The coronavirus disease-2019 (COVID-19) pandemic is an ongoing pandemic caused by severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2). Although COVID-19 pandemic is not a direct surgical problem, its impact on the surgical units has been substantial. Many operations have been cancelled or deferred due to risk of disease spread and staff shortages. Operating theatres are identified as areas of high risk of disease transmission due to aerosol generation during the surgical procedures and prolonged patient contact. Resource limitations, including lack of personal protective equipment (PPE) and limited availability of testing, continue to expose the surgical community to COVID-19. In this review, evidence and consensus guidelines on surgical practice during the COVID-19 pandemic are summarised and described. Immediate action is deemed essential to ensure unhindered provision of surgical care while optimizing the use of limited resources and ensuring staff safety.

Since the severe acute respiratory syndrome (SARS) outbreak in 2003, human coronaviruses (hCoVs) have been identified as causative agents of severe acute respiratory tract infections. Two more hCoV outbreaks have since occurred, the most recent being SARS-CoV-2, the causative agent of coronavirus disease 2019 (COVID-19). The clinical presentation of SARS and MERS is remarkably similar to COVID-19, with hyperinflammation causing a severe form of the disease in some patients. Previous studies show that the expression of the SARS-CoV E protein is associated with the hyperinflammatory response that could culminate in acute respiratory distress syndrome (ARDS), a potentially fatal complication. This immune-mediated damage is largely caused by a cytokine storm, which is induced by significantly elevated levels of inflammatory cytokines interleukin (IL)-1beta and IL-6, which are partly mediated by the expression of the SARS-CoV E protein. The interaction between the SARS-CoV E protein and the host protein, syntenin, as well as the viroporin function of SARS-CoV E, are linked to this cytokine dysregulation. This review aims to compare the clinical presentation of virulent hCoVs with a specific focus on the cause of the immunopathology. The review also proposes that inhibition of IL-1beta and IL-6 in severe cases can improve patient outcome.

The severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) viral genome is an RNA virus consisting of approximately 30,000 bases. As part of testing efforts, whole genome sequencing of human isolates has resulted in over 1,600 complete genomes publicly available from GenBank. We have performed a comparative phylogenetic analysis of the sequences, in order to detect common mutations within the population. Analysis of variants occurring within the assembled genomes yields 417 variants occurring in at least 1% of the completed genomes, including 229 within the 5’ untranslated region (UTR), 152 within the 3’UTR, 2 within intergenic regions and 34 within coding sequences.

(1) Background: It is unclear whether the reported presenting clinical features of coronavirus disease 2019 (COVID-19) are useful in identifying high-risk patients for early testing and isolation in the emergency department (ED). We aimed to compare the exposure history, clinical, laboratory, and radiographic features of ED patients who tested positive and negative for COVID-19; (2) Methods: We conducted a case-control study in seven EDs during the first five weeks of the COVID-19 outbreak in Hong Kong. Thirty-seven laboratory-confirmed COVID-19 patients were compared with 111 age- and gender-matched controls; (3) Results: There were no significant differences in patient characteristics and reported symptoms between the groups, except patient-reported fever. A positive travel history or contact history was the most significant predictor for COVID-19 infection. After adjustment for age and presumed location of acquiring the infection in Wuhan/Hubei, patient-reported fever (OR 2.6, 95% CI 1.1 to 6.3), delayed presentation (OR 5.0, 95% CI 2.0 to 12.5), having medical consultation before ED presentation (OR 7.4, 95% 2.9 to19.1), thrombocytopenia (OR 4.0, 95% CI 1.6 to 9.7), raised lactate dehydrogenase (OR 5.9, 95% CI 1.9 to 18.5), haziness, consolidation or ground-glass opacity on chest radiography (OR 5.6, 95% CI 2.0 to 16.0), and bilateral changes on chest radiography (OR 13.2, 95% CI 4.7 to 37.4) were associated with a higher odds of COVID-19 separately while neutrophilia was associated with a lower odds (OR 0.3, 95% CI 0.1-0.8); and (4) Conclusions: This study highlights several features that may be useful in identifying high-risk patients for early testing and isolation while waiting for test result. Further studies are warranted to verify the findings.

It is unclear why European countries have been more severely affected by COVID-19 than East Asian countries. In this ecological study we compared the COVID-19 epidemics (cumulative incidence and mortality rates), host genetic susceptibility and national responses (testing intensity) in all European versus all Western Pacific/East Asian countries reporting cases in the first month of the epidemic. The host-genetic-susceptibility assessment was limited to the frequency of the D-allele of the angiotensin converting enzyme-1 (ACE-1) which has been found to be positively associated with COVID-19 incidence within European countries. Despite earlier epidemics, countries from the Western Pacific reported lower cumulative numbers of COVID-19 cases/100 000 inhabitants than European countries (P=0.0002). The Western Pacific countries also reported fewer cumulative COVID-19 deaths/100 000 (P=0.0024). Whilst there was little difference in the cumulative number of tests conducted/100 000, the percent of COVID-19 tests reported positive was higher in Europe than the Western Pacific (P=0.0076). The frequency of the ACE-1 D-allele was lower in the Western Pacific than European countries (P=0.0007). Our results suggest that a combination of different testing strategies and host genetic susceptibility contribute to difference in severity of the COVID-19 epidemics in the Western Pacific/East Asia and Europe.

The Coronavirus Disease 2019 (COVID-19) is a new viral infection caused by the severe acute respiratory coronavirus 2 (SARS-CoV-2). Genomic analyses have revealed that SARS-CoV-2 is related to Pangolin and Bat coronaviruses. In this report, a structural comparison between the Sars-CoV-2 Envelope and Membrane proteins from different human isolates with homologous proteins from closely related viruses is described. The analyses here reported show the high structural similarity of Envelope and Membrane proteins to the counterparts from Pangolin and Bat coronavirus isolates. However, the comparisons have also highlighted structural differences specific of Sars-CoV-2 proteins which may be correlated to the cross-species transmission and/or to the properties of the virus. Structural modelling has been applied to map the variant sites onto the predicted three-dimensional structure of the Envelope and Membrane proteins.

Several epidemiological models are being used around the world to project the number of infected individuals and the mortality rates of the COVID-19 outbreak. Advancing accurate prediction models is of utmost importance to take proper actions. Due to a high level of uncertainty or even lack of essential data, the standard epidemiological models have been challenged regarding the delivery of higher accuracy for long-term prediction. As an alternative to the susceptible-infected-resistant (SIR)-based models, this study proposes a hybrid machine learning approach to predict the COVID-19 and we exemplify its potential using data from Hungary. The hybrid machine learning methods of adaptive network-based fuzzy inference system (ANFIS) and multi-layered perceptron-imperialist competitive algorithm (MLP-ICA) are used to predict time series of infected individuals and mortality rate. The models predict that by late May, the outbreak and the total morality will drop substantially. The validation is performed for nine days with promising results, which confirms the model accuracy. It is expected that the model maintains its accuracy as long as no significant interruption occurs. Based on the results reported here, and due to the complex nature of the COVID-19 outbreak and variation in its behavior from nation-to-nation, this study suggests machine learning as an effective tool to model the outbreak. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research.

The severity of the ongoing COVID-19 outbreak demand from countries to adopt extreme measurements of social isolation to stop the spread and flatten the curve of contamination. Although social isolation measures may have negative impacts on economy, historically it has been showed to be more effective in saving lives and less damaging to economy than not adopting these measurements during a viral pandemic. In Brazil, despite the positioning of the president against social isolation due to the consequent economic recession, the rapid spread of the virus has worried the governors of the Brazilian states, which are thus managing stringent social isolation measurements to avoid the advance of the virus. Since one of the main strategies to guarantee progress and economic growth in Brazil has been the exploitation of natural resources from the Amazonia biome, here we discuss the importance of this biome to Brazilian economy during the post-pandemic recession and highlights potential strategies to burst the economy without promoting Amazonia destruction. We show that, together with the REDD+ and the Amazon Fund, the Forest bonds represents good strategies to burst Brazilian economy in a sustainable way, showing that it is possible to improve the commodities without increasing Amazonia deforestation or the greenhouse gases emissions. Amazonia is a biome of global importance for the avoidance of another global crisis, which will occur if we reach the climatic tipping point of 1.5°C. Thus, governmental actions should go towards its preservation, not exploitation and depletion. The commitment of the government with environmental conservation is paramount so that these economic strategies have positive results, especially in a post-pandemic scenario, where the economy will be extremely weakened. The COVID-19 brings us a lesson regarding how our attitudes can impact the world, and what we can expect from a global crisis. Perhaps we can apply these lessons and focus on change our economy towards a sustainable direction to avoid another global crisis in the years to come.

The natural history of COVID-19 caused by SARS-CoV-2 is extremely variable, ranging from asymptomatic infection, to pneumonia, and to complications eventually fatal. We propose here the first model, explaining how the outcome of first, crucial 10-15 days after infection, hangs on the balance between the cumulative dose of viral exposure and the efficacy of the local innate immune response (natural IgA and IgM antibodies, MBL). If SARS-CoV-2 runs the blockade of this innate immunity and spreads from the upper airways to the alveoli in the early phases of the infections, it can replicate with no local resistance, causing pneumonia and releasing high amounts of antigens. The delayed and strong adaptive immune response (high affinity IgM and IgG antibodies) that follows, causes severe inflammation and triggers mediator cascades (complement, coagulation, and cytokine storm) leading to complications often requiring intensive therapy and being, in some patients, fatal. Strenuous exercise and high flow air in the incubation days and early stages of COVID-19, facilitates direct penetration of the virus to the lower airways and the alveoli, without impacting on the airway’s mucosae covered by neutralizing antibodies. This allows the virus to bypass the efficient immune barrier of the upper airways mucosa in young and healthy athletes. In conclusion, whether the virus or the adaptative immune response reach the lungs first, is a crucial factor deciding the fate of the patient. This “quantitative and time-sequence dependent” model has several implications for prevention, diagnosis, and therapy of COVID-19.

The fast spread of COVID-19 constitutes a worldwide challenge to the public health, educational, and trade systems, affecting the overall wellbeing of human societies. The high transmission and mortality rates of this virus, and the unavailability of a vaccine or treatment, resulted in the decision of multiple governments to enact measures of social distancing. Thus, it is of general interest to consider the validity of the proposal for keeping a social distancing of at least 2 m from other persons to avoid the spread of COVID-19. The exposure to the bioaerosol can result in the deposition of the pathogen in the respiratory tract of the host causing disease and an immunological response. In the atmospheric context, the work evaluates the effect of aerodynamic diameter (size) of particles in carrying RNA copies of the novel coronavirus. A SARS-CoV-2 carrier person talking, sneezing, or coughing at distance of 2 m can still provide a pathogenic bioaerosol load with submicron particles that remain viable in air for up to 3 hours for exposure of healthy persons near and far from the source in a stagnant environment. The deposited bioaerosol creates contaminated surfaces, which if touched can act as a path to introduce the pathogen by mouth, nose, or eyes and cause disease.

Several outbreak prediction models for COVID-19 are being used by officials around the world to make informed-decisions and enforce relevant control measures. Among the standard models for COVID-19 global pandemic prediction, simple epidemiological and statistical models have received more attention by authorities, and they are popular in the media. Due to a high level of uncertainty and lack of essential data, standard models have shown low accuracy for long-term prediction. Although the literature includes several attempts to address this issue, the essential generalization and robustness abilities of existing models need to be improved. This paper presents a comparative analysis of machine learning and soft computing models to predict the COVID-19 outbreak. Among a wide range of machine learning models investigated, two models showed promising results (i.e., multi-layered perceptron, MLP, and adaptive network-based fuzzy inference system, ANFIS). Based on the results reported here, and due to the highly complex nature of the COVID-19 outbreak and variation in its behavior from nation-to-nation, this study suggests machine learning as an effective tool to model the outbreak.

The high transmissibility and replication of SARS-CoV-2 have been attributed to enhanced protein functions which are dependent on protein folding. Our in silico study endeavored to scrutinize SARS-CoV-2 ORF1ab by analyzing the conserved folding patterns of its transcribed proteins. Accordingly, the findings indicated that SARS-CoV-2 ORF1ab shares domain-specific fold-fingerprints with a spectrum of unrelated organisms. Closer observation revealed slight changes in folding patterns engendered with small variation in the intrinsic amino acid sequence. By correlating with the evolvability-potential of RNA-viruses and COVID-19 pandemic, we hypothesize that SARS-CoV-2 could undergo fast recombination with the host, SARS-CoV-2 minor variants and other viral species resulting in a reservoir of SARS-CoV-2 quasispecies. It is highly possible that natural selection will cause a future emergence of evolved SARS-CoV-2-descendants. Nonetheless, we hope that this insightful study will assist in elucidating SARS-CoV-2 protein functionalities, development of vaccines, and the possibility and nature of future emergence.

Two conundrums have puzzled COVID-19 investigators: 1) morbidity and mortality is rare among Infants and young children and 2) rates of morbidity and mortality exhibit very large variances across nations, locals and even within cities. These differences correlate with rates of Haemophilus influenzae type B (Hib) and pneumococcal vaccination, which are almost universal among infants and vary widely by geography among adults and the elderly. The higher the rate of vaccination, the lower the COVID-19 morbidity and mortality. Vaccination rates with other vaccines, including BCG and poliovirus, do not correlate with COVID-19 risks. Notably, both Hib and pneumoccoci are common co-infections with influenza and coronaviruses and are associated with more severe disease and risk of death. Whether the vaccines simply protect against COVID-19 complications, directly protect against COVID-19 infection by inducing cross-reactive immunity, or are markers for some other types of protection such as availability of better healthcare, is not yet known. What is known is that improving coverage rates of Hib and pneumococcal vaccination has significantly lowered severe morbidity and mortality in influenza epidemics and might have similar efficacy for mitigating coronavirus outbreaks. If infants and children are valid indicators, the beneficial effects might be very significant. The possibility that anti-viral proteins in milk (e.g., lactoferrin) protect against COVID-19 is also explored.

Water quality retrieval for small urban waterbodies by remote sensing get used to be difficult due to coarse spatial resolution of the remote sensing imagery. The recently launched Sentinel-2 produces imagery with a spatial resolution of 10 m. It provides an opportunity to solve the problem of retrieving water quality for small waterbodies. Additionally, many water management issues also require fine resolution of imagery, e.g. illegal discharge to an urban waterbody. Since illegal discharges are an important issue for urban water management, chemical oxygen demand (COD), total phosphorous (TP), and total nitrogen (TN) were chosen as the target parameters for water quality retrieval in this study. COD, TP and TN, however, are non-optically active parameters. There were limited studies in the past to retrieve these parameters in comparison with optically active parameters, e.g. Chlorophyll-A etc. This study compared three machine learning models, namely Random Forest (RF), Support Vector Regression (SVR), and Neural Networks (NN), to investigate the opportunity to retrieve the above non-optically active parameters. Results showed that R² of TP, TN, and COD by NN, RF and SVR were 0.94, 0.88, and 0.86, respectively. The performances of water quality retrieval for these non-optically active parameters were significantly improved by the optimized machine learning models. These models hence solved the problem to use remote sensing data to retrieve these non-optically active water quality parameters and provided a new monitoring strategy for small waterbodies. Water quality mapping obtained by Sentinel-2 imagery provided a full spatial coverage of the water quality characterization for the entire water surface. Compared with water samples collecting and testing, it greatly reduced labor cost, reagents cost, and waste treatment cost. It also may help identify illegal discharges to urban waterbodies. The method developed in this research provides a new practical and efficient water quality monitoring strategy in managing water with consideration of environmental sustainability.

We applied a generalized SEIR epidemiological model to the recent SARS-CoV-2 outbreak in the world, with a focus on Italy and its Lombardia, Piemonte, and Veneto regions. We focus on the application of a stochastic approach in fitting the model numerous parameters using a Particle Swarm Optimization (PSO) solver, to improve the reliability of predictions in the medium term (30 days). We analyze the official data and the predicted evolution of the epidemic in the Italian regions, and we compare the results with data and predictions of Spain and South Korea. We link the model equations to the changes in people’s mobility, with reference to Google’s COVID-19 Community Mobility Reports. We discuss the effectiveness of policies taken by different regions and countries and how they have an impact on past and future infection scenarios.

Recent emergence of novel coronavirus (SARS-CoV-2) all over the world has resulted more than 33,106 global deaths. To date well-established therapeutics modules for infected patients are unknown. In this present initiative, molecular interactions between FDA-approved antiviral drugs against the Hepatitis-C virus (HCV) have been investigated theoretically against the RNA-dependent RNA polymerase (RdRp) of SARS-CoV-2. HCV and SARS-CoV-2 are both +ssRNA viruses. At 25^o C beclabuvir, a non-nucleoside inhibitor of the RdRp_HCV can efficiently bind to RdRp _SARS-CoV-2 (ΔG_AutoDock = -9.95 kcal mol^-1) with an inhibition constant of 51.03 nM. Both the ΔG_London and ΔG_{GBVI / WSA} values were - 9.06 and - 6.67 kcal mol^-1, respectively for binding of beclabuvir to RdRp_SARS-CoV-2. In addition, beclabuvir has also shown better binding free energy with RdRp_SARS-CoV-2 (ΔG_vina = -8.0 kcal mol^-1) than that observed with the Thumb 1 domain of RdRp_HCV (ΔG_vina = -7.1 kcal mol^-1). InterProScan has suggested the RNA-directed 5'-3' polymerase activity exists within 549^th to 776^th amino acid residues of RdRp_SARS-CoV, where the major amino acid residues interacting being I591, Y621, C624, D625, A690, N693, L760, D762, D763, and E813-N817. Molecular interaction suggests occupancy of beclabuvir inside the active site environment of the RdRp_SARS-CoV-2, the enzyme essential for viral RNA synthesis. In conclusion, results suggest beclabuvir may serve as an anti-SARS-CoV-2 drug.

With the rapid spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that results in coronavirus disease 2019 (COVID-19), corporate entities, federal, state, county and city governments, universities, school districts, places of worship, prisons, health care facilities, assisted living organizations, daycares, homeowners, and other building owners and occupants have an opportunity to reduce the potential for transmission through built environment (BE) mediated pathways. Over the last decade, substantial research into the presence, abundance, diversity, function, and transmission of microbes in the BE has taken place and revealed common pathogen exchange pathways and mechanisms. In this paper, we synthesize this microbiology of the BE research and the known information about SARS-CoV-2 to provide actionable and achievable guidance to BE decision makers, building operators, and all indoor occupants attempting to minimize infectious disease transmission through environmentally mediated pathways. We believe this information is useful to corporate and public administrators and individuals responsible for building operations and environmental services in their decision-making process about the degree and duration of social-distancing measures during viral epidemics and pandemics.

Coronavirus Disease 2019 (COVID-19), caused by SARS-CoV-2 (Severe Acute Respiratory Syndrome - Coronavirus-2) of the family Coronaviridae, appeared in Wuhan, Hubei province, China being its epicenter in December 2019. This disease was declared as posing Public Health International Emergency by World Health Organization on January 30, 2020, attained the status of a very high-risk category on February 29, and now having a pandemic status (March 11, 2020). COVID-19 has presently spread to more than 195 countries/territories while killing nearly 19,600 humans out of cumulative confirmed cases accounting to more than 430,000 within a short period of just a few weeks. The majority of deaths have been reported in Italy and China. Researchers worldwide are pacing with high efforts to counter the spread of this virus and to design effective vaccines and therapeutics/drugs. Few of the studies have shown the potential of animal-human interface and zoonotic links in the origin of SARS-CoV-2. Exploring the possible zoonosis and revealing the factors responsible for its initial transmission from animals to humans will pave ways to design and implement effective preventive and control strategies to counter COVID-19. The present review presents an overview of COVID-19 and the causative virus SARS-CoV-2, with particular emphasis on the role of animals and their jumping the cross-species barriers, experiences learned from SARS- and MERS-CoVs, zoonotic links, and spillover events, transmission to humans and rapid spread., highlighting in very brief the preventive and control measures along with a few of the recent research developments to counter this pandemic virus/disease.

One notable features of the SARS-CoV-2 genome is that the spike protein of SARS-CoV-2 has a functional polybasic (furin) cleavage site (RRAR) at the S1–S2 boundary through the insertion of 12 nucleotides encoding PRRA. To date, the furin cleavage site (FCS) remains an experimentally uncharted territory both structurally and functionally. For instance, whether or not FCS is actually cleaved, before or after viral cell entry or exit, still remains to be experimentally investigated. With currently available structural data, this article presents a computational structural characterization of the FCS inserted into SARS-CoV-2 spike glycoprotein, and puts forward a set of structural hypothesis against the hypothesis of SARS-CoV-2 from purposeful manipulation: (1), the inserted FCS does not alter, neither stabilize nor de-stabilize, the overall structure of SARS-CoV-2 spike glycoprotein; (2), the net structural consequence of FCS is the insertion of a furin cleavage site into SARS-CoV-2 spike glycoprotein, whose S1 and S2 subunits will still be bonded together even if the FCS is actually cleaved by furin protease.

The COVID-19 pandemic is placing unprecedented stress on healthcare systems around the world. Although Radiation Oncology Departments are not at the frontline of fighting this infectious disease, it is important to implement COVID-19 policies to reduce risk of staff and patient exposure, and to limit the risk of department shutdown or downtime. This brief report describes the policy implemented at George Washington University Radiation Oncology to manage the risks of COVID-19. This includes a General Statement related to the priorities of the Radiation Oncology department, a screening procedure for new and follow-up patients, management policies for critical and non-critical patients with COVID-19 or under quarantine, a policy for the management of patients currently under treatment who are diagnosed or placed in quarantine, a clinical escalation action plan, guidelines for staff meetings and travel, and procedure management. This policy was implemented at George Washington University Radiation Oncology after the first case of COVID-19 was reported in Washington DC on March 7, 2020.

The transnational spread of coronavirus (2019-nCoV) first detected in Wuhan is causing global panic; thus, accelerated research into clinical intervention is of high necessity. The spike glycoprotein structure has been resolved, and its affinity to human angiotensin-converting enzyme 2 (ACE-2) has been experimentally validated. Here, using computational methods, a metastable conformation of 2019-nCoV-RBD/ACE-2 complex has been revealed and FDA-database of approved drugs have been docked into the interface. Darunavir has been discovered as high ligand affinity candidate capable of disrupting communication between 2019-nCoV-RBD and ACE-2. Darunavir, in addition to its previously known anti-HIV protease inhibitor is now repurposeable for the treatment 2019-nCoV disease acting via disruption of cellular recognition, binding and invasion.

A novel coronavirus (SARS-CoV-2) that is initially found to trigger human severe respiratory illness in Wuhan City of China in 2019, has killed 2,718 people in China by February 26, 2020, and which has been recognized as a public health emergency of international concern as well. And the virus has spread to more than 38 countries around the world. However, the drug has not yet been officially licensed or approved to treat SARS-Cov-2 infection. NSP12-NSP7-NSP8 complex of SARS-CoV-2, essential for viral replication and transcription, is generally regarded as a potential target to fight against the virus. According to the NSP12-NSP7-NSP8 complex (PDB ID: 6NUR) structure of SARS, two homologous models were established for virtual screening in the present study, namely NSP12-NSP7 interface model and NSP12-NSP8 interface model. Seven compounds (Saquinavir, Tipranavir, Lonafarnib, Tegobuvir, Olysio, Filibuvir, and Cepharanthine) were selected for binding free energy calculations based on virtual screening and docking scores. All seven compounds can combine well with NSP12-NSP7-NSP8 in the homologous model, providing drug candidates for the treatment and prevention of SARS-CoV-2.

Less aligned emphasis has been given to naming the 2019 novel coronavirus and pandemic disease. Global profusion of squab names has found their ways in daily communication, and our survey promises to articulate that many of them may have contributed to backlash against Chinese people. Here, based on brief critical reviews on the naming of coronavirus and human coronaviruses, we scrutinize a clear sense of pros and cons of previous multifarious names and punctuate heuristic introspection of naming practices. Our findings suggest that full-fledged official names are duly contribute to the resilience of healthy collective usages in current infodemic scenario.

A new compound containing a triene, a tetrahydropyran ring and glycine ester functionalities, restricticin B (1), together with four known compounds (2-5) were obtained from the EtOAc extract of the marine fungus Penicillium janthinellum. The planar structure of 1 was determined by detailed analyses of MS, 1D and 2D NMR data. The relative and absolute configurations of 1 were established via analyses of NOESY spectroscopy data, comparison of optical rotation values with those of restricticin derivatives reported and electronic circular dichroism (ECD). All the compounds were screened for their anti-neuroinflammatory effects in lipopolysaccharide (LPS)-induced BV-2 microglia cells. Restricticin B (1) and N-acetyl restricticin (2) exhibited anti-neuroinflammatory effects by suppressing the production of pro-inflammatory mediators in activated microglial cells.

Iron and zinc are essential micronutrients required for growth and health. Deficiencies of these nutrients are highly prevalent among populations, but can be alleviated by supplementation. Cross-sectional studies in humans showed positive association of serum zinc levels with hemoglobin and markers of iron status. Dietary restriction of zinc or intestinal specific conditional knock out of ZIP4 (SLC39A4), an intestinal zinc transporter, in experimental animals demonstrated iron deficiency anemia and tissue iron accumulation. Similarly increased iron accumulation has been observed in cultured cells exposed to zinc deficient media. These results together suggest a potential role of zinc in modulating whole body iron metabolism. Studies in intestinal cell culture models demonstrate that zinc induces iron uptake and transcellular transport via induction of divalent metal iron transporter-1 (DMT1) and ferroportin (FPN) expression, respectively. It is interesting to note that intestinal cells are exposed to very high levels of zinc through pancreatic secretions, which is a major route of zinc excretion from the body. Therefore, zinc appears to be modulating the iron metabolism possibly via regulating the DMT1 and FPN1 levels. Herein we critically reviewed the available evidence to hypothesize novel mechanism of Zinc-DMT1/FPN axis in regulating intestinal iron absorption and tissue iron accumulation to facilitate future research aimed at understanding the yet elusive mechanisms of iron and zinc interactions.

The NA61/SHINE experiment studies hadron production in hadron-hadron, hadron-nucleus and nucleus-nucleus collisions. The physics programme includes the study of the onset of deconfinement and search for the critical point as well as reference measurements for neutrino and cosmic ray experiments. For strong interactions, future plans are to extend the programme of study of the onset of deconfinement by measurements of open-charm and possibly other short-lived, exotic particle production in nucleus-nucleus collisions. This new programme is planned to start after 2020 and requires upgrades to the present NA61/SHINE detector setup. Besides the construction of a large acceptance silicon detector, a 10-fold increase of the event recording rate is foreseen, which will necessitate a general upgrade of most detectors.

The Biginelli reaction is a highly versatile reaction, which leads to dihydropyrimidinones/thiones. This scaffold is reported as being a privileged structure due to its ability to interact with biological targets. Synthesis of ethyl 4-(2-fluorophenyl)-6-methyl-2-thioxo-1-(p-tolyl)-1,2,3,4-tetrahydropyrimidine-5-carboxylate was achieved through the Biginelli reaction using a functionalized thiourea. In silico studies demonstrated that the compound title showed good potential for interacting with ecto-5’-nucleotidase, which has been considered as a target in designs for anti-cancer drugs.

(RADA)4 nanoscaffolds are excellent candidates for use as peptide delivery vehicles: they are relatively easy to synthesize with custom bio-functionality, and assemble in situ to allow a focal point of release. This enables (RADA)4 to be utilized in multiple release strategies by embedding a variety of bioactive molecules in an all-in-one ‘construct’. One novel strategy focuses on the local, on-demand release of peptides triggered via proteolysis of tethered peptide sequences. However, the spatial-temporal morphology of self-assembling nanoscaffolds may greatly influence the ability for enzymes to both diffuse into as well as actively cleave substrates. Fine structure and its impact on overall affect on peptide release is poorly understood. In addition, fractal networks observed in nanoscaffolds are linked to the fractal nature of diffusion in these systems. Therefore, matrix morphology and fractal dimension of virgin (RADA)4 and mixtures of (RADA)4 and matrix metalloproteinase 2 (MMP-2) cleavable substrate modified (RADA)4 were characterized over time. Sites of high (GPQG+IASQ, CP1) and low (GPQG+PAGQ, CP2) cleavage activity were chosen. Fine structure was visualized using established according to established methods. After 2 hrs of incubation, nanofiber networks showed an established fractal nature, however nanofibers continued to bundle in all cases as incubation times increased. It was observed that despite extensive nanofiber bundling after 24 hrs of incubation time, the CP1 and CP2 nanoscaffolds were susceptible to MMP-2 cleavage. The properties of these engineered nanoscaffolds characterized herein illustrate that they are an excellent candidate as an enzymatically initiated peptide delivery platform.

In the recent past about 40 botulinum neurotoxin (BoNT) subtypes belonging to serotypes A, B, E, and F pathogenic to humans were identified among hundreds of independent isolates. BoNTs are the etiological factors of botulism and represent potential bioweapons, but are also recognized pharmaceuticals for efficient counteraction of hyperactive nerve terminals in a variety of human diseases. The detailed biochemical characterization of subtypes as the basis for development of suitable countermeasures and possible novel therapeutic applications is lagging behind the increase in new subtypes. Here we report the primary structure of a ninth subtype of BoNT/F. Its amino acid sequence diverges by at least 8.4% at the holotoxin and 13.4% on the enzymatic domain level from all other known BoNT/F subtypes. We found that BoNT/F9 shares the scissile Q⁵⁸/K⁵⁹ bond in its substrate vesicle associated membrane protein 2 with the prototype BoNT/F1. Comparative biochemical analyses of four BoNT/F enzymatic domains showed that the catalytic efficiencies decrease in the order F1 > F7 > F9 > F6 and vary by up to factor eight. K_M values increase in the order F1 > F9 > F6 ≈ F7, whereas k_cat decreases in the order F7 > F1 > F9 > F6. Comparative substrate scanning mutagenesis studies revealed a unique pattern of crucial substrate residues for each subtype. Based upon structural coordinates of F1 bound to an inhibitor polypeptide the mutational analyses suggest different substrate interactions in the substrate binding channel of each subtype.

The considerable biological and medicinal activities of pyridazines has stimulated considerable research on efficient syntheses of these derivatives. In the last decade, microwave irradiation has generally been used for the energy source. As demonstrated in recent studies, pressure reactor “Q-tubes” may be used to accelerate several of these reactions in a more optimal and safer manner (compared to microwaves). In these studies there has been postulated a pathway for the formation of pyridazino[5,4,3-de][1,6]naphthyridine derivatives . In this paper we consider this pathway, and an alternate pathway, for several reactions. Contrary to the suggestion in these studies the pathway in which initial dimerization of malononitrile was postulated could be excluded based on chemical evidence. The reactions performed were the reaction of arylhydrazonals 1a,b with benzylidinemalononitrile which afforded in Q-tube the 3-acyl-4-aryl-1-phenyl-6-amino-1,4-dihydropyridazines, and the reaction of arylhydrazonals 1a,b, malononitrile 9 and aromatic aldehydes 10a-g in Q-tubes which afforded the tricyclic systems 12a-n whose structure could be established by X-ray crystal structure determination. In conclusion, we have added to the work of the recent studies by excluding a reaction pathway for one of their reaction products.

Abstract Background: Diabetes mellitus is dramatically increasing in the wide world. The managing of diabetes care emphasized the self-management education and support into patients’ care and family care. Objective: to review and synthesizes the effectiveness of DSME strategies involving family as a key person to provide social support for diabetes mellitus self-management of glycemic uncontrolled patients Method: Three databases through PubMed, CINAHL, and Scopus were reviewed to assess the relevant articles. The following search terms: “type 2 diabetes,” “self-management,” “family support,” and “glycemic uncontrolled.” We summarized details of family support on self-management among glycemic uncontrolled patients for 14 existing studies. Results: A total of 22 intervention studies were identified. Those studies have a heterogeneous of the education strategies, support perceived, follow-ups strategies and outcomes among type 2 DM. Family integration on diabetes self-management education (DSME) has a positive impact on several outcomes including, self-care behaviors, psychological outcomes, self-efficacy and clinical outcomes Conclusions: This systematic review found robust data related to the integration of family support on diabetes self-management among glycemic uncontrolled patients. Consequently, the improvement in outcomes was identified. Implications: The findings suggest model of family engagement is better and needed for sustaining the diabetes care in the long-term care

Glacier dynamics play an important role in the mass balance of many glaciers, ice caps and ice sheets. In this study we exploit Radarsat-2 (RS-2) Wide Fine (WF) data to determine the surface speed of Svalbard glaciers in the winters of 2012/2013 and 2013/2014 using Synthetic Aperture RADAR (SAR) offset and speckle tracking. The RS-2 WF mode combines the advantages of the large spatial coverage of the Wide mode (150 x 150 km) and the high pixel resolution (9m) of the Fine mode and thus has a major potential for glacier velocity monitoring from space through offset and speckle tracking. Faster flowing glaciers (1.95 m d^-1 - 2.55 m d^-1) which are studied in detail are Nathorstbreen, Kronebreen, Kongsbreen and Monacobreen. Using our Radarsat-2 WF dataset, we compare the performance of two SAR tracking algorithms, namely the GAMMA Remote Sensing Software and a custom written MATLAB script (GRAY method) that has primarily been used in the Canadian Arctic. Both algorithms provide comparable results, especially for the faster flowing glaciers and the termini of slower tidewater glaciers. A comparison of the WF data to RS-2 Ultrafine and Wide mode data reveals the superiority of RS-2 WF data over the Wide mode data.

Mitochondrial pyruvate dehydrogenase complex (PDHC) is essential for the brain glucose and neurotransmitter metabolism, dysregulated in many pathologies. Using specific inhibitors of PDHC in vivo, we determine biochemical and physiological responses to PDHC dysfunction. Dose dependence of the responses to membrane-permeable dimethyl acetylphosphonate (Ac-PMe2) is non-monotonous. Primary decreases in glutathione and its redox potential, methionine and ethanolamine are alleviated with increasing PDHC inhibition, the alleviation accompanied by physiological changes. Comparison of 39 brain biochemical parameters after administration of four phosphinate and phosphonate analogs of pyruvate at a fixed dose of 0.1 mmol/kg reveals no primary, but the secondary changes, such as activation of 2-oxoglutarate dehydrogenase complex (OGDHC) and decreased levels of glutamate, isoleucine and leucine. The accompanying decreases in RMSSD of ECG and freezing time are most pronounced after administration of me-thyl acetylphosphinate and dimethyl acetylphosphonate. The levels of PDHA1 expression and phosphorylation, sirtuin 3 and total protein acetylation are not significantly changed by the PDHC inhibitors that affect the brain protein succinylation and glutarylation. Thus, decreased production of the tricarboxylic acid cycle substrate acetyl-CoA by inhibited PDHC is compen-sated by increased degradation of amino acids through the cycle with activated OGDHC, increas-ing total protein succinylation and decreasing anxiety indicators.

Abstract: Hand gestures are an essential kind of nonverbal communication that let people engage and be expressive with one another. As technology has progressed, hand gesture recognition systems have become an important field of study, providing a link between human activities and artificial intelligence. An overview of the field of hand gesture recognition systems is given in this abstract, along with an explanation of its importance, difficulties, and possible uses. This abstract begins by examining the role that hand gestures have in communication. It then goes on to show the cultural significance and wide range of variations in gestural expressions that occur in many contexts and societies. Artificial intelligence systems face a unique problem in comprehending and interpreting these motions, which calls for the combination of computer vision, machine learning, and pattern recognition approaches. The abstract goes on to address why people develop hand motions. The purpose behind creating systems to recognize hand gestures is also covered in the abstract, with a focus on how these systems could improve human-computer interaction in several fields, including robotics, virtual reality, healthcare, and assistive technologies. These systems have the potential to enhance accessibility, efficiency, and user experience by allowing people to engage with devices and interfaces naturally and intuitively. The abstract also discusses environmental elements including backdrop clutter and lighting, as well as the technological difficulties in identifying and understanding hand gestures, which include variations in hand forms, orientations, and movements. To tackle these obstacles, strong algorithms that can record and interpret complex hand movements in real-time must be developed.

Abstract: Field studies with large-stemmed plants Artemisia dubia (wormwood) have been carried out at Vėžaičiai Branch LAMMC since 2018. According to three years of experimental results, annual dry matter (DM) yield varied from 7.94 to 10.14 t ha-1. Growing conditions, nitrogen ap-plication level and harvesting time had significant impacts on Artemisia dubia productivity. This article presents the results of research on preparation and use of Artemisia dubia biomass for en-ergy conversion and evaluation of technological-technical and environmental parameters of these plants utilization for solid biofuel. For experiments were used 6 variants of Artemisia dubia samples, which were grown in 2021: plants were cut three times and two fertilization options were used: (1) no fertilization and (2) fertilization with 180 kg ha-1 of nitrogen fertilizer. These harvested plants were chopped, milled and were pressed into the pellets. The physi-cal-mechanical characteristics (mоisture content, density and strength) of Artemisia dubia pellets were investigated. During this study, it was found that the density in dry mass (DM) of pellets ranged from 1119.86 to 1192.44 kg m-3. The pellet moisture content ranged from 8.80 to 10.49%. After testing of pellet strength, it was found that the pellets which were made from plant bio-mass PK-1-1, were the most resistant to compression, they withstood 560.36 N of pressure. Dry fuel lower heating value (LHV) of pellets was sufficiently high and was very close to pine saw-dust pellets, it varied from 17.46±0.25 MJ kg-1 to 18.14±0.28 MJ kg-1. The ash content of burned pellets ranged from 3.62±0.02% to 6.47±0.09%. Emissions of harmful pollutants – CO2, CO, NOx and unburnt hydrocarbons (CxHy) did not exceed the maximum permissible levels. Summariz-ing the results of investigated properties, cоmbustion and emissiоns of Artemisia dubia pellets, it can be concluded that this biofuel can be used for production of pressed biofuel and it is charac-terized by sufficient high quality, efficient combustion and permissible emissions to the envi-ronment.

The practical problem-solving of incidental unreliability of intraoperative neuromonitoring may be the simultaneous neurophysiological recording and the inspection of the surgical field through the camera (the "Real-time neuromonitoring" concept). This would allow the immediate warning of the surgeon on the possibility of the spinal structures insult during but not after the application of the standard procedures in scoliosis surgery (the “Interactive verbal surgeon-neurophysiologist neuromonitoring" concept). This study aimed to compare the advantages, utility, reliability, and time-consuming of both intraoperative neuromonitoring scenarios using non-invasive and innovative recordings from peroneal nerves (PER) versus tibialis anterior muscles (TA) with surface electrodes of motor evoked potentials (MEP) bilaterally as a result of transcranial magnetic (TMS) or electrical (TES) stimulations. Studies were performed in two similar groups ("Real-time neuromonitoring", N=60 and "Interactive verbal sur-geon-neurophysiologist neuromonitoring", N=60) of patients treated surgically because of mainly Lenke 2 type idiopathic scoliosis (IS) pre- (T0), intra- (T1 – before surgical procedures, T2 – after surgical procedures) and postoperatively (T3) as well as in healthy volunteers (N=60, Control). The cumulative parameters of MEP amplitudes and latencies recorded from PER compared to those recorded from TA in healthy volunteers were approximately 67% (1100µV) lower at p=0.007 and 10.6% shorter (3.1 ms) at p=0.04, respectively. A similar trend was also observed in patients from both groups at each follow-up stage. MEP recordings from TA and PER in patients of both groups differed similarly in T0-T3 compared to controls at p=0.008-0.04. MEP parameters in all patients induced by TMS (T0) and TES (T1) did not differ. The parameters of MEP amplitudes recorded from TA and PER intraoperatively in T1 and T2 differed at p=0.04-0.03, indicating the bilateral improvement of neural spinal conduction due to the surgical intervention. Parameters of TMS-induced MEP amplitudes in T3 further increased bilaterally compared to the tests recorded in T0 at p=0.03-0.02. In both groups of patients, an average 51,8 BIS level of anaesthesia minimally affects the variability of the MEP amplitude, especially in PER recordings when ap-plied TES strength was at 98.2 mA. The number of MEP parameter fluctuations, mainly the amplitudes decreasing, was strictly associated with the neurophysiologist's warnings due to transpedicular screws implantation, corrective rods implantation, distraction, derotation, and compression procedures, respectively; at p=0.04-0.03 more in patients from the “Interactive S-N neuromonitoring” group. The average duration of the surgery was significantly shorter (p=0.04) by about 1 hour in the "Real-time neuromonitoring" group. The number of two-way communications between the surgeon and neurophysiologist and vice versa in the "Real-time neuromonitoring" group was reduced by approximately half at p=0.008. The study's results prove the advantages of using the "Real-time neuromonitoring" procedure in increasing safety and non-invasiveness, shortening the time, and lowering the costs of surgical treatment of patients with pathological lateral curvature of the spine. The modifications of the MEP nerve conduction recording technology with surface electrodes from nerves presented in this study enable precise and reliable information on the patient's neurological condition at every stage of applied surgical procedures, even in conditions of slight fluctuations in the anaesthesia.

In Korea, the underground silo structure for low- and intermediate-level radioactive waste disposal facilities has been constructed and operated since 2014. Large-scale earthquakes occurred in 2016 and 2017, respectively, in Gyeongju and Pohang areas near the underground silo structures, and interest in the stability of the underground silo increased significantly. In this paper, one-dimensional free field analyses have been carried out before the three-dimensional silo dynamic analyses subjected to earthquake loadings. As an additional study, a new form of finite element equilibrium equation is derived in terms of relative motions, which is essentially the same equation as that expressed in terms of total motions where the base shear force is applied for earthquake load. The accuracy of conventional finite element solutions is evaluated by directly comparing them with closed-form solutions by frequency domain analysis such as SHAKE91.

Wildfires are important natural drivers of forest stands dynamics, strongly influencing on their natural regeneration and ecosystem services. This paper presents a comprehensive analysis of spatiotemporal burnt area (BA) patterns over the period 2000–2022 in the Middle Volga region of the Russian Federation on the base of remote sensing time series, considering the impact of cli-matic factors on forest fires. The temporal trends were assessed with the Mann-Kendall nonpara-metric statistical test and Theil-Sen’s slope estimator using the LandTrendr algorithm on the Google Earth Platform (GEE). The accuracy assessment indicated a high overall accuracy (> 84%) and F-score value (> 82%) for forest burnt area detection as evaluated against 581 test sites of ref-erence data. The results revealed that the fire occurrences in the region were mainly irregular with the highest frequency of 7.3 over a 22-year period. The total forest BA was about 280 thousand ha, which equals to 1.7% of the land surface area or 4.0% of the total forested area under study in the Middle Volga region. The coniferous forest stands are the most fire-prone ecosystems accounting for 59.0 % of the total BA; deciduous stands accounts for 25.1%; and insignificant fire occurrences were registered in young forests and shrub lands. On a seasonal scale, temperature generally has a greater impact on the BA than precipitation and wind speed.

This study presents a novel approach to enhance Large Language Models (LLMs) like Alpaca by dynamically integrating real-time information. This method addresses the issue of content hallucination and data relevancy by automatically collecting and integrating current data from credible sources into model prompts. Experiments show a significant improvement in accuracy and a decrease in content hallucination, with a manageable increase in response time. The research underscores the potential of real-time data integration in making LLMs more accurate and contextually relevant, setting a foundation for future advancements in dynamic data processing in AI.

The reflection and transmission spectra of a metamaterial formed by omega-shaped elements with pre-calculated optimal parameters on a silicon substrate have been recorded in the terahertz range at oblique incidence for the s- and p- polarizations of the incident wave. The spectra were interpreted within the dipole radiation theory of electromagnetic waves. Both measurement results and analysis provide evidence sup-porting the presence of a pronounced polarization anisotropy impact in the reflection and transmission of the metamaterial. The potential of these materials to be utilized in the development of devices that control the polarization properties of THz radiation across a wide spectral range is examined.

Active front-end (AFE) rectifiers have been becoming widely employed in power systems to achieve unity power factor and harmonic mitigations. The typical modeling approaches applied for AFE rectifiers in the literature mostly relied on two baselines named the detailed model and the time average model. The former approach deals with the switching element model (SEM) leads to significant harmonics in currents with distorted waveforms. The latter approach uses the average-value model (AVM) to overcome the currents’ harmonics as well as provide fast responses. However, even the AVM baseline has contained problems during the starting stage (lack of control signals) and over the dead-time periods which causes serious issues in the implementation process. This paper presents an improved dynamic AVM for AFE rectifiers by precisely considering the issues mentioned above along with the practical starting procedure and desirable initialization. The studied AFE rectifier is developed using the voltage-oriented control (VOC) technique based on the different modeling methodologies including SEM, Conventional AVM, and the proposed AVM. The performance of all models is analyzed and compared using simulation results with MATLAB/Simulink R2023a Function blocks for all the algorithm parts and SimScape elements for the electrical circuit model. The simulation results illustrate that the performance of the proposed AVM approach can closely resemble the behavior of the SEM baseline with low harmonic distortion.

The study’s objective is to analyse the mechanical properties of steel pipe piles as a part of a trestle bridge subjected to five years of natural marine corrosion degradation. Sixteen tensile specimens were extracted from the steel pipe piles in the splash, tidal, and immersion zones. The experimental tensile test results are used to establish regression equations defining elastic modulus, yield strength, strain hardening index, and strength coefficient for the true stress-strain curves of the three regions. A non-linear time-dependent mathematical model is exploited to identify the corrosion degradation using the data from one single corrosion degradation measurement campaign. The analysis indicated that the splash zone experiences the most severe corrosion degradation, and there are progressive losses in the mechanical properties of each zone as the corrosion degradation progresses. The established relationships of the mechanical properties, as a function of the ratio of corroded plate thickness to the as-built one, can be used as a fast-engineering approach to identify the mechanical properties of severely corroded piles. The corrosion degradation allowance is also defined using the First Order Reliability Method, accounting for existing uncertainties covered by the partial safety factors. By examining the impact of marine corrosion on the mechanical properties of marine structures and developing predictive models to assess the corrosion’s effect on material strength and corrosion allowance, the study aims to improve offshore structures’ safety, design, and maintenance.

Modern electrical power systems integrate renewable generation, with solar generation being one of the pioneers worldwide. In Latin America, the greatest potential and development of solar generation is found in Chile through the National Electric System. However, its energy matrix faces a crisis of drought and reduction of emissions that limits hydroelectric generation and involves the definitive withdrawal of coal generation. The dispatch of these plants is carried out by the system operator, who uses a simplified mechanism, called “economic merit list” and which does not reflect the real costs of the plants to the damage of the operating and marginal cost of the system. This inefficient dispatch scheme fails to optimize the availability of stored gas and its use over time. Therefore, a real-time redispatch model is proposed that minimizes the operation cost function of the power plants, integrating the variable generation cost as a polynomial function of the net specific fuel consumption, adding gas volume stock restrictions and water reservoirs. In addition, the redispatch model uses an innovative "maximum dispatch power" restriction, which depends on the demand associated with the automatic load disconnection scheme due to low frequency. Finally, by testing real simulation cases, the redispatch model manages to optimize the operation and dispatch costs of power plants, allowing the technical barriers of the market to be broken down with the aim of integrating ancillary services in the short term, using the power reserves in primary (PFC), secondary (SCF), and tertiary (TCF) frequency control.

We show that, as in the case of the principle of minimum action in classical and quantum mechanics, there exists an even more general principle in the very fundamental structure of quantum space-time: This is the principle of minimal group that allows to consistently and simultaneously obtain a natural description of the spacetime dynamics and the physical states admissible in it. The theoretical construction is based on the physical states that are average values of the generators of the Metaplectic group Mp(n) , the double covering of SL(2C) in a vector representation, with respect to the coherent states carrying the spin weight. Our main results here are: (i) There exists a connection between the dynamics given by the Metaplectic group symmetry generators and the physical states (mappings of the generators through bilinear combinations of the basic states). (ii) The ground states are coherent states of the Perelomov-Klauder type defined by the action of the Metaplectic group which divide the Hilbert space into even and odd states mutually orthogonal. They carry a spin weight 1/4 and 3/4 respectively from which, two other basic states can be formed. (iii) The physical states, mapped bilinearly with the basic 1/4 and 3/4 spin weight states, plus their symmetric and antisymmetric combinations, have spin contents s = 0, 1/2, 1, 3/2 and 2. (iv) The generators realized with the bosonic variables of the harmonic oscillator introduce a natural supersymmetry and a superspace whose line element is the geometrical Lagrangian of our model. (v) From the line element as operator level, a coherent physical state of spin 2 can be obtained and naturally related to the metric tensor. (vi) The metric tensor is naturally discretized by taking the discrete series given by the basic states (coherent states) in the n number representation, reaching the classical (continuous) space-time for n going to infinity. (vii) There emerges a relation between the eigenvalue of our coherent state metric solution and the black hole area (entropy) as A_{bh}/4 l_{p}^{2} = I alpha I relating the phase space of the metric found g_{ab} and the black hole area A_{bh} through the Planck length l_{p}^{2} and the eigenvalue I alpha I of the coherent states. As a consequence of the lowest level of the quantum discrete space-time spectrum, eg the ground state associated to n = 0 and its characteristic length, there exists a minimum entropy related to the black hole history.

Abstract: rainfall is the main driver of soil erosion. With the daily rainfall, riverine flow discharge and sediment load in the upper Lianjiang River watershed from 1990 to 2020, extreme rainfall events were defined by the 95th percentile method and minimum event interval time (MIT), change trends and mutations of yearly rainfall, riverine flow discharge and sediment load were identified using the Mann-Kendall test, then the optimum lag response time and antecedent rainfall were investigated with the multiple linear regression. The results showed that (1) the sediment changed significantly and abruptly in 1995 due to the expansion of the garden land. (2) Compared with ordinary rainfall, extreme rainfall events explained more variations of riverine flow and sediment with a higher degree, and had a more significant effect on the lag time of runoff and sediment. (3) Garden land expansion in extreme rainfall scenarios resulted in longer lag times for runoff and sediment and decreased demand for antecedent rainfall with more pre-event time. Therefore, taking the rainfall event as a breakthrough, analyzing the antecedent rainfall and the lag response of riverine flow discharge and sediment load is conducive to revealing the response mechanism of riverine flow discharge and sediment load and improving the simulation accuracy of riverine flow and sediment under extreme rainfall condition, thus help for the soil erosion control under extreme rainfall.

The investigation of the spatiotemporal propagation characteristics of the "meteorological-agricultural-hydrological" drought system, under diverse climatic conditions, is crucial for the development of a robust drought warning system and the effective implementation of proactive drought prevention and resilience strategies. To achieve this, the current study utilizes the Soil and Water Assessment Tool (SWAT) model to simulate key components of the watershed water cycle, such as evaporation, surface water, soil water, and groundwater. Specifically, the Standardized Precipitation Evapotranspiration Index (SPEI), the Standardized Soil Moisture Index (SSMI), and the Nonlinear Joint Hydrological Drought Index (NJHDI) are employed to characterize meteorological drought, agricultural drought, and hydrological drought, respectively. By analyzing the correlation between these types of drought, the propagation characteristics of the "meteorological-agricultural-hydrological" drought system are elucidated using the rigorous strongest correlation coefficient method. The Yellow River Basin (YRB) is chosen as the case study for this research. Results showed that (1) The propagation time from meteorological to agricultural drought exhibited distinct seasonal characteristics, with durations of 5-6 months in spring, 2-3 months in summer, 3-5 months in autumn, and 6-8 months in winter. Compared to 1961-1990, the propagation time increased in spring and summer but decreased in autumn and winter during 1991-2010 for most YRB regions. (2) The agricultural to hydrological drought propagation showed no clear seasonal differences but increased over time. Specifically, zone C (arid/semi-arid with moderate temperatures) had shorter propagation time of 1-5 months, while zones B (transitional plateau to mid-latitude) and E (semi-arid/semi-humid temperate continental climate) experienced longer propagation time of 7-12 months. (3) Despite the extended timescales, agricultural-hydrological drought correlation was weaker than meteorological-agricultural linkage. This is because meteorological deficits directly reduce soil moisture, rapidly inducing agricultural drought. However, groundwater sustaining baseflow during agricultural drought delays streamflow deficits, prolonging the agricultural-hydrological propagation time.

In this paper, we derive Jeffreys divergence, generalized Fisher divergence and corresponding De Bruijn identities on space-time random field. First, we determine the relation between Fisher information on the space-time random field in one of the space-time points and the ratio of Jeffreys divergence on a space-time random field at distinct space-time positions to the square of coordinate difference. In addition, we also find identities between the partial derivative of the Jeffreys divergence and the generalized Fisher divergence with respect to space-time variables, i.e. the De Bruijn identities, between two space-time random fields obtained by different parameters under the same Fokker-Planck equations. At the end of this paper, we present three examples of the Fokker-Planck equations on space-time random fields, identify their density functions, and derive the Jeffreys divergence, generalized Fisher information, generalized Fisher divergence, and accompanying De Bruijn identities.

An approach to the implementation of neural network for real-time cryptographic data protection with symmetric keys oriented on embedded systems is presented. This approach is valuable especially for onboard communication systems in unmanned aerial vehicles (UAV) because of suitability to hardware implementation. In this study we evaluate the possibility to build such a system in hardware implementation at FPGA. The proposed integrated neural network approach for real-time cryptographic data protection was based on theoretical foundations of neural network cryptographic data protection, new algorithms and structures of neural network data encryption and decryption, modern hardware components with programmable structure. The development and implementation of the on-board neural network system for cryptographic data protection in real-time are based on the following principles: variable composition of equipment; modularity; conveyorization and spatial parallelism; hardware and software modification and making them suitable for data encryption and decryption. The tabular-algorithmic method for calculation of the scalar product has been improved that makes it possible to perform fast calculation of the scalar product of fixed-point and floating-point input data by bringing them to the largest common order of weights and forming tables of macro-partial products. Components of neural network cryptographic data encryption and decryption have been developed on the processor core supplemented by specialized scalar product calculation modules. The specialized hardware for neural network cryptographic data encryption was developed using VHDL for equipment programming in the Quartus II development environment ver. 13.1 and the appropriate libraries, and implemented on the basis of the FPGA EP3C16F484C6 Cyclone III family.

As sessile organisms, plants face a wide range of abiotic stresses, with salinity being a prominent constraint affecting their growth, development, and productivity, particularly in arid and semi-arid regions. This study focused on understanding how salinity impacts Jatropha curcas, an important oilseed plant for biodiesel production. By examining the anatomy and ultrastructure of stomata and chloroplasts, we investigated the effects of prolonged salinity stress on J. curcas. This stress led to changes in stomatal density, stomatal index, and ostiole aperture, which case an imbalance of water conductivity in the xylem. Through transmission electron microscopy, we explored the subcellular organization of J. curcas chloroplasts and their contribution to plant photosynthetic efficiency, providing insights into their role in this process. Notably, salinity treatment resulted in a significant increase in starch granules accumulation, leading to impaired the granal and stromal grana lamellae, destroying this ultrastructure. Our findings indicate that the anatomy and ultrastructure of chloroplasts play a crucial role in influencing photosynthetic efficiency and hydraulic conductivity. This study offers new perspectives on the structure and function of chloroplasts in J. curcas, presenting innovative opportunities to develop strategies that enhance biofuel production in areas with high soil salinity.

Durability of concrete requires a dense microstructure which can be achieved by using self-compacting concrete (SCC). Both calcined clay (CC) and rice husk ash (RHA) are promising supplementary cementitious materials (SCMs) that can partially replace cement, but their use in SCC is critical due to their higher water demand and specific surface area (SSA) compared to cement. Empirical method of SCC design was adopted considering the physical properties of both CC and RHA. The influence of partially substituting cement at 20 vol-% with binary and ternary blends of CC and RHA were investigated. The fresh properties of SCC were investigated using a variety of tests. The time dependent workability was monitored by plunger method and flow resistance determined based on the rheological measurements of SCC. The evolution of the hydrate phases of the binder in SCC was determined by thermogravimetric analysis, while the durability was evaluated by rapid chloride migration test. Cement partial replacement with 20 vol-% CC has no significant effect on SCC fresh, time dependent, compressive strength and durability properties. 20 vol-% RHA on the other hand requires higher dosage of SP to achieve self-compactability and increased the viscosity of SCC. Its workability retention is only up to 30 min after mixing and exhibited higher flow resistance. It consumes more CH and improves compressive strength and chloride resistance of SCC. The ternary blending with CC and RHA yielded better fresh SCC properties compared to the binary blend with RHA, while an improved chloride penetration resistance could be achieved compared to binary CC blend.

It is common knowledge that estimating the height component of GNSS stations in general is much more problematic than estimating the horizontal position. Many different effects, such as tectonic signals, non-tectonic signals, atmospheric delay, noise, etc., are known to affect the height component of GNSS stations more than the horizontal component. However, the height component of GNSS stations is still poorly estimated. In this study, the height time series of 37 continuous GNSS stations covering the 2014–2019 date range is used from the Turkish National Permanent GNSS Network-Active (TUSAGA-Active). Since it is easier to interpret the effects of the height component due to its topographic features and seasonal changes being more effective than in the rest of the country, stations were chosen in the Eastern Anatolia region of Turkey. The daily coordinates of the GNSS stations were obtained as a result of the GAMIT/GLOBK software solution. By applying time series analysis to the daily coordinate values of the stations, statistically significant trends, periodic and stochastic components of the stations were determined. As a result of the analysis, the vertical velocities of the GNSS stations and the standard deviations of the vertical velocities were determined. Furthermore, when the height components of continuous GNSS stations were examined, it was seen that there were seasonal effects, and it was investigated whether the height components were related to meteorological parameters. For that, simple linear regression analysis was performed to determine how dependent the height components of the continuous GNSS stations were on meteorological parameters. As a result of the analysis, the height components of the continuous GNSS stations are dependent on meteorological parameters such as temperature, pressure, relative humidity, wind speed, and precipitation. In addition, height component time series analysis of continuous GNSS stations was performed by using Autoregressive Moving Average (ARMA) models from linear time series methods. As a result of the study, the performance of the ARMA modeling results again indicated the dependence of the height component of the continuous GNSS stations on the meteorological parameters.

Disasters related to climate change on our water resources are on the rise in terms of scale and severity. Therefore, predicting groundwater levels (GWL) is a crucial means to aid adaptive capacity towards disasters related to climate change in our water resources. In this study Gradient Boosting (GB) regression modelling approach for GWL prediction as a function of rainfall and antecedent GWL is used. Firstly, we sought to demonstrate the effects of rainfall changes on our groundwater resources through a Mann-Kendall trend analysis. Secondly, we evaluated the relationship between the input and response variables and determined the optimal lag times between the variables using autocorrelations and cross-correlations. Lastly a predictive model was developed for eight groundwater stations in the Upper Crocodile. 50 % of the groundwater stations revealed declining trends, while 25% had no trends and the other 25% showed an increasing trend. Generally low cross-correlation maximum (CCmax) were obtained, with the highest CCmax being 0.299 at an optimal lag of 2-month. While the highest autocorrelation was 0.969 at a 1-month lag. The best groundwater predictive model had R2 and MSE of 0.66 and 0.06, respectively. The stations that generally performed better had both high autocorrelation and cross-correlation coefficients. GB model performed satisfactorily in predicting GWL for most of the stations in the study area. Therefore, GB can be used for GWL prediction in the Upper Crocodile.

We firstly present two popular space discretization models of the Nonlinear Schrödinger Equation in dark solitons motion : the Direct-Discrete model and the Ablowitz-Ladik model. Applying the midpoint scheme to the space discretization models, we get two time-space discretization models: the Crank-Nicolson method and the New-Difference method. Secondly, we demonstrate that the solutions of the two space discretization models converge to the solution of the Nonlinear Schrödinger Equation. Also, we prove that the convergence order of the two time-space discretization models are O(h2+τ2) in discrete L2-norm error estimates. Finally, the numerical experiments agree well with the proven theoretical results.

Elucidating physicochemical processes in the degradation of pollutants may optimize their removal from water sources. This work presents a set of elementary steps in the photocatalytic degradation of carbamazepine (CBZ), assuming a steady state approximation in an Advanced Oxidation Process (AOP) combining short-wave ultraviolet radiation (UVC), homogeneous reagent (H₂O₂) and heterogeneous (TiO₂) catalyst. Elementary steps include excitation of both reagent/catalysts by UVC photons, adsorption of CBZ on the excited TiO₂, or its oxidation by hydroxyl radicals. Assuming the steady state approximation on the intermediate products (excited TiO₂, CBZ- excited TiO₂ complex, and hydroxyl radicals), leads to rate laws for degradation of CBZ, in which UVC radiation, TiO₂, and H₂O₂ are pseudo first order at all concentrations or intensities having no direct influence on CBZ pseudo-order, whereas CBZ shifts from pseudo first order at low concentrations to pseudo-zero order at high concentrations. Several experiments to test the mechanism were conducted, by varying CBZ, H₂O₂, and TiO₂ concentrations, and UVC radiation intensities. Measured results indeed fit the suggested mechanism for the first three, but irradiation intensity appears to shift CBZ influence from pseudo-second to pseudo-first order with increased intensities. Corrected elementary steps are proposed to fit the results.

The mathematical modelling of reverse logistics inventory systems ignores that returned items may arrive out of sequence, i.e. with different number of remanufacturing times. Moreover, such modelling assumes that the retuned items may retain the same quality upon recovery regardless of how many times they have been previously remanufactured. This paper develops a new mathematical expression of the percentage of retuned items that can be remanufactured a finite number of times. The proposed expression is modelled as a function of the expected number of times an item can be remanufactured on its life cycle and the number of times an item can be technologically (or optimally) remanufactured based on its quality upon recovery. The model developed in this paper considers joint production and remanufacturing options. The return rate is a varying demand dependent rate, which is a decision variable with demand, product deterioration, manufacturing and remanufacturing rates being arbitrary functions of time. The model considers the initial inventory of returned items in the mathematical formulation, which enables decision makers to adjust all functions and input parameters for subsequent cycles. Illustrative examples indicate that dependent purchasing price of recovery items and the incorporation of remanufacturing investment cost significantly impact on the optimal remanufacturing policy.

Deep learning has been applied in many areas that have had a significant impact on applications that improves real-life challenges. The success of deep learning in a wide range of areas is due in part to the use of activation functions, which are particularly effective at solving non-linear problems. Activation functions are a key focus for researchers in artificial intelligence who aim to improve the performance of neural networks. This article provides a comprehensive explanation and comparison of different activation functions, with a focus on the arc tangent and its variations specifically. The paper presents experimental results that show that variations of the arc tangent using irrational numbers such as pi, the golden ratio, and Euler’s number, as well as a self-arctan function, produce promising results. Since we experimented with promising activation functions on two different problems, and datasets, we reached a result that different irrationals work well for different problems. In other words, arctan ϕ gives the best results mostly for multiclass classification and arctan e gives the best results for time series prediction problems. The paper focuses on a multi-class classification problem applied to the Reuters Newswire dataset and a time-series prediction problem on Türkiye energy trade value to show the impacts of activation functions.

Anomaly Detection in real time data is accepted as a vital research area. Clustering has effectively been tried for this purpose. As the datasets are real time, the time of generating of the data is also important. In this article, we introduce a mixture of partitioning and agglomerative hierarchical approach to detect anomalies from such datasets. It is a two-phase method which follows partitioning approach first and then agglomerative hierarchical approach. The dataset can have mixed attributes. In phase-1, a unified metric defined on mixed attributes is used. The same is also used for merging of similar clusters in phase-2. Also, we have kept the track of time attribute of each data instance which produces the clusters with their lifetimes in phase-1. Then in phase-2, we merge the similar clusters. While merging, the similar clusters, the lifetimes of the corresponding clusters with overlapping cores are to be superimposed producing fuzzy time intervals. This way, each cluster will have an associated fuzzy lifetime. The data instances either belonging sparse clusters or not belonging to any of the clusters can be treated as anomalies. The efficacy of the algorithms can be established using both complexity analysis as well as experimental studies.

Cities exposed their vulnerabilities during COVID-19 pandemic. Unprecedented policies restricted human activities but left an unique opportunity to quantify anthropogenic effects on urban air pollution. This study aimed to address the key questions of urban development behind the restrictions with the goal of supporting sustainable transition. Data from ground stations and Sentinel-5P satellite were used to assess the temporal and spatial anomalies of NO2. Beijing China was selected for a case study because this mega city maintained a “dynamic zero-COVID” policy with adjusted restrictions, allowing us better to track the effects. The time-series decomposition and prediction regression model were employed to estimate the normal NO2 levels in 2020. The anomalies between the observations and predictions as the deviation were identified due to the policy interventions and quantified different effects using spatial stratified heterogeneity statistics. The top three restrictions showing dominant effects were workplace closures, restricted public transport usage, and school closures, accounting for 54.8%, 52.3%, and 46.4% of NO2 anomalies, respectively; and they are directly linked to the mismatch of employment and housing (deter-mining the commuting patterns), educational inequality and the long-term unsolved road con-gestion. Promoting the transformation of urban spatial structure will effectively alleviate air pollution.

It is expected that scholars will continue to strengthen the depth and breadth of theoretical research on RIS, so as to provide a higher theoretical upper bound for the engineering application of RIS. While making breakthroughs in academic research, great progress has been made in engineering application research and industrialization promotion. This article will provide an overview of RIS engineering applications, mainly including the typical features, typical classifications, and typical deployment scenarios of RIS. Then the challenges and candidate solutions of the RIS are systematically and deeply analyzed, involving the cascade channel decoupling for solving the RIS beamforming, the influences and solutions of RIS regulation constraints, RIS system architecture of network controlled mode, the integrated channel regulation and information modulation, TDD mechanism used for RIS. Future trends and challenges are also provided.

Drinking water quality is a major concern, especially in African countries. This manuscript aims to analyze the chemical composition of Lioua’s groundwater in order to determine the geological processes influencing the chemical elements' composition and origin. Therefore, chemometrics techniques such as multivariate statistical analysis (MSA) and time series methods (TSM) are used. Indeed, MSA includes a component analysis (PCA) and a cluster analysis (CA), while au-tocorrelation analysis (AA) supplemented by simple spectral density analysis (SDA) is used for TMS. PCA displays three main factors explaining a total variance (TV) of 85.01 %. Factors 1, 2, and 3 are 68.72%, 11.96%, and 8.89 % of TV, respectively. In the CA, three groups were controlled by TDS and EC. G1 reveals a close association between SO42−, K+, Ca2+, and TDS; G2 reveals a close association between Na+, Cl−, Mg2+, and EC; G3 shows the dissociation of bicarbonates HCO3− and NO3− from other chemical elements. AA shows a linear interrelationship of EC, Mg2+, Na+, K+, Cl−, and SO42−. However, NO3− and HCO3− indicate uncorrelated characteristics with other parameters. For SDA, the correlograms of Mg2+, Na+, K+, Cl−, and SO42− have a similar trend with EC. None-theless, pH, Ca2+, HCO3− and NO3− exhibit multiple peaks related to the presence of several dis-tinct cyclic mechanisms. The methods enabled the authors to conclude that the geochemical processes influencing the chemical composition are: (i) dissolution of evaporated mineral depos-its, (ii) water-rock interaction, and (iii) evaporation process. In addition, Groundwater exhibits two bipolar characteristics, one recorded with negative and positive charges on pH and Ca+ and another recorded only with negative charges on HCO3− and NO3−. On the other hand, SO42-, K+, Ca2+, and TDS are the major predominant elements in the groundwater’s chemical composition. The major participation of salts and chlorides is in the electrical conductivity of water. The dominance of the lithological factor in the overall mineralization of the Plio-Quaternary surface aquifer waters. The origins of HCO3− and NO3− are different. Indeed, carbonated for HCO3- has a carbonate origin, whereas NO3– has an anthropogenic origin. The salinity was affected by Mg2+, SO42-, Cl-, Na+, K+, and EC. Ca2+, HCO3− and NO3− are resulted from human activity fertilizers, the carbonate facies outcrops, and domestic sewage.

This paper attempts to describe the large-scale (solar system-like, astronomical-scale) systems of the known world using the physical models and mathematical tools of relativistic quantum mechanics. The value of H_x can be introduced and approximated as an analogue of the constant ℏ. Based on the quantum mechanical approach, the proper time scale of the solar system can be determined.

The search for fundamental determinants of bio-molecular chirality is a hot topic in biology, clarifying the meaning of evolution and the enigma of life's origin. Non-biological and biological entities obey the universal laws of nature grounded on space-time symmetry (STS) transformations. The fabric of STS is the immense subject of our review. The symmetry, encompassing behavior of elementary particles and galaxy structure, imposes its fundamental laws on all hierarchical levels of the biological world. Objects across spatial scales may be classified as chiral or achiral concerning a specific space-related symmetry transformation - mirror reflection. Chirality as a symmetry element reflecting the property of asymmetry (or dissymmetry) is the critical structural feature of natural systems, including sub-atomic particles and living matter. The inheritance of molecular symmetry from the symmetry of elementary particles points to the bi-directional causal pathway of prevalent bio-chirality. We assume that both extremities of spatial dimension naturally influence this transitional state of biological matter. The current review promotes an integrative holistic approach to the new experimental results in fast-developing and divergent branches of STS and biological chirality. The generalized view on biological chirality is a necessary and promising avenue for adequately understanding the link between protein folding, cell morphology, neuronal signaling, and laterality of cognitive functions.

For more than half a century, Manfred Deistler has been contributing to the construction of the rigorous theoretical foundations of the statistical analysis of time series and more general stochastic processes. Half a century of unremitting activity is not easily summarized in a few pages. In this short note, we chose to concentrate on a relatively little-known aspect of Manfred's contribution which nevertheless had quite an impact on the development of one of the most powerful tools of contemporary time series and econometrics: dynamic factor models.

The environmental issues we are currently facing require long-term prospective efforts for sustainable growth. Renewable energy sources seem to be one of the most practical and efficient alternatives in this regard. Understanding a nation's pattern of energy use and renewable energy production is crucial for developing strategic plans. No previous study has been performed to explore the dynamics of power consumption with the change in renewable energy production on a country-wide scale. In contrast, a number of deep learning algorithms demonstrated acceptable performance while handling sequential data in the era of data-driven predictions. In this study, we developed a scheme to investigate and predict total power consumption and renewable energy production time series for eleven years of data using a Recurrent Neural Network (RNN). The dynamics of the interaction between the total annual power consumption and renewable energy production are investigated through extensive Exploratory Data Analysis (EDA) and a feature engineering framework. The performance of the model is found satisfactory through the comparison of the predicted data with the observed data, visualization of the distribution of the errors and Root Mean Squared Error (RMSE) value of 0.084. Higher performance is achieved through the increase in the number of epochs and hyperparameter tuning. The proposed framework can be used and transferred to investigate the trend of renewable energy production and power consumption and predict the future scenarios for different communities. Incorporation of the cloud-based platform into the proposed pipeline may lead to real-time forecasting.

Aiming at the problem of low charging efficiency caused by the scattered sensor nodes in traditional wireless rechargeable sensor networks (WRSNs), a UAV-assisted WRSN Online Charging Strategy Based on Dynamic Queue and Improved K-means (UOCS) is proposed. The scheme assumes that the energy consumption of nodes is unpredictable, and only generates charging requests when the energy is lower than a threshold, and performs on-demand responses to nodes that issue charging requests. The scheme combines the characteristics of one-to-one charging of UAVs, the selection and allocation timing of waiting queues and the number of UAVs, and the improved K-means partitioning based on space-time coordination(SPKM), which simplifies the problem of coordinated charging of multiple UAVs and maximizes energy. Using the efficiency and charging success rate, the optimal charging trajectory can be found under the constraint that the node will not starve to death due to power shortage. Finally, a simulation comparison experiment is carried out with the existing UAV charging scheduling strategy. UOCS achieves the optimal node survival rate with low algorithm complexity.

This paper presents a new implementation of a 2-box absolute flow model, enabling the calculation of CO₂ transfer between the atmosphere and a mixing reservoir representing the combined effect of the terrestrial and ocean regions. The model uses published values of anthropogenic CO₂ fossil fuel emissions (CO2ff), atmospheric CO₂ mixing ratio and nuclear weapons bomb yields, to compute atmospheric d¹³C and Δ¹⁴C time-series. The level of agreement between the calculated values and the accepted values for d¹³C is within ± 0.05 ‰, and for Δ¹⁴C is within ± 3‰, spanning 200 years. The model contains only seven internal parameters; these are varied to optimize the fit, leading to reasonable parameter values, indicating the validity of the method. In addition, the model shows how a rising CO₂ exchange influx fluctuating with surface temperature is compatible with a nett atmospheric CO₂ sink. The study demonstrates that a single CO₂ residence time applies to both the ¹⁴C bomb pulse and the anthropogenic flux, contrary to the conventional view that "bomb radiocarbon and anthropogenic CO₂ do not behave identically." [Joos 1994 Nature 370, 181–182]. It is shown that the difference in behaviour, claimed to be due to seawater chemistry, is not significant in practice since fluctuating bulk carbon dominates fluctuating isotopic ratio. A new analysis of the airborne fraction indicates that the assumption of a time-dependent exchange influx results in a similar residence time for bulk CO₂ as for ¹⁴C in the nuclear bomb pulse, reinforcing the view that the ¹²C, ¹³C and ¹⁴C carbon isotopic forms of CO₂ behave similarly in the carbon cycle.

Recent developments in mid-infrared (MIR) spectroscopic ellipsometry enabled by quantum cascade lasers (QCLs) resulted in a drastic improvement in signal-to-noise ratio compared to conventional thermal emitter based instrumentation. Thus, it was possible to reduce the acquisition time for high-resolution broadband ellipsometric spectra from multiple hours to less than 1 second. This opens up new possibilities for real-time in-situ ellipsometry in polymer processing. To highlight these evolving capabilities we demonstrate the benefits of a QCL based MIR ellipsometer by investigating single and multilayered polymer films. The molecular structure and reorientation of a 2.5m thin biaxially oriented polyethylene terephtalate film is monitored during a stretching process lasting 24.5 s to illustrate the perspective of ellipsometric measurements in dynamic processes. In addition, a polyethylene/ethylene vinyl alcohol/polyethylene multilayer film is investigated at continuously varying angle of incidence ( 0∘ – 50∘) in 17.2 s, highlighting an unprecedented sample throughput for the technique of varying angle spectroscopic ellipsometry in the MIR spectral range. The obtained results underline the superior spectral and temporal resolution of QCL ellipsometry and qualify this technique as suitable method for advanced in-situ monitoring in polymer processing.

Here we studied Bianchi type-III string cosmological models generated by means of a cloud of strings with particles connected to them in the framework of Lyra geometry considering five-dimensional space-time. To obtain the exact solutions of field equations we consider that the shear scalar and the scalar expansion are proportional, σ∞θ which leads to D=cn and secondly we adopt the assumption considering the Reddy String Condition, ρ+λ=0. From the two different cases obtained here, first case leads to the Bianchi type-III string cosmological model in Lyra geometry in five-dimensional space-time and the second case leads to the five-dimensional vacuum universe in general relativity. The Geometrical and physical properties of the model universe are studied comparing with the present day’s observational findings. The model universe obtained here starts with the big bang and as time progresses both particle density ρp and energy density ρ decreases with expansion of our Universe.

In systems biology, biological phenomena are often modeled by ODE and distributed in the de facto standard file format SBML. The primary analyses performed with such models are dynamic simulation, steady-state analysis, and parameter estimation. These methodologies are mathematically formalized, and libraries for such analyses have been published. Several tools exist to create, simulate, or visualize models encoded in SBML. However, setting up and establishing analysis environments is a crucial hurdle for non-modelers. Therefore, easy access to perform fundamental analyses of ODE models is a significant challenge. We developed SBMLWebApp, a web-based service to execute SBML-based simulations, steady-state analysis, and parameter estimation directly in the browser without the need for any setup or prior knowledge to address this issue. SBMLWebApp visualizes the result and numerical table of each analysis and provides a download of the results. SBMLWebApp allows users to select and analyze SBML models directly from the BioModels Database. Taken together, SBMLWebApp provides barrier-free access to an SBML analysis environment for simulation, steady-state analysis, and parameter estimation for SBML models. SBMLWebApp is implemented in Java™ based on an Apache Tomcat® web server using COPASI, the SBSCL, and LibSBMLSim as simulation engines. SBMLWebApp is licensed under MIT with source code available from https://github.com/TakahiroYamada/SBMLWebApp. The program runs online at http://simulate-biology.org.

In this paper, the problem of observer-based adaptive sliding mode control is discussed for nonlinear systems with sensor and actuator faults. The time-varying actuator degradation factor and external disturbance are considered in the system simultaneously. In this study, the original system is described as a new normal system by combining the state vector, sensor faults and external disturbance into a new state vector. For the augmented system, a new sliding mode observer is designed, where a discontinuous term is introduced such that the effects of sensor and actuator faults and external disturbance will be eliminated. In addition, based on a tricky design of the observer, the time-varying actuator degradation factor term is developed in the error system. On the basis of the state estimation, an integral-type adaptive fuzzy sliding mode controller is constructed to ensure the stability of the closed-loop system. Finally, the effectiveness of the proposed control methods can be illustrated with a numerical example.

Motorcycles can be considered a new form of smart vehicle when taking into account their small and modern structure and due to the fact that nowadays they are used in the new role of ambu-lance to rapidly reach emergency patients in large cities with traffic congestion. However, there is no study regarding the measuring of access time for motorcycle ambulances in large cities of Thailand. Therefore, this study aims to compare access times to patients between motorcycle ambulances and conventional ambulances, including analysis of the use of AED installed on motorcycle ambulances to contribute to the sustainable development of public health policies. In two hundred and seventy-one operations, it was found that the times from dispatch to vehicle (ambulance and motorlance) being en route and times from the 1669 center (center for com-mand-and-control EMS in Thailand) call receipt to arrival on scene (response time) of motorcycle ambulances was shorter than that of a conventional ambulance and the use of AED on a motor-cycle ambulance can increase the chances of survival for patients with cardiac arrest outside the hospital in public places where AEDs are not available and in all cases where access to an AED was less than eight minutes.

System-of-systems (SoS) approach is often used for simulating disruptions to business and infrastructure system networks allowing for integration of several models into one simulation. However, the integration is frequently challenging as each system is designed individually with different characteristics, such as time granularity. Understanding the impact of time granularity on propagation of disruptions between businesses and infrastructure systems and finding the appropriate granularity for the SoS simulation remain as major challenges. To tackle these, we explore how time granularity, recovery time, and disruption size affect the propagation of disruptions between constituent systems of an SoS simulation. To address this issue, we developed a High Level Architecture (HLA) simulation of 3 networks and performed a series of simulation experiments. Our results revealed that time granularity and especially recovery time have huge impact on propagation of disruptions. Consequently, we developed a model for selecting an appropriate time granularity for an SoS simulation based on expected recovery time. Our simulation experiments show that time granularity should be less than 1.13 of expected recovery time. We identified some areas for future research centered around extending the experimental factors space.

This paper contains a concise overview of the deployment of scattered solar power plants in Poland, mainly from the perspective of their communication networks, and how the recent development of the Polish 4G networks has a very positive impact for the performance of the whole monitoring system (production control and video-surveillance), with a special emphasis on video-analytics, due to its higher bandwidth demand. All the information will be shown from the point of view of the solar photovoltaics developer I+D Energías, and therefore it constitutes a real user’s experience.

Direct yaw moment control (DYC) is an effective way to alter the behaviour of electric cars with independent drives. Controlling the torque applied to each wheel can improve the handling performance of a vehicle making it safer andfaster on a race track. The state-of-the-art literature covers the comparison of various controllers (PID, LPV, LQR, SMC, etc.) using ISO manoeuvres. However, more advanced comparison on important characteristics of the controllers performance is missed, such as the robustness of the controllers under changes in the vehicle model, steering behaviour, use of the friction circle and, ultimately, lap time on a track. In this study, we have compared the controllers according to some of the aforementioned parameters on a modelled race car. Interestingly, best lap times are not provided by perfect neutral or close-to-neutral behaviour of the vehicle, but rather by allowing certain deviations from the target yaw rate. In addition, a modified PID controller showed that its performance is comparable to other more complex control techniques such as MPC.

Transport Layer Security (TLS) is a cryptographic protocol that provides communications security between two peers and it is widely used in many applications. To reduce the latency in TLS handshake session resumption using pre-shared key (PSK) had been used. But current methods in PSK mode handshake uses a fixed session key multiple times for the lifetime of session ticket. Reuse of fixed session key should be very careful in the point of communications security. It is vulnerable to replay attacks and there is a possibility of tracking users. Paired token (PT) is a new secondary credential scheme that provides pre-shared key in stateless way in client-server environment. Server issues paired token (public token and secret token) to authenticated client. Public token represents signed identity of client and secret token is a kind of shared secret between client and server. Once client is equipped with PT, it can be used for many symmetric key based cryptographic applications such as authentication, authorization, key establishment, etc. It was also shown that it can be used for one-time authenticated key establishment using the time-based one-time password (TOTP) approach. In this paper we apply the PT and TOTP approach to TLS to achieve stateless one-time authenticated session resumption. Server executes full handshake of TLS 1.3 and issues PT to authenticated client. Then client and server can execute one-time authenticated session resumption using PT in stateless way in server side. In every runs of session resumption distinct session keys are established that the same PT can be used safely for longer lifetime. If anonymous PT is used with renewal issuing, user privacy, untraceability and forward security can be achieved easily. It will provide a huge performance gain in large-scale distributed services.

New experimental and clinical findings question the historic view of hydrocephalus and its 100-year-old classification. In particular, real-time MRI evaluation of CSF flow and detailed insights into brain water regulation on the molecular scale indicate the existence of at least three main mechanisms that determine the dynamics of neurofluids. (i) Inspiration is a major driving force (ii) Adequate filling of brain ventricles by balanced cerebrospinal fluid upsurge is sensed by cilia (iii) The perivascular glial network connects the ependymal surface to the pericapillary Virchow-Robin spaces. Hitherto, these aspects have not been considered a common physiologic framework improving knowledge and therapy for severe disorders of normal-pressure and post-haemorrhagic hydrocephalus, spontaneous intracranial hypotension and spaceflight disease.

Classical enzyme kinetics are summarized and linked with modern discoveries here. The time course of sequential catalytic events by biological macromolecular enzyme is analyzed at the molecular level; the relationships between catalytic efficiency (turnover number), catalytic rate/velocity, the amount of time taken and physical/biochemical conditions of the system are discussed. This writing tries to connect the microscopic molecular behavior of enzyme to kinetic data obtained in experiment, and the hypothesis proposed here provide an interpretation to previous experimental observations and can be testified by future experiments.

In this article we study the presence of chaos in supersymmetric(SUSY) quantum mechanics. For that purpose we present a form of 4-point out of time order correlator(OTOC) for SUSY quantum mechanical systems using tensor product formalism. We calculate the 4-point OTOC for SUSY 1D harmonic oscillator and find that the OTOC is exactly equal to that of 1D bosonic harmonic oscillator system. In similar manner using the eigenstate representation of supersymmetric systems we calculate the generalized higher order out of time order correlator. The higher order OTOC is a more sensitive measure of chaos than the usual 4-point correlator used in literature. Finally, we calculate the generalized 2N-point OTOC for SUSY 1D harmonic oscillator.

We use a system biology approach to translate the interaction of Bacillus Calmette-Gurin (BCG) + interleukin 2 (IL-2) for the treatment of bladder cancer into a mathematical model. The model is presented as a system of differential equations with the following variables: number of tumor cells, bacterial cells, immune cells, and cytokines involved in the tumor-immune response. This work investigates the delay effect induced by the proliferation of tumor antigen-specific effector cells after the immune system destroys BCG-infected urothelium cells following BCG and IL-2 immunotherapy in the treatment of bladder cancer. For the proposed model, three equilibrium states are found analytically. The stability of all equilibria is analyzed using the method of Lyapunov functionals construction and the method of linear matrix inequalities (LMIs).

GIGANTEA (GI) is a gene involved in multiple biological functions, which were analysed and are partially conserved in a series of mono- and dicotyledonous plant species. The identified biological functions include control over the circadian rhythm, light signalling, cold tolerance, hormone signalling and photoperiodic flowering. The latter function is a central role of GI, as it involves a multitude of pathways, both dependent and independent of the gene CONSTANS(CO) as well as on the basis of interaction with miRNA. The complexity of gene function of GI increases due to the existence of paralogs showing changes in genome structure as well as incidences of sub- and neofunctionalization. We present an updated report of the biological function of GI, integrating late insights into its role in floral initiation, flower development and flower volatile production.

Feature selection is a crucial step to overcome the curse of dimensionality problem in data mining. This work proposes Recursive k-means Silhouette Elimination (RkSE) as a new unsupervised feature selection algorithm to reduce dimensionality in univariate and multivariate time-series datasets. Where k-means clustering is applied recursively to select the cluster representative features, following a unique application of silhouette measure for each cluster and a user-defined threshold as the feature selection or elimination criteria. The proposed method is evaluated on a hydraulic test rig, multi sensor readings in two different fashions: (1) Reduce the dimensionality in a multivariate classification problem using various classifiers of different functionalities. (2) Classification of univariate data in a sliding window scenario, where RkSE is used as a window compression method, to reduce the window dimensionality by selecting the best time points in a sliding window. Moreover, the results are validated using 10-fold cross validation technique. As well as, compared to the results when the classification is pulled directly with no feature selection applied. Additionally, a new taxonomy for k-means based feature selection methods is proposed. The experimental results and observations in the two comprehensive experiments demonstrated in this work reveal the capabilities and accuracy of the proposed method.

For more than one hundred years, scientists dispute the correct interpretation of Lorentz transformations within the framework of the special theory of relativity of Albert Einstein. On the one hand, the changes in length, time and mass with increasing velocity are interpreted as apparent due to the observer dependence within special relativity. On the other hand, real changes are described corresponding to the experimental evidence of mass increase in particle accelerators or of clock delay. This ambiguity is accompanied by an ongoing controversy about valid Lorentz-transformed thermodynamic quantities such as entropy, pressure and temperature. In this paper is shown that the interpretation problem of the Lorentz transformations is genuinely anchored within the postulates of special relativity and can be solved on the basis of the thermodynamic approach of matter-energy equivalence, i.e. an energetic distinction between matter and mass. It is suggested that the velocity-dependent changes in state quantities are real in each case, in full agreement with the experimental evidence.

On 9 March 2020, two cases of COVID-19 were reported in Burkina Faso. As of 10 April 2020, a total number of 484 cases (404 cases in the Kadiogo province) were reported nationwide. Real-time forecasts of COVID-19 are important to inform decision-making in the country. Here, we propose an approach that tests the performance of four models (Exponential Growth model, the Generalized Growth model (GGM), the Generalized Logistic Growth, and Richards Growth model) to select the model that best fit data and to generate short-term forecasting (5-, 10-, and 15-day forecasts from 11 to 25 April 2020) in Kadiogo, the epicenter of the outbreak. Using daily number of confirmed COVID-19 cases, the results suggests that GGM performed the best out of the 4 models. Overall, our GGM predictions suggested an average total number of cumulative cases of 514 (95% CI, 464–559), 629 (95% CI, 559–691), and 750 (95% CI, 661–840) between 11 to 15 April, 16 to 20 April, and 20 to 25 April 2020, respectively. COVID-19 in this province was best approximated by sub exponential growth rather than exponential or logistic growth. Current data suggest that COVID-19 cases would continue to increase over the next 15-days.

We present and benchmark FilterNet, a flexible deep learning architecture for time series classification tasks, such as activity recognition via multichannel sensor data. It adapts popular CNN and CNN-LSTM motifs which have excelled in activity recognition benchmarks, implementing them in a many-to-many architecture to markedly improve frame-by-frame accuracy, event segmentation accuracy, model size, and computational efficiency. We propose several model variants, evaluate them alongside other published models using the Opportunity benchmark dataset, demonstrate the effect of model ensembling and of altering key parameters, and quantify the quality of the models’ segmentation of discrete events. We also offer recommendations for use and suggest potential model extensions. FilterNet advances the state of the art in all measured accuracy and speed metrics on the benchmarked dataset, and it can be extensively customized for other applications.

We study the center of energy (CE) before and after the separation of superposed wave from a moving medium (MM). It is assumed that two out-of-phase mechanical transverse waves propagating from the opposite directions on a medium moving at non-relativistic speeds are superposed and the superposed portion (SP) is separated from the MM at that moment. We consider the CE of the SP before and after the separation from the MM. The location of CE (LCE) of the SP seems to be at the center of it at the moment of superposition. The SP rotates due to the separation from the MM since the velocity of each portion symmetric with respect to the center of the SP is equal in magnitude and opposite in direction. The magnitudes of velocities of the symmetric portions become different as soon as the SP begins to rotate with the separation from the MM. Then the energies of their symmetric portions are not the same, so the LCE of the SP is not at the center of it. As a result, the LCE of it looks different before and after the separation from the MM. We must find a solution to keep the LCE of the SP constant. We propose that two out-of-phase mechanical transverse waves (MTWs) propagating from the opposite directions on a MM originally have hidden difference in relativistic energy and it suddenly appears within the range observable in Newtonian mechanics when the SP starts rotating. This means that the point of view of hidden difference in relativistic energy is necessary to keep the LCE constant.

In this paper, initially a mathematical model is formulated for transient frequency of power system considering time delays which occur while transmitting the control signals in open communication infrastructure. Time delay negligence in a power system leads to improper measurement of frequency variation in power system. The study of impact of time delays on the stability of power system is performed by estimating the decay rate of frequency wave form using Kalman Filter (KF). In power system, there is a possibility of multiple time delays. This paper also focusses on developing Interacting Multiple Model (IMM) Algorithm with multiple model space using Kalman Filter (KF) as state estimator tool. The multiple time delays in power system is considered as multiple model space. The result shows that KF provides better estimate of correct model for a particular input-set. The qualitative properties of Riccati difference equation (RDE) in terms of state error covariance of IMMKF are also analyzed and presented.

Risk modelling along with multi-objective optimization problems have been at theepicenter of attention for supply chain managers. In this paper, we introduce a datasetfor risk modelling in sophisticated supply chain networks based on formal mathematical models. We have discussed the methodology and simulation tools used to synthesize the dataset. Additionally, the underlying mathematical models are discussed in granular details along with providing directions to conducting statistical analyses or neural machine learning models. The simulation is performed using MATLAB ™Simulink and the models are illustrated as well.

A multi-server parallel system dispatches the incoming job, which contains kn tasks into n servers. A job is considered to be computed if all the tasks associated with the job are processed. One job’s tasks can be encoded into at least kn “replicas” such that the job is considered to be served if any kn replicas finishing computation. In this paper, we analyze the random scheduling policy of a multi-server computing system under discrete time model in terms of Quality of Exponent (QoE), which is defined as the probability exponent that a typical job can be computed within a given number of time slots. We let kn/n be a constant. Assuming that any task of any job can be randomly dispatched by a “scheduler” to any server, and computing each task takes exactly one time slot. We divide the calculation of probability exponent into two parts, exponent of numerator and exponent of denominator. For the denominator, we give the almost exact exponent using Lagrange multiplier method, while for the numerator, an upper bound of the numerator’s exponent is provided. In addition, we also express the exponent in terms of information theoretical quantities and reconsider both of exponents in the context of large deviation theory.

The surface enhanced fluorescence（SEF）detection bases by plasmonic nanopillars array with nanoparticles has opened up a new gate in the application of biological imaging and sensing. The fluorescence enhancement of the probe molecule depends on its position in equilibrium, which is close to the hot spot leading to the electromagnetic field enhancement, but not too close to the metal surface resulting in quenching. Here, a large scale SiO₂-Ag-cicada wing SEF substrate was fabricated by magnetron sputtering with correction enhancement factor of 797.6. Thereinto the cicada wing provides the skeleton of the nanopillars array structure, the deposited Ag constructs two kinds of hot spots, and SiO₂ forms a separation layer to prevent quenching. Moreover, the substrate exhibited good reproducibility, high sensitivity with low limits of detection (LOD) and high stability for oxidation resistance. We propose that SEF substrate with modification of SiO₂ can not only improve the enhancement performance, but also expanding its application in the biological investigations.

Aiming at the current optimization problem of electric vehicle charging path planning, a charging path optimization strategy for electric vehicles under the "Traffic-Price-Distribution" mode is proposed. This strategy builds an electric vehicle charging and navigation system based on road traffic network model, real-time electricity price model and distribution network model. Based on Dijkstra shortest path algorithm and Monte Carlo time-space prediction method, the goal is to minimize the charging cost of electric vehicles. Optimal charging path. The simulation results of MATLAB and MATPOWER show that the electric vehicle charging path optimization strategy can better solve the local traffic congestion problem and improve the safety and stability of the distribution network on the basic of fully considering the convenience of electric vehicle charging.

Today massive amounts of sequenced metagenomic and -transcriptomic data from different ecological niches and environmental locations are available. Scientific progress depends critically on methods that allow extracting useful information from the various types of sequence data. Here, we will first discuss types of information contained in the various flavours of biological sequence data, and how this information can be interpreted to increase our scientific knowledge and understanding. We argue that a mechanistic understanding is required to consistently interpret experimental observations, and that this understanding is greatly facilitated by the generation and analysis of dynamic mathematical models. We conclude that, in order to construct mathematical models and to test mechanistic hypotheses, time-series data is of critical importance. We review diverse techniques to analyse time-series data and discuss various approaches by which time-series of biological sequence data was successfully used to derive and test mechanistic hypotheses. Analysing the bottlenecks of current strategies in the extraction of knowledge and understanding from data, we conclude that combined experimental and theoretical efforts should be implemented as early as possible during the planning phase of individual experiments and scientific research projects.

Giant plasmonic surface enhancement has been observed in gold coated micron sized inverse pyramids entrapping a gold nanoparticle. The amplification of both surface enhanced Raman and photoluminescence signals was found to be dependent on the diameter of trapped gold nanoparticle and around 50-fold enhancement was detected for 250nm diameter sample relatively to the 50nm one. Finite differential time domain simulations, performed to determine the near-field distribution in the structure, showed that when the nanoparticle protrudes into the hotspot zone of the void, coupling of electromagnetic field occurs and the plasmon-related near-field enhancement is concentrated into the close vicinity of the nanoparticle, mainly into the close gaps around the tangential points of the curved sphere and the flat pyramid surface. This results in a more than 15 times increase of the near-field intensity, compared to the empty void.

Perception and motor interaction with physical surroundings can be analyzed by the changes in probability laws governing two possible outcomes of neuronal activity, namely the presence or absence of spikes (binary states). Perception and motor interaction with physical environment are accounted partly by the reduction in entropy within the probability distributions of binary states of neurons in distributed neural circuits, given the knowledge about the characteristics of stimuli in physical surroundings. This reduction in the total entropy of multiple pairs of circuits in networks, by an amount equal to the increase of mutual information among them, occurs as sensory information is processed successively from lower to higher cortical areas or between different areas at the same hierarchical level but belonging to different networks. The increase in mutual information is partly accounted by temporal coupling as well as synaptic connections as proposed by Bahmer and Gupta [1]. We propose that robust increases in mutual information, measuring the association between the characteristics of sensory inputs and neural circuits connectivity patterns, are partly responsible for perception and successful motor interactions with physical surroundings. It is also argued that perception from a sensory input is the result of networking of many circuits to a common circuit that primarily processes the given sensory input.

Real-time energy management of a converter-based microgrid is difficult to determine optimal operating points of a storage system in order to save costs and minimise energy waste. This complexity arises due to time-varying electricity prices, stochastic energy sources and power demand. Many countries have imposed real-time electricity pricing to efficiently control demand side management. This paper presents a particle swarm optimisation (PSO) for the application of real-time energy management to find optimal battery controls of a community microgrid. The modification of the PSO consists in altering the cost function to better model the battery charging/discharging operations. As optimal control is performed by formulating a cost function, it is suitably analysed and then a dynamic penalty function in order to obtain the best cost function is proposed. Several case studies with different scenarios are conducted to determine the effectiveness of the proposed cost function. The proposed cost function can reduce operational cost by 12% as compared to the original cost function over a time horizon of 96 hours. Simulation results reveal the suitability of applying the regularised PSO algorithm with the proposed cost function, which can be adjusted according to the need of the community, for real-time energy management.

For many years, the programmable positioners have been widely applied in structures of modern electro-pneumatic final control elements. The positioner consists of an electro-pneumatic transducer, embedded controller and measuring instrumentation. Electro-pneumatic transducers that are used in positioners are characterized by a relatively short mean time-to-failure. The practical and economical method of a reasonable prolongation of this time is proposed in this paper. It is principally based on assessment and minimizing the effort of the embedded controller. For this purpose, were introduced: the control value variability, mean-time and the cumulative controller's effort. The diminishing of controller effort has significant practical repercussions, because it reduces the intensity of mechanical wear of the final control element components. On the other hand, the reduction of the cumulative effort is important in the context of process economy due to limitation of the consumption of energy of compressed air supplying the final control element. Therefore, the minimization of introduced effort factors has an impact on increasing the functional safety and economics of the controlled process. As a result of the performed simulations, the recommendations regarding the selection of the structure and tuning of positioner controller were elaborated. The simulations were performed in the Matlab-Simulink environment with the use of the liquid level control system in which a phenomenological model of a final control element was deployed. It has been proven that under appropriate conditions, it is possible to extend significantly the lifetime of the final control element and simultaneously enhance the control quality factors.

In this study, we actually want to explore the time-fractional Phi-four equation via two methods, the exp _a function method and the hyperbolic function method. We transform a fractional order dierential equation into an ordinary differential equation using a wave transformation and the fractional derivative in conformable form. Then, the resulting equation has successfully been explored for new explicit exact solutions. The procured solutions are simply showed the effectiveness and plainness of the projected methods.

Time recordings of impulse-type oscillation responses are short and highly transient. These characteristics may complicate the usage of classical spectral signal processing techniques for a) describing the dynamics and b) deriving discriminative features from the data. However, common model identification and validation techniques mostly rely on steady-state recordings, characteristic spectral properties and non-transient behavior. In this work, a recent method, which allows reconstructing differential equations from time series data, is extended for higher degrees of automation. With special focus on short and strongly damped oscillations, an optimization procedure is proposed that fine-tunes the reconstructed dynamical models with respect to model simplicity and error reduction. This framework is analyzed with particular focus on the amount of information available to the reconstruction, noise contamination and non-linearities contained in the time series input. Using the example of a mechanical oscillator, we illustrate how the optimized reconstruction method can be used to identify a suitable model and to extract features from uni-variate and multivariate time series recordings in an engineering-compliant environment. Moreover, the determined minimal models allow for identifying the qualitative nature of the underlying dynamical systems as well as testing for the degree and strength of non-linearity. The reconstructed differential equations would then be potentially available for classical numerical studies, such as bifurcation analysis. These results represent a physically interpretable enhancement of data-driven modeling approaches in structural dynamics.

New technologies such as "connected" and "autonomous" vehicles are going to change the future of traffic signal control and management and possibly will introduce new traffic signal systems that will be based on floating car data (FCD). The use of floating car data to regulate, in real-time, traffic signal systems has the potential for an increased sustainability of transportation in terms of energy efficiency, traffic safety and environmental issues. However, research has never explored how not "connected" vehicles would benefit by the implementation of such systems. This paper explores the use of floating car data to regulate in real-time traffic signal systems in terms of cooperative-competitive paradigm between "connected" vehicles and conventional vehicles. In a dedicated laboratory, developed for testing regulation algorithms, results show that "invisible vehicles" for the system (which are not "connected") in most simulated cases also benefit when real time traffic signal settings based on floating car data are introduced. Moreover, the study estimates the energy and air quality impacts of signal regulation by evaluating fuel consumption and pollutant emissions. Specifically, the study demonstrates that significant improvements in air quality are possible with the introduction of FCD regulated traffic signals.

Modified FOLFIRINOX is effective for advanced pancreatic cancer but frequently causes severe neutropenia. The present study was designed to investigate the influence of severe neutropenia on clinical outcomes in advanced pancreatic cancer patients receiving modified FOLFIRINOX. Fifty-one advanced pancreatic cancer patients who received modified FOLFIRINOX during January 2014 and May 2018 were subjects of the present study. Adverse events, including neutropenia, were graded according to the Common Terminology Criteria for Adverse Events, version 4.0. Median overall survival (OS) was determined as the primary endpoint, while median time to treatment failure (TTF), overall response rate (ORR), and the incidence of other adverse events were measured as secondary endpoints. Severe neutropenia (grade≥3) occurred in 39 patients (76.4%), in which high level of total bilirubin (>0.6mg/dL) was a significant risk as assessed by a multivariate logistic regression analysis. Median duration of OS was significantly longer in patients with severe neutropenia than in those without it (15.2 months versus 7.2 months, P=0.032). Moreover, there was a significant correlation between OS and the grade of neutropenia (R=0.306, P=0.029). ORR tended to be higher, though not significantly, in patients with severe neutropenia. In contrast, the incidence rates of other adverse events were not different between the two groups. Severe neutropenia is an independent predictor of prognosis in advanced pancreatic cancer patients received modified FOLFIRNOX therapy.

Physical Unclonable Functions (PUFs) based on the retention times of the cells of a Dynamic Random Access Memory (DRAM) can be utilised for the implementation of cost-efficient and lightweight cryptographic protocols. However, as recent work has demonstrated, the times needed in order to generate their responses may prohibit their widespread usage. In order to address this issue, the Row Hammer PUF has been proposed by Schaller et al. [1], which leverages the row hammer effect in DRAM modules to reduce the retention times of their cells and, therefore, significantly speed up the generation times for the responses of PUFs based on these retention times. In this work, we extend the work of Schaller et al. by presenting a run-time accessible implementation of this PUF and further reducing the time required for the generation of its responses. Additionally, we also provide a more thorough investigation of the effects of temperature variations on the the Row Hammer PUF and briefly discuss potential statistical relationships between the cells used to implement it. As our results prove, the Row Hammer PUF could potentially provide an adequate level of security for Commercial Off-The-Shelf (COTS) devices, if its dependency on temperature is mitigated, and, may therefore, be commercially adopted in the near future.