module both context extraction and optimizer are continuously learning and improving their performance. The solution is following Service Oriented Architecture principles. The generic solution is developed and then applied to two very different manufacturing processes. This paper proposes a holistic solution to achieve context sensitivity for Flexible Manufacturing Systems, whereby the knowledge created by applying the context sensitivity approach can be used for (self-) optimization of manufacturing processes.

We estimate general public’s willingness to pay (WTP) for agricultural soil quality protection and improvement in Spain (n=1000) and the UK (n=984) using data from a cross-sectional survey via Qualtrics panels in March-April 2021. We use a double bound dichotomous choice contingent valuation approach to elicit the individuals’ WTP. We investigate the effect of uncertainty on the success of policies aiming at achieving soil protection. In addition, to understand the heterogeneity in individuals’ WTP for agricultural soil quality protection and improvement we model individuals’ WTP through individual’s awareness and attitudes toward agricultural soil quality protection and the environment, trust in institutions; risk and time preferences; pro-social behavior and socio-demographics in Spain and the UK. We found that there is significant public support for agricultural soil quality protection and improvement in Spain and the UK. We also found that the support does not vary significantly under uncertainty of success of policies aiming at achieving soil protection. However the individual’s reasons for supporting agricultural soil quality protection and improvement are found to depend on the level of uncertainty and country. Hence, promoting public support for soil protection needs to be tailored according to the level of general public’s perceived uncertainty and geographic location.

The adoption of Industry 4.0 technology is essential for achieving energy efficiency, a critical aspect of Sustainable Development Goal (SDG) no. 12 aimed at addressing climate change. Understanding the impact of Sustainable Attitudes, Digital Trust, and Uncertainty Avoidance is crucial in the decision-making process regarding the implementation of Industry 4.0 technology. The slow adoption of Industry 4.0 technology is attributed to non-technological factors or people, while the need to achieve energy efficiency targets necessitates examining the influence of Sustainable Attitudes and Digital Trust as supporting variables, along with Uncertainty Avoidance, on technology adoption. The study employed a quantitative explanatory research approach to investigate the relationships and impacts of variables. It centered on employees of Schneider Electric Indonesia, a global Industry 4.0 technology provider, with a sample of 189 employees selected through random sampling, incorporating a 5% margin error from Slovin formula. The analysis involved the use of Structural Equation Modeling (SEM) with AMOS version 26. Industry 4.0 technology is essential for fostering innovation and business expansion, leading to a sustainable environment by offering principles, guidance, and energy-efficient technologies for companies. The involvement of Sustainable Attitude, moderated by Uncertainty Avoidance, is vital in reaching energy efficiency goals through Industry 4.0 implementation.

In the century and a half since Maxwell first conjured his “finite being” which Lord Kelvin subsequently dubbed a “daemon”, researchers have explored the connections between non-equilibrium thermodynamics, entropy, and information theory. In recent years various Thermodynamic Uncertainty Relations (TURs) have been derived to inform upon the relationship between the entropy production and the precision possible in thermodynamic machines and processes. In this paper the recently derived TURs are applied to the hypothetical thermodynamic economy as described by Parker [1]. The TURs define the lower bound on the total entropy production of the economy. Changes in this entropy production rate has consequences on the stability of the economic system and plays a central role in the business cycle. This new perspective has important implications for policy makers, researchers, and other economic actors.

Quantum gravity theories rely on a minimal measurable length for their formulations, which clashes with the classical formulation of the uncertainty principle and with Lorentz invariance from general relativity. These incompatibilities led to the development of the generalized uncertainty principle (GUP) from string theories and its various modifications. GUP and covariant formulations of the uncertainty principle are discussed, together with implications for space-time quantization.

We study the connection between the phase and the amplitude of the wave function and the conditions under which this relationship exists. For this we use model of particle in a box. We have shown that the amplitude can be calculated from the phase and vice versa if the log Analytical uncertainty relations are satisfied.

Methods to handle uncertainty in economic evaluation have gained much attention in the literature and the cost-effectiveness acceptability curve (CEAC) is the most widely used method to summarize and present uncertainty associated with program costs and effects in cost-effectiveness analysis. Some researchers have emphasized the limitations of the CEAC for informing decision and policy makers as the CEAC is insensitive to radial shifts of the joint distribution of incremental costs and effects in the North-East and South-West quadrants of the cost-effective plane (CEP). Furthermore, it has been pointed out that the CEAC does not incorporate risk-aversion in valuing uncertain costs and effects. In the present article we show that the cost-effectiveness affordability curve (CEAFC) captures both dimensions of the joint distribution of incremental costs and effects on the CEP and is therefore sensitive to radial shifts of the joint distribution on the CEP. Furthermore, the CEAFC also informs about the budget impact of a new intervention as it estimates the joint probability an intervention is both affordable and cost-effective. Moreover, we show that the cost-effectiveness risk-aversion curve (CERAC) allows to incorporate risk-aversion into the analysis and can therefore be used to inform decision-makers who are risk-averse. We use data from a published cost-effectiveness model of palbociclib in addition to letrozole versus letrozole alone for the treatment of oestrogen-receptor positive, HER-2 negative, advanced breast cancer to demonstrate the differences between CEAC, CEAFC and CERAC and show how these can jointly be used to inform decision and policy makers.

Industrial dynamical systems often exhibit multi-scale response due to material heterogeneities, operation conditions and complex environmental loadings. In such problems, it is the case that the smallest length-scale of the systems dynamics controls the numerical resolution required to effectively resolve the embedded physics. In practice however, high numerical resolutions is only required in a confined region of the system where fast dynamics or localized material variability are exhibited, whereas a coarser discretization can be sufficient in the rest majority of the system. To this end, a unified computational scheme with uniform spatio-temporal resolutions for uncertainty quantification can be very computationally demanding. Partitioning the complex dynamical system into smaller easier-to-solve problems based of the localized dynamics and material variability can reduce the overall computational cost. However, identifying the region of interest for high-resolution and intensive uncertainty quantification can be a problem dependent. The region of interest can be specified based on the localization features of the solution, user interest, and correlation length of the random material properties. For problems where a region of interest is not evident, Bayesian inference can provide a feasible solution. In this work, we employ a Bayesian framework to update our prior knowledge on the localized region of interest using measurements and system response. To address the computational cost of the Bayesian inference, we construct a Gaussian process surrogate for the forward model. Once, the localized region of interest is identified, we use polynomial chaos expansion to propagate the localization uncertainty. We demonstrate our framework through numerical experiments on a three-dimensional elastodynamic problem

Predictive simulation of concurrent debris flow using only pre-disaster information has proven to be difficult as a result of problems in predicting the location of debris-flow initiation (i.e., slope failure). However, because catchment topography has concave characteristics, with all channels in a catchment joining each other as they flow downstream, it is possible to predict damage to downstream area using relatively inaccurate initiation points. Based on this, this paper presents methodologies employing debris-flow initiation points generated randomly using statistical slope failure prediction. A many-case simulation across numerous initiation points was performed to quantify the effect of slope-failure location in terms of deviations in the predicted water level and terrain deformation. It was found that the relative standard deviation diminished as the points approached the downstream area, indicating a location-based predictability effect.

In this paper, we relate Poisson’s summation formula to Heisenberg’s uncertainty principle. They both express Fourier dualities within the space of tempered distributions and these dualities are furthermore the inverses of one another. While Poisson’s summation formula expresses a duality between discretization and periodization, Heisenberg’s uncertainty principle expresses a duality between regularization and localization. We define regularization and localization on generalized functions and show that the Fourier transform of regular functions are local functions and, vice versa, the Fourier transform of local functions are regular functions.

In order to obtain stable and reliable configuration transformation ability, reliability optimization design is regarded as an effective way to reduce the probability of kinematic function failure for the constrained metamorphic mechanism. Based on the structural composition principle of multi-configuration source metamorphic mechanism that can operate in an under-actuated state, the modularized calculation methods are established for the force analysis of augmented Assur groups including metamorphic kinematic joints. According to the equivalent resistance gradient model of metamorphic mechanisms, with considering the uncertainties in the link dimensions, masses, and compliance parameters et al, a probabilistic evaluation method for describing the configuration transformation ability of the constrained metamorphic mechanism is established. Based on reliability evaluation and reliability sensitivity analysis, a reliability optimization design method for improving the configuration transformation ability is proposed, and then the optimization design is carried out for tolerances of random variables focusing on those structural parameters with higher reliability sensitivity, so that the optimized results can satisfy the requirements of both reliability and economic simultaneously. Finally, the feasibility and effectiveness of the proposed method is verified by the illustration of a paper folding metamorphic mechanism. The research provides the foundation of reliability design of metamorphic mechanisms to obtain the high-probability repeated execution ability of configuration transformation, it also has theoretical and practical significance to promote the engineering application of metamorphic mechanisms.

In recent years, the rapid expansion of urban areas has led to changes in land use, a reduction in pervious surfaces, and a disturbance in the balance between surface waters and groundwater. These urban changes, combined with inadequate drainage systems and misdirection of runoff, have resulted in severe urban floods, causing numerous fatalities and damage to urban areas. As a result, investigating the effects of flood occurrences and determining proper mitigation measures to reduce flood damage is essential. This paper aims to establish a novel economic-hydrologic model to evaluate the efficacy of Low-impact Development (LID) measures in urban Bronx River catchment, NYC. The model examines reduced peak discharge of flood and imposed damage. Initially, the EPA’s SWMM is used to simulate urban flooding based on recent extreme events. LID scenarios are then implemented, including individual and combined scenarios, to mitigate the adverse impacts of flood occurrences of high to low frequency. By means of the HEC-GeoRAS tool, highly detailed flood inundation maps are generated and seamlessly incorporated into the flood damage estimation model, namely HAZUS, to obtain estimates of the potential damage caused by floods. Using the reduced monetary damages and the implementation cost of LID measures, the benefit-to-cost (BC) ratio is then calculated. Findings indicated that the combined LID scenario is the most effective approach in peak flow reduction. Moreover, Permeable pavement outperforms infiltration trench, bio retention cell, and rain barrel in terms of benefit-to-cost ratio, with rain barrel showing the lowest ratio. The proposed evaluation system for LID measures, with a focus on flood damage reduction as a key benefit, provides valuable insights for identifying optimal scenarios and facilitating informed decision-making.

In a limestone quarry mine, fragmentation is a crucial outcome of blasting operations. The optimization of blasting operations greatly benefits from the prediction of rock fragmentation. The main factors that affect fragmentation are rock mass characteristics, blast geometry, and explosive properties. This paper is a step towards the implementation of machine learning and deep learning algorithms for predicting the extent of fragmentation (in percentage) in opencast mining. Various parameters can affect fragmentation. But, in this paper initially, ten parameters (spacing, drill hole diameter, burden, average bench height, powder factor, number of holes, charge per delay, uniaxial compressive strength, specific drilling, and stemming) are collected to train the model. However, due to a weak correlation with rock fragmentation, drill diameter, Average bench height, compressive strength, stemming, and charge per delay are eliminated to reduce model complexity. A total of 219 data sets having five input features i.e., the number of holes, spacing, burden, specific drilling, and powder factor are used to develop the models. To predict rock fragmentation due to blasting in limestone quarry mines, both machine learning models (Random Forest Regression (Bagging), Support Vector Regression, and XG Boost Regression (Boosting)), as well as a deep learning model (Neural Network Regression), are applied to develop a model that can optimize the prediction of fragmentation. The Artificial neural network model optimization showed that the model with architecture 64-32-16-1 can perform well giving MSE (mean squared error) values of 41.32 and 28.59 on training and test data respectively. The R2 value for both training and test is 0.83. Random Forest regression is also performing well compared to SVR and XG boost with the MSE value 12.37 and 9.89 on training and testing data respectively. Here, the R2 value for both sets are 94%. Based on the permutation importance and Shapely plot values, the powder factor has the highest impact, and the burden has the lowest impact on fragmentation.

(1) Background: The emergence of antibiotic-resistant bacteria is a growing concern in the field of medicine. This has led to the need for alternative treatments for bacterial infections. (2) Methods: The present study aimed to investigate the synergistic effect of a water-based extract of S. fru-tescens and penicillin G on the growth of E. faecalis. (3) Results: The study found that the combina-tion of the plant extract and penicillin G had a synergistic effect on the inhibition of bacterial growth, with a significantly larger inhibition zone observed compared to penicillin G alone. The MIC and MBC tests also demonstrated that the combination of the plant extract and penicillin G was more effective against E. faecalis than penicillin G alone, with a dilution of 1 in 4 yielding 100% inhibition of growth. (4) Conclusion: Although the specific mechanism of action was not dis-cussed in the study, the results suggest that the combination of the plant extract and penicillin G may offer a promising alternative treatment for antibiotic-resistant infections. Further research is needed to determine the active compounds in the plant extract and to investigate the synergistic effect of the combination on other strains of bacteria.

Controlling the selectivity of detonation initiation reaction of explosive is a Holy Grail, and it seems to be an “idiot's daydream”, by strengthening external electric field, to reduce the explosive sensitivity. The effects of external electric fields on the initiation reactions in NH2NO2∙∙∙NH3, a model system of the nitroamine explosive with alkaline additive, were investigated at the MP2/6-311++G(2d,p) and CCSD/6-311++G(2d,p) level. The concerted effect in the intermolecular hydrogen exchange is characterized by an index of the imaginary vibrations. Due to the weakened concerted effects by the electric field along the −x-direction opposite to the “reaction axis”, the dominant reaction changes from the hydrogen exchange to 1,3-intramolecular hydrogen transference with the increase of the field strengths. Furthermore, the stronger the field strengths, the higher the barrier heights become, indicating the lower sensitivities. Therefore, by strengthening the field strength and adjusting the orientation between the field and “reaction axis”, not only can the reaction selectivity be controlled, but also the sensitivity can be reduced, in particular under a super-strong field. Thus, a traditional concept, which the explosive is dangerous under the super-strong external electric field, is broken theoretically. Compared to the neutral medium, the low sensitivity of the explosive with alkaline can be achieved under the stronger field. Employing atoms in molecules, reduced density gradient and surface electrostatic potentials, the origin of the reaction selectivity and sensitivity change is revealed. This work provides a new idea to the technical improvement for adding the external electric field into the explosive system.

Early stroke diagnosis remains a big challenge in healthcare partly due to the lack of reliable diagnostic blood biomarkers, which in turn leads to increased rates of mortality and disability. Current screening methods are optimised to identify patients with a high risk of cardio-vascular disease, especially among the elderly. However, in young adults and children, these methods suffer low sensitivity and specificity and contribute to further delays in their triage and diagnosis. Accordingly, there is an urgent need to develop reliable blood biomarkers for triaging patients suspected of stroke in all age groups, especially children and young adults. This review explores some of the existing blood biomarkers, as single biomarkers, or biomarker panels, and examine their sensitivity and specificity for predicting stroke. A review was performed on PubMed and Web of Science for journal articles published in English during the period 2001 to 2021 which contained information regarding biomarkers of stroke. In this review article, we provide comparative information on the availability, clinical usefulness, and time-window periods of eight single blood biomarkers and six biomarker panels that have been used for predicting stroke in emergency situations. The outcomes of this review can be used in future research for developing more effective stroke biomarkers.

Early diagnosis and treatment have always been highly desired in the fight against cancer, and detection of circulating tumor DNA (ctDNA) has recently been touted as highly promising for early cancer screening. Consequently, the detection of ctDNA in liquid biopsy gains much attention in the field of tumor diagnosis and treatment, which has also attracted research interest from the industry. However, traditional gene detection technology is difficult to achieve low cost, real-time and portable measurement of ctDNA. Electroanalytical biosensors have many unique advantages such as high sensitivity, high specificity, low cost and good portability. Therefore, this review aims to discuss the latest development of biosensors for minimal-invasive, rapid, and real-time ctDNA detection. Various ctDNA sensors are reviewed with respect to their choices of receptor probes, detection strategies and figures of merit. Aiming at the portable, real-time and non-destructive characteristics of biosensors, we analyze their development in the Internet of Things, point-of-care testing, big data and big health.

Ultrasonic measurements are used in civil engineering for structural health monitoring of concrete infrastructures. The late portion of the ultrasonic wavefield, the coda, is sensitive to small changes in the elastic moduli of the material. Coda Wave Interferometry (CWI) correlates these small changes in the coda with the wavefield recorded in intact, or unperturbed, concrete specimen to reveal the amount of velocity change that occurred. CWI has the potential to detect localised damages and global velocity reductions alike. In this study, the sensitivity of CWI to different types of concrete mesostructures and their damage levels is investigated numerically. Realistic numerical concrete models of concrete specimen are generated and damage evolution is simulated using the discrete element method. In the virtual concrete lab, the simulated ultrasonic wavefield is propagated from one transducer using a realistic source signal and recorded at a second transducer. Different damage scenarios reveal a different slope in the decorrelation of waveforms with the observed reduction in velocities in the material. Finally, the impact and possible generalizations of the findings are discussed and recommendations are given for a potential application of CWI in concrete at structural scale.

The COVID-19 pandemic has created huge damage to society and brought panics around the world. Such panics can be ascribed to the seemingly deceptive features of the COVID-19: compared to other deadly viral outspreads, it has medium transmission and mortality rates. As a result, the severity of the causative coronavirus, SARS-CoV-2, was deeply underestimated by the society at the beginning of the COVID-19 outbreak. Based on this, in this review, we define the viruses with features similar to those of SARS-CoV-2 as the Panic Zone viruses. To contain those viruses, accurate and fast diagnosis followed by effective isolation and treatment of patients are pivotal at the early stage of virus breakouts. This is especially true when there is no cure or vaccine available for a transmissible disease, which is the case for current COVID-19 pandemic. As of January 2021, more than two hundred kits for the COVID-19 diagnosis on the market are surveyed in this review, while emerging sensing techniques for SARS-CoV-2 are also discussed. It is of critical importance to rationally use these kits for the efficient management and control of the Panic Zone viruses. Therefore, we discuss guidelines to select diagnostic kits at different outbreak stages of the Panic Zone viruses, SARS-CoV-2 in particular. While it is of utmost importance to use nucleic acid-based detection kits with low false negativity (high sensitivity) at the early stage of an outbreak, the low false positivity (high specificity) gains its importance at later stages of the outbreak. When a society is set to reopen from the lock-down stage of the COVID-19 pandemic, it becomes critical to have antibody based immunoassay kits with high specificity to identify people who can safely return to the society after their recovery of SARS-CoV-2 infections. Given that the emergence of mutant viruses at the beginning of 2021 has complicated current battle against the COVID-19, we also discussed approaches and guidelines to detect viral mutants in the middle of the second wave of the pandemic that started at the end of 2020. Finally, since a massive attack from a viral pandemic requires a massive defense from the whole society, we urge both government and private sectors to research and develop more affordable and reliable point-of-care testing (POCT) kits, which can be used massively by the general public (and therefore called as massive POCT) to contain Panic Zone viruses in future.

Densimetric Froude (Fr) is the minimum velocity required to prevent sediment deposition in pipes. Prediction of Fr is of utmost importance in numerous applications in civil engineering. In this paper through using a new hybrid method. Genetic Algorithm (GA) is used for optimum selection of membership functions of Adaptive Neuro-Fuzzy Inference System (ANFIS), and Singular Value Decomposition (SVD) method is used to compute the linear parameters of ANFIS’s results section (ANFIS-GA/SVD). Also, two different target functions are known as training error (TE) and prediction error (PE) by Pareto curve, the trade-off between these functions is selected as the optimal modeling point. First, different models will be presented using the parameters affecting Fr prediction, classifying them in different groups; then the Fr parameter will be predicted for all the different models through utilizing three different sets of data and the ANFIS-GA/SVD technique. The results of the models indicate that the best Fr prediction is obtained when independent parameters such as volumetric sediment concentration (CV), ratio of median diameter of particle size to pipe diameter (d/D), ratio of median diameter of particle size to hydraulic radius (d/R) and overall friction factor of sediment (λs) use as input variables in prediction of Fr. A sensitivity analysis is also conducted for the purpose of examining the effect of each of the dimensionless parameters on Fr prediction accuracy. Comparing the results of the suggested models with the existing regression-based equations shows that ANFIS-GA/SVD (R²=0.986, MAPE=4.397, RMSE=0.206, SI=0.053, ρ=0.026, BIAS=-0.025) is more accurate than the rest of the models.

Since the beginning, humans advanced their civilization by making better tools to improve their lives. Tools and products were designed for better living considering manufacturing issues, cost and time as predominant criteria. It has become clear that not considering environment and society, both at local/global levels, has now become a major impediment affecting living conditions on a large portion of the Earth and in many societies. Design methodologies should lead to creative solutions with consideration to engineering and economics for practicality but also to environmental and social constraints for sustainability. We propose a comprehensive design methodology based on multidisciplinary design to include the knowledge of humanities, environmentalists, science and engineering, and allowing for experts’ inputs from these areas to provide a holistic approach to engineering design . For example, experts in humanities are expected to interact with stakeholders to evaluate their value systems to provide guidance for the design. The methodology that we synthesize is new and combines (i) Societal level impacts at all scales, (ii) Environmental impacts and (iii) Engineering design with economic impacts, including uncertainty considerations. The proposed design methodology is called Social-Environmental-Economical-Engineering Framework (SEEEF). It can utilize concepts and tools such as Circular Design, Doughnut Economics, design based on environmental life cycle analysis, among others. SEEEF is quantity based and provides steps for evaluating any project or product in an objective manner and will help train engineers in design for sustainability. It also provides non-engineers with a significant role in design to increase their understanding of the hard constraints of engineering. Ultimately, SEEEF allows society to take an informed decision considering short/long term and local/global impacts of the design and the pertinent uncertainties.