A novel approach has been suggested to use isoelectric points of viral and human proteins to quickly identify proteins that are effective in not allowing virus particles to attach to human receptor cells by virtue of their electrical charge. The method has been applied to SARS CoV-2 to suggest potentially important human proteins that can be suitable for making anti-viral drugs.

The COVID-19 pandemic is one of the largest challenges in medicine and health care worldwide in recent decades, and it is infecting and killing increasing numbers of people every day. In this paper, we discuss the possible relationships among lysosomotropism, increasing lysosomal pH, and the SARS-CoV-2 infection and disease process, and we deduce a possible approach for treatment and prophylaxis. Lysosomotropism is a biological characteristic of small molecules, such as (hydroxyl)chloroquine, amitriptyline, NB 06, or sertraline, which is present in addition to intrinsic receptor-mediated or enzymatic pharmacological effects. Lysosomotropic compounds affect prominent inflammatory messengers, such as IL1B, CCL4, CCL20, and IL6, as well as cathepsin L dependent viral entry (fusion) into host cells. Therefore, this heterogeneous group of compounds is a promising candidate for the prevention and treatment of SARS-CoV-2 infections, as well as influenza A infections and cytokine release syndrome (CRS) triggered by bacterial or viral infections. Patients who have already taken medications with lysosomotropic compounds for other pre-existing conditions may benefit from this treatment in the COVID-19 pandemic. Increased lysosomal pH levels play an important role in the disease process in common skin disorders, such as psoriasis and atopic dermatitis, thus suggesting that affected individuals might benefit from their particular conditions in the COVID-19 pandemic. We suggest data analysis of patients with these diseases, and who are treated with lysosomotropic compounds, and, if the results are promising, subsequent clinical testing of off-label therapy with clinically approved lysosomotropic compounds in the current COVID-19 pandemic and future influenza A pandemics.

The disproportionate incidences of COVID-19-related hospitalization and mortality for different age groups and various underlying health conditions is a result of a complex social predisposition to the exposure, resistance, and tolerance for the infection. Based on the observed data as well as the molecular mechanisms for viral entry and replication, cellular senescence related to aging, obesity, hypertension, and diabetes appears to be strongly correlated with the SARS-CoV-2 infections resulting in higher COVID-19 related complications and mortality. Establishing such a correlation may allow us to better explain the pathobiology as well as the differential nature of the SARS-CoV-2 infections and consider targeted control and therapeutic strategies to combat the disease.

Covid-19 is often related to hyperinflammation that drives lung or multi-organ injury. The immunopathological mechanisms that cause excessive inflammation following SARS-Cov-2 infection are under investigation while different approaches to limit hyperinflammation in affected patients are being proposed. Here, a computational protein-protein interaction network approach was used on recently available data to identify possible Covid-19 inflammatory mechanisms and bioactive genes. First, network analysis of putative SARS-Cov-2 cellular receptors and their directly associated proteins, led to the mining of a robust neutrophil response signature and multiple relevant inflammatory genes. Second, analysis of RNA-seq datasets of lung epithelial cells infected with SARS-Cov-2 revealed that infected cells specifically expressed neutrophil-attracting chemokines, further supporting the likely role of neutrophils in Covid-19 inflammation. Third, analysis of RNA-seq datasets of bronchoalveolar lavage fluid from Covid-19 patients, identified neutrophil-specific genes and chemokines. Different immunoregulatory and neutrophil-relevant molecules mined here such as, TNFR, IL8, CXCR1, CXCR2, ADAM10, GPR84, MME-neprilysin, ANPEP and LAP3 are druggable and might be therapeutic targets in efforts to limit SARS-Cov-2 inflammation in severe clinical cases. The role of neutrophils in Covid-19 needs to be studied further.

Mycophenolate mofetil was reported to have broad in vitro activity against different viruses and had been tried in combination with IFN-β in treating MERS infection. We tested the pharmacological activity of mycophenolate mofetil using SARS-CoV-2 infected Vero cells. The half-maximal effective concentration (EC₅₀) of mycophenolate mofetil against SARS-CoV-2 was 0.47 μM while that of remdesivir was 0.77 μM. Molecular docking results of mycophenolate mofetil to potential target proteins of COVID-19 suggested that mycophenolate mofetil might inhibit SARS-CoV-2 mainly by interacting with DHODH and IMPDH2. Furthermore, mycophenolate mofetil as an immunosuppressant may be a good therapeutic option for the management of hyperinflammation in patients with severe COVID-19. Based on its high potency against SARS-CoV-2 in Vero E6 cells, its good pharmacokinetics and clinical safety profile, mycophenolate mofetil deserves further exploration as potential treatment for COVID-19.

The recently emerged coronavirus named Severe Acute Respiratory Syndrome Coronavirus 2 (SARS- CoV-2) is the newest threat to human health. It has already infected more than half a million people worldwide, leading to a lot of deaths. Although it causes mild flu-like disease in most patients, lethality may increase to more than 20% in elderly subjects, especially those with comorbidities, like hypertension, diabetes or lung and cardiac disease, and the mechanisms are still elusive. Common symptoms at the onset of illness are fever, cough, myalgia or fatigue, headache, and diarrhea or constipation. Interestingly, respiratory viruses have also placed themselves as relevant agents for CNS pathologies. Here we discuss several CNS related features, referred by several patients, especially at the beginning of the disease. Thus, we also discuss the possibility by which SARS-CoV-2 may affect the olfactive system of patients, either directly or indirectly.

The global pandemic of SARS-CoV-2, the causative viral pathogen of COVID-19, has driven the biomedical community to action – to uncover and develop anti-viral interventions. One potential therapeutic approach currently being evaluated in numerous clinical trials is the agent remdesivir, which has endured a long and winding developmental path. Remdesivir is a nucleotide analog prodrug that perturbs viral replication, originally evaluated in clinical trials to thwart the Ebola outbreak in 2014. Subsequent evaluation by numerous virology laboratories demonstrated the ability of remdesivir to inhibit coronavirus replication, including SARS-CoV-2. Here, we provide an overview of its mechanism of action, discovery, and the current studies exploring its clinical effectiveness.

Purpose: Pandemic Novel Coronavirus (SARS-CoV-2) has emerger centered from wuhan, China. Structurally homologous spike protein of SARS-CoV-2 receptor is taxonomically homologous with SARS-Cov and SARS associated bat coronavirus. Still now scientists are trying to find out proper vaccine and treatments for this disease. Methods: Systematically we modeled and compared the structure of SARS-CoV-2 spike protein along with Bat Cov, Bat SARS Cov and SARS Cov Urbani. S1 and S2 unit of the coronavirus (SARS-CoV-2) are attached with ACE2 and furin, here we docked 5 Ca+ chelating drugs with these two proteins. Results: Structural comparison with all these spike proteins revealed that less significant but not negligible difference exists among them. Inserted stable nucleotide sequences and corresponding surface exposed peptidal region may be considered as epitope. Docking result with Toxicokinetics and half life of Penicillamine can effectly inhibit the attachment site of spike protein of coronavirus (SARS-CoV-2). Conclusions: Docking summery and the pharmacokinetics with toxicokinetics index recommend that Penicillamine can able to inhibit the infection of SARS-CoV-2.

Diabetes is a risk factor for mild cognitive impairment (MCI) and dementia. However, how the clinical characteristics of type 2 diabetic patients with MCI are linked to sarcopenia and/or its criterion remain to be elucidated. Japanese patients with type 2 diabetes mellitus were categorized into the MCI group for MoCA-J (the Japanese version of the Montreal cognitive assessment) score <26, and into the non-MCI group for MoCA-J ≥26. Sarcopenia was defined by a low skeletal mass index along with low muscle strength (handgrip strength) or low physical performance (walking speed <1.0 m/s). Univariate and multivariate-adjusted odds ratio models were used to determine the independent contributors for MoCA-J <26. Among 438 participants, 221 (50.5%) and 217 (49.5%) comprised the non-MCI and MCI groups, respectively. In the MCI group, age (61 ± 12 vs. 71 ± 10 years, p < 0.01) and duration of diabetes (14 ± 9 vs. 17 ± 9 years, p < 0.01) were higher than those in the non-MCI group. Patients in the MCI group exhibited lower hand grip strength, walking speed, and skeletal mass index, but higher prevalence of sarcopenia. Only walking speed (rather than muscle loss or muscle weakness) was found to be an independent determinant of MCI after adjusting for multiple factors, such as age, gender, BMI, duration of diabetes, hypertension, dyslipidemia, smoking, drinking, eGFR, HbA1c, and history of coronary heart diseases and stroke. In subgroup analysis, a group consisting of male patients aged ≥65 years, with BMI <25, showed a significant OR for walking speed. This is the first study to show that slow walking speed is a sole determinant for the presence of MCI in patients with type 2 diabetes. It was suggested that walking speed is an important factor in the prediction and prevention of MCI development in patients with diabetes mellitus.

To date, viral RNA detection is almost the only way to confirm SARS-CoV-2infectionin practice.However, variousreasons can cause low sensitivity for RNA detection, and thisposes aserious challenge to disease control. We tested the performance of detecting total antibody(Ab) and IgM levels in serum by the methods of chemiluminescence, enzyme-linked immunosorbent assay (ELISA), and colloidal golddetection. The datashowed that the sensitivity and specificity for detecting total Ab and IgM levels were high by all three methods, and the sensitivity was higher for detecting total Ab than for detecting IgM. Evidence from studieshas shown thatviral RNA testingcombinedwith serological testing could increase the diagnostic sensitivity while maintaining a high specificity. Specific serology testsfor SARS-CoV-2 havegreat value for clinical practice and public health.

The emerging of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) outbreak is associated with high morbidity and mortality rates globally. One of the most prominent characteristics of coronavirus disease-19 (COVID-19) is lymphopenia which is in contrast to other viral infections. This controversy might be explained by the evaluation of impaired innate and adaptive immune responses during the SARS-CoV-2 infection. During the innate immune response, poly-ADP-ribose polymerase (PARP) hyperactivated due to virus entry and extensive DNA damage sequentially leading to NAD+ depletion, ATP depletion and finally cell death. In contrast to the immune response against viral infections, cytotoxic T lymphocytes decline sharply in SARS-CoV-2 infection which might be due to infiltration and trapping in the lower respiratory tract. In addition, there are more factors proposed to involve in lymphopenia in COVID-19 infection like the role of CD38 which functions as NADase and intensifies NAD depletion which in turn affects NAD+ dependent Sirtuin proteins, as the regulators of cell death and viability. Lung tissue sequestration following cytokine storm supposed to be another reason for lymphopenia in COVID-19 patients. Protein 7a as one of the virus-encoded proteins induces apoptosis in various organ-derived cell lines. These mechanisms proposed to induce lymphopenia, although there are still more studies needed to clarify the underlying mechanisms for lymphopenia in COVID-19 patients.

Nitric oxide has been implicated in symptoms of ethanol withdrawal in animal models. Zebrafish have been used as models to study neurobehavioral effects of ethanol (EtOH) withdrawal, but the mechanisms associated with these effects are not yet clear. Adult zebrafish were treated with 1% EtOH for 20 min per day for 8 days, injected with the nitric oxide synthase 2 (NOS-2) inhibitor aminoguanidine (50 mg/kg), and allowed to experience withdrawal (WD) in their hometanks for 7 days. EtOH WD increased anxiety-like behavior in the novel tank test, an effect that was blocked by aminoguanidine. EtOH WD also increased brain levels of nitrite, an effect that was partially blocked by aminoguanidine. These results underline a novel mechanism by which NOS-2 controls anxiety-like responses to ethanol withdrawal, with implications for the mechanistic study of symptoms associated with chronic ethanol abuse.

Best Management Practices (BMPs) are commonly used to control pollution in the river basins. Prioritization of BMPs helps improve the efficiency and effectiveness of pollution reduction, especially in Critical Source Areas (CSAs) that produce the highest pollution loads. Recently, the Dez River in Khuzestan, Iran, has become highly eutrophic from the overuse of fertilizers and pesticides. In this basin, dry and irrigated farming produce 77.34% and 6.3% of the Total Nitrogen (TN) load, and 83.56% and 4.3% of the Total Phosphorus (TP) load, respectively. In addition, residential, pasture, and forest land uses together account for 16.36% of the TN and 12.14% of the TP load in this area. The Soil and Water Assessment Tool (SWAT) was implemented to model the Dez River basin and evaluate the applicability of several BMPs, including point source elimination, filter strips, livestock grazing, and river channel management, in reducing the entry of pollution loads to the river. Sensitivity analysis and calibration/validation of the model was performed using the SUFI-2 algorithm in the SWAT Calibration Uncertainties Program (SWAT-CUP). The CSAs were identified using individual (sediment, TN, TP) and combined indices, based on the amount of pollution produced. Among the BMPs implemented, the 10 m filter strip was most effective in reducing TN load (42.61%), and TP load (39.57%).

We reelaborate on the basic properties of PT symmetry from a geometrical Perspective. The transfer matrix associated with these systems induces a Möbius transformation in the complex plane. The trace of this matrix classifies the actions into three types that represent rotations, translations, and parallel displacements. We find that a PT invariant system can be pictured as a complex conjugation followed by an inversion in a circle. We elucidate the physical meaning of these geometrical operations and link them with measurable properties of the system.

Oil spills from offshore drilling and coastal refineries often cause degradation of coastal wetlands that can take a long time to recover. Early oil detection may prevent losses and speed up recovery if monitoring of the initial oil extent, oil impact, and recovery are in place. Satellite imagery data can provide a cost-effective alternative to expensive airborne imagery or labor intensive field campaigns for monitoring effects of oil spills on wetlands. However, these satellite data may be restricted in their ability to detect and map ecosystem recovery post-spill given their spectral measurement properties and temporal frequency. In this study, we assessed whether spatial and spectral resolution, and other sensor characteristics influence the ability to detect and map vegetation stress and die-off due to oil. We compared how well three satellite multispectral sensors: WorldView2, RapidEye and Landsat EMT+, match the ability of the airborne hyperspectral AVIRIS sensor to map oil-induced vegetation stress, recovery, and die-off after the DeepWater Horizon oil spill in the Gulf of Mexico in 2010. We found that finer spatial resolution (3.5m) provided better delineation of the oil-impacted wetlands and better detection of vegetation stress along oiled shorelines in saltmarsh wetland ecosystems. As spatial resolution become coarser (3.5m to 30m) the ability to accurately detect and map stressed vegetation decreased. Spectral resolution did improve the detection and mapping of oil-impacted wetlands but less strongly than spatial resolution, suggesting that broad-band data may be sufficient to detect and map oil-impacted wetlands. AVIRIS narrow-band data performs better detecting vegetation stress, followed by WorldView2, RapidEye and then Landsat 15m (pan sharpened) data. Higher quality sensor optics and higher signal-to-noise ratio (SNR) may also improve detection and mapping of oil-impacted wetlands; we found that resampled coarser resolution AVIRIS data with higher SNR performed better than either of the three satellite sensors. The ability to acquire imagery during certain times (midday, low tide, etc.) or a certain date is also important in these tidal wetlands; WorldView2 imagery captured at high-tide detected a narrower band of shoreline affected by oil likely because some of the impacted wetland was below the tideline. These results suggest that while multispectral data may be sufficient for detecting the extent of oil-impacted wetlands, high spectral and spatial resolution, high-quality sensor characteristics, and the ability to control time of image acquisition may improve assessment and monitoring of vegetation stress and recovery post oil spills.

A model of space-time is presented, whose origin is set before the appearance of any universe. It uses concepts of previous work, a central one being that the information about the laws of nature is stored locally everywhere in space such that all physical processes obey them. Since information storage is bound to matter or energy, space is assumed to consist of dark energy (DE), which thus assumes an importance far beyond its function as a stopgap for an energy deficit in our universe ( U ). The primordial state (PS) is a timeless and relatively large space of 3 dimensions described by a Wheeler DeWitt (WDW) equation, whose solution is interpreted as an arrangement of very many space quanta and possesses properties that remind of the time-honored concept of the unmoved mover. The comparison of WDW solutions belonging to different mass densities of the DE favors one in which the space quanta are mini black holes. This allows the space quanta to be imagined as mass points which are kept at a distance compatible with the quantum volume by the Pauli principle or something equivalent. Triggered by a dramatic increase in probability, the PS is followed by a time-controlled sequence of quantized expansion states (ES i ). The concomitant emergence of time occurs in a lightning huge crash, consuming almost all DE of the PS. The subsequent quantized ES i emerge from a WDW equation, novelly derived from a solution of the modified cosmic momentum equation, which classically describes an exponential space expansion (CS). The huge volme of the ES i is also used as size of the PS. Information is assumed to be geometrically stored in arrangements of space quanta similar to how genetic information is stored in DNA molecules. During the CS information must be transferred from already existing "information quanta" to new ones. This requires time, slows down the expansion and is taken into account by a friction term in the cosmological momentum equation thus modified. Since the Friedmann-Lemaître (FL) equation remains fully valid, the model is widely based on recognized equations, so that some results may be understood as interpretations.

The principle of the constancy of the velocity of light, which stated that the light velocity is invariant to the motion of the emitter, was well established and directly proven by experiments. Interestingly, the further assumption that the light velocity is independent of the motion of the observer was, arguably, never directly proven by any experiment for a century. This paper proposed the design of two experiments to directly test this assumption, which tried to address some perceived technical difficulty in such experiments. One is to directly measure the light speed as to moving sensors. The experiment setup is designed in such a way that the concerns of time synchronization and dilation can be avoided. Another experiment is to test the isotropy of the light speed to a moving particle in the electromagnetic accelerator by measuring the momentum to acceleration ratio. The experiment result, if positive, will provide direct and solid proof of the assumption. Otherwise, it may imply a need for further investigation. Since the light speed invariance to moving observers is a key assumption of some fundamental physical theory, either way, the experiments will have significant meanings.

The endocannabinoid system (ECS) is a complex neuromodulatory network in the human body that have enormous impact in developing the central nervous system, as well as modulates various physiological processes. The ECS is made up of anandamide, 2-arachidonoylglycerol (2-AG), which are responsible for the synthesis and degradation of endocannabinoids, and enzymes such as FAAH and MAGL ensures the precise control over endocannabinoid levels. The ECS relies on CB1 receptors found in the central nervous system (CNS), and CB2 receptors found in the peripheral nervous system and immune cells. Dysregulation of the ECS has been associated with several health conditions, prompting investigations into its association with phytocannabinoids to harness their therapeutic potential. The ECS is intricately linked with the nervous system, and it is known to modulate neuronal activities in the CNS, influencing pain and mood modulation. The ECS is also associated with neuropathies-peripheral nerve damage leading to symptoms such as weakness, numbness and pain as evidenced in diabetic neuropathy, Guillain-Barré Syndrome, carpal tunnel syndrome, peripheral neuropathy, and postherpetic neuralgia. The focus of this review is on the interplay between 2-arachidonoylglycerol and anandamide amidohydrolase in neuropathy, elucidating their roles in pain regulation, inflammation, and specific neuropathies. The knowledge generated is expected to have broad implications, providing comprehensive insights into neuropathic conditions and how to improve on them. Ultimately, this understanding may lead to more effective treatments and improved quality of life for individuals affected by these conditions.

Iron is an essential nutrient and necessary for biological functions from DNA replication and repair to transcriptional regulation, mitochondrial respiration, electron transfer, oxygen transport, photosynthesis, enzymatic catalysis, and nitrogen fixation. However, due to iron’s propensity to generate toxic radicals which can cause damage to DNA, proteins, and lipids, multiple processes regulate uptake and distribution of iron in living systems. Understanding how intracellular iron metabolism is optimized and how iron is utilized to regulate other intracellular processes is important to our overall understanding of a multitude of biological processes. One of the tools that the cell utilizes to regulate a multitude of functions is ligation of the iron-sulfur (Fe-S) cluster cofactor. Fe-S clusters comprised of iron and inorganic sulfur are ancient components of living matter on earth that are integral for physiological function in all domains of life. Here we will explore the ways in which the cell utilizes Fe-S clusters to sense the intracellular environment and respond to maintain equilibrium.

In the present work we propose a dynamical mathematical model of the lung cells inflammation process in response to SARS-CoV-2 infection. In this scenario the main protease Nsp5 enhances the inflammatory process, increasing the levels of NF kB, IL-6, Cox2, and PGE2 with respect to a reference state without the virus. In presence of the virus the translation rates of NF kB and IkB arise to a high constant value, and when the translation rate of IL-6 also increases above the threshold value of 7 pg mL^-1 s^-1 the model predicts a persistent over stimulated immune state with high levels of the cytokine IL-6. Our model shows how such over stimulated immune state becomes autonomous of the signals from other immune cells such as macrophages and lymphocytes, and does not shut down by itself. We also show that in the context of the dynamical model presented here, Dexamethasone or Nimesulide have little effect on such inflammation state of the infected lung cell, and the only form to suppress it is with the inhibition of the activity of the viral protein Nsp5.To that end, our model suggest that drugs like Saquinavir may be useful. In this form, our model suggests that Nsp5 is effectively a central node underlying the severe acute lung inflammation during SARS-CoV-2 infection. The persistent production of IL-6 by lung cells can be one of the causes of the cytokine storm observed in critical patients with COVID19. Nsp5 seems to be the switch to start inflammation, the consequent overproduction of the ACE2 receptor, and an important underlying cause of the most severe cases of COVID19.

Blocking the interaction of the SARS-CoV-2 spike protein with the human angiotensin-converting enzyme II (hACE2) protein serves as a therapeutic strategy for treating COVID-19. Traditional Chinese medicine (TCM) treatments containing bioactive products could alleviate the symptoms of severe COVID-19. However, the emergence of SARS-CoV-2 variants has complicated the process of developing broad-spectrum drugs. As such, the aim in this study was to explore the efficacy of TCM treatments for SARS-CoV-2 variants through targeting the interaction of the viral spike protein with the hACE2 receptor. The antiviral activity was systematically evaluated using a pseudovirus system. Scutellaria baicalensis was found to be effective against SARS-CoV-2 infection through affecting the interaction of viral spike protein with the hACE2 protein. The active molecules of S. baicalensis were identified and analyzed. Baicalein and baicalin, a flavone and a flavone glycoside, respectively, found in S. baicalensis, exhibited strong inhibitory activities, targeting the viral spike protein or hACE2 protein, respectively. Under optimized conditions, virus infection was inhibited by 98% via baicalein-treated pseudovirus and baicalin-treated hACE2. In summary, we identified the potential SARS-CoV-2 inhibitors of the interaction of the Omicron spike protein and hACE2 receptor from S. baicalensis. Future studies on the therapeutic application of baicalein and baicalin against SARS-CoV-2 variants are needed.

Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2) infection triggers various events from molecular to tissue level, which in turn is given by the intrinsic characteristics of each patient. Given the molecular diversity characteristic of each cellular phenotype, the possible cytopathic, tissue and clinical effects are difficult to predict, which determines the heterogeneity of COVID-19 symptoms. The purpose of this article is to provide a comprehensive review of the cytopathic effects of SARS-CoV-2 on various cell types, focusing on the development of COVID-19, which in turn may lead, in some patients, to a persistence of symptoms after recovery from the disease, a condition known as long COVID. We describe the molecular mechanisms underlying virus-host interactions, including alterations in protein expression, intracellular signaling pathways, and immune responses. In particular, the article highlights the potential impact of these cytopathies on cellular function and clinical outcomes, such as immune dysregulation, neuropsychiatric disorders, and organ damage. The article concludes by discussing future directions for research and implications for the management and treatment of COVID-19 and Long-COVID.

The COVID-19 pandemic continues to affect individuals across the globe, with some individuals experiencing more severe disease than others. The relatively high frequency of re-infections and breakthrough infections observed with SARS-CoV-2 highlights the importance of extending our understanding of immunity to COVID-19. Here we aim to shed light on the importance of antibody titers and epitope utilization in protection from re-infection. Health care workers are highly exposed to COVID-19 and are therefore also more likely to become re-infected. We utilized quantitative, multi-antigen, multi-epitope SARS-CoV-2 protein microarrays to measure IgG and IgA titers against various domains of the nucleocapsid and spike proteins. Potential re-infections in a large, diverse health care worker cohort (N=300) during the second wave of the pandemic were identified by assessing the IgG anti-N titers before and after the second wave. We assessed epitope coverage and antibody titers between the 'single infection’ and ‘re-infection’ groups. Clear differences were observed in the breadth of the anti-N response before the second wave, with the epitope coverage for both IgG (p=0.019) and IgA (p=0.015) being significantly increased in those who did not become re-infected compared to those who did. Additionally, the IgG anti-N (p=0.004) and anti-S titers (p=0.018) were significantly higher in those not re-infected. These results highlight the importance of the breadth of elicited antibody epitope coverage following natural infection in protection from re-infection and disease in the COVID-19 pandemic.

The COVID-19 pandemic first observed in December 2019 caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has influenced every individual on the planet. The virus has influenced our lifestyle, education, economy and the environment. Though the vaccines against COVID-19 have provided protection against the disease; new strains of the virus have lowered the efficiency of the vaccines. There is still no effective therapy for the treatment of the disease. Understanding the protein structure of the virus may lead to the development of effective therapies for the disease. We recently mapped the structural proteins and non-structural proteins of SARS-CoV-2. The accessory proteins (Open reading frames, Orfs) of SARS-CoV-2 modulate the host environment to favor virus replication. This paper reports mapping the accessory proteins (Orfs) of SARS-CoV-2.

Currently, studies are being conducted on the possible role of the cytoprotective effect of biologically active substances in conditions of cerebral hypoxia or cardiomyopathies. At the same time, oxidative stress is considered as one of the important mechanisms of cellular cytotoxicity and a target for the action of cytoprotectors. The aim of this study is to search for derivatives of 3-(arylmethyl)-6-methyl-4-phenylpyridin-2(1H)-ones. The probability of cytoprotective action was assessed by two tests by measuring cell viability (with neutral red dye and MTT test). It was found that some derivatives of 3-(arylmethyl)-6-methyl-4-phenylpyridin-2(1H)-ones under the conditions of our experiment have a pronounced cytoprotective activity, providing better cell survival in vitro, including the MTT test and conditions of blood hyperviscosity. To correlate the obtained results in vitro, molecular docking of the synthesized derivatives was also carried out. The standard drug omeprazole (co-crystallized with the enzyme) was used as a standard. It was shown that all synthesized derivatives of 3-(arylmethyl)-6-methyl-4-phenylpyridin-2(1H)-ones had higher affinity for the selected protein than the standard gastro-cytoprotector omeprazole. The studied derivatives of 3-(arylmethyl)-6-methyl-4-phenylpyridin-2(1H)-ones also fully satisfy Lipinski's rule of thumb of five (RO5), which increases their chances for possible use as orally active drugs with a good ability to absorption and moderate lipophilicity. Thus, the results obtained make it possible to evaluate derivatives of 3-(arylmethyl)-6-methyl-4-phenylpyridin-2(1H)-ones as having a relatively high cytoprotective potential.

The Coronavirus disease 2019 (COVID-19) pandemic began in Jan. 2020 in Wuhan, China with a new coronavirus designated SARS-CoV-2. The principle cause of death from COVID-19 disease quickly emerged as Acute Respiratory Distress Syndrome (ARDS). A key ARDS pathogenic mechanism is the “Cytokine Storm”. This is a dramatic increase in the blood of inflammatory cytokines. In the last 2 years of the pandemic new pathology has emerged in COVID-19 survivors in which a variety of long-term symptoms emerge. This condition is called “Long COVID”. The spike protein on the surface of the virus (target for the new mRNA/DNA vaccines) is composed of joined S1-S2 subunits. Upon S1 bind-ing to the human ACE2 receptor on cells, the S1 subunit is cleaved and the S2 subunit me-diates entry of the virus. The S1 protein is then released into the blood, which might be one of the pivotal triggers for initiation and/or perpetuation of the cytokine storm. In this study, we tested the hypothesis that the spike S1 protein may activate inflammatory sig-naling and cytokine production independent of the virus. Our data support a potential role for spike S1 activation of inflammatory signaling and cytokine production in human lung and intestinal epithelial cells in culture. These data support a potential role for the SARS-CoV-2 spike S1 protein in COVID-19 pathogenesis.

Abstract: Oleuropein, the major compound of olive leaves, has been reported to exert numerous pharmacological properties, including anti-inflammatory, antidiabetic and anticancer. The purpose of this study is to evaluate, for the first time, the effect of oleuropein-rich leaf extracts (ORLE) in already-developed colon tumours colon tumours arising in an Apc (adenomatous polyposis coli) mutated PIRC rats (F344/NTac-Apcam1137). Here, we were able to investigate in parallel the anti-cancer effect of ORLE, both in vivo and in vitro, and its anti-inflammatory effect on macrophages, which represents a critical and abundant population in most solid tumours microenvironment. We found that in vivo ORLE treatment promoted apoptosis and attenuated iNOS activity both in colon tumours as in peritoneal macrophages of PIRC rats. We confirmed in vitro using primary RAW264.7 cells: ORLE reduced iNOS activity in parallel with COX-2 and pro-inflammatory cytokines, such as IL-1, IL-6 and TGF-. These findings suggest that ORLE possess a strong anti-inflammatory activity, which could be crucial for dampening the pro-tumourigenic activity elicited by a chronic inflammatory state generated by either tumour cells or tumour-associated macrophages.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is causing pandemic of coronavirus disease 2019 (COVID-19). The worldwide transmission of COVID-19 from human to human is spreading like wildfire, affecting almost every country in the world. In the past 100 years, the globe did not face microbial pandemic similar in scale to COVID-19. Taken together, both previous outbreaks of other members of the coronavirus family (SARS-CoV and MERS-CoV) did not produce even 1% of the global harm already inflicted by COVID-19. There are also four other CoVs capable of infecting humans (HCoVs), which circulate continuously in the human population, but their phenotypes are generally mild, and these HCoVs received relatively little attention. These dramatic differences between infection with HCoVs, SARS-CoV, MERS-CoV, and SARS-CoV-2 raise many questions, such as: Why is COVID-19 transmitted so quickly? Is it due to the some specific features of the viral structure? Are there some specific human (host) factors? Are there some environmental factors? The aim of this review is to collect and concisely summaries the possible and logic answers to these questions.

SARS-CoV-2 is a newly emerging, highly transmissible, and pathogenic coronavirus in humans, which has caused global public health emergency and economic crisis. To date, millions of infections and thousands of deaths have been reported worldwide, and the numbers continue to rise. Currently, there is no specific drug or vaccine against this deadly virus; therefore, there is a pressing need to understand the mechanism through which this virus enters the host cell. Viral entry into the host cell is a multistep process in which SARS-CoV-2 utilizes the receptor binding domain of the spike glycoprotein (S) to recognize ACE2 receptors on the human cells; this initiates host cell entry by promoting viral-host cell membrane fusion through large scale conformational changes in the S protein. Receptor recognition and fusion are critical and essential steps of viral infections and are key determinants of the viral host range and cross-species transmission. In this review, we summarize the current knowledge on the origin and evolution of SARS-CoV-2 and the roles of key viral factors. We discuss the RNA dependent RNA polymerase structure of SARS-CoV-2, its significance in drug discovery, and explain the receptor recognition mechanisms of coronaviruses. We provide a comparative analysis of the SARS-CoV and SARS-CoV-2 S proteins, receptor-binding specificity, and discuss the differences in their antigenicity based on biophysical and structural characteristics.

The novel coronavirus COVID- 19 disease is extremely contagious and has been spread worldwide. First COVID-19 case was identified in December, 2019 and within three months, more than one million affected cases and over 65,000 deaths have been reported. SARS-coronavirus 2 (SARS-CoV-2) also known as 2019-nCoV is a causative agent of COVID-19 disease and belongs to the SARS CoV (Severe Acute Respiratory Syndrome corona virus) family. The SARS-CoV-2 enters the human body by binding its viral surface spike protein with the host angiotensin-converting enzyme 2 (ACE2) receptors and cause infection. To prevent the virus entry and its transmission in the human body, we focused on the two domains of ACE2: i) the N-terminal extracellular binding domain (18-740 residues) reported for coronavirus spike interaction, and ii) the C-terminal cytoplasmic region (762-805 residues) to prevent the virus transmission. Therefore, we proposed: i) inhibition of receptor binding domain (RBD) of SARS-CoV-2 and human ACE2 protein may prevent the virus entry to the host and ii) inhibition of phosphorylation at Ser-787 of ACE2 protein may prevent the transmission of the virus in the COVID-19 patients. In the past, the critical role of Ser 787 in human ACE2 protein has been experimentally verified in SARS-CoV transmission, that upon binding to the receptor, SARS- CoV induces CKII- mediated phosphorylation of ACE2 at Ser-787 that in-turn facilitate virus entry to host cells, followed by replication and activation of ACE2, initiates downstream signaling leading to lung fibrosis. Therefore, in this study, we have suggested post-translational modification (PTM) O-β-GlcNAcylation, and two compounds Chloroquine and 2-hydroxybenzohydrazine might share the common pathways to prevent the COVID-19 infection in human. The addition of O-β-GlcNAcylation at same or neighboring Ser/ Thr residues results in phosphorylation inhibition and a change in protein structural and functional confirmations. Thereby, using neural networking methods, we have identified Ser/ Thr residues in ACE2 that are potential sites for phosphorylation and / or O-β-GlcNAcylation. Molecular docking showed that UDP-GlcNAc has more binding affinity with Ser-787 than the phosphoryl group. Moreover, chloroquine and 2-hydroxybenzohydrazine also showed great potential to bind at Ser-787 that may result in inhibition of Ser-787 phosphorylation and downstream signaling. Furthermore, O-β-GlcNAcylation, chloroquine and 2-hydroxybenzohydrazine showed their high affinity at ACE2-SARS-CoV-2receptor binding domain that may prevent the entry of SARS-CoV-2 into human body. In conclusion, inhibition of human ACE2 phosphorylation at Ser-787 and ACE2-SARS-CoV-2 binding domain could be promising targets against SARS-CoV-2 infection.

Understanding how the coronaviruses invade our body is an essential point, and the expression profile of coronaviruses receptor may help us to find where the coronavirus infects our body. We found that the coronavirus receptors, including angiotensin-converting enzyme 2 (ACE2) for SARS-CoV and SARS-Cov-2, are digestion-related enzymes in human enterocytes. Coronaviruses are continually altering the binding receptor and binding modes during their evolution, but the potential target cell in the small intestine is constant when in the lung is inconstant. Enterocytes may act as a conserved cell reservoir for coronaviruses, which may be partially explained by the Red Queen hypothesis. We also found that coronaviruses receptors could be elevated in the presence of both invasive bacteria and their counterpart, probiotics. We demonstrated here that enterocytes act as a conserved cell reservoir for coronaviruses during their evolutions, which should not be ignored in the investigation of coronavirus diagnosis and treatment strategies.

Like all RNA viruses, SARS-CoV-2 shows high mutation rate, which has led to the emergence of new variants. Among them, Gamma and Delta developed at the turn of 2020-2021 in Amazonas and India, two ecoregions characterized by hot-humid weather, very similar to that of the summer season due to climate change in Italy, the first Western country to have been hit hard by COVID-19 and to experience lockdown restrictions in a democratic framework of 58,85 million people. Methods: during the state of national health emergency and the Draghi government, the Civil Defense Department daily released the aggregate data coming from the Ministry of Health, the Higher Institute of Health, the Independent Provinces and the Italian Regions, in order to inform about the pandemic situation in Italy. Among these data there were the number of deaths, hospitalizations in intensive care units (ICU), non-ICU patients, contagions, and performed swabs. By means of a team effort, we have collected and elaborated all these data, comparing COVID-19 pandemic in Italy during the summers 2020 (following the nationwide lockdown), 2021 and 2022. Results: from summer 2020 (before mass vaccination) to summers 2021 (after primary mass vaccination) and 2022 (after booster mass vaccination) all pandemic trend indicators have showed a sharp worsening in Italy. COVID-19 deaths increased by ≈298% and ≈834%, ICU hospitalizations by ≈386% and ≈310%, non-ICU hospitalizations by ≈224% and ≈600%, contagions by ≈627% and ≈6.850% (i.e. ≈68,50 times), swabs by ≈354% and ≈370%, and the mean positivity rate passed from ≈1% to ≈2% and ≈20%, respectively. Conclusions: SARS-CoV-2 can be transmitted in any climate, including areas with hot and humid weather, and the emergence of variants adapted to hot-humid climate may result in summer COVID-19 outbreaks, even in neither tropical nor subtropical countries. Although COVID-19 vaccines can confer cross-protection against newly emerging variants, this cross-immunity is obviously not absolute but limited, also taking into account that vaccine protection wanes considerably after 6 months. It follows that a subject vaccinated at the beginning of winter will not be completely covered in the height of summer, without forgetting the unvaccinated. As a final remark, the long nationwide lockdown made it possible to flatten SARS-CoV-2 circulation and, therefore, its negative impact on Italy during summer 2020.

Protection suit is vital for fireman’ safety. Traditional protection suit just physically protects fireman from empyrosis, but can not locate the body injury position from the impact debris. Herein, we present a wearable impact debris positioning system for fireman protection suit alarming based on accelerometer array. The wearable piezoelectric accelerometers with distribute regularly on the suit to detect the vibration on different body parts, which is conducive to positioning injured body parts. In addition, the injured parts can be displayed on the dummy body model on the upper computer with a higher localization accuracy of 4 cm. The positioning alarm system has a rapid response time of 0.11ms attributing to smart signal processing method. This work provides a reliable and smart method for locating and assessing the body injury position from the impact debris, which is significant for fire disaster commander to rescue injured fireman in time.

This article presents an innovative approach to the design of a safe adaptive backstepping control system. Tailored specifically for underactuated marine robots, the system utilizes simple sensors for enhanced practicality and efficiency. Given their operation in diverse oceanic environments fraught with various sources of uncertainties, ensuring the system's safe and robust behavior holds paramount importance in the control literature. To address this concern, this paper introduces a control strategy designed to ensure robustness at both the kinematic and dynamic levels. By emphasizing the compensation for the system uncertainties, the design integrates a straightforward fuzzy system structure. To further ensure the system's safety, a funnel surface is defined, followed by the design of a suitable nonlinear sliding surface as a function of the funnel and tracking error. Specifically leveraging the Lyapunov function, the study formally establishes the Semi-globally Practically Finite-time Stability of the closed-loop system, validated through simulations conducted on underactuated marine robots.

Responsible, efficient, and environmentally conscious energy consumption practices are increasingly essential for ensuring the reliability of the modern electricity grid. This study focuses on leveraging time series analysis to improve forecasting accuracy, crucial for various application domains where real-world time series data often exhibit complex, non-linear patterns. Our approach advocates for utilizing Long Short Term Memory (LSTM) and Bidirectional LSTM (BiLSTM) models for precise time series forecasting. To ensure a fair evaluation, we compare the performance of our proposed approach with traditional neural networks, time-series forecasting methods, and conventional decline curves. Additionally, individual models based on LSTM, Bi-LSTM, and other machine learning methods are implemented for a comprehensive assessment. Experimental results consistently demonstrate that our proposed model outperforms all benchmarking methods in terms of mean absolute error (MAE) across most datasets. Addressing the imbalance between activations by consumer and prosumer groups, our predictions show superior performance compared to several traditional forecasting methods, such as the Autoregressive Integrated Moving Average model (ARIMA) and Seasonal Autoregressive Integrated Moving Average model (SARIMA). Specifically, the root mean square error (RMSE) of Bi-LSTM is 5.35%, 46.08%, and 50.6% lower than LSTM, ARIMA, and SARIMA, respectively, on the May test data.

Predicting anomalies in manufacturing assembly lines is crucial for reducing time and labor costs and improving processes. For instance, in rocket assembly, premature part failures can lead to significant financial losses and labor inefficiencies. With the abundance of sensor data in the Industry 4.0 era, machine learning (ML) offers potential for early anomaly detection. However, current ML methods for anomaly prediction have limitations, with F1-measure scores of only 50% and 66% for prediction and detection, respectively. This is due to challenges like the rarity of anomalous events, scarcity of high-fidelity simulation data (actual data is expensive), and the complex relationships between anomalies not easily captured by traditional ML approaches. Specifically, these challenges relate to two dimensions of anomaly prediction: predicting when anomalies will occur and understanding the dependencies between them. This paper introduces a new method called Robust and Interpretable 2D Anomaly Prediction (RI2AP) designed to address both dimensions effectively. RI2AP is demonstrated on a rocket assembly simulation, showing up to a 30-point improvement in F1 measure compared to current ML methods. This highlights its potential to enhance automated anomaly prediction in manufacturing. Additionally, RI2AP includes a novel interpretation mechanism inspired by a causal-influence framework, providing domain experts with valuable insights into sensor readings and their impact on predictions. Finally, the RI2AP model is deployed in a real manufacturing setting for assembling rocket parts. Results and insights from this deployment demonstrate the promise of RI2AP for anomaly prediction in manufacturing assembly pipelines.

In the last decade Electromagnetic Induction (EMI) measurements have been increasingly used for investigating the soil salinization caused by the use of brackish or saline water as irrigation source. EMI measurements proved to be a powerful tool for providing spatial information of the investigated soil because of the strict correlation between the output geophysical parameter, i.e. the electrical conductivity, to soil moisture and salinity. In addition, their non-invasive nature and their capability to collect a high number of data over broad areas and in relatively short time, makes these measurements attractive for monitoring flow and transport dynamics, otherwise undetectable with conventional measurements. In an experimental field, EMI measurements were collected during the growth season of the tomato, irrigated with three different irrigation strategies. The data were collected over three months in a time-lapse mode in order to visualize changes in electrical conductivity to be associated with soil salinity. A rigorous time-lapse inversion procedure has been set for modeling the soil salinization induced by brackish irrigation water.

In this work, a novel approach is presented for the multivariate prediction of hydrological variables, employing a specialized type of recurrent neural network (RNN) known as the long-term memory network (LSTM).The central objective is to develop a flood prediction model using data collected from two hydrological stations situated along the Atrato River in the Choc´o Department, Colombia. The interconnected variables of water level, water flow, and water precipitation are analyzed in this model to enhance flood prediction accuracy. The LSTM model, integrated into an RNN structure, is designed to capture the complex dynamics and cross-correlations inherent in these hydrological variables. Validation involves a comparison with linear and nonlinear Nonlinear AutoRegressive with eXogenous input (NARX) models, considering factors such as estimation error and computational time. Additionally, the study conducts further analysis for water level prediction under two scenarios: utilizing only outflow and inflow measurements or predicting outflow alongside measuring inflow, with the objective of utilizing the proposed approach as a flood early warning system. † This article is a revised and expanded version of a paper entitled Multivariable NARX based Neural Networks Models for Short-term Water Level Forecasting, which was presented at International Conference on Time Series and Forecasting (ITISE-2023), Gran Canaria, Spain, 12th-14th July, 2023.

Electrospun ultrathin fibers based on binary compositions of polylactide (PLA) and poly(ε-caprolactone) (PCL) with different ratios of polymer components from 0/100 to 100/0 wt. % were studied. Using a combination of thermal (DSC) and relaxation (EPR) measurements, the effect of biopolymers content on the characteristics of the crystal structure of PLA and PCL and the diffusive rotational mobility of the stable TEMPO radical in the amorphous regions of PLA/PCL compositions was shown. It was revealed that after PLA and PCL blending, significant changes in the degree of crystallinity of PLA, PCL segmental mobility, sorption capacity of the radical as well as activation energy of rotational diffusion in the amorphous regions of PLA/PCL composition are observed. The characteristic region of biopolymers’ composition from 50/50 to 30/70 % PLA/PCL blends ratio was found, where the inversion transition of PLA from dispersive medium to dispersive phase where an inversion transition is assumed when the continuous medium of the PLA transforms into a discrete phase. The performed studies made it possible for the first time to carry out a comprehensive analysis of the effect of the system component ratio on the structural and dynamic characteristics of the PLA/PCL film material at the molecular level.

To reduce the redundant counting of TDC in high-speed applications and thus reduce power consumption, a TDC architecture is proposed to quantify the difference between two adjacent rows of pixel signals. This structure can remove the identical part between two rows of pixel signals by adjusting the start and stop signal of the TDC, which will reduce the number of flipping of D flip-flops in the TDC. In the 110nm CMOS process, simulation results show that this design achieves an effective number of bits (ENOB) of 4.72 bits and a Figure of Merit (FoM) of 104.7 fJ/step - 162.3 fJ/step with a power consumption ranging from 60 µW to 93 µW. Compared with traditional counting methods, the proposed TDC can reduce counting power consumption by 30%.

In an effort to enhance the efficiency and safety of open-pit mines, this study explores the optimization of end slope road parameters and slope structures, specifically focusing on unmanned driving lanes.A significant aspect of the study is the development of a truck trajectory offset model, which considers the different reaction times between automated sensors and human drivers in adapting to environmental changes. To test these concepts, the study uses numerical simulations to confirm the stability of the proposed end slope designs. Using Victory West Mine No. 1 as a case study, the research determines the optimized width for unmanned driving lanes and the maximum angle for safe steepening of end slopes. The findings indicate that the optimized unmanned lane width for NTE240 mining dump trucks is 1743 mm, allowing for a 2-degree increase in the slope angle at the south end slope. This optimization leads to a steep mining stripping volume of 3.2735 million m³ and a coal output of 2.49628 million tons, maintaining a stripping ratio of 1.31m³/t. These results demonstrate that the unmanned driving road width optimization not only ensures slope safety but also significantly boosts the economic benefits of steep mining in open-pit mines.

The aim of the research was to determine the most productive tree cutting methods and the factors influencing them in flat and sloping terrain in tree plantations in Brazil. The study utilized drone-captured video material from eucalyptus and pine plantations harvesting operations. In both terrains, two cutting methods were compared, differing in the felling to the side method used: either at the edge of the harvesting front or inside. In addition, on flat terrain, the efficiency of forward felling was studied in relation to the aforementioned methods. In slope terrain, the machines were also equipped with a winch assistance system. Statistical tests were used to determine the most productive cutting methods by analyzing differences in productivity. With an average tree size of 0.3 m3, cutting productivity was 45 m3/E0h in pine cuttings and 55 m3/E0h in eucalyptus cuttings. The average cutting productivity on flat terrain was about 11 m3/E0h higher than on sloping terrain, mainly due to the time spent attaching the winch assistance system, which was a necessary phase of the work in sloping terrain. The research results suggest that it would be the most productive to use sideways felling inside the harvesting front method. However, the need for further research is evident if we want to precisely identify the harvesting conditions and work phases in the tree cutting cycle affecting the differences in the productivity of the harvesting chain in tree plantations.

When dealing with limited sample sizes or missing values in time series data, achieving high accuracy in forecasting becomes challenging. This challenge is particularly common in wind energy forecasting, where many wind farms lack complete data. In this paper, we employ the widely-used ARIMA (Autoregressive Integrated Moving Average) model for time series forecasting. To address the difficulties posed by limited sample sizes and missing values, we adopt a unique approach by utilizing multiple ARIMA models for different datasets. A key distinction from other multiple models methods for time series forecasting lies in each model within our approach using its own dataset, making the fusion process more intricate. To overcome this, we incorporate neural networks and two techniques for model fusion: direct fusion with a learning mechanism and compensation for missing data through training. For each ARIMA model, we introduce a noise estimation method to circumvent the white noise assumptions inherent in ARIMA models. Finally, we apply these methods to wind energy prediction, and our experimental results showcase a significant enhancement in forecasting accuracy.

The hyper-arid environment is characterized by high inter-annual climatic fluctuations. The yearly average rainfall can change substantially for several years, forming wet or dry sub-periods. Observing rainfall trends over a sub-period can lead to a false perception of a change in the trend but may in fact represent a periodic cycle when examined on a larger time scale. We aimed to better characterize the rainfall regime prevailing in the hyper-arid Arava Valley (Israel/Jordan), and to examine the response of vegetation to annual rainfall. We hypothesized that annual and perennial vegetation would respond differently to wet and dry sub-periods, and that grazing activities will impact vegetation growth. We used a time series of monthly rainfall, from which we calculated Standard Precipitation Index (SPI), and calculated proxies of perennial and annual vegetation over the last four decades using Landsat-derived Normalized Difference Vegetation Index (NDVI). We found no clear trend in rainfall amounts during this period, however we did identify wet and dry sub-periods which were statistically distinct in rain and in vegetation patterns from each other. The highest levels of correlation between rainfall and the NDVI derived proxies were found when examining average rainfall over a period of two- three years for the annual vegetation and over four years for perennial vegetation. Using the Mann-Kendall test, we identified a time lag of two to four years, with the proxies of annual vegetation responding faster than the proxies of perennial vegetation, to shifts between wet and dry sub-period. In addition, we found a consistent difference between natural vegetation cover in Jordan (grazed) and Israel (non-grazed), favoring the development of natural vegetation on the Israeli side. We conclude that integrating between long-term remote sensing satellite imagery and climatic records revealed the greater resilience of perennial vegetation in the hyper-arid region to climatic fluctuation, and enabled us to identify the vegetation’s sensitivity to anthropogenic impact.

This paper provides the first-time ever analytic modelling for Pointwise Stationary Fluid Flow Approximation(PSFFA) model of the non-stationary

Computational simulations of three-dimensional flow around a NACA 0018 wing with an aspect ratio of AR=5 are carried out using the Unsteady Reynolds-Averaged Navier-Stokes (URANS) equations with the Shear-Stress Transport turbulence model closure. Simulations were performed to capture the aerodynamic stall hysteresis using a developed Pseudo-Transient Continuation (PTC) method based on a dual time step approach in the CFD code OpenFOAM. The flow was characterized by an incompressible Mach number of M=0.12 and a moderate Reynolds number Re=0.67×10^6. The results obtained indicate the presence of a noticeable aerodynamic hysteresis in the static dependencies of the force and moment coefficients, as well as the manifestation of bi-stable flow separation patterns, accompanied by the development of asymmetry in the stall zone. The URANS simulation results are in good agreement with the experimental data obtained for the NACA 0018 finite aspect ratio wing from the low-speed wind tunnel under the same test conditions. A new phenomenological bifurcation model of aerodynamic stall hysteresis under static and dynamic conditions is formulated, which is proven to be able to closely match the experimental data.

During the 2008 financial crisis, central banks (such as the Fed) adopted quantitative easing (QE) policy to stimulate their economies and overcome severe economic and financial recessions. However, apart from stimulating the economy by issuing a substantial amount of currency to purchase long-term bonds and suppress interest rates, QE policy also contributed to a boom in the real estate and construction sectors. Therefore, this study employs Data Envelopment Analysis to measure the business performance (BP) of construction companies and explores the impact of QE policy on the BP of construction companies between 2004 and 2015 using ordinary least squares regression. Also, we examine the moderating role of debt ratio on the relationship. Focused on publicly listed construction companies in Taiwan, this research reveals three encouraging find-ings. Firstly, QE policy indeed enhanced the BP of Taiwanese construction companies. Secondly, the performance improvement of construction companies due to QE policy shows a time-diminishing trend, suggesting the importance of seizing the initial policy benefits of QE implementation. Lastly, construction companies with appropriate financial leverage may exhibit better BP. These findings can provide valuable insights for relevant government entities and de-cision-makers in the industry for policy and investment decisions.

The most commonly used real-time augmentation services in China are International GNSS Service (IGS) real-time service, PPP-B2b service, and BeiDou Satellite-Based Augmentation System (BDSBAS) dual-frequency augmentation service. However, the research on the performance evaluation, comparison and application scope of these three products is still unknown and unrevealed. This article introduces methods for obtaining real-time augmentation information and real-time orbit and clock offset recovery. Based on real-time orbit and clock offset accuracy, positioning accuracy, and positioning availability, it systematically evaluates the performance of CNES, PPP-B2b, and BDSBAS augmentation information. The results indicate that the radial accuracy of CNES and PPP-B2b real-time orbit products is consistent, and the Root Mean Square (RMS) is better than 5 cm. CNES can achieve centimeter-level accuracy in both along-track and cross-track components, surpassing the decimeter-level accuracy of PPP-B2b. Both services demonstrate consistent accuracy in real-time clock offset, with PPP-B2b showing similar standard deviations (STD) of 0.16 ns for different satellites. However, for CNES, the STD of real-time clock offset varies for GPS, BDS-3 MEO, and BDS-3 IGSO satellites, with values of 0.10 ns, 0.19 ns, and 0.60 ns, respectively. The accuracy and availability of the two PPP services exhibit the same levels, with centimeter-level accuracy achieved after convergence and positioning availability exceeding 99%. Therefore, the difference between the two services in application areas depends on the acquisition of augmentation information. However, BDSBAS, which concentrates on code observations, demonstrates lower accuracy in real-time orbit and clock offset, positioning accuracy, and positioning availability compared to the other two services. Its primary application is in the aviation and maritime domains, where there is a greater need for service integrity, continuity, and reliability.

The electrical energy supply relies on the satisfactory operation of insulators. The ultrasound recorded from insulators in different conditions has a time series output, which can be used to classify faulty insulators. The random convolutional kernel transform (Rocket) algorithms use convolutional filters to extract various features from time series data. This paper proposes the combination of Rocket algorithms, machine learning classifiers, and empirical mode decomposition (EMD) methods, such as complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), empirical wavelet transform (EWT), and variational mode decomposition (VMD). The results show that the EMD methods combined with MiniRocket, significantly improve the accuracy of logistic regression in insulator fault diagnosis. The proposed strategy achieves respectively an accuracy of 0.992 using CEEMDAN, 0.995 with EWT, and 0.980 with VMD. These results highlight the potential of incorporating EMD methods in insulator failure detection models to enhance the safety and dependability of power systems.

Explosive boiling is a fast phase transition from an ultra-thin liquid film to vapor under an extremely high heat flux, which typically has been studied using molecular dynamics simulation (MDS) method. The present MDS study investigated the explosive boiling of a liquid argon nanofilm over different solid copper surfaces with different nanowall patterns including parallel and cross nanowalls. For each surface, atomic motion trajectories, number of liquid and vapor argon atoms, heat flux, and, mainly, onset time of explosive boiling were investigated. The simulation results indicated that explosive boiling occurs earlier on parallel and cross nanowall surfaces than on an ideally smooth surface, regardless of the topology and configuration of nanowalls. Moreover, the results revealed that by using the cross nanowall surfaces, the onset time of explosive boiling decreased by 0.7–4% compared to the parallel nanowall surfaces. In addition, it was found that the onset time of explosive boiling strongly depends on the potential energy barrier and the movement space between nanowalls for both parallel and cross nanowall surfaces. Furthermore, simulation findings showed that even though increasing the height of cross nanowalls increases heat flux and temperature of the fluid argon domain, it does not necessarily result in a shorter onset time of explosive boiling. These findings demonstrate the capability of cross nanowall surfaces for explosive boiling, thereby could be utilized in future surface design for thermal management applications.

This paper investigates reachable set estimation and state-feedback controller design for linear time-delay control system with bounded disturbances. First, by constructing an appropriate Lyapunov-Krasovskii functional, we obtained a delay-dependent condition, which determed the admissible bounding ellipsoid for the reachable set of the system we considered. Then, a sufficient condition in form of liner matrix inequalities is given to solve the problem of controller design with reachable set estimation. Finally, by minimizing the volume of the ellipsoid and solving the liner matrix inequality, we obtain the desired ellipsoid and controller gain. A comparative numerical example is given to verify the usefulness of our result.

The phytoplankton community in 66 reservoirs ubicated at the hydrographic basin of the Ebro River (Spain) was analyzed. Samples were collected in the summer season during the period 2010-2015, with the main purpose of evaluate the Trophic State and Ecological Potential according to the Water Framework Directive of the European Union. In this work we presented a representative overview of several consecutive years of the state of the reservoirs and their associated trophic status; methodology was to obtain statistically significant relationships between the phytoplankton community and main physicochemical water quality variables (water transparency, pH, chlorophyll a, total and soluble phosphorus, anoxic zone depth, nitrite, nitrate and ammonium concentrations). We provide data for retrospective comparative of the state of the reservoirs with previous and current studies in order to improve the management of these reservoirs, and to serve as data reference for future works in a context of global change (climate and pollution).

Accurate determination of the concentration of alcohols and their metabolites is important in forensics and in several life science areas. A new method has been developed to quantify alcohols and their oxidative products using a headspace gas chromatography- mass spectrometer and isotope-labelled internal standards. This work demonstrates the application of this method, specifically in quantifying the concentration of oxidative products of alcohol and other minor alcohols found in hand sanitizers, which have become an essential household item since the COVID-19 pandemic. Apart from the major component, the minor alcohols found in hand sanitizers include methanol, isopropanol, and n-propanol. In ethanol-based hand sanitizers, a significant amount of acetaldehyde was observed as an oxidation product, while in isopropanol-based hand sanitizers, acetone was observed. The concentration of acetaldehyde in ethanol-based hand sanitizers significantly increased with storage time and temperature, whereas no such increase of acetone concentration was observed in isopropanol-based hand sanitizers with storage time and temperature.

Floods are some of the most devastating crop disasters in Southeast Asia. The Pampanga River Basin in the Philippines is a representative flood-prone area, where cultivation patterns are varied according to the flood risk. However, quantitative analyses on the effects of flooding on cultivation patterns remain quite limited. Accordingly, this study analyzes MODIS LAI data (MCD15A2H) from 2007 to 2022 to evaluate annual and geographical differences in cultivation patterns in the Candaba municipality of the basin. The analysis consists of two stages of hierarchical clustering: a first stage for area classification and a second stage for the classification of annual LAI dynamics. As a result, Candaba is divided into four areas, which are found to be partly consistent with the observed flood risk. Subsequently, the annual LAI dynamics in each area are divided into two or three clusters. The obvious differences among the clusters are caused by flooding in the late rainy season, delaying the start of planting in the dry season. The clusters also indicate that the cultivation patterns slightly changed over the 16 years of the study period. The results of this study suggest that the two-stage clustering approach provides an effective tool for the analysis of MODIS LAI data when considering cultivation patterns characterized by annual and geographical differences.

Chromium poisoning strongly influences the performance of solid oxide fuel cell (SOFC) stacks. A three-dimensional, time-dependent, computational fluid dynamics model of a single channel of a F10 SOFC stack in Forschungszentrum Jülich GmbH is developed to investigate chromium poisoning for different stack designs, temperatures and absolute air humidities. The model takes into account both chemical and electrochemical mechanisms of chromium poisoning and is able to predict the spatial distribution of SrCrO4 and Cr2O3. The voltage degradation over 100 kh can be simulated quantitatively. According to the simulation results, chromium poisoning is almost eliminated by the application of the protective coating fabricated by the atmospheric plasma-spraying (APS) technology. Besides, with the help of the APS protective coating, operating a SOFC stack with less dehumidified air at 650 oC is possible according to the simulation.

Changes in sea level exhibit nonlinearity, nonstationarity, and multivariable characteristics, making traditional time series forecasting methods less effective in producing satisfactory results. To enhance the accuracy of the predictions of changes in sea level, this study introduced an improved VMD–EEMD–LSTM hybrid model. This model decomposes satellite altimetry data from near the Dutch coast using VMD, resulting in components of the Intrinsic Mode Function (IMF) with various frequencies and a residual sequence. EEMD further dissects the residual sequence obtained from VMD into second-order components. These IMFs decomposed by VMD and EEMD are utilized as features in the LSTM model for making predictions, culminating in the final forecasted results. The experimental results demonstrated significant improvements in the predictive performance compared with the VMD–LSTM model. The RMSE (root mean square error) decreased by an average of 58.68%, the MAE (mean absolute error) reduced by an average of 59.96%, and the R2 (coefficient of determination) increased by an average of 49.85% compared with the VMD–LSTM model. In comparison with the EEMD–LSTM model, the RMSE decreased by an average of 26.95%, the MAE decreased by an average of 28.00%, and the R2 increased by an average of 6.53%. The VMD–EEMD–LSTM model exhibited significantly improved predictive performance.

The current study primarily aimed to simulate detonation initiation via turbulent jet flame acceleration in partial-premixed H2-air mixtures. Different vertical concentration gradients were generated by varying the duration of hydrogen injection (referred to as diffusion time) within an enclosed channel filled with air. H2-air mixtures with average hydrogen concentrations of 22.5% (lean mixture) and 30% (near stoichiometric mixture) were investigated at diffusion times of 3, 5, and 60 seconds. Numerical results show that the vertical concentration gradient has a major influence on the early-stage of flame acceleration (FA). In the stratified lean mixture, detonation began in all the diffusion times, and comparing the flame-speed graphs showed that a decrease in the diffusion time and an increase in the mixture inhomogeneity speeded up the flame propagation and the jet flame to detonation transition occurrence in the channel. In the stratified H2-air mixture with an average hydrogen concentration of 30%, transition from a turbulent jet flame to detonation occurred in all the cases, and the mixture inhomogeneity weakened the FA and delayed the detonation initiation.

Given a set of distinct non-negative integers X^n and a target certificate S in parametrized form: ∃X^k⊆X^n,∑_(x_i∈X^k)▒x_i =S (k=|X^k |,n=|X^n |). We present a polynomial solution of the subset sum problem with time complexity T≤O(kn)≤O(n^2) and space complexity S≤O(((n-1)n)/2)≤O(n^2 ), so that P = NP.

Collagen is a triple-helical protein unique to the extracellular matrix, conferring rigidity and stability to tissues such as bone and tendon. For the [(PPG)10]3 collagen-mimetic peptide at room temperature, our molecular dynamics simulations show that these properties result in a remarkably ordered first hydration layer, of water molecules hydrogen-bonded to the backbone-carbonyl (bb-CO) oxygen atoms. This originates from the following observations. The radius of gyration attests that the PPG triplets are organized along a straight line, so that all triplets (excepting the ends) are equivalent. The solvent accessible surface area (SASA) for the bb-CO oxygens shows a repetitive regularity for every triplet. This leads to bb-CO⋯HOH occupancy following a similar regularity, similar also to the crystal-phase 0-2-1 water occupancy in the P-P-G triplet. The regularity is maintained in spite of the sub-nsec water exchange rate, because the bb-CO sites rarely remain vacant. The manifested ordered first-shell water molecules are expected to produce a cylindrical electrostatic potential around the peptide, that might guide cation diffusion in its vicinity.

The research paper provides a mathematical framework for understanding phase shift in wave phenomena, bridging theoretical foundations with real-world applications. It emphasizes the importance of phase shift in physics and engineering, particularly in fields like telecommunications and acoustics. Key equations are introduced to explain phase angle, time delay, frequency, and wavelength relationships. The study also introduces the concept of time distortion due to a 1° phase shift, crucial for precise time measurements in precision instruments. The research also addresses infinitesimal wave energy loss related to phase shift, enriching our understanding of wave behavior, and impacting scientific and engineering disciplines.

Climate change is expected to affect the agricultural sector in ways that are often unclear to predict. If in the short- medium-terms farmers may adapt to climate change by adjusting agricultural practices, in the long-term these adjustments may become insufficient, forcing farmers to change their farming systems. The extent and direction in which these farming system transitions will occur is still a subject underexplored in the literature. We propose a new framework to explore the marginal effect of climate change on the choice of the farming system, while controlling the effect of other drivers known to also influence farming system choice. Using a spatial-explicit farming system choice-model developed by a previous study in an extensive agricultural region of southern Portugal, we applied a space-for-time approach to simulate the effect of climate change on the future dynamics of the farming systems in the study area. Results suggest that climate change will force many farmers to change the farming system in a foreseeable future. The extent of the projected changes in farming systems is likely to trigger significant social, economic, or environmental impacts, which should require early attention from policy makers.

In this paper, to make the missile intercept the maneuvering target, the parallel approaching guidance law is developed. In order to estimate the target maneuver more accurately and reduce that’s influence on guidance accuracy, the distance-scalar disturbance observer is employed. Specifically, the estimation accuracy of the observer designed is regardless of the relative distance. The finite-time prescribed performance is employed to ensure that the line-of-sight angular rate is capable of converging to a predesigned small region in the specified finite time. All signals of interception system can guarantee ultimately uniformly boundedness proved by the Lyapunov stability theory. Finally, the availability of the parallel approaching guidance law is demonstrated by the numerical simulation.

In the evolving landscape of higher education, St. Michael's College (SMC) recognizes the significant impact of the COVID-19 pandemic on student welfare. This paper emphasizes the critical importance of addressing mental health, time management, and stress in the college environment, underlining their influence on academic success and personal growth. The call to action urges students to prioritize their well-being. Diverse cultural and religious perspectives, including Islam, emphasize the importance of a balanced mind and body. The wide-ranging literature review uncovers empirically supported strategies for mental health, time management, and stress reduction. Personal autonomy emerges as crucial in addressing these challenges. This study underscores the importance of these dimensions, calls for student action, and highlights the diversity of perspectives contributing to holistic well-being.

This study presents finite-time synchronization of chaotic fractional order systems with disturbance uncertainty and unknown time delay. First, a PID sliding surface is presented. Then, for the finite-time synchronization of the master and slave systems, a robust sliding-adaptive control method is provided. Update rules have been retrieved to estimate the system parameters by using the Lyapunov function while establishing the stability of the suggested mechanism and assuring the convergence of errors to zero. The proposed approach was used to solve the fractional order system of Gensou-Tsei with time-varying parameters, and the simulation results show that the presented approach performs well.

In this study, the apparent variation range of acoustical parameters were investigated in a concert hall. The initial time delay gap (ITDG) was evaluated in terms of its just noticeable difference (JND) through two instruments, the cello and the trumpet. Even though the IDTG values were prolonged over the measurements and were not significantly varied, an ITDG range of 22 - 220 ms in increments of 91 steps was produced electro-acoustically in an anechoic chamber with a loudspeaker. The result of JNDc ((delta gap/gap) was rated 0.059 and 0.081 at 50% “different” judgement ranges for the cello and trumpet tracks, respectively. The effective duration of the autocorrection function (ACF) of the continuous brainwaves (CBWs) within the alpha (8-13 Hz) frequency range on the left hemisphere responding to 91-step ITDG increments revealed that the continuous ratio minimum Tauee e of CBW was approximately equal to 0.28 s on the trumpet. The continuous ratios of Tauee e_min ((Tauee e_min_rear - Tauee e_min_front)/ Tauee e_min_front) of CBWs were constantly at 0.4. Furthermore, a homologous period of resonance between the subjective JNDc and Tauee e values of the ACF of CBWs in the alpha range allowed us to conclude that the subjective JND of the ITDG in a room is related to the W_IACC value of the interaural cross-correlation function, which reflect the characteristics of source signal themselves and arouse the activities of brain on the right hemisphere (p < 0.01).

Psychological readiness following Anterior Cruciate ligament Reconstruction (ACLR) correlates with different return to sport outcomes. However, the relationship between strength and power and psychological readiness remains unexplored. The aim of this study was to investigate the relationship between the Anterior Cruciate Ligament Return to Sport After Injury (ACL-RSI) scores and various hamstrings and quadriceps strength and power variables. Twelve participants (20.7±2.5 years old; 174.2±7.5 cm; 70.2±8.5 kg; 18.2±8.3% of body fat) who had an ACLR nine months or more before the study completed the ACL-RSI questionnaire and isokinetic strength testing of the hamstrings and quadriceps (60°.s-1 and 180°.s-1). Based on ACL-RSI scores, they were divided into “cases” and “controls”, deemed not psychologically ready and psychologically ready to return to previous sport performance (PILOS), respectively. The main findings are that quadriceps’ and hamstrings’ rate of torque development (RTD) and time since surgery were determinants of psychological readiness following ACLR. Furthermore, compared to controls cases showed significantly lower angle-specific quadriceps and hamstrings torque close to extension and lower RTD, but no difference in peak torque. These results suggest that physiotherapists should facilitate athletes’ RTS by focusing on the restoration of RTD and strength at angles close to full knee extension.

Influence of reaction time on silver nanoparticles-catalyzed oxidation of 3,3’,5,5’-tetramethylbenzidine (TMB) was evaluated via probing the oxidation process by recording the UV-Visible absorbance of the resulting colored products of TMB oxidation by hydrogen peroxide in the presence of silver nanoparticles as peroxides mimics. The time curve of the oxidation process was constructed, revealed that the concentration of the oxidation product was increased by increasing the reaction time and then levled off because the nanozyme active nodes were saturated after a certain reaction time and consequently the steady-state condition can be observed. The maximal activity of the silver nanozymes was achieved after a very short reaction time as short as 3.0 min and after this time, the color intensity of the oxidation products remained constant, revealing saturation of active nodes of nanoparticles with nanozyme-substrate.

Modern electrical power systems integrate ancillary services to provide security and quality of service in real-time operation because of the intense variations in frequency caused by the massive development and uncertainty of solar-wind generation. Therefore, this ancillary services market focuses on power reserves for secondary and tertiary frequency control. Adjusting reserves and dispatching plants is a manual instruction executed by the system operator to maintain the frequency in the normal operating state (49.80≤f≤50.20 Hz). However, in the absence of an economic model for real-time power reserve reallocation in the ancillary services market, the reserve adjustments made by the system operator are not always optimal since they generate a displacement between the scheduled and actual marginal costs. Then, this work proposes a methodology for operating the ancillary services market in real-time through a dynamic and hourly mathematical model that integrates the variability of solar-wind generation, the demand monitoring curve, and the trajectory of the marginal cost. This model minimizes power reserve costs, which are governed by hourly price auctions, for candidate plants classified as supra/infra-marginal and can optimally reallocate power reserves for secondary and tertiary frequency control.

Asthma is a chronic respiratory disease that impairs breathing. Management of asthma presents a significant challenge due to its inherent variability; that is, its symptoms can substantially differ among individuals, thereby complicating the prediction and management of exacerbations. Furthermore, individuals with asthma often have unique triggers that precipitate symptoms or attacks. The identification of these triggers can often prove to be a challenging, and at times, an impractical attempt. To address this, our research proposes a practical, personalized alert system, predicated on individual lung function tests conducted under varying environmental conditions classified by air-quality sensors. To validate this concept, we conducted an observational pilot study involving healthy individuals. We recruited twelve healthy participants and monitored their responses across a broad spectrum of environments, characterized by varying air quality, temperature, and humidity conditions. The lung function for each participant, assessed using peak expiratory flow (PEF) values, was recorded in each of these environments. Our results highlighted substantial variability in pulmonary responses to different environments. Utilizing these insights, we proposed a personalized alarm system that provides real-time air-quality monitoring and issues alerts when environmental conditions may potentially become unfavorable. We also explored the feasibility of employing basic machine learning techniques to predict PEF values in the aforementioned environmental conditions. This proposed system has the potential to empower individuals in actively safeguarding their respiratory health and mitigating discomfort caused by environmental conditions, especially in cases of asthma patients. By enabling timely and personalized interventions, the system aims to provide individuals with the necessary tools to minimize exposure to asthma’s possible triggers.

In this paper we consider a failed cold standby systems and obtain some stochastic bounds on the idle time of such systems. It is assumed that, at a time t the failure of a cold standby system is observed, but the actual time of the failure is a time before t. The idle time of the inactive cold standby system is measured as the time between the exact time of failure, which is a random variable, and time t. We use the idle times of the components of the system to obtain lower and upper bounds on the lifetime of the standby system to which they are assembled. We bring some examples to show that the results and the conditions imposed to get them are achievable.

Turbidity is an important water quality parameter, especially for drinking water. The ability to actively monitor the turbidity level in Drinking Water Distribution Systems is of critical importance to the safety and wellbeing of the public. Traditional turbidity monitoring methods involve manual collection of water samples at set locations and times followed by laboratory analysis, which are labor intensive and time consuming. Fiber-optic measurement permits real-time, in-situ turbidity monitoring. But the current technology is based on plastic fibers, which suffer from high optical attenuation and hence are unsuitable for large-scale remote monitoring. In this paper, we report the demonstration of a fiber-optic turbidity sensor based on multi-mode glass fibers. The system uses a single fiber to both deliver laser light into the water sample and collect the back-scattered light for detection. A balanced-detection scheme is utilized to remove the common-mode noise to enhance the turbidity sensitivity. Highly linear turbidity responses are obtained and a turbidity resolution as low as 0.1 NTU is achieved. The test unit is also shown to have excellent reproducibility against repeated measurements and good stability against temperature changes. Turbidity measurement in real environmental matrices such as tap water and pond water is also reported with an assessment of the impact of flow rate. This work demonstrates the feasibility of future large scale distributed fiber-optic turbidity monitoring networks.

This paper discusses two major applications of SU(3) theory, the first being the question of quantum brachistochrones and problems in time optimal control theory, the second being the use of spherical tensor decompositions of the special functions which are associated to SU(3) via the isotropic oscillator. We discuss the derivation of the quantum brachistochrone problem more generally from the perspective of von Neumann equations and matrix mechanics, arriving at an equivalent formulation to the Lagrangian method. Discussion is given to the application of such advanced methods in quantum computation, and some future directions that may be amenable to a similar sort of an

In econometrics and finance, volatility modelling has long been a specialised field for addressing a variety of issues pertaining to the risk and uncertainties of an asset. This study presents a robust framework, through a step-by-step design, that is relevant for effective Monte Carlo simulation (MCS) with empirical verifications to estimate volatility using the Generalized Autoregressive Score (GAS) model. The framework describes an organised approach to the MCS experiment that includes "background (optional), defining the aim, research questions, method of implementation, and summarised conclusion". The method of implementation is a workflow that consists of writing the code, setting the seed, setting the true parameter a priori, data generation process, and performance assessment through meta-statistics. Among the findings, it is experimentally demonstrated in the study that the GAS model with a lower unconditional shape parameter value can generate a dataset that adequately reflects the behaviour of financial time series data, relevant for volatility estimation. This dynamic framework is intended to help interested users on MCS experiments utilising the GAS model for reliable volatility calculations in finance and other areas.

This paper proposed a time two-mesh (TT-M) finite difference numerical scheme to improve the efficiency of solving the symmetric regularized long wave (SRLW) equation. The TT-M Crank-Nicolson discretization and finite difference method are employed in time and space approximation respectively. The scheme involves three main steps: firstly, the time interval is divided into coarse and fine time meshes, then the nonlinear system is solved on the coarse time mesh; secondly, coarse numerical solutions on the fine time mesh are computed using an interpolation formula based on the solutions derived in the step one; lastly, the TT-M finite difference numerical solutions can be obtained through constructing the linearized fine time mesh system using Taylor’s formula. Compared to the currently existing TT-M numerical methods, the novelty of this study is that the nonlinear term including derivatives is linearized by Taylor’s formula for a function with three variables, whose error analysis is more complex. Finally, some numerical examples, including computational time and accuracy, preservation of conservation laws, are given to verify the efficiency of the scheme. By comparing it with the standard nonlinear finite difference scheme, this method can reduce CPU time without sacrificing accuracy.

Overuse and misuse of antibiotics can pose the risks of spreading mutant strains that show antimicrobial resistance (AMR), with negative impact on management of bacterial infections and economic implications on healthcare systems. Research and development of natural antibacterial agents could be a priority in the next years to improve number of effective antibacterial molecules and to reduce AMR phenomenon and its development. The present study identified the most effective concentration and contact time of Thymus vulgaris L. essential oil (TEO) to obtain bactericidal effect in vitro against different Gram + and Gram – bacterial strains. Six clinically isolated (wild types) bacterial strains, (Citrobacter freundii, Enterococcus feciorum, Proteus mirabilis, Acinetobacter cioffi, Pasteurella putrefaciens, Klebsiella pneumoniae) and two ATCC (Staphylococcus aureus and Streptococcus mutans) were tested after 1min, 3min and 5min of contact with TEO. Preliminary results on S. aureus after 24h incubation revealed a TEO concentration of 9.28mg/mL (w/v) that completely inhibited bacteria growth keeping cell viability. Total suppression of bacterial growth at all tested contact times was observed for all tested bacterial strains and the results were confirmed after 48h incubation. Bacterial growth suppression was confirmed even with presence of organic component. These preliminary results showed in vitro antimicrobial efficacy of TEO against different Gram+ and Gram- bacterial strains. Future studies are necessary to confirm the reproducibility of these results even on other strains and to define exactly the molecular mechanisms of EOs, in order to consider TEO as a valid alternative of classic antibiotic therapies and subsequently to reduce the occurrence of AMR.

The natural frequency of buildings decreases during a strong-motion earthquake, and the structure loses its stiffness. As a result, understanding the damaging process in the structure owing to changes in structural properties is critical during a seismic excitation. The time-frequency technique can detect the damaged building’s time-varying frequency contents. Wigner distributions (WD), Wigner-Ville distributions (WVD), pseudo-Wigner-Ville distributions (PWVD), smoothed pseudo-Wigner-Ville distributions (SPWVD), and synchrosqueezing transforms (SST) have all become popular in recent years for a variety of earthquake engineering applications, including building damage detection. This study proposes the local maximum synchrosqueezing transform (LMSST) for detecting frequency shifts in buildings during strong earthquakes. The data presented in the research show that the suggested method outperforms as compared to the conventional time-frequency methods for detecting frequency shifting in earthquake-damaged structures.

Fluctuating stock prices make it difficult for investors to see investment opportunities. One tool that can help investors overcome this is forecasting techniques. Long Short-Term Memory (LSTM) is one of deep learning methods used in forecasting time series. The training and success of deep learning is strongly influenced by the selection of hyperparameters. This research uses a hybrid method between the Genetic Algorithm (GA) and LSTM to find a suitable model for predicting stock prices. GA is used in optimizing the architecture such as the number of epochs, window size, and the number of LSTM units in the hidden layer. Tuning optimizer is also carried out using several optimizers to achieve the best value. From method that has been applied, it shows that the method has a good level of accuracy with MAPE values below 10% in every optimizer used. The error rate generated is quite low, in case-1 with a minimum RMSE value of 93.03 and 94.40, & in case-2 with an RMSE value of 104.99 and 150.06 during training and testing. A fairly stable and small value is generated by setting it using the Adam Optimizer.

Knowledge about ecological conditions and processes in centers of endemism (CoEs) is still limited with respect to various systematic groups of organisms, ecosystem types, ecological conditions and ecosystem functions. Endemics and CoEs play a prominent role in nature conservation monitoring and management, and in the organization of zoos, aquaria and botanical gardens. We review the meaning of different groups of organisms and indicators for the characterization of endemic-rich regions, the importance of heterogeneity in space, continuity in time, isolation and ex-situ management for the survival of endemic and threatened species. Currently, conversion of land cover and land use change are the most important causes of biodiversity decline and extinction risk of endemic and endangered species concentrated in CoEs. These are followed by climate change, including severe weather, and then natural processes such as volcanism, landslides or tsunamis. For conservation purposes the management of zoos, aquaria, botanical gardens, of regional land use, and social aspects of the diversity of endemics and CoEs have to be taken into account as well. We need better answers to questions about the relationship between ecoregions, CoEs and regional awareness of biodiversity, which is linked with human culture including the management of zoos, aquaria and botanical gardens, aesthetics, wellbeing, health and trade.

NMR spin relaxation times have been an instrumental tool in deciphering the local en-vironment of ionic species, the various interactions they engender and the effect of these interactions on their dynamics in conducting media. Of particular importance has been their application in studying the wide range of electrolytes for energy storage, on which this chapter is based. Here we highlight some of the research carried out on electrolytes in recent years using NMR relaxometry techniques. Specifically, studies on liquid electro-lytes, such as ionic liquids and organic solvent; on semi-solid-state electrolytes, such as ionogels and polymer gels, and solid electrolytes such as glasses, glass ceramics and polymers. Although this chapter focuses on a small selection of materials, we believe they demonstrate the breadth of application and the invaluable nature of NMR relaxometry.

Precision Agriculture is the ability to handle variations in productivity within a field and maximize financial return, optimize resource utilization and minimize impact of the environment. It is also the process of automated data collection, cloud storage and utilization to build robust decision support system. In case of Ethiopia, due to poor communication infrastructure coverage and absence of the state-of-the-art technology in the agriculture sector, implementing precision farming system is a challenging tasks in the domain area. In this work, we proposed a fusion of multiple sensors using IOT and IIOT infrastructure to collect critical data from farming fields to develop precision farming facility for decision makers. The main purpose was to monitor weather variability, automate irrigation process, extract critical soil properties. In addition, we have used time series data collected from sensor devices to build forecasting model. Fusion of multiple IoT device provide a mechanism in the agriculture area to deal with real-time monitoring of crops. It is cost-effective technology and required low-energy with edge computing sensor device. We employed the Message Queuing Telemetry Transport (MQTT) protocol to connect the Industrial/Internet of Things (I/IoT) to the cloud server. The communication between system user and sensor device has been done via cloud using Node-RED platform, web android APIs. The cloud-based Eco-system allows us to aggregate, visualize, and analyze live streams output from each sensor in real-time manner. Finally, we have built time series forecasting model using records collected by each sensor device. Using the multi-variate time series data-set, we have obtained about 99 forecasting accuracy on some important variables. Finally, we have developed mobile and web-based application for the end-user to monitor the proposed system remotely.

InSAR and associated analytic methods enable relative surface deformation measurements from low Earth orbit with a potential accuracy of centimeters to millimeters. However, assessing the actual accuracy can be quite difficult. The analytic methods are complicated enough that naive analytic error propagation is infeasible, and, in many settings, InSAR practitioners lack sufficient ground truth to assess results. Phase noise due to partial decorrelation from changes in the scattering properties of the ground is a prominent source of accuracy loss. In this paper we present a method to assess the loss of precision due to this component of phase noise. The proposed method consists of generating synthetic data stacks whose statistical properties match those of the actual input SAR data stacks, and then using the synthetic data for an ensemble calculation. The spread of the results of the ensemble calculation indicates the loss of precision. We show examples of the ensemble analysis at a mining operation in South Africa, and demonstrate the ability to estimate the precision of two InSAR deformation retrieval methods on a point-by-point and epoch-by-epoch basis.

Background: Digital clinical measures collected via various digital sensing technologies such as smartphones, smartwatches, wearables, ingestible, and implantable are increasingly used by individuals and clinicians to capture health outcomes or behavioral and physiological characteristics of individuals. Time series classification (TSC) is very commonly used in modeling digital clinical measures. While deep learning models for TSC are very common and powerful, there exist some fundamental challenges. This review presents non-deep learning models commonly used for time series classification in biomedical applications that achieve high performance. Objective: We performed a systematic review to characterize the techniques used in time series classification of digital clinical measures throughout all stages of data processing and model building. Methods: We conducted a literature search on PubMed, and the Institute of Electrical and Electronics Engineers (IEEE), Web of Science, and SCOPUS databases using a range of search terms to retrieve peer-reviewed articles reporting academic research on digital clinical measures in the five year period between June 2016 and June 2021. We identified and categorized research studies based on the types of classification algorithms and sensor input types. Results: We found 452 papers in total from four different databases: PubMed, IEEE, Web of Science Database, and SCOPUS. After removing duplicates and irrelevant papers, 135 articles remained for detailed review and data extraction. Among these, engineered features using time series methods that were subsequently fed into widely-used machine learning classifiers was the most commonly used technique and also most frequently achieved the best performance metrics (77 out of 135 articles). Statistical modeling (24 out of 135 articles) algorithms were the second most common and also second best classification technique. Wavelet-based classification models (8 out of 135 articles) were also common. Electroencephalogram (29 out of 135 articles) was the most common data type used as an input. Accuracy was the most commonly reported performance metric, with 67.65% of articles reporting on accuracy. In this review paper, we provide summaries of signal pre-processing methods, feature engineering and selection methods, time series models, as well as model interpretations. Importantly, we found that about 50% of the papers only report one performance metric, which may result in a skewed view of overall performance. Conclusion: While high time series classification performance has been achieved in digital clinical, physiological, or biomedical measures, no standard benchmark datasets, modeling methods, or reporting methodology exist. There is no single widely used method for time series model development or feature interpretation– many different methods have proven successful.

Breaking up prolonged sitting by short bouts of light physical activities including standing and walking has been shown to be beneficial for people with type 2 diabetes (T2D). This paper presents the development of an android mobile app to deliver a just-in-time adaptive intervention (JITAI) to reduce sedentary time in people with T2D. A total of six design workshops were conducted with seven experts to identify design requirements, a behavioural framework, and required contextual adaptations for the development of a bespoke mobile app (iMOVE). Moreover, a focus group was conducted among people with T2D as potential end-users (N=10) to ascertain their perceptions of the app. Feedback from the focus group was used in subsequent iterations of the iMOVE app. Data were analysed using an inductive qualitative thematic analysis. Based on workshops, key features of iMOVE were developed, including simplicity (e.g., navigation, login), colours and font sizes, push notifications, messaging algorithms and a triggering system for breaking up sitting time and moving more. Based on the user testing results, a goal setting tab was added, font sizes were made larger, the brightness of colours was reduced, and a colour indicator was used to indicate device connectivity with an activity tracker. A user-centric app was developed to support people with T2D to transition from sedentary to active lifestyles.

Generalisation from the local 4-dimensional spacetime form for a proper time interval provides an alternative basis for a unified theory contrasting with the more familiar approach of appending extra dimensions of space. In both cases the main goal is to derive a structure of matter that accounts for empirical observations from the properties of the additional components. Previous work has focussed upon the direct and significant connections that this new approach makes with the Standard Model of particle physics while also providing a link with dark matter models. In this paper we elaborate on how a further sector of generalised proper time, incorporating the key component of negative pressure, can account for the apparent dark energy fuelling the accelerating large-scale expansion of the universe. Through comparison with existing dark energy models we elucidate the elementary composition of this sector, its potential relation to an effective cosmological constant term, and the nature of possible interaction with the visible and dark matter sectors.

Purpose: ETV is indicated for treating obstructions of major CSF pathways. The outcome evaluation often yields success rates of only +- 70% for shunt independency. Hence, compromised CSF absorption seems to occur more often than expected. We searched for parameters suitable to assess the involved CSF dynamics. Material and Methods: This was a prospective study in 58 paediatric patients (7.7 yrs. mean age) between 2000 and 2020 with aqueductal stenosis (11/58), obstruction of the aqueduct due to tumor growth (22/58),and connatal hydrocephalus (9/58). The average follow-up interval was 4.7 years. Head circumferences, Evans- and fronto-occipital horn ratios before and 3 months after ETV were obtained as Delta-indices. Furthermore ETV success score (ETVSS), the patency of the aqueduct pre- and postoperatively as well as of the stoma were assessed by flow void signs on MRI. Evaluation on MRI also included the shape of the floor of the 3^rd ventricle and whether or not the septum pellucidum showed signs of perforation. Four patients were analysed pre- and postoperatively via real-time MRI. At least the educational status regarding protected or unprotected education was analyzed. Results:The prevalence of a bowing of the floor of the 3^rd ventricle was 72%, and the ETVSS was 71.0%. In 26 children a septal perforations or an open aqueduct prior to ETV (19) could be identified. Mean ER and FOHR were reduced by 0.03 and 0.05 , respectively. Maintained open (flow void on postop MRI) or perforation could successfully be carried out during endoscopic surgery in 44 patients (79%). The disproportionate increase of head circumference abated in 79.4% of patients. New shunt insertion occurred in 16 patients (27.5%). Intraoperatively upward CSF flow was detected in all cases. Statistical analyses(ANOVA) showed significant results for unprotected education, postoperative ER and FOHR but not for open stoma. Conclusion: The identification of flow through the stoma on postoperative MRI seems to be a necessary but not sufficient condition for ETV success. In our study, ventricular volumes were used as parameters to determine success rates as well as unprotected education. Furthermore, enabling upward CSF flow driven by inspiration seems crucial for successful ETV.

Cellular Nonlinear Networks (CNN) are a concept introduced in 1988 by Leon Chua and Lin Yang as a bio-inspired architecture, capable of massively parallel computation. Later on, CNN have been enhanced by incorporating designs that incorporate memristors to profit from their processing and memory capabilities. In addition, Stochastic Computing (SC) can be used to optimize the quantity of required processing elements; thus it provides a lightweight approximate computing framework, quite accurate and effective, though. In this work, we propose utilization of SC in designing and implementing a memristor-based CNN. As a proof of the proposed concept, an example of application is presented. This application combines Matlab and a FPGA in order to create the CNN. The implemented CNN has then been used to perform three different real-time applications on a 512x512 gray-scale and a 768x512 color image: storage of the image, edge detection, and image sharpening. It has to be pointed out that the same CNN has been used for the three different tasks, with the sole change of some programmable parameters. Results show an excellent capability with significant accompanying advantages, like the low number of needed elements further allowing for a low cost FPGA-based system implementation, something confirming the system’s ability for real time operation.

Fatigue risk to commercial pilots operating under global pandemic conditions had not been in-vestigated prior to COVID-19. Examining how pilots slept during COVID-19 pandemic-specific flights can provide a precedent for estimating fatigue risk for future public health emergencies. Twenty (n=20) pilots flying across five COVID-19 humanitarian missions between Brazil and China wore a sleep-tracking device (the Zulu watch), which has been validated for the estimation of sleep timing (sleep onset and offset), duration, efficiency, and sleep depth (Wake, Interrupted, Light, or Deep Sleep) throughout the mission period. Pilots also reported sleep timing, duration and subjective quality of their in-flight rest periods using a sleep diary. To our knowledge, this is the first report of commercial pilot sleep behavior during ultra-long-range operations under COVID-19 pandemic conditions. Moreover, these analyses provide an estimate of sleep depth during in-flight sleep, which has not been reported previously in the literature.

Nighttime lights (NTL) are a popular type of data for evaluating economic performance of regions and economic impacts of various shocks and interventions. Several validation studies use traditional statistics on economic activity like national or regional Gross Domestic Product (GDP) as a benchmark to evaluate the usefulness of NTL data. Many of these studies rely on dated and imprecise Defence Meteorological Satellite Program (DMSP) data and use aggregated units such as nation-states or the first sub-national level. Yet applied researchers who draw support from validation studies to justify their use of NTL data as a proxy for economic activity increasingly focus on smaller and lower level spatial units. This study uses a 2001-19 time-series of GDP for over 3100 US counties as a benchmark to examine the usefulness of the recently released version 2 VIIRS nighttime lights (V.2 VNL) products as proxies for local economic activity. Contrasts are made between cross-sectional predictions for GDP differences between areas and time-series predictions of GDP changes within areas. Disaggregated GDP data for various industries are used to examine what types of economic activity are best proxied by NTL data and comparisons are also made with the predictive performance of earlier NTL data products.

Predictive maintenance (PM) algorithms are widely applied for detecting operational anomalies on industrial processes to trigger maintenance before a possible breakdown; however, much less focus has been devoted to the use of such PM predictions as feedback in automated process control mechanisms. They usually integrate preventive solutions to protect the machines, usually causing downtimes. The premise of this study is to develop a holistic adaptive process scheduling mechanism that incorporates PM analysis as a safety component to optimize the operation mode of an industrial process toward preventing breakdowns while maintaining its availability and operational state, thereby reducing downtimes. As PM is largely a data-driven approach; hence, relies on the setup, we first compare different PM approaches and identify a one-class support vector machine (OCSVM) as the best performing option for the anomaly detection on our setup. Then, we propose a novel pipeline to integrate maintenance predictions into a real-time adaptive process scheduling mechanism. It schedules for the most suitable operation, i.e., optimizing for machine health and process efficiency, according to the abnormal readings. To demonstrate the pipeline on action, we implement our approach on a small-scale conveyor belt system utilizing our Internet of Things (IoT) framework. The results show that our PM-based adaptive process control provides an efficient process with less or no downtime. We also conclude that a PM approach does not provide sufficient efficiency without its integration into an autonomous planning process.

Extensive, but remote oil and gas fields of the United States, Canada, and Russia require the construction and operation of extremely long pipelines. Global warming and local heating effects lead to rising soil temperatures and thus a reduction in the sub-grade capacity of the soils; this causes changes in the spatial positions and forms of the pipelines, consequently increasing the number of accidents. Oil operators are compelled to monitor the soil temperature along the routes of the remoted pipelines in order to be able to perform remedial measures in time. They are therefore seeking methods for the analysis of volumetric diagnostic information. To forecast soil temperatures at the different depths we propose compiling a multidimensional dataset, defining descriptive statistics; selecting uncorrelated time series; generating synthetic features; robust scaling temperature series, tuning the additive regression model to forecast soil temperatures.

With the development of designing and manufacturing level for micro-electromechanical system (MEMS) gyroscopes, the control circuit system becomes a key point to determine their internal performances. Nevertheless, phase delay of electron components may result in some serious hazards. This paper describes a real-time circuit phase delay correction system for MEMS vibratory gyroscopes. A detailed theoretical analysis is provided to clarify the influences of circuit phase delay on the in-phase and quadrature (IQ) coupling characteristics and zero rate output (ZRO) utilizing force-to-rebalance (FTR) closed-loop detection and quadrature correction system. By deducing the relationship between amplitude-frequency, phase-frequency of MEMS gyroscope and the phase relationship of the whole control loop, a real-time correction system is proposed to automatically adjust the phase reference value of phase-locked loop (PLL) and thus compensate for the real-time circuit phase delay. The experimental results show that the correction system can accurately measure and compensate the circuit phase delay in real time. Furthermore, the unwanted IQ coupling can be eliminated and the ZRO is decreased by 755% to 0.095°/s. This correction system realizes a small angle random walk of 0.978°/√h, and a low bias instability of 9.458°/h together with a scale factor nonlinearity of 255 ppm at room temperature. Besides, the thermal drift of ZRO is reduced to 0.0034°/s/°C at a temperature range from -20°C to 70°C.

We reduplicate the Book “Dark Energy” by M. Li, X-D. Li, and Y. Wang, given zero-point energy calculation with an unexpected “length’ added to the ‘width’ of a graviton wave just prior to specifying the creation of ‘gravitons’, using the Rosen and Israelit model of a nonsingular universe. In doing so we are in addition to obtaining a wavelength 10^30 times greater than Planck’s length so we can calculate DE, may be able to with the help of the Rosen and Israelit model have a first approximation as to the arrow of time, and a universe with massive gravity. We have left the particulars of the nonsingular starting point undefined but state that the Rosen and Israelit model postulates initial temperatures of 10^-180 Kelvin and also a value of about Planck temperature, at 10^-3 centimeters radii value which may satisfy initial conditions asked by t’Hooft for describing an arrow of time. A key assumption is that the DE is formed at 10^-3 cm, after an expansion of 10^30 times in radii, from the Planck length radius nonsingular starting point. The given starting point for DE in this set of assumptions is where there is a change in the cosmic acceleration, to a zero value, according to Rosen and Israel, with time t = 1.31 times 10^-42 seconds. Which may be where we may specify a potential magnitude, V, which has ties into inflaton physics. The particulars of the model from Rosen and Israelit allow a solution to be found, without discussion of where that nonsingular starting point came from, a point the author found in need of drastic remedies and fixes.

Smartphones have become an integral part of our everyday lives and keep us busy while doing other primary activities such as driving, cycling or walking in traffic. The problem of digital distraction among drivers has been largely addressed, and interest is growing also on vulnerable road users as well: In fact, high percentages of pedestrians and cyclists are accustomed to checking their devices while moving in traffic. This research links to the presented theme and aims to investigate the extent to which digital distraction in the form of social media app checking influences pedestrian behavior. The focus of the study is specifically on signalized intersections. An outdoor, eye-tracking experiment was conducted on a specific route consisting of various elements typical of urban areas. Participants were asked to walk the predefined route twice, en-countering three signalized intersections: the first time they were asked to walk with their smartphone in hand, the second time without. The recordings of each participant's route were then analyzed, examining reaction time, crossing time and speed, fixations, and gaze paths. The results show a clear impact of digital devices on pedestrians' attention by increasing their reaction and crossing times and decreasing crossing speeds. In addition, the analysis of fixations found that 82.54% of the time was devoted to the smartphone, while interest in other street ele-ments decreased from 16.64% to 4.03%.

In this paper, we propose a multi-zone service control scheme to maximize the performance of each service zone when a large number of cellular service zones and D2D (Device-to-Device) service zones are composed to 5G cellular network. This paper also improves performance of service zone by dividing traffic into real-time traffic and non-real-time traffic in order to minimize traffic interference. Real-time traffic and non-real-time traffic have a significant impact on communication performance. We propose a new self-detection traffic interference control technique to improve the QoS and throughput of D2D and C2D communication in a cellular network, STICS (Self-detecting Traffic Interference Control Scheme). The proposed STICS scheme distinguishes between short-term traffic congestion process and long-term traffic congestion process according to traffic characteristics to detect and control traffic. When the proposed scheme is applied to the 5G-based cellular network environment, it is expected that the traffic type will be efficiently classified by self-detecting the traffic according to the flow. Such classified traffic is less sensitive to communication between the D2D and C2D links, thereby reducing traffic overload. We evaluate the performance of the proposed scheme through simulation and show that the proposed scheme is more efficient than other comparison schemes.

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

Both types of surgical guides – with and without metal sleeves are founded beneficial in clinical studies. The aim of this in vitro study was to compare time of surgical procedure in dental implantology depending of used type of the surgical guide. Ten three dimensional (3D) printed models of lower jaws were prepared based on complete virtual model of patient clinical conditions with missing teeth 37, 46 and 47. Five of this models were used for implant sites preparation performed with use of surgical guide without metal sleeves and dedicated surgical kit, and next five were used for the same procedure performed with surgical guide with metal sleeves and dedicated surgical kit. Time of implant site preparation were measured and noted. Statistical analysis was performed using Student’s t test for independent samples. Difference of time in both groups were founded as statistically significant (t=-9.94; df =28; p=0.0000). Type of surgical guide is important factor which can impact on time of implant site preparation and all surgical procedure.

Lake Malombe fish stocks have been depleted by chronic overfishing. Various management approaches (co-management, command control, and ecosystem-based management to fisheries) have been used to manage the fishery. However, the lack of an accurate predictive model has hampered their success. Therefore, we developed and tested a time series model for Lake Malombe fishery. The seasonal fish biomass and CPUE trends were first observed and both were non-stationary. The second-order differencing was applied to transform the non-stationary data into stationary. Autocorrelation functions (AC), partial autocorrelation function (PAC), and Akaike information criterion (AIC) were estimated, which led to the identification and construction of autoregressive integrated moving average (ARIMA) models, suitable in explaining the time series and forecasting. The results showed that ARIMA (1,2,1) provided a better prediction than its counterparts. The model satisfactorily predicted that by 2032, both fish biomass and CPUE will decrease to 3204.6 tons and 59.672 respectively, signifying the potential threat to Lake Malombe fishery. The model justified the necessity of taking precautionary measures to avoid the total collapse of the fishery.

Three different approaches show that, contrary to a longstanding conviction older than 160 years, the advance of Mercury perihelion can be achieved in Newtonian gravity with a very high precision by correctly analysing the situation without neglecting Mercury’s mass. General relativity remains more precise than Newtonian physics, but Newtonian framework is more powerful than researchers and astronomers were thinking till now, at least for the case of Mercury. The Newtonian formula of the advance of planets' perihelion breaks down for the other planets. The predicted Newtonian result is indeed too strong for Venus and Earth. Therefore, it is also shown that corrections due to gravitational and rotational time dilation, in an intermediate framework which analyzes gravity between Newton and Einstein, solve the problem. By adding such corrections, a result consistent with the one of general relativity is indeed obtained. Thus, the most important results of this paper are two: i) It is not correct that Newtonian theory cannot predict the anomalous rate of precession of the perihelion of planets' orbit. The real problem is instead that a pure Newtonian prediction is too strong. ii) Perihelion's precession can be achieved with the same precision of general relativity by extending Newtonian gravity through the inclusion of gravitational and rotational time dilation effects. This second result is in agreement with a couple of recent and interesting papers of Hansen, Hartong and Obers. Differently from such papers, in the present work the importance of rotational time dilation is also highlighted.

Monitoring drought impacts in forest ecosystems is a complex process, because forest ecosystems are composed of different species with heterogeneous structural compositions. Even though forest drought status is a key control on the carbon cycle, very few indices exist to monitor and predict forest drought stress. The Forest Drought Indicator (ForDRI) is a new monitoring tool developed by the National Drought Mitigation Center (NDMC) to identify forest drought stress. ForDRI integrates 12 types of data, including satellite, climate, evaporative demand, ground water, and soil moisture, into a single hybrid index to estimate tree stress. The model uses Principal Component Analysis (PCA) to determine the contribution of each input variable based on its covariance in the historical records (2003–2017). A 15-year time series of 780 ForDRI maps at a weekly interval were produced. The ForDRI values at a 12.5km spatial resolution were compared with normalized weekly Bowen ratio data, a biophysically based indicator of stress, from nine AmeriFlux sites. There were strong and significant correlations between Bowen ratio data and ForDRI at sites that had experienced intense drought. In addition, tree ring annual increment data at eight sites in four eastern U.S. national parks were compared with ForDRI values at the corresponding sites. The correlation between ForDRI and tree ring increments at the selected eight sites during the summer season ranged between 0.46 and 0.75. Generally, the correlation between the ForDRI and normalized Bowen ratio or tree ring increment are reasonably good and indicate the usefulness of the ForDRI model for estimating drought stress and providing decision support on forest drought management.

Objective: Identify factors associated with excessive day time sleepiness (EDS) in individuals with obstructive sleep apnea (OSA), and analyze the effects that obesity and gender have on excessive daytime sleepiness in such individuals. Methods: A total of 160 people were selected for this study. All the people have completed a clinical evaluation, and whose apnea-hypopnea index (AHI) > 10 events/hour of sleep on polysomnography were included in the study from the Department of otolaryngology. Results: The Mean age was 43.87±11.34 years, mean EDS score was 14.09± 4.91, and mean AHI was 43.88±20.66 events/hour of sleep. Male presented lower mean age, higher EDS scores, and more time in apnoea, whereas females presented with higher mean age, lower EDS scores, and less time in sleep apnea. The EDS score showed best correlation with duration of apnoea (r = 0.448; p < 0.01), peripheral oxygen saturation (SpO₂; r = -0.458; p < 0.01) and AHI (r = 0.484; p < 0.01). The mean body mass index (BMI) was 27.41±3.86 kg/m². Normal, Overweight, obese, and morbidly obese were observed in 20%, 61%, 18%, and 0.6% of cases, respectively. Severity of the disease best correlated with BMI (r = 0.421; p < 0.01). Conclusions: OSA is predominant in males (M/F 5:1), and obese population. Females diagnosed with OSA have higher mean age. However, EDS scores and time spent in sleep apnoea is lower in females. Higher BMI is associated with EDS, irrespective of gender.