Metallic substrates and polymer adhesive in composite-metal joints have a relatively large coefficient of thermal expansion (CTE) mismatch, which is a barrier in the growing market of electric vehicles and their battery structures. It is reported that adding carbon nanotubes (CNTs) to the adhesive reduces the CTE of the CNT enhanced polymer adhesive multi-material system, therefore when used in adhesively bonded joints it would, theoretically, result in low CTE mismatch in the joint system. The current article presents the influence of two specific mass ratios of CNTs on the CTE of the enhanced polymer. It was observed that the addition of 1.0 wt% and 2.68 wt% of multi-walled CNTs (MWCNTs) decreased the CTE of the polymer adhesive from 7.5e-5 1/C (pristine level) to 5.87e-5 1/C and 4.43e-5 1/C, respectively by 22% and 41% reduction. The reduction in the CTE was predicted, theoretically, which showed that CTE should have been reduced to 3.6e-5 1/C (52% reduction) and 1.4e-5 1/C (81% reduction). This may be due to the fact that, Raman spectroscopy of the MWCNTs identified defects in the raw material, and scanning electron microscopy (SEM) identified agglomeration of MWCNTs on the surface and cross-section of the modified polymers.

Purpose: To compare the therapeutic efficacy and safety of dorzolamide/timolol fixed-combination in newly diagnosed primary open angle glaucoma patients. Methods: In this prospective, interventional case series, newly diagnosed primary open angle glaucoma (POAG) patients that had not been treated for glaucoma were included. Patients were started on Cosopt twice a day (BID) for 1 month and then switched to three times a day (TDS) for additional 1 month. Patients underwent comprehensive ophthalmic examination, diurnal intraocular pressure (IOP), blood pressure (BP) and 24-hours heart rate (HR) measurements at baseline, month 1( BID), and month 2( TDS). IOP, systolic and diastolic pressures were measured at 8:00 AM,12:00 AM, 4:00 PM, 8:00 PM and 12:00 PM. Throughout the study, all adverse events were recorded and monitored by the investigators. Results: In 31 POAG patients that completed the study ,mean baseline IOP was 23.1±3.15 mmHg . IOP was decreased significantly 16.5 ± 2.21 at 1 month (P < 0.0001) and 13.9 ± 2.23 mmHg at 1 and 2 month follow up. (P < 0.0001) IOP was significantly lower in month 2 compared to month 1 (P = 0.0004). While Cosopt BID significantly reduced the mean 24-hour systolic BP and mean 24-hour HR from baseline (P < 0.0001), the mean 24-hour systolic BP and HR remained unchanged 2ith Cosopt TDS compared to BID (P = 0.62). Conclusions: Cosopt TDS has a superior IOP-lowering effect than Cosopt BID in POAG patients with comparable safety profile.

This paper presents a robust and efficient fault detection and diagnosis framework for handling small faults and oscillations in synchronous generator (SG) systems. The proposed framework utilizes the Brunovsky form representation of nonlinear systems to mathematically formulate the fault detection problem. A differential-flatness model of SG systems is provided to meet the conditions of the Brunovsky form representation. A combination of high-gain observer and group method of data handling neural network is employed to estimate the trajectory of the system and to learn/ approximate the fault and uncertainties associated functions. The fault detection mechanism is developed based on output residual generation and monitoring so that any unfavourable oscillation and/or fault occurrence can be detected rapidly. Accordingly, an average L1-norm criterion is proposed for rapid decision making of faulty situations. The performance of the proposed framework is investigated for two benchmark scenarios which are actuation fault and fault impact on system dynamics. The simulation results demonstrate the capacity and effectiveness of the proposed solution for rapid fault detection and diagnosis in SG systems in practice, and thus enhancing service maintenance, protection, and life cycle of SGs.

Alzheimer’s disease (AD) is a genetically complex senile neurodegeneration with unknown etiology. The first gene discovered to be mutated in early-onset AD, the amyloid precursor protein (APP), has been widely assumed as a causal factor in the disease cascade due to its generation of Aβ species. APP has an evolutionarily conserved biological role and activates a signaling program with notable similarities to integrin—a cell adhesion receptor with a wide array of functions. Intriguingly, several AD genome-wide association study (GWAS) candidate genes, including the SHARPIN locus recently reported by us and others, influence signaling of the integrin pathway. Integrins are focal adhesion regulators and serve in nervous system development, synaptic plasticity, and Tau phosphorylation. These observations suggest that the function of APP probably goes beyond Aβ generation in AD. Aging—the strongest risk factor for AD—is associated with various clock-like events in cells. For instance, neurons are continuously impacted by stochastic ‘hits’ to their genomes in aging, in the forms of DNA damage, insertion-deletions, copy-number variations (CNVs) and other types of somatic mutations. DNA damage and somatic mutations can result in neoplastic changes and cancer in mitotically active cells. However, their consequences in post-mitotic cells such as aging neurons are less defined. The current hypothesis holds that the stochastic loss of DNA sequence data at random loci in aging affects longer genes by chance more frequently. As a result, the biological processes coordinated by long genes may be more vulnerable to such random aging effects. Curiously, as shown by us and others, long genes are strongly enriched for synapse- and cell adhesion-related ontologies, more than any other biological process or cellular compartment. In addition, among various cell types, neurons possess the highest levels of long gene expression and are therefore more vulnerable to such harmful effects. The long gene vulnerability hypothesis provides a simple link between aging and the genetic landscape of AD and warrants new strategies for disease modification.

Purpose: To compare the efficacy and safety of graft-free short tunnel small flap (STSF) technique with that of scleral patch graft (SPG) in Ahmed glaucoma valve (AGV) implantation. Design: Randomized clinical trial. Participants: Eighty-eyes of eighty patients with medically uncontrolled glaucoma including 41 in STSF and 39 eyes in SPG. Methods: Patients were enrolled and assigned randomly to STSF or SPG. Main Outcome Measures: tube exposure, Intraocular pressure (IOP), number of glaucoma medications, best corrected visual acuity (BCVA), surgical complications, and success rate ( defined as intraocular pressure (IOP) >5 mmHg, ≤21 mmHg, and IOP reduction ≥20% from baseline at two consecutive visits after three months, no reoperation for glaucoma). Results: only one case in SPG developed tube exposure at 1-year follow-up. The cumulative probability of success during the first year of follow-up was 70% in the STSF and 65% in SPG (P = 0.36). IOP decreased significantly from 29.6 ± 8.6 mmHg at baseline to 16.4 ± 3.6 mmHg at the final follow-up in STSF (p = 0.001). The corresponding numbers for SPG were 30.9 ± 11.2 and 15.8 ± 4.7, respectively (p = 0.001). The final IOP was comparable between both groups (p = 0.65). Mean ± standard deviation of the number of glaucoma medications was 1.8 ± 0.9 in STSF and 1.6 ± 0.9 in SPG at final follow-up (P = 0.32). Postoperative complications developed in 8 patients (19%) in STSF and 9 patients (23%) in SPG (P = 0.81). Conclusions: STSF and SPG techniques had comparable complication rate at one-year follow-up. Both techniques were comparable in terms of success rate, postoperative IOP, and glaucoma medications.

Numerous genes expression lead to inflammation in the individuals’ lungs that have chronic obstructive pulmonary disease (COPD) may be affected by epigenetic alteration. Important epigenetic processes include methylation of DNA and different histones post-translational changes, including ubiquitination, phosphorylation, methylation, SUMOylation and acetylation. Smoking can trigger the enzymes that control these epigenetic changes. According to the majority of publications, both environmental and genetic variables have a substantial role in the development of COPD. Although, the information about COPD epigenetic is not much but, a better perception of the disease pathophysiology and identifying new markers to create novel therapeutics for patients can be achieved via a better understanding of the epigenetic processes involved.

Sehezar area is located in southern Tonokabon in Mazandaran province, north of Iran, near the Tarom – Hashdtjin belt. The existence of granitoid masses in the region can be important in terms of the potential of mineralization. Geochemical anomaly separation from the background is one of the important steps in mineral exploration. In the past decades, geochemical anomalies have been identified by means of various methods. Some of these separation methods include: statistical analysis methods (like univariate, bivariate, multivariate statistics), spatial statistical methods and fractal and multi-fractal methods. To identify the anomalous area, 71 stream sediment samples were collected from the area and analyzed by the ICP-MS method, and then interpreted. Initially, data were normalized and afterwards, univariate analysis (threshold limit and screening (P.N) methods) was used, in which results of the probable and definite anomaly of the threshold method were confirmed by the P.N screening method. Finally, the maps of the anomal zones were drawn. Then, bivariate analysis (Pearson correlation coefficients) and multivariate analysis on normal data were performed on SPSS software, in which factor analysis and cluster analysis were used for multivariate analysis. As a result of using the factor analysis method, six factors were identified and factor maps were drawn by the Surfer software. Also, by using cluster analysis, the variables were divided into two groups. In order for a better separation of the geochemical anomaly from the background, in addition to the threshold method, the Concentration - Area fractal method was used. Here, the fractal geometry using full-logarithmic graphs of the Concentration - Area obtained is capable of separating the stairs of different sections (background, threshold, and anomaly) with respect to the angle coefficient of the Concentration - Area plot. Then, in conclusion, results of these methods were compared and investigated, and finally, the anomalies area maps of the Au, Ag, Cu, Fe, W elements were drawn by Concentration - Area fractal and threshold methods and anomalous zones were introduced.

Metal 3D printing technology is a promising manufacturing method, especially in the case of complex shapes. The quality of the printed product is still a challenging issue for mechanical applications. The anisotropy of the microstructure, imperfections, and residual stress are some of the issues that diminish the mechanical properties of the printed sample. The simulation could be used to investigate some technical details, and this research has tried to computationally study the metal 3D printing of austenitic stainless steel to address austenite microstructure and local yield strength. Two computational codes were developed in Visual basics 2015 to simulate the local heating/cooling curve and subsequent austenite microstructure. A stochastic computational code was developed to simulate austenite grain morphology based on calculated thermal history. Then Hall-Pitch equation was used to estimate the yield strength of the printed sample. These codes were used to simulate the effect of temperature of the printer’s chamber on microstructure and subsequent yield strength. The austenite grain topology is more columnar at a lower temperature. The percentage of the equiaxed zone will be increased at a higher chamber’s temperature. Almost a fully equiaxed austenite microstructure will be achieved at 800 C chamber’s temperature, but the last printed layer, which is columnar and can be removed by cutting then. The estimated local austenite grain size and the local yield strength in the equiaxed regions are in the range of 15 to 30 μm and 270 to 330 MPa at 800 C temperature of printer’s chamber, respectively.

The powder bed fusion (PBF) metal additive manufacturing (AM) method uses an energy source like a laser to melt the metal powders. The laser can locally melt the metal powders and creates a solid structure as it moves. The complexity of the heat distribution in laser PBF metal AM is one of the main features that need to be accurately addressed and understood to design and manage an optimized printing process. In this research, the dependency of local thermal rates and gradients on print after solidification (in the heat-affected zone) was numerically simulated and studied to provide information for designing the print process. The simulation results were validated by independent experimental results. The simulation shows that the local thermal rates are higher at higher laser power and scan speed. Also, the local thermal gradients increase if the laser power increases. The effect of scan speed on the thermal gradients is opposite during heating versus cooling times. Increasing the scan speed increases the local thermal gradients in the cooling times and decreases the local thermal gradients during the heating. In addition, these simulation results could be used in artificial intelligence (AI) and machine learning for developing digital additive manufacturing.

Computer simulation relies on well-defined experimental supported theories, models, and numerical methods. Computer simulation is an alternative to fill the gaps where the experiment is pricy or limited in engineering. Computer simulation is a high-fidelity computational technique that can work as an engineering innovation tool to try imaginative ideas beyond the available technologies. The accuracy of current computer simulation results is reliable enough to accelerate innovation for new materials and mechanical technologies without experimental validation. So, we may need to urge relying on computer simulation results without any experimental validation to accelerate engineering innovations. In addition, using high-fidelity computer simulation for engineering innovation acceleration without validation will be promoted if it is credited as a new classification. This subject and a related justification's classification in engineering have been discussed.

The Sol Hamed (SH) area is a part of the Arabian-Nubian Shield (ANS) ophiolites occurred within Onib-Sol Hamed suture zone in the southern Eastern Desert of Egypt. The ophiolitic assemblages in this area are represented by serpentinite, metagabbro and arc assemblages represented by metavolcanics. They later intruded by gabbroes and granites. Geochemically, the compatible trace elements (Cr=2426–2709 ppm, Ni=1657–2377 ppm and Co=117–167 ppm) enrichment in SH serpentinites indicate derivation from a depleted mantle peridotite source. They show affinity to the typical metamorphic peridotites. The normative compositions reflect harzburgitic mantle source. Their Al2O3 contents (0.05–1.02 wt. %) are akin to oceanic and active margin peridotites and Pan-African serpentinites. The Cr and TiO2 contents indicate supra-subduction zone (SSZ) environment. Their Al2O3/SiO2 and MgO/SiO2 ratios support the SSZ affinity and are similar to ANS peridotites with fore-arc setting. Moreover, their Al2O3 and CaO depletion is typical of fore-arc peridotites. Structurally, the area represents four deformational events can be well-known in the Neoproterozoic rocks (D1, D2, D3 and D4); D1: E–W thrust faults and related E–W (F1) folds; D2: NW–SE thrust faults and related NW–SE (F2) folds were formed; D3: conjugate NNW-trending sinistral and NNE-trending dextral transpression, as well as N–trending tight folds (F3) and D4: is E–W dextral strike-slip and dip-slip normal faults striking NNW–SSE to N–S and E–W may be related to Red Sea rifting. There are major three fault sets affected the area. The first set trend mainly NE-SW and is manifested in the volcanic-sedimentary assemblage and Gabal SH and have important role in mineralization. The second set trend E-W affecting all the basement rocks and disturbs the first fault set. The third set trend N-S affected all the rock units. Magnesite mineralization in SH serpentinites is cryptocrystalline formed due to hydrothermal alteration of the serpentinite host rocks. It is occur as snow-white veins and stock-works. These characteristics are typical of Kraubath type magnesite deposits. Gold mineralization is confined to malachite-bearing quartz veins, smoky quartz veins and alteration zones. Malachite-bearing quartz veins trending NW-SE cut through gabbroic rocks and exhibit mylonitic structure. They are fractured containing malachite and disseminated sulfide minerals. Smoky quartz veins trending NE-SW with SE steeply dipping intrude the meta-andesite. They are intensively sheared containing iron oxides in the fissures. The gold grades increase with arsenopyrite occurrences. On the other hand, the barren quartz veins are nearly vertical with E-W directions. Alteration zones with NW-SE trend and nearly vertical dip intrude metagabbros and metavolcanics. Hematite, limonite, goethite and fresh pyrite characterize these zones. They occur mainly neighboring the auriferous quartz veins.

Background: Hypoxia-inducible factors (HIFs) are transcription factors that get activated and stabilized in the heterodimerized form under hypoxic conditions. The three members of the HIF alpha factors share high structural similarity but have tissue-specific expression patterns. A majority of studies have reported the importance of the HIF1A and HIF2A activity in the survival, proliferation, metastatic potential, and metabolic regulation of hypoxic cancer cells. However, the importance of the expression pattern and activity of HIF3A in a variety of cancers remains unknown. Method and materials: The expression profile of 13 different types of The Cancer Genome Atlas (TCGA) cancer samples were downloaded, normalized and differential gene expression analysis (DGE) was performed to compare the expression pattern of HIF alpha family members in cancer and adjacent normal tissues, as well as at different stages and tumor-sizes. Receiver operating characteristic (ROC) test and survival analysis were carried out to estimate the diagnostic potential of HIF alpha isomers in different cancers, as well as the survival rate of patients with the varying expression levels of HIF alpha factors. Results: The expression status of HIF3A was notably less in all cancer samples in contrast to their adjacent normal tissues. The expression degree of HIF1A varied among distinct types of cancer and the expression degree of HIF2A was lower in nearly all types of cancers. The expression level of HIF alpha isomers did not significantly correlate with different sizes of tumor samples and stages of different tumor tissue samples. HIF3A had very weak diagnostic potential, while HIF2A had better diagnostic potential in most types of cancers compared to HIF1A. Patients who had a higher level of HIF3A had better survival, while the higher expression levels of HIF1A and HIF2A were associated with worse survival in many types of cancers. Conclusion: Our study shows the heterogenous expression pattern of HIF alpha subunits in distinctive kinds of cancers and the influence of HIF3A expression level in the survival of patients with varying types of cancers.

Brain stem cells (neural stem cells or NSCs) and neurons of a chosen kind reprogramming is a potential technique for cell therapy. It is possible to reprogram non-neuronal cells, for example, by using a predetermined group of factors, nuclear transfer, and the induced transcriptional factors (TFs) expression in a related lineage of cells, and non-coding microRNAs (miRNAs). Researchers have additionally been attempting to improve reprogramming methods, whether it is by employing unique sets of biomolecules and particular TFs or by delivering relevant miRNA and Biomolecules. The technique of miRNA mediated is intriguing for its capability to quickly create a range of biologically desirable cell types for therapy from different lineages of cells. Current findings have made significant advancements towards changing the somatic cells to diverse particular neuronal subgroups with greater efficiency, using reprogramming of miRNA-mediated neural cells, despite the fact that the precise processes need to be discovered. To further understand how miRNAs might direct somatic cells to become neural, we need to look at the latest research on their function in neural reprogramming over the differentiated cells. Recent findings on the role of miRNAs in the initiation of cell reprogramming and the determination of the neuronal subtype's destiny are the primary focus of this comprehensive overview. Furthermore, we cover the far more latest results concerning certain miRNAs' activity in controlling different phases of neuronal differentiation, which contributes in comprehending the interaction network of miRNAs and their receptors.

The growth of solid particles during liquid phase sintering was modeled by the Cellular Automata method. The binary phase diagram and Fickian approach for the diffusion process were applied to simulate the chemical composition variation in liquid and solid phases during sintering. The Oswald-Ripening effect was considered during the dissolution of the solid phase in the liquid phase. It is used to define the probability of solid-phase dissolution by the liquid phase and develop the model to simulate the alloy with solid solubility. So, the microstructure could be modeled in the liquid phase sintering process.

Metal 3D printing technology is a promising manufacturing method. The quality of the printed product can pass for mechanical application, if the anisotropy of the microstructure, imperfections, deformation, and residual stress of the printed sample could be lower than the appropriate level or if they are fully illuminated. Thermal stress is one of the significant reasons for deformation in the 3D printed samples. Thermal stresses are the direct consequence of the local temperature gradient. In this research, the effect of the temperature printer’s chamber (from room temperature to 900 C) was studied on thermal stress and subsequent total deformation in the printed sample. The printed sample is a six-layers-printed walk, which could be considered as a building block of other complex shapes and give us inside about deformation. The computational results show a meaningful reduction in thermal stress and deformation at the higher temperature of the printer’s chamber. The lower final deformation of the printed sample is an important subject, especially for samples with complex shapes.

We present software on total energy calculation by quantum mechanics first principle method with a graphic user interface (GUI). Total energy calculation in this software is based on numerical analysis of time-dependent density functional (the used numerical method is finite difference time domain). QUMEC package has been equipped by common exchange-correlation energy terms with electron spin polarization calculation. With this package, users can calculate the total energy of the free particle, bulk materials, and materials with free surfaces at the atomic scale. The package is tested by several physical subjects, i.e., the surface energy of nano-LiCoO₂ and diffusion constant of lithium atoms in LiNi­_0.5Mn_1.5O₄.

This code provides computational facilities to simulate current versus time during the charging of Li-ion cells at desire constant voltage by considering multiscale physical phenomena. This code only considers a powder of active materials (at microscale or nanoscale) and a small part of electrolyte around it as a half cell. Then it is extended to a complete cell by applying correct boundary conditions. This code is very useful by modifying code parameters to understand the effect of the complex shape of active materials powder (surface area and powder size), kind of electrolyte, and the applied voltages on the charging response of Li-ion cell. As a summary, a microscale approach to the design of Li-ion cells has been provided via this code.

Drug-induced liver disease represents one of the main problems in the therapeutic field. As with most pathologies, also in this case various risk factors can be recognised, such as age, sex, but also the gut microbiota. Therefore all drugs on the market can, in fact, cause hepatotoxicity of varying degrees. Drugs used in the treatment of IBD can also cause these adverse effects and even lead to DILI. In this review, the various classes of drugs used in the treatment of IBD were taken into consideration and the various adverse effects on the liver were illustrated. In particular, a major role has emerged regarding hepatotoxicity of immunosuppressants. Biological drugs, however, seem to be responsible for these complications to a much lesser extent.

Alzheimer's disease (AD) is a progressive neurodegenerative brain disorder. One of the important therapeutic approaches of AD is the inhibition of β‐site APP cleaving enzyme‐1 (BACE1). This enzyme plays a central role in the synthesis of the pathogenic β-amyloid peptides (Aβ) in Alzheimer's disease. A group of potent BACE1 inhibitors with known x-ray structures (PDB ID 5i3X, 5i3Y, 5iE1, 5i3V, 5i3W, 4LC7, 3TPP) were studied by molecular dynamics simulation and binding energy calculation employing MM_GB(PB)SA. The calculated binding energies gave Kd values 0.139 µM, 1.39 nM, 4.39 mM, 24.3 nM, 1.39 mM, 29.13 mM and 193.07 nM, respectively. These inhibitors showed potent inhibitory activities in enzymatic and cell assays. The Kd values were compared with experimental values, the structures were discussed in view of the energy contributions to binding. Drug likeness of these inhibitors is also discussed. Accommodation of ligands in the catalytic site of BACE1 is discussed depending on the type of fragment involved in each structure. Molecular dynamics (MD) simulations and energy studies were used to explore the recognition of the selected BACE1 inhibitors by Asp 32, Asp228 and the hydrophobic flap. The results show that selective BACE1 inhibition may be due to the formation of strong electrostatic interactions with Asp32 and Asp228 and a large number of hydrogen bonds, π-π and Van der Waals interactions with the amino acid residues located inside the catalytic cavity. Interactions with the ligands show a similar binding mode with BACE1. These results help to rationalize the design of selective BACE1 inhibitors.

The main regulatory factors during the adaptation of cancer cells to hypoxic stress are the hypoxia-inducible factors (HIFs), which are being increasingly recognized as an interesting and challenging target for the design of new chemotherapeutic molecules. HIF2A was found to have an large internal hydrophilic cavity within its PAS-B domain, unique to this sub-unit and is suggested to be a possible ligand-binding site. Regulation of HIF2A by cellular molecules is still greatly unknown. In This paper we have employed in-silico techniques, such as molecular docking and dynamic simulation, to design new direct inhibitors against HIF-2A subunit via targeting one of its critical domains and the final top screened molecules have been tested on hypoxic cancer cells for further validation of their inhibitory potential. we targeted the hydrophilic cavity inside the PAS-B domain of the HIF2A to identify novel molecules with a high binding capacity. Virtual Screening methodology was used for molecular docking of NSC library against the target domain inside the HIF2A PAS-B domain with the top 5% compounds with significant MolDock and Re-rank scores were selected for further analysis. The NSC 106416, NSC 217021, NSC 217026, and NSC 215639 compounds were selected based on their docking scores. NSC 215639 had the minimum polar solvation energy and also had a relative strong binding energy. NSC 217026 had the strongest binding energy among other compounds.

Ferric uptake regulation protein is a repressor protein which binds an AT rich region of DNA (the iron box). Fur binds as a dimer in a helix turn helix mode and it is activated by iron(II) and other divalent transition metal ions at elevated concentrations in a process to regulate the ion uptake. Each transition metal ion induces certain conformational changes to aid the Fur binding, both the N-terminal and C-terminal domains take part in binding to DNA in addition to His 88 and His 86 residues. The process is discussed in view of experimental reports. Fe(II), Mn(II) and Co(II) activate Fur to bind DNA experimentally but Zinc plays a structural role and does not activate Fur to bind DNA.

We present theoretical designs of high performance optical filters in integrated silicon photonic nanowire resonators. We use mode interference in formed by zig-zag waveguide coupled Sagnac loop reflectors (ZWC-SLRs), tailored to achieve diverse filtering functions with good performance. These include compact bandpass filters with improved roll-off, optical analogues of Fano resonances with ultrahigh spectral extinction ratios (ERs) and slope rates, and resonance mode splitting with high ERs and low free spectral ranges. The analysis verifies the feasibility of multi-functional integrated photonic filters based on ZWC-SLR resonators for flexible spectral engineering in diverse applications.

Rebar cages form the skeletal framework of reinforced concrete structural members. They comprise longitudinal and transverse reinforcing bars, usually connected by tie wires. Past studies have identified tie-wire connections as the "weak links" in large, prefabricated rebar cages, leading to their failure during site handling and construction stages. Recent research has explored the use of mechanical U-bolt connectors to bolster stability and safety of rebar cages while reducing their vulnerability and risk of failure. This paper presents a comprehensive experimental and numerical investigation of a new type of mechanical connector, referred to as "Cage Clamp", to enhance rebar cage stability and safety. Component-level experimental tests are conducted to determine the force-displacement response of Cage Clamp connectors for crossbar connections in different degrees of freedom. The results highlight the remarkable strength and stiffness of Cage Clamp connectors, significantly outperforming traditional tie-wire connections. Furthermore, full-scale experimental tests are performed on a circular underground pile-shaft rebar cage with Cage Clamp connectors to assess its behavior under various site handling conditions. The data obtained from these experiments are used to construct and refine detailed 3D finite element models of the rebar cages, capable of capturing the complex behavior of rebar cages with Cage Clamp connectors. Using the numerical model, a sensitivity analysis is performed to assess the effect of the number of Cage Clamps on the deflection and stability of the rebar cage. The findings show that using cage clamps to connect pick-up rebar with lateral reinforcement at every 8 to 10 feet is sufficient to ensure the stability of the cage during typical site handling conditions. The results of this study serve as a basis for establishing analysis, design, fabrication, and handling guidelines for rebar cages, contributing to safe and efficient construction practices.

Identity is the basis and foundation of the cultural landscape. Despite the emphasis of international documents and charters on its various aspects and necessity, today, the cultural landscape is threatened by extinction due to changes of many parameters. Accordingly, it is crucial to find an approach that can sustain cultural identity and its values in the changing world of the twenty-first century. Given the qualitative and the changing nature of cultural identity, achieving an approach which can lead to its continuation is not straightforward. By reviewing and analyzing international conventions and documents, on the one hand, and expert opinions, on the other hand, the present study, therefore, aimed to find out how the approach of cultural landscape values conservation can lead to the continuation of cultural identity in the natural and cultural heritage of Uraman Takht. This paper employed qualitative research methods as a basis for data collection and analysis, which primarily involved the use of content analysis along with field observations and interviews with stakeholders and indigenous residents.

We propose and theoretically investigate integrated photonic filters based on coupled Sagnac loop reflectors (SLRs) formed by a self-coupled wire waveguide. By tailoring coherent mode interference in the device, three different filter functions are achieved, including Fano-like resonances, wavelength interleaving, and varied resonance mode splitting. For each function, the impact of device structural parameters is analyzed to facilitate optimized performance. Our results theoretically verify the proposed device as a compact multi-functional integrated photonic filter for flexible spectral shaping.

We theoretically investigate integrated photonic resonators formed by two mutually coupled Sagnac loop reflectors (MC-SLRs). Mode interference in the MC-SLR resonators is tailored to achieve versatile filter shapes with high performance, which enable flexible spectral engineering for diverse applications. By adjusting the reflectivity of the Sagnac loop reflectors (SLRs) as well as the coupling strength between different SLRs, we achieve optical analogues of Fano resonance with ultrahigh spectral slope rates, wavelength interleaving / non-blocking switching functions with significantly enhanced filtering flatness, and compact bandpass filters with improved roll-off. In our designs the requirements for practical applications are considered, together with detailed analyses of the impact of structural parameters and fabrication tolerances. These results highlight the strong potential of MC-SLR resonators as advanced multi-functional integrated photonic filters for flexible spectral engineering in optical communications systems.

This paper presents a novel adaptive fault-tolerant neural-based control design for wind turbines with unknown dynamic and unknown wind speed. By utilizing the barrier Lyapunov function in the analysis of the Lyapunov direct method, the constrained behavior of the system is provided in which the rotor speed, its variation and generated power remain in the desired bounds. In addition, input saturation is also considered in terms of smooth pitch actuator bounding. Furthermore, by utilizing a Nussbaum-type function in designing the control algorithm, the unpredictable wind speed variation is captured without requiring accurate wind speed measurement, observation or estimation. Moreover, with the proposed adaptive analytic algorithms, together with the use of radial basis function neural networks, a robust adaptive and fault-tolerant control scheme is developed without the need for precise information about the wind turbine model nor the pitch actuator faults. Additionally, the computational cost of the resultant control law is reduced by utilizing a dynamic surface control technique. The effectiveness of the developed design is verified using theoretical analysis tools and illustrated by numerical simulations on a high-fidelity wind turbine benchmark model with different fault scenarios. Comparison of the achieved results to the ones that can be obtained via an available industrial controller shows the advantages of the proposed scheme.

Background: Twitter is a major tool for communication in emergencies such as natural disasters. This online social network allows the user to produce content, and it is not designed exclusively for news releases, as opposed to other service providers. Aim: The aim of this study is to investigate Twitter uses in natural disasters and pandemics. Methods: The included studies reported the role of Twitter in natural disasters. The studies that report in settings other than the natural disasters (such as man-made disasters) and other social media were excluded. Electronic databases for a comprehensive literature search including MEDLINE, Web of Science, CINAHL, PsycINFO, Cochrane Register of Controlled Trials (CENTRAL) and EMBASE were used to identify the records that match the mentioned inclusion criteria published till May 2020. The study characteristics were extracted from the qualified studies including year of publication, findings, and geographical location of the study conduct. A narrative synthesis for this literature review was used. Results: The search identified 822 articles of which 780 articles were removed, 256 were not available, 311 papers were not relevant, 16 were duplicated articles, and 197 were non-related to the emergencies. 45 articles met the selection criteria and were included in the review. eleven themes were found in the narrative synthesis including early warning, disseminating information and misinformation, advocacy, personal gains, assessment, various roles of organizations, public mood, geographical analysis, charity, using influencers, and trust. Conclusions: It is recommended that influential individuals be identified in each country and community before disasters occur so that the necessary information can be disseminated in response to disasters. Preventing the spread of misinformation is one of the most important issues in times of disaster, especially pandemics. Disseminating accurate, transparent, and prompt information from relief organizations and governments can help. Also, analyzing Twitter data can be a good source for understanding the mental state of the community, estimating the number of injured people, estimating the points affected by natural disasters, and modeling the prevalence of epidemics. Therefore, various groups such as politicians, the government, non-governmental organizations, aid workers, and the health system can use this information to plan and implement interventions.

Appeals to governments for implementing basic income are contemporary. The theoretical backgrounds of the basic income notion, only prescribe transferring equal amounts to individuals irrespective of their specific attributes. However, the most recent basic income initiatives all around the world are attached to certain rules with regard to the attributes of the households. This approach is facing significant challenges to appropriately recognize the vulnerable groups. A possible alternative for setting rules with regard to the welfare attributes of the households is to employ artificial intelligent algorithms that can process unprecedented amounts of data. Can integrating machine learning change the future of basic income by perdition of vulnerable to future poverty households? In this paper, we utilize a multidimensional and longitudinal welfare data comprising one and a half million individual data and a Bayesian beliefs network approach to examine the feasibility of predicting households’ vulnerability to future poverty based on the existing households’ welfare attributes.

Background: ChatGPT is becoming a new reality. Where do we go from here? Objective: to show how we can distinguish ChatGPT-generated publications from counterparts produced by scientist. Methods:By means of a new algorithm, called xFakeBibs, we show the significant difference between ChatGPT-generated fake publications and real publications. Specifically, we triggered ChatGPT to generate 100 publications that were related to Alzheimer’s disease and comorbidity. Using TF-IDF, using the real publications, we constructed a training network of bigrams comprised of 100 publications. Using 10-folds of 100 publications each, we also 10 calibrating networks to derive lower/upper bounds for classifying articles as real or fake. The final step was to test xFakeBibs against each of the ChatGPT-generated articles and predict its class. The algorithm successfully assigned the POSITIVE label for real ones and NEGATIVE for fake ones. Results: When comparing the bigrams of the training set against all the other 10 calibrating folds, we found that the similarities fluctuated between (19%-21%). On the other hand, the mere bigram similarity from the ChatGPT was only (8%). Additionally, when testing how the various bigrams generated from the calibrating 10-folds against ChatGPT we found that all 10 calibrating folds contributed (51%-70%) of new bigrams, while ChatGPT contributed only 23%, which is less than 50% of any of the other 10 calibrating folds. The final classification results using the xFakeBibs set a lower/upper bound of (21.96-24.93) number of new edges to the training mode without contributing new nodes. Using this calibration range, the algorithm predicted 98 of the 100 publications as fake, while 2 articles failed the test and were classified as real publications. Conclusions: This work provided clear evidence of how to distinguish, in bulk ChatGPT-generated fake publications from real publications. Also, we also introduced an algorithmic approach that detected fake articles with a high degree of accuracy. However, it remains challenging to detect all fake records. ChatGPT may seem to be a useful tool, but it certainly presents a threat to our authentic knowledge and real science. This work is indeed a step in the right direction to counter fake science and misinformation.

Lack of water resources in Iran, especially in recent years, has faced the agricultural sector as the most important consumer of water resources, with serious challenges. In Iran, the agricultural sector accounted for more than 90% of water consumption. However, the focus on domestic production and self-sufficiency policy in staples (wheat, barley, maize and rice) has been emphasized in general agriculture's policy. This study was conducted to estimate the imported virtual water from the imports of basic products in Iran using defined indicators during 1961-2013. Also this study investigated the possibility of achieving self-sufficiency due to the limited water resources in Iran. The results of this study showed with the increase in cereal imports, virtual water imports from 0.28 billion cubic meters in 1961 increased to 17.6 billion cubic meters in 2013 and on average about 60% of virtual water imports in strategic products is related to wheat imports during the past 53 years. Other products in cereal (barley, maize and rice) are also indicative of the general trend of increasing imports of virtual water in development plans. The estimated long-run elasticity of virtual water imports in the cereal group compared with the country's water resources also showed that with a one percent reduction in renewable water resources of the country, the virtual water import in the main cereal group will increase equivalent to 2.89 percent and the determination coefficient more than 90 percent also confirms this negative relationship. According to this result and the emphasis on the fact that renewable water resources per capita in the country is falling increasingly, it cannot be expected that domestic production could compensate for the imports of the cereal group and virtual water imports with current technology, without increasing the water productivity and without additional harm to water resources.

This paper presents the results of an experimental investigation about target detecting with passive sonar in Persian Gulf. Detecting propagated sounds in the water is one of the basic challenges of the researchers in sonar field. This challenge will be complex in shallow water (like Persian Gulf) and noise less vessels. Generally, in passive sonar the targets are detected by sonar equation (with constant threshold) which increase the detection error in shallow water. Purpose of this study is proposed a new method for detecting targets in passive sonars using adaptive threshold. In this method, target signal (sound) is processed in time and frequency domain. For classifying, Bayesian classification is used and prior distribution is estimated by Maximum Likelihood algorithm. Finally, target was detected by combining the detection points in both domains using LMS adaptive filter. Results of this paper has showed that proposed method has improved true detection rate about 27% compare other the best detection method.

Radiotheranostics refers to pairing of radioactive imaging biomarkers with radioactive therapeutic compounds that deliver ionizing radiation. Given the introduction of very promising radio-pharmaceuticals, the radiotheranostics approach is creating a novel paradigm in personalized, targeted radionuclide therapies (TRTs), also known as radiopharmaceuticals (RPTs). Radiother-apeutic pairs targeting somatostatin receptors (SSTR) and prostate-specific membrane antigens (PSMA) are increasingly used to diagnose and treat patients with metastatic neuroendocrine tumors (NETs) and prostate cancer. In parallel, radiomics and artificial intelligence (AI) as important areas in quantitative image analysis are paving the way for significantly enhanced workflows in diagnostic and theranostic fields, from data and image processing to clinical decision support, improving patient selection, personalized treatment strategies, response prediction, and prognostication. Furthermore, AI has the potential for tremendous effectiveness in patient dosimetry which copes with complex and time-consuming tasks in the RPT workflow. The present work provides a comprehensive overview of radiomics and AI application in radio-theranostics, focusing on pairs of SSTR- or PSMA‑targeting radioligands, describing the funda-mental concepts and specific imaging/treatment features. Our review includes ligands radio-labeled by 68Ga, 18F, 177Lu, 64Cu, 90Y, and 225Ac. Specifically, contributions by radiomics and AI towards improved image acquisition, reconstruction, treatment response, segmentation, restag-ing, lesion classification, dose prediction, and estimation as well as ongoing developments and future directions are discussed.

Ion channels are integral membrane proteins that facilitate the movement of ions across cell membranes, playing a key role in a range of biological processes. The high cost and time required for wet lab experiments to characterize ion channels has spurred the development of computational methods for this purpose. In our previous work, we demonstrated the effectiveness of protein language models for ion channel prediction, using a logistic regression classifier to distinguish ion channels from non-ion channels (TooT-BERT-C) and transporters from non-transporters (TooT-BERT-T). In this study, we build upon this approach by using a combination of classical machine learning classifiers and a Convolutional Neural Network (CNN) with fine-tuned representations from ProtBERT, ProtBERT-BFD, and MembraneBERT to discriminate ion channels from non-ion channels. The results of our experiments demonstrate that TooT-BERT-CNN-C, a combination of the representations from ProtBERT-BFD and a CNN, outperforms existing state-of-the-art methods for predicting ion channels, with a Matthews Correlation Coefficient (MCC) of 0.86 and an accuracy of 98.35% on an independent test set.

Lake Manzala, the largest of the northern Delta lakes of Egypt, has a great economic importance as a major natural resource of fish and salt. Unfortunately, the lake is suffering from the high inputs of pollutants from industrial, domestic, and agricultural sources. The present study aims to develop the proposed water quality management scenarios to assess and control lake pollution, the pollution sources as well as the pollution spread from the lake to the connected Mediterranean Sea. To apply study methodology, the work tasks divided into two main parts, the first part involved in modelling the lake environments by using Delft3D-WAQ hydrodynamic and water quality model to study the current status and predict the dynamic state of the Lake. This model was calibrated and validated by using various water quality datasets to simulate different scenarios. In the second part, the required lake water quality improvement scenarios were developed to solve the lake water quality problems. The study results showed that the first three developed scenarios that focusing on treatment drain effluent using primary, secondary treatment and surface wetland techniques respectively have a limited efficiency on lake water quality improvement. While the fourth scenario that involved on using biological biofilm techniques can improve lake water quality parameters. Moreover, the fifth scenario that proposed adding a new artificial inlet has a limitation due to the noted increases in lake salinity levels. The sixth scenario that proposed a diversion of some drains can improve lake water quality parameters but it can lead to a decrease in Lake water level. From water quality view point, the last scenario that applying a combination biological biofilm activated technique and also adding a new artificial inlet at northern lake region can represent the optimum scenario. Hopefully, this research will preserve the lake environment and contribute to the benefit of the man health as well. This approach could be extended to the hydrodynamic studies in similar large, shallow lakes anywhere in the world.

An efficient procedure for the synthesis of polyhydroxyl aromatic compounds (phloroglucide analogs) is described. In this procedure a reaction was done between different 4-substituted phenols and 2,6-bis(hydroxymethy) phenols. The reactions proceed in the presence of catalytic amount of silica gel supported boric tri-sulfuric anhydride (SiO2-BTSA) in excellent yields. 16 compounds were synthesized (I1-I16). Chemical structures of all compounds were confirmed by spectroscopic methods. We optimized the chemical reactions in the presence of different acidic catalysts, different solvents and also different temperatures. Catalytic amounts of SiO2-BTSA in dichloroethane (DCE) was the best conditions. Some of the synthesized compounds were screened for their antimicrobial activities. Antifungal and antibacterial activities of the synthesized compounds were evaluated by broth micro dilution method as recommended by CLSI. Some of the tested compounds show good in vitro biological properties.

Although lithium-ion batteries have extensively been used in various applications because of their high energy capacity, fracture and failure, the by-products of large strains and stresses caused by fast charging and discharging need yet to be addressed. The size effects on the mechanical behavior of the nano-sized structures are significant; however, the classical elasticity theory may not consider such effects. On the other hand, surface stress theory, as a robust and potential theory, is suitable in considering size effects in nano-scale structures. Therefore, in this paper, in order to involve the surface stress effects on the fracture behavior of Li-ion batteries, the following steps are taken. Firstly, a phase-field model is used to determine the evolution of the concentration profile. Subsequently, the stress distribution is obtained by using the surface stress theory combined with chemical equations for a planar electrode. Afterward, by using the weight function method for an edge crack in the plate, the stress intensity factor is derived for all time steps and possible crack lengths during the process. It is found that with increasing phase boundary thickness parameter or decreasing phase-separation phenomenon, the surface mechanics parameters become more influential. Furthermore, in the presence of positive surface stress, the diffusion-induced stress distribution decreases, which in turn reduces the stress intensity factor. In addition, in this paper, the two states of surface stress are compared either for elastic or total strain. Concerning stresses and concentrations, the results indicate a big difference at the beginning of the deintercalation process showing, in particular, 2% for stresses, but the differences diminish gradually.

A series of supported CuO-based nanoparticle catalysts were prepared by impregnation method and used for the synthesis of glycerol carbonate from glycerol and CO2 in the presence of 2-cyanopyridine as a dehydrant and DMF as a solvent. The effects of supports (activated alumina, silicon dioxide, graphene oxide, graphene, and activated carbon), CuO loading amount, calcination temperature, and reaction parameters on the catalytic activity of catalyst were investigated in detail. XRD, FTIR, SEM, BET, and CO2-TPD were used for the characterization of the prepared catalysts. It is found that CuO/Al2O3 shows a higher catalytic activity, which depends on the CuO loading amount and calcination temperature. The surface area and amount of basic sites of the catalyst exhibit crucial effect on the catalytic activity of CuO/Al2O3. Furthermore, there is a synergistic effect between the catalyst and 2-cyanopyridine that the former has higher activation ability for glycerol and the latter acts not only as a dehydrant, but also as a promoter for CO2 activation. Recycling experiments reveal that this catalyst can be reused at least five cycles without any inactivation. Based on the experiment results and FTIR characterization, a possible reaction mechanism for the carbonylation of glycerol and CO2 is proposed.

The dynamics of suspended sediment involves inherent non-linearity and complexity as a result of the presence of both spatial variability of the basin characteristics and temporal climatic patterns. As a result of this complexity, the conventional sediment rating curve (SRC) and other empirical methods produce inaccurate predictions. Deep neural networks (DNNs) have emerged as one of the advanced modeling techniques capable of addressing inherent non-linearity in hydrological processes over the last few decades. DNN algorithms are used to perform predictive analysis and investigate the interdependencies among the most pivotal water quantity and quality parameters i.e., discharge, suspended sediment concentration (SSC), and turbidity. In this study, the Long short-term memory (LSTM) algorithm of DNNs is used to model the discharge-suspended sediment relationship for the Stony Clove Creek. The simulations were run using primary data on discharge, SSC and turbidity. For the development of the DNN models and examining the effects of input vectors, combinations of different input vectors (namely discharge, and SSC) for the current and previous days are considered. Furthermore, a suitable modelling approach with an appropriate model input structure is suggested based on model performance indices for the training and testing phases. The performance of developed models is assessed using statistical indices such as root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). Statistically, the performance of DNN-based models in simulating the daily SSC performed well with observed sediment concentration series data. The study demonstrates the suitability of the DNN approach for simulation and estimation of daily SSC, opening up new research avenues for applying hybrid soft computing models in hydrology.

Recent astronomical observations with respect to measurements in distant supernovas, cosmic microwave background and weak gravitational lensing confirm that the Universe is undergoing a phase of accelerated expansion and it has been proposed that this cosmological behavior is caused by a hypothetical dark energy which has a strong negative pressure that allows explain the expanding universe. Several theoretical ideas and models related dark the energy includes the cosmological constant, quintessence, Chaplygin gas, braneworld and tachyonic scalar fields. In this paper, we have obtained new relativistic stellar configurations considering an anisotropic fluid distribution with a charge distribution which could represents a potential model of a dark energy star. In order to investigate the effect of a quadratic equation of state in this anisotropic model we specify particular forms for the gravitational potential that allow solving the Einstein-Maxwell field equations. For these new solutions we checked that the radial pressure, metric coefficients, energy density, anisotropy factor, charge density , mass function are well defined and are regular in the interior of the star. The solutions found can be used in the development of dark energy stars models satisfying all physical acceptability conditions but the causality condition and strong energy condition are violated. We expect that these models have multiple applications in astrophysics and cosmology.

Motivated for improving the efficiency and reliability of wind turbine energy conversion, this paper presents an advanced control design that enhances the power regulation efficiency and re-liability. The constrained behaviour of the wind turbine is taken into account, by using the barrier Lyapunov function in the analysis of the Lyapunov direct method. This, consequently, guarantees that the generated power remains within the desired bounds to satisfy the grid power demand. Moreover, a Nussbaum-type function is utilized in the control scheme, to cope with the unpre-dictable wind speed. This eliminates the need for accurate wind speed measurement or estimation. Furthermore, via properly designed adaptive laws, a robust actuator fault-tolerant capability is integrated into the scheme, handling the model uncertainty. Numerical simulations are performed on a high-fidelity wind turbine benchmark model, under different fault scenarios, to verify the effectiveness of the developed design. Also, a Monte-Carlo analysis is exploited for the evaluation of the reliability and robustness characteristics against the model-reality mismatch, measurement errors and disturbance effects.

Long-term health consequences are influenced by circumstances that occur during pregnancy. The confluence of the maternal and fetal circulations occurs at the placenta, which is the first or-gan to develop. Placental pathology can provide an accurate diagnosis of inflammation inside the amniotic sac. The pathological alterations of preterm placentas provide evidence for the causes of numerous perinatal pathologies, including spontaneous preterm births. This retrospec-tive study aimed to re-examine placentas regarded as normal by the Obstetrics and Gynecology Department in our institution so that grading and staging of any evident inflammatory response could be evaluated and associated with baby gender. Eighty-four full-term placentas were col-lected after delivery. Placentas that were considered normal and were not sent to the histo-pathology department were taken. Morphological examination of fresh placenta was conducted. Full thicknesses of placenta samples were taken from central and marginal regions of placental disc. Fetal and maternal inflammatory responses syndrome were assessed. Villitis of unknown etiology (VUE) and chronic deciduitis also were evaluated in the placenta. Immunohistochemis-try (IHC) was also performed to evaluate patterns of inflammation in the placenta using an-ti-CD8 and anti-CD68 antibodies. The correlation between the silent pathologies and clinical complications or involvement of developing fetal inflammatory response syndrome was meas-ured. In this study, seventeen (20%) maternal inflammatory samples and ten (12%) fetal inflam-matory samples showed inflammatory responses. The frequency of chronic deciduitis and villi-tis of unknown etiology was more remarkable among Saudi pregnant women than the previ-ously reported findings in the literature of placental pathology. The prevalence of fetal and ma-ternal inflammatory response was greater in the placentas of mothers of males than in placentas of female mothers. Grading placental inflammation usually predict the degree of maternal an-ti-fetal cellular rejection. Fetal inflammatory response syndrome usually goes unnoticed in Sau-di Arabia. There are differences in the inflammatory response in the placenta based on the sex of the newborn. Increasing the number of placental samples that must be sent for microscopic in-spection may be preferable due to their significance in identifying the causes of chronic disor-ders.

In this paper, we present some new models for anisotropic compact stars within the framework of 5-dimensional Einstein-Gauss-Bonnet (EGB) gravity with a linear and nonlinear equation of state considering a metric potential proposed for Thirukkanesh and Ragel (2012) and generalized for Malaver (2014). The new obtained models satisfy all physical requirements of a physically reasonable stellar object. Variables as energy density, radial pressure and the anisotropy are dependent of the values of the Gauss-Bonnet coupling constant

In this paper, we found a new model for compact star with charged anisotropic matter distribution considering an extended version of the Chaplygin equation of state. We specify a particular form of the metric potential Z(x) that allows us to solve the Einstein-Maxwell field equations. The obtained model satisfies all physical properties expected in a realistic star such that the expressions for the radial pressure, energy density, metric coefficients, measure of anisotropy and the mass are fully well defined and are regular in the interior of star. The solution obtained in this work can have multiple applications in astrophysics and cosmology.

In this paper, we found new classes of exact models to the Einstein-Maxwell system of equations which describe the internal structure of a compact star made of strange matter considering the equation of state proposed by Rocha, Bernardo, de Avellar and Horvath in 2019. It has been assumed that this matter is composed of equal number of up, down and strange quarks and a small amount of electrons required to reaching the charge neutrality. If this hypothesis is correct, the neutron stars could be strange stars or hybrid stars with a thin crust of nuclei where the temperatures and pressures are capable of converting hadronic matter into this new stable phase of quarks. We have chosen a particular form of gravitational potential Z(x) that depends on an adjustable parameter related to degree of anisotropy of the models and the new solutions can be written in terms of elementary and polynomial functions. The obtained models satisfy all physical features expected in a realistic star and the expressions for mass, density and stellar radius are comparable with the experimental results.

The exchange coupling between two ferromagnetic thin films, one with magnetically hard and the other with soft phases, separated by a thin non-magnetic layer is studied. Nd-Fe-B/ Ti / Fe thin film heterostructures were fabricated using DC magnetron sputtering on Si substrates which were heated in situ at 650°C using a house-built vacuum-compatible heater. The effect of the thickness of the Ti buffer layer and the annealing temperature on the formation of various phases of NdFeB was investigated. The effect of the thickness of the non-magnetic Ti spacer layer on the exchange coupling strength between the hard phase Nd-Fe-B ferromagnetic thin layer and the soft phase transition metal Fe layer was experimentally investigated. Hysteresis loops of multilayer thin films indicate an antiferromagnetic coupling was observed when the thickness of the spacer layer was 2 nm. This is within the range of antiferromagnetic coupling calculation based on RKKY theory predictions.

New antibacterial agents are needed to overcome the increasing number of infectious diseases caused by pathogenic microorganisms due to the emergence of multi-drug resistant strains. In this context, halogens, especially Iodine is known since ages for its antimicrobial activity. Therefore, especially triiodides encapsulated in organometallic complexes can be helpful as new agents against microorganisms. The aims of this work was to study the biological activity of [Na(12-Crown-4)₂]I₃ against gram positive Streptococcus pyogenes, Streptococcus faecalis, the spore forming bacteria Bacillus subtilis and gram negative bacteria Escherichia coli, Proteus mirabilis, Pseudomonas aeruginosa and Klebsiella pneumoniae, as well as the yeast Candida albicans. The antimicrobial and antifungal activities of the triiodide were determined by zone of inhibition plate studies. [Na(12-Crown-4)₂]I₃ exhibited potent antimicrobial activity on gram positive Streptochocci and the yeast C. albicans. Furthermore, the gram negative bacteria P. aeruginosa and K. pneumoniae were less effectively inhibited, while E. coli and P. mirabilis proved to be even resistant.

The spread of the Coronavirus pandemic has been accompanied by an infodemic. The false information that is embedded in the infodemic affects people’s ability to have access to safety information and follow proper procedures to mitigate the risks. This research aims to target the falsehood part of the infodemic, which prominently proliferates in news articles and false medical publications. Here, we present NeoNet, a novel supervised machine learning text mining algorithm that analyzes the content of a document (news article, a medical publication) and assigns a label to it. The algorithm is trained by TFIDF bigram features which contribute a network training model. The algorithm is tested on two different real-world datasets from the CBC news network and Covid-19 publications. In five different fold comparisons, the algorithm predicted a label of an article with a precision of 97-99 %. When compared with prominent algorithms such as Neural Networks, SVM, and Random Forests NeoNet surpassed them. The analysis highlighted the promise of NeoNet in detecting disputed online contents which may contribute negatively to the COVID-19 pandemic.

Two important types of metal oxide nanoparticle catalysts Copper (II) oxide (CuO) and Cerium oxide (CeO₂) are prepared by a suitable method which was traditional precipitation (PT) method at calcination temperature of 400^oC for 5h and used for the synthesis of glycerol carbonate GC (C₄H₆O₄) from the direct reaction by the carbonylation of Glycerol GL (C₃H₈O₃) with Carbone Dioxide. The precipitation (PT) was an important route for the preparation of nanoparticles catalyst. The effects of performance of (CuO and CeO₂) nanoparticle catalysts on the conversion of glycerol GL, yield of glycerol carbonate GC, selectivity of glycerol carbonate are researched. XRD, XPS, BET, FT-IR, CO₂-TPD, H₂-TPR are used for the characterization of the prepared catalysts. Comparing the optimal performance between them under reaction conditions were 150 ^oC, 4MPa (40 bar.), 5h, and both CuO and CeO₂ catalyst amount 37.6 % (based on ratio of glycerol weight) by using 2-pyridinecarbonitrate (C₆H₄N₂) as dehydrating agent and dimethylformamide (DMF), (C₃H₇NO) as solvent. The glycerol conversion (X_GL), glycerol carbonate yield (Y_GC) and glycerol carbonate selectivity (S_GC) over 0.7g CuO are 57.151%, 47.524%, and 83.156%, respectively, and glycerol carbonate yield over 0.7 CeO₂ is 36.2185% or 35.076%, and the yield of GC could reach as high as 78.234% over 1.73g CeO₂, the both catalysts could be easily regenerated by washing with methanol and water after a reaction and then dried at 60 ^oC overnight after that calcination at 400 ^oC for 5h without loss of activity after five recycling times, In addition to, the (ICP- MS) results confirmed that the leaching of CuO and CeO₂ was below the detection limit.

Rapid post-earthquake damage diagnosis of bridges can guide decision-making for emergency response management and recovery. This can be facilitated using digital technologies to remove the barriers of manual post-event inspections. Prior mechanics-based Finite Element (FE) models can be used for post-event response simulation using the measured ground motions at nearby stations; however, the damage assessment outcomes would suffer from uncertainties in structural and soil material properties, input excitations, etc. For instrumented bridges, these uncertainties can be reduced by integrating sensory data with prior models through a model updating approach. This study presents a sequential Bayesian model updating technique, through which a linear/nonlinear FE model, including soil-structure interaction effects, and the foundation input motions are jointly identified from measured acceleration responses. The efficacy of the presented model updating technique is first examined through a numerical verification study. Then, seismic data recorded from the San Rogue Canyon Bridge in California are used for a real-world case study. Comparison between the free-field and the foundation input motions reveals valuable information regarding the soil-structure interaction effects at the bridge site. Moreover, the reasonable agreement between the recorded and estimated bridge responses shows the potentials of the presented model updating technique for real-world applications. The updated FE model is considered as the digital twin of the bridge and can be used to analyze the bridge and monitor the structural response at element, section, and fiber levels to diagnose the location and severity of any potential damage mechanism.

This paper provides a structured and practical roadmap for practitioners to integrate Learning from Demonstration (LfD) into manufacturing tasks, with a specific focus on industrial manipulators. Motivated by the paradigm shift from mass production to mass customization, it is crucial to have an easy-to-follow roadmap for practitioners with moderate expertise, to transform existing robotic processes to customizable LfD-based solutions. To realize this transformation, we devise the key questions of "What to Demonstrate", "How to Demonstrate", "How to Learn", and "How to Refine". To follow through these questions, our comprehensive guide offers a questionnaire-style approach, highlighting key steps from problem definition to solution refinement. The paper equips both researchers and industry professionals with actionable insights to deploy LfD-based solutions effectively. By tailoring the refinement criteria to manufacturing settings, the paper addresses related challenges and strategies for enhancing LfD performance in manufacturing contexts.

Zinc finger proteins (ZFP) play important roles in cellular processes. The DNA binding region of ZFP consists of 3 zinc finger DNA binding domains connected by amino acid linkers, the sequence TGQKP connects ZF1 and ZF2, and TGEKP connects ZF2 with ZF3. Linkers act to tune the zinc finger protein in the right position to bind its DNA target, the type of amino acid residues and length of linkers reflect on ZF1-ZF2-ZF3 interactions and contribute to the search and recognition process of ZF protein to its DNA target. Linker mutations and the affinity of the resulting mutants to specific and nonspecific DNA targets were studied by MD simulations and MM_GB(PB)SA. The affinity of mutants to DNA varied with type and position of amino acid residue. Mutation of K in TGQKP resulted in loss in affinity due to the loss of positive K interaction with phosphates, mutation of G showed loss in affinity to DNA, WT protein and all linker mutants showed loss in affinity to a nonspecific DNA target, this finding confirms previous reports which interpreted this loss in affinity as due to ZF1 having an anchoring role, and ZF3 playing an explorer role in the binding mechanism. The change in ZFP-DNA affinity with linker mutations is discussed in view of protein structure and role of linker residues in binding.

Dietary factors are one of the risk factors that can impact the brain chemistry, which leads to mental distress. Based on our data mining approach, we found that mental distress in men is associated with eating unhealthy food. Our aim in this paper is to apply results from our big data analytics approach to inform system dynamics (SD) modeling to investigate the causal relationships between brain structures, nutrients from food and dietary supplements, and mental health. We perform descriptive analysis based on a large data set to estimate the SD modeling parameters. Finally, we calibrate the model towards a time series data collected for individuals on their dietary and distress patterns. The results reveal that bridging these different methodologies leads to further insights from the SD model and decreases the error of calibrated parameter values. Future research is needed to validate our initial results for investigating the relationship between mental distress and dietary intake.

model checking techniques are often used for the verification of software systems. Such techniques are accompanied with several advantages. However, state space explosion is one of the drawbacks to model checking. During recent years, several methods have been proposed based on evolutionary and meta-heuristic algorithms to solve this problem. In this paper, a hybrid approach is presented to cope with the SSE problem in model checking of systems modeled by GTS with an ample state space. Most of existence proposed methods that aim to verify systems are applied to detect deadlocks by graph transformations. The proposed approach is based on the fuzzy genetic algorithm and is designed to decline the safety property by verifying the reachability property and detecting deadlocks. In this solution, the state space of the system is searched by a fuzzy genetic algorithm to find the state in which the specified property is refuted/verified. To implement and evaluate the suggested approach, GROOVE is used as a powerful designing and model checking toolset in GTS. The experimental results indicate that the presented hybrid fuzzy method improves speed and performance by comparing other techniques

By studying game theory, we find that the Nash equilibrium does not exist in some games or, if there is in one, does not describe a real event. Here we show that the matching pennies game with pure strategy has the solution, but this solution is a game that happens simultaneously with this game. Using this solution, we will answer the Brookings Institution's question of boycotting the elections whether the opposition's boycott of elections is, in different circumstances, a defeated strategy. A strategy that its result in most cases is a failure or political suicide and the least possible consequence by the opposition. The findings of this article show that in general, it can be concluded that the election boycott strategy is a dominated strategy, and most of the opposition groups that have used it have failed and they donated the playground to the ruling group.

Marfan syndrome is an autosomal dominant genetic connective tissue disorder that results from mutations in the fibrillin-1 gene located on chromosome band 15q 15–21. Fibrillin, a glycoprotein, is widely expressed throughout the body and contribute to elasticity and force-bearing capacity of connective tissue. In the eye, fibrillin is a key constituent of the ciliary zonules, which suspend the crystalline lens in place. The zonular defect leads to ectopia lentis that is hallmark of Marfan ocular abnormalities and occurs in 60 to 80% of cases. Other less common ocular features of Marfan syndrome are increased axial length, astigmatism, and flat cornea. Visual function in Marfan syndrome could be affected in several ways: ectopia lentis, refractive error, amblyopia, retinal detachment, cataract, and glaucoma. Management of subluxated lens starts with correction of refractive error with eyeglasses in mild cases. In more severe cases, especially when lens bisects the pupil, complete correction of refractive error is impossible without removing subluxated lens. The best method for visual rehabilitation after lens extraction is still debated. Aphakic Artisan implantation at the time of subluxated lens removal results in good visual outcomes with acceptable safety profile. Studies with longer term follow up and larger sample populations are needed to evaluate the safety of this procedure in patients with Marfan syndrome.

Cancer is one of the leading causes of death, making timely diagnosis and prognosis very important. Utilization of AI (artificial intelligence) enables providers to organize and process patient data in a way that can lead to better overall outcomes. This review paper aims to look at the varying uses of AI for diagnosis and prognosis and clinical utility. PubMed and EBSCO databases were utilized for finding publications from January 1, 2013, to December 22, 2023. Articles were collected using key search terms such as “artificial intelligence” and “machine learning.” Included in the collection were studies of the application of AI in determining cancer diagnosis and prognosis using multi-omics data, radiomics, pathomics, clinical and laboratory data. The resulting 89 studies were categorized into eight sections based on the type of data utilized and then further subdivided into two subsections focusing on cancer diagnosis and prognosis, respectively. 8 studies integrated more than one form of omics, namely genomics, transcriptomics, epigenomics, and proteomics. Incorporating AI into cancer diagnosis and prognosis alongside omics and clinical data represents a significant advancement. Given the considerable potential of AI in this domain, ongoing prospective studies are essential to enhance algorithm interpretability and to ensure safe clinical integration.

In this paper, a model of the hub covering the location-routing problem with simultaneous delivery and pickup should be designed in order to minimize the total costs, minimize the maximum vehicles travel time and minimize the amount of CO_2 gas emissions. Two-stage stochastic programming is used in this paper to control the uncertainty parameters of the problem. The mathematical model aims to decide the appropriate location of hubs and vehicle routing for simultaneous delivery and pickup of products. In this regard, the results obtained from the LP-Metric method show that there is a conflict between the objective functions, and with the reduction of the amount of CO_2 gas emissions and the reduction of the maximum vehicles travel time, the total costs have increased. On the other hand, by examining the economical factor, it was observed that by reducing this factor, the robustness factors are changed and the total costs of the network are reduced. MOIWO and MOALO were used to solve the problem in large scale, the result of which was the existence of a significant difference between the calculation time averages. Also, the efficiency of these methods was very high compared to the LP-Metric method and the maximum relative difference was less than 2.83%. The results of numerical examples show a higher efficiency of the MOALO than the MOIWO.

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.

Glucosamine is the most common nutrition supplement used in the United States. It is promoted not only as a pain reliever but also a therapy to slow osteoarthritis. Although studies with 3,800 patients with osteoarthritis of the knee or hip found that glucosamine was no better than placebo, it continues to be used for these two indications due to its perceived safety. However, reports of an increased intraocular pressure are alarming, especially in glaucoma. Glycosaminoglycans play a key role in the physiology of the cornea, conventional outflow of aqueous humor and the retina. In theory, long-term treatment with glucosamine could reach a concentration that impacts the morphology and function of ocular tissues. We recommend ophthalmologists counsel glaucoma patients about the potential risks of glucosamine and encourage cessation in uncontrolled intraocular pressure.

Climate change has become a major agenda item in international relations and in national energy policy-making circles around the world. This article studies the surprising evolution of the energy policy, and more particularly the energy transition, currently happening in the Arabian Gulf region, which features some of the world’s largest exporters of oil and gas. Qatar, Saudi Arabia and other neighboring energy exporters, plan to export blue and green hydrogen across Asia as well as towards Europe in the years and decades to come. Although poorly known and understood abroad, this recent strategy doesn’t threaten the current exports of oil and gas (still needed for a few decades) but prepares the evolution of their national energy industries towards the future decarbonized energy demand of their main customers in East and South Asia, and beyond. The world’s largest exporters of Liquefied Natural Gas, Qatar, has established industrial policies and projects to upscale CCUS, which can enable blue hydrogen production, as well as natural carbon sinks domestically via afforestation projects.

This study presents the consecutive modification of orange peel (OP) by NaOH and sodium dodecyl sulfate (SDS) for simultaneous elimination of basic dyes from the binary system and modeling the adsorption process using an intelligent tool. The natural and modified biosorbents were characterized by variety of analyses such as: field emission scanning electron microscopy with energy dispersive X-ray, N2 physisorption and Fourier transform infrared spectroscopy techniques. The influence of various variables on dye removal like pH, the quantity of biosorbents, dyes concentration, contact time, and temperature in the binary system were investigated and optimized by an artificial neural network (ANN) model as an intelligent tool. The optimum quantity of the sorbent was found to be 0.30 g for orange peel (OP) and 0.25 g for NaOH-treated OP (NOP) and SDS-decorated NOP (SNOP) at pH = 7. The kinetics and thermodynamics investigations showed that the removal of dyes obeyed the pseudo-second-order kinetic model and were spontaneous and exothermic in nature. Moreover, in order to describe the mechanism of sorption process, desorption studies of dyes were carried out. The desorption percentages of methylene blue (MB) in water and HCl were found to be in the range of 1.93%–4.76% and 18.87%–28.76%, respectively; in addition, the desorption percentages of crystal violet (CV) in water and HCl were obtained to be in the range of 4.11%–7.41% and 32.84%–43.00%, respectively; which could be a recommendation ion exchange or electrostatic attachment of dyes onto biosorbents. The ANN predictions matched with the experimental data very well (0.95308 < R2 < 0.99191 and 0.98335 < R2 < 0.99773 for MB and CV, respectively) which indicated high accuracy of the ANN model. In addition, the relative importance of each parameter was calculated by Garson’s equation.

As the outbreak of COVID-19 has accelerated, an urgent need for finding strategies to combat the virus is growing. Thus, gaining more knowledge on the pathogenicity mechanisms of SARS-CoV-2, the causing agent of COVID-19, and its interaction with the immune system is of utmost importance. Although this novel virus is not well known yet, its structural and genetic similarity with SARS-CoV as well as the comparable pattern of age-mortality relations suggest that the previous findings on SARS can be applicable for COVID-19. Therefore, a systems biology study was conducted to investigate the most important signaling pathways activated by the virus. The results were then validated through a literature review on COVID-19 and the other closely related viruses, SARS and MERS. Interferons have shown to play a crucial role in the defense against coronavirus diseases. CoV can impede the interferon induction in humans. Moreover, STAT1, a key protein in the interferon-mediated immune response, is antagonized by the virus. This could explain the increased response threshold of immune cells to IFNs during CoV infections. A vivid correlation between the innate immune response threshold and the fatality rates in COVID-19 can be found. Differences in the dynamics of the interferon-related innate immune responses in children, adults, and elderly may explain the reported fatality rates. The increased mortality rates in the elderly can be explained by the higher threshold of interferon-mediated immune responses. Earlier induction of interferons in children and their less developed immune system could contribute to their near to zero fatality rate. Administration of interferon-inducing agents, such as poly (ICLC), could reduce the mortality of SARS at the very early stages of the disease. Interferon-γ combination with an interferon-I might induce synergistic effects and maximize the benefits. However, in-depth research is needed to validate it and determine the optimum dosage and timing to prevent unwanted results. Such interventions can act as a double-edged sword and aid the imbalance of the immune reactions, which may occur at the later stages of the disease. With the advancement of the disease and the virus overload, the responses would shift toward immnopathogenic over-reactions and probably cytokine storm. Moderating the activity of the immune system and supportive care in such conditions might be the optimum approach.

Helicobacter pylori is the most common human gastric infection. H. pylori stool antigen lateral flow immunochromatography assay (HpSA-LFIA) is considered one of the most cost-effective and rapid non-invasive assays (active tests). The evaluation of this test is crucial for accuracy and utility assurance. This study aimed to evaluate the polyclonal antibody-based HpSA-LFIA in comparison to a monoclonal antibody-based ELISA kit. Methodology: Stool samples were collected from 200 gastric patients for HpSA-LFIA and semi-quantitative HpSA-ELISA. Statistical analysis of the diagnostic values was performed using MedCalc software. Chi-square tests were used to determine the effects of gender and age. Results: The obtained results found that HpSA-LFIA achieved promising sensitivity (93.75%) and NPV (98.00%). However, it had poor specificity, PPV, and accuracy, respectively, 59.76%, 31.25%, and 65.31%. LR+ & LR- were 2.33% & 0.1%, respectively. Gender had no significance on the di-agnostic parameters of HpSA-LFIA. Age groups had irrelevant sensitivity; however, specificity was significantly higher in patients over 45 years. Conclusion: It was concluded that HpSA-LFIA was not accurate enough to be the sole test for di-agnosis and needs other confirmatory tests in case of positive conditions

Natural radioactivity of 238U, 232Th and 40K for liquid and solid samples collected from Sidi Salem dam (Tunisia) and Aïn Dalia dam (Algeria) were measured using TERRA detector of rays (gam-ma, beta, and alpha), Atomic adsorption and Gamma-ray spectrometry were used to analyze the levels of radionuclides and toxic heavy metals, respectively. Toxic heavy metals (Fe, Bb, Zn, Ni, Cu, Cr and Cd) and associated health risks in surface water and sediment of dams have been in-vestigated in this present study. The radioactivity mean rates in the water samples were 1.72±0.01, 0.068±0.01 and 94.6±1.04 Bql-1 for 238U, 232Th and 40K respectively (Tunisia dam) and were 1.9± 0.24, 0.09±0.01 and 131.43±1.03 Bql-1 for 238U, 232Th and 40K respectively (Algeria dam). The 238U, 232Th and 40K radioactivity mean concentration measured in the sediment samples were 2.67±0.01, 0.18±0.012 and 197.87±2.01 Bqkg-1 respectively (Tunisian dam) and were 4.34±0.05, 0.27±0.05 and 287.61±3.34 Bqkg-1 respectively (Algeria dam). The activity concentration of 40K was higher than that of 238U and 232Th for the water and sediment samples. The mean activity concen-trations follow the order 40K > 238U > 232Th. The cumulative impact can be a serious immediate radiological health burden on the ecosystem, environment and people's health in this study area and can be transposable to any other similar region. The good knowledge of monitoring quality and quantity for the transboundary water resources and the international collaborations are es-sential to safeguard human health (women’s breasts cancer, thyroid cancer, neurological im-pact…), and avoid the conflicts, especially during these climatic upheavals of drought.

Social media addiction has attracted the attention of researchers especially during the COVID era because negative emotions generated from the pandemic may have increased social media addiction. The present study aimed to investigate the role of negative emotions and social media addiction on health problems during and after the COVID lockdown. A survey was conducted with 2926 participants aged between 25 and 45 years. The data collection period was between 2nd September and 13th October 2020. Partial Least Square Structural Equation Modeling was conducted for data analysis by controlling the respondents' working time, leisure time, gender, education, and age. Our study showed that social media addiction and time spent on social media impact health. Interestingly, while anxiety about COVID increased social media addiction, fear about COIVD reduced social media addiction. Also, long working hours contributed most to people’s health issues, and its impact on social media addiction and hours was much higher than negative emotions, where males faced more health challenges than females. The impacts of negative emotions generated by the COVID on social media addiction and health issues should be reconsidered. Government and employers' control of people's working time stress should prioritize solving social media addiction-related issues.

Methamphetamine (METH) has the potential to disrupt the activities of neurotransmitters in the Central Nervous System (CNS) and cause neurotoxicity through various pathways. These pathways include increased production of reactive nitrogen and oxygen species, hypothermia, and induction of mitochondrial apoptosis. In this study, we investigated the long-term effects of METH addiction on the structural changes in the amygdala of postmortem human brains, as well as the involvement of the CREB/BDNF and Akt-1/GSK3 signaling pathways. We examined ten male postmortem brains, comparing control subjects with chronic METH users, using immunohistochemistry, Real-time PCR (to measure levels of CREB, BDNF, Akt-1, GSK3, and TNF-α), Tunnel assay, stereology, and assays for ROS, GSSG, and GPX. The findings revealed that METH significantly reduced the expression of BDNF, CREB, Akt-1, and GPX, while increasing the levels of GSSG, ROS, RIPK3, GSK3, and TNF-α. Furthermore, METH was found to induce inflammation and neurodegeneration in the amygdala, with ROS production mediated by the CREB/BDNF and Akt-1/GSK3 signaling pathways.

COVID-19 is an infectious disease spreading through human touch. This study explored the risk perception and knowledge towards COVID-19 infection among Bangladeshi adult participants. Two self-administered online surveys were administered at two different time points from 26-31 March 2020 (Early lockdown) and 11-16 May 2020 (Late lockdown) through social media on 1005 respondents (322 and 683 participants, respectively) during COVID-19 lockdown period in Bangladesh. Univariate and multiple linear regression models were used to examine factors associated with risk perception and knowledge towards COVID-19. The mean knowledge (8.4 vs. 8.1, P=0.022) and risk perception (11.2 vs. 10.6, P < 0.001) scores differ significantly between early and late lockdown. Compared to the early lockdown period, the scores for perceived risk of contracting COVID-19 decreased significantly while public knowledge about COVID-19 was lower but not statistically significant. Female participants who practiced high quarantine particularly those who did so at the public health order during the lockdown reported increased knowledge towards the spread of COVID-19 and perceived high risk of contracting COVID-19. Education intervention using awareness to increase public knowledge and perception towards COVID-19 in Bangladesh should target male participants who practiced low quarantine and are less worried about the spread of such novel coronavirus even as the physical distancing persists.

Nowadays, model checking is applied as an accurate technique to verify software systems. The main problem of model checking techniques is the state space explosion. This problem occurs due to the exponential memory usage by the model checker. In this situation, using meta-heuristic and evolutionary algorithms to search for a state in which a property is satisfied/violated is a promising solution. Recently, different evolutionary algorithms like GA, PSO, etc. are applied to find deadlock state. Even though useful, most of them are concentrated on finding deadlock. This paper proposes a fuzzy algorithm in order to analyze reachability properties in systems specified through GTS with enormous state space. To do so, we first extend the existing PSO algorithm (for checking deadlocks) to analyze reachability properties. Then, to increase the accuracy, we employ a Fuzzy adaptive PSO algorithm to determine which state and path should be explored in each step to find the corresponding reachable state. These two approaches are implemented in an open-source toolset for designing and model checking GTS called GROOVE. Moreover, the experimental results indicate that the hybrid fuzzy approach improves speed and accuracy in comparison with other techniques based on meta-heuristic algorithms such as GA and the hybrid of PSO-GSA in analyzing reachability properties.

In the presented paper, a comprehensive study will be done on shape factor analysis of MoS2-GO in H2O-C2H6O2 based hybrid nanoliquids associated with effect and influence of transverse magnetic field and thermal radiation. The effect of variation in different parameters and nanoliquids shapes under temperature and velocity distribution is explored and also non-linear thermal radiation will be analyzed. Algorithms are introduced in proportion to mathematical modeling based on their numerical results and comparative curves for further explanation. In addition, it will be done research for influence and effect of new significant parameters emerged to the model to do sensitivity analysis and also their output results are demonstrated, examined and compared together by presenting graphs and tables. Based on detailed discussions, authentication of attained results designates the high accuracy of applied methods deployed to solve presented model in the paper. Our results satisfy that our used approach is accurate, highly reliable and also effective. All mentioned steps will be described throughout the literature.

We analyze the thermal convection thresholds and linear characteristics of the primary and secondary instabilities for viscoelastic fluids saturating a porous horizontal layer heated from below by a constant flux. Galerkin method is used to solve the eigenvalue problem by taking into account the elasticity of the fluid, the ratio between the viscosity of the solvent and the total viscosity of the fluid and the lateral confinement of the medium. For the primary instability, we found out that depending on the rheological parameters, two types of convective structures may appear when the basic conductive solution loses its stability: stationary long wavelength instability as for Newtonian fluids and oscillatory convection. The effect of the lateral confinement of the porous medium by adiabatic walls is to stabilize the oblique and longitudinal rolls and therefore selects transverse rolls at the onset of convection. In the range of the rheological parameters where stationary long wave instability develops first, we use a parallel flow approximation to determine analytically the velocity and temperature fields associated to the monocellular convective flow. The linear stability analysis of the monocellular flow is performed, and the critical conditions above which the flow becomes unstable are determined. The combined influence of the viscoelastic parameters and the lateral confinement on the characteristics of the secondary instability is quantified. The major new findings concerning the secondary instabilities may be summarized as follows: (i) For concentrated viscoelastic fluids, computations showed that the most amplified mode of convection corresponds to oscillatory transverse rolls which appears via a Hopf bifurcation. This pattern selection is independent of both the fluid elasticity and the lateral confinement of the porous medium; (ii) For diluted viscoelastic fluids, the preferred mode of convection is found to be oscillatory transverse rolls for a very laterally confined medium. Otherwise stationary or oscillatory longitudinal rolls may develop depending on the fluid elasticity. Results also showed the destabilizing effect of the relaxation fluid elasticity and the stabilizing effect of the viscosity ratio for the onset of both primary and secondary instabilities.

Two experiments were conducted to develop a Nano green manganese and assess its effect on grapevines. The first experiment was microbiological, where several incorporation strategies were utilized to enrich the yeast with manganese, as follows: (1) adding manganese to the liquid medium (growth phase), (2) adding after 24 h of incubation (non-growth phase). As the results, growth phase reduced the possibility of medium contamination. Manganese concentration in the yeast cells was increased as that of manganese sulphate in the medium. Manganese incorporation in yeast cells was 99.93% higher than that of the medium at 0.0195 g/l of manganese. Although the concentration of manganese in the medium raised the OD of yeast cell biomass, manganese sulphate had no passive influence on it. The second experiment was horticultural, done on Flame Seedless grapevines, since both active fresh and frozen yeast enriched with manganese resulted from the first experiment were sprayed twice a year at 10 or 20 cm3/l comparing with manganese sulphate, and manganese chelate. The results demonstrated that yeast extracts in both forms increased the yield and improved fruit properties. The most effective treatment on vegetative growth, yield and fruit properties was the frozen yeast enriched with manganese at 20 cm3/l.

Despite being considered as natural components of many ecosystems, forest fires pose significant threats to the environment and human health. In order to ensure public safety and effective fire suppression planning, it is necessary to develop reliable prediction models to mitigate forest fire danger. These models should account for specific environmental conditions. The advent of big data in recent years has opened new avenues for improving forest fire predictions. Machine learning techniques, surpassing traditional forecasting methods, have shown significant promise in this area. By applying the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology to a real-world dataset, this paper explores the application of machine learning approaches to understand forest fire patterns and predict fire danger. We consider six distinct time stages and incorporate feature selection to refine our predictions. It is important to note that forest fire behavior models are not universally effective due to geographical variations in data. Nevertheless, advanced decision-making techniques are vital in forest fire management. Our research presents a systematic exploration of the topic, comparing various machine learning models, thereby providing a comprehensive baseline for future investigations in this crucial environmental arena.

Abstract This study delved into the application of various machine learning (ML) models for the classification of lung cancer levels. Through meticulous monitoring of parameters such as minimum child weight and learning rate, efforts were made to mitigate overfitting while optimizing model performance. The Deep Neural Network (DNN) emerged as a standout performer, showcasing robust performance across training, validation, and testing stages. Ensemble methods like voting and bagging also demonstrated promising results. However, Support Vector Machine (SVM) models with the Sigmoid kernel faced challenges in achieving satisfactory performance. Overall, the investigation sheds light on the efficacy of different ML models in lung cancer level classification and underscores the importance of parameter tuning to address overfitting concerns.

In response to the burgeoning global demand for premium agricultural products, particularly within the competitive nut market, this paper introduces an innovative methodology aimed at enhancing the grading process for almonds and their shells. Leveraging state-of-the-art Deep Convolutional Neural Networks (CNNs), specifically the AlmondNet-20 architecture, our study achieves exceptional accuracy exceeding 99%, facilitated by the utilization of a 20-layer CNN model. To bolster robustness in differentiating between almonds and shells, data augmentation techniques are employed, ensuring the reliability and accuracy of our classification system. Our model, meticulously trained over 1000 epochs, demonstrates remarkable performance, boasting an accuracy rate of 99% alongside a minimal loss function of 0.0567. Rigorous evaluation through test datasets further validates the efficacy of our approach, revealing impeccable precision, recall, and F1-score metrics for almond detection. Beyond its technical prowess, this advanced classification system offers tangible benefits to both industry experts and non-specialists alike, ensuring globally reliable almond classification. The application of deep learning algorithms, as showcased in our study, not only enhances grading accuracy but also presents opportunities for product patents, thereby contributing to the economic value of our nation. Through the adoption of cutting-edge technologies such as the AlmondNet-20 model, we pave the way for future advancements in agricultural product classification, ultimately enriching global trade and economic prosperity.

The accurate forecasting of solar power output is essential for ensuring the stable and efficient operation of grid-connected photovoltaic (PV) systems, especially when faced with variable weather patterns. This research focuses on the integration of Machine Learning (ML) methodologies and climatic parameters to predict solar panel energy generation, with a specific emphasis on addressing consumption-production imbalances. Leveraging a dataset sourced from the Kaggle platform, the study is conducted in the context of Estonia, aiming to optimize solar energy utilization in this geographic region. The dataset, obtained from Kaggle, encompasses comprehensive information on climatic variables, including sunlight intensity, temperature, and humidity, alongside corresponding solar panel energy output. Leveraging machine learning algorithms, including XGBoost regression and neural networks, our model aims to identify complex patterns and relationships within the datasets. By tailoring the model to Estonia's climatic nuances, we seek to enhance the accuracy of energy production forecasts and, consequently, better manage the challenges associated with consumption-production imbalances. Furthermore, the research investigates the adaptability of the proposed model to diverse climatic conditions, ensuring its applicability for similar endeavors in other geographical locations. The performance of the forecasting model will be evaluated using error metrics like mean absolute error (MAE) and root mean squared error (RMSE). This study leverages a comprehensive dataset from Kaggle and utilizes advanced machine learning techniques to generate valuable insights. These insights can inform the development of sustainable energy policies and practices, ultimately leading to a more efficient and reliable renewable energy infrastructure.

Smartphones are prone to SMS phishing due to the rapid growth in the availability of smart mobile technologies driven by Internet connections. Also, detecting phishing SMS is a challenging task due to the unstructured nature of SMS text data with non-linear complex correlations. In this concern, considering the recent advancements in the domain of cybersecurity, we have proposed a hybrid deep learning framework that extracts robust features from SMS texts followed by an automatic detection of Phishing SMS. Due to combining the potential capability of individual models into one hybrid framework, it has outperformed various other individual machine learning and deep learning models. The proposed Phishing Detection framework is an effective hybrid combination of pretrained transformer model, MPNet (Masked and Permuted Language Modeling), with supervised ConvNets (CNN) and Bi-directional Gated Recurrent Units (GRU). It is intended to successfully detect unstructured short phishing text messages that contain complex patterns.

The present paper deals with the stochastic modeling of bio-colonization for the computation of stochastic hydrodynamic loading on jacket-type offshore structures. It relies on a multidisciplinary study gathering biological and physical research fields that accounts of uncertainties at all the levels. Indeed, bio-colonization of offshore structures is a complex phenomenon with two major but distinct domains (i) marine biology whose processes are modeled with biomathematics methods and (ii) hydrodynamic processes. This paper aims to connect these two domains. It proposes a stochastic model for the marine organism’s growth and then continues with transfers for assessment of drag coefficient and forces probability density functions that accounts for marine growth evolution. A case study relies on the characteristics (growth and shape) of the blue mussel (Mytilus edulis) in northeastern Atlantic.

Using optical characterization, it is evident that the spin state of the spin crossover molecular complex [Fe{H2B(pz)2}2(bipy)] (pz = tris (pyrazol-1-1y)-borohydride, bipy = 2,2’-bipyridine) depends on the ferroelectric polarization of an adjacent thin film of the polymer ferroelectric polyvinylidene fluoride-hexafluoropropylene (PVDF-HFP). The UV-Vis spectroscopy reveals that room temperature switching of [Fe{H2B(pz)2}2(bipy)] molecules in bilayers of PVDF-HFP/[Fe{H2B(pz)2}2(bipy)] as a function of ferroelectric polar polarization. The electric polarity dependence of bilayers of PVDF-HFP/[Fe{H2B(pz)2}2(bipy)] shows a strong dependence on the thickness of the PVDF-HFP layer. The PVDF-HFP/[Fe{H2B(pz)2}2(bipy)] interface may affect polarization retention in the PVDF-HFP thin film limit.

Early detection of cancer increases the probability of recovery. This paper presents an intelligent decision support system (IDSS) for the early diagnosis of cancer based on gene expression profiles collected using DNA microarrays. Such datasets pose a challenge because of the small number of samples (no more than a few hundred) relative to the large number of genes (on the order of thousands). Therefore, a method of reducing the number of features (genes) that are not relevant to the disease of interest is necessary to avoid overfitting. The proposed methodology uses the information gain (IG) to select the most important features from the input patterns. Then, the selected features (genes) are reduced by applying the grey wolf optimization (GWO) algorithm. Finally, the methodology employs a support vector machine (SVM) classifier for cancer type classification. The proposed methodology was applied to two datasets (Breast and Colon) and was evaluated based on its classification accuracy, which is the most important performance measure in disease diagnosis. The experimental results indicate that the proposed methodology is able to enhance the stability of the classification accuracy as well as the feature selection

The aim of this paper is to analyze the five grand parks in Dubai, United Arab Emirates (UAE) and provide a geoprocessing approach to different aspects such as sport, health, leisure, recreation, and public wellbeing. The study uses a hybrid of qualitative and quantitative approach as methodology. Sustainability offerings, accessibility for people of determination and special needs, typology and the geolocations of the grand parks plays crucial role in residents’ wellbeing. The paper concludes with recommendations for Dubai government to design new and innovative approaches to manage wellbeing of urban public places into the leisure environment for residents.

Several studies suggest that COVID-19 may be accompanied by symptoms such as a dry cough, muscle aches, sore throat, and mild to moderate respiratory illness. The symptoms of this disease indicate the fact that COVID-19 causes noticeable negative effects on the lungs. Therefore, considering the health status of the lungs using X-rays and CT scans of the chest can significantly help diagnose COVID-19 infection. Due to the fact that most of the methods that have been proposed to COVID-19 diagnose deal with the lengthy testing time and also might give more false positive and false negative results, this paper aims to review and implement artificial intelligence (AI) image-based diagnosis methods in order to detect coronavirus infection with zero or near to zero false positives and false negatives rates. Besides the already existing AI image-based medical diagnosis method for the other well-known disease, this study aims on finding the most accurate COVID-19 detection method among AI methods such as machine learning (ML) and artificial neural network (ANN), ensemble learning (EL) methods.

Background: To evaluate the effect of systemic erythropoietin, as well as oral steroids, in the management of recent onset non-arteritic anterior ischemic optic neuropathy (NAION). Method: Ninety-nine eyes of 99 patients diagnosed with NAION within 5 days of onset were included in this single masked randomized clinical trial. Thirty-four patients were randomized into group 1 (systemic erythropoietin), group 2 (oral steroids), and group 3 (control). Group A received 10,000 units of erythropoietin twice a day for three days. Group B received oral prednisone 75 mg daily for two weeks followed by a tapering dose (70 mg for 5 days, 60 mg for 5 days, and 5 mg reductions thereafter every 5 days). Functional and structural outcomes were analyzed at 3 and 6 months following treatment. Best corrected visual acuity (BCVA) was the main outcome measure, and mean deviation (MD) of visual field (VF) test and peripapillary retinal nerve fiber layer thickness (PRNFLT) were secondary outcome measures. Results:The mean BCVA (±SD) at the time of presentation was 1 ± 0.56, 1.01 ± 0.6, and 0.94 ± 0.47 logMAR in groups A, B, and C, respectively (P = 0.140); corresponding values were 0.72 ± 0.45, 0.83 ± 0.46, and 0.78 ± 0.4 logMAR (P = 0.417), and at 6-month follow-up, they were 0.70 ± 0.44, 0.73 ± 0.35, and 0.75 ± 0.39 logMAR, respectively (P = 0.597). Fifty-five percent of patients in group A vesus 34.3% in group B, and 31.2% in group C had an improvement of at least 3 lines in the BCVA values at the 6th-month follow-up visit. (P= 0.04) The mean deviation (MD) at the time of presentation was 19.67 ± 6.2, 20.83 ± 4.83, and 18.94 ± 6.92 decibels (db), respectively (P= 0.483).The corresponding values at month 3 were 18.22 ± 7.5, 19.82 ± 7.15, and 17.65 ± 7.22 db, (P = 0.848); and at month 6 they were 16.56 ± 7.08, 18.15 ± 6.57, and 15.9 ± 5.97 db, respectively. (P = 0.699) PRNFLT at presentation was 189 ± 58, 193 ± 64, and 199 ± 62 micrometers, respectively (P = 0.779), which decreased to 110 ± 45, 127 ± 37, and 119 ± 37 at month 3 (P = 0.423). The corresponding values for month 6 were 88 ± 12, 74 ± 25, and 71 ± 18, respectively (P = 0.041). Conclusion: The findings of our study indicate the beneficial effects of systemic erythropoietin in preserving the function and structure of the optic nerve in recent onset NAION.

The average age of in-service bridges has increased in recent years in the United States. To address this issue, structural health monitoring and damage identification approaches can be employed to prioritize maintenance/replacement of aging bridges. Among the damage identification and operational health monitoring approaches, finite element (FE) model updating methods can offer a solution to evaluate the mechanics-based characteristics of bridges. However, in a real-world setting, unidentifiability and mutual dependency between model parameters, modeling errors, especially due to boundary conditions, as well as ill-conditioning of updating algorithms can pose challenges to the application of FE model updating methods. To address these challenges, this study presents a two-step FE model updating approach. In the first step, modal-based model updating is used to estimate linear model parameters mainly related to the stiffness of boundary conditions and material properties. In the second step, in order to refine parameter estimation accounting for nonlinear response behavior of the bridge, a time-domain model updating is carried out. In this step, boundary conditions are fixed at their final estimates using modal-based model updating. To prevent the convergence of updating algorithm to local solutions, the initial estimates for nonlinear material properties are selected based on their corresponding final estimates in the modal-based model updating. To validate the applicability of the two-step FE model updating approach, a series of forced-vibration experiments are designed and carried out on a pair of decommissioned and deteriorated prestressed bridge I-girders. After carrying out the two-step FE model updating, the final estimates of concrete compressive strength are shown to provide reasonable assessment of the damage extent in the girders.

It has now known that at least 10% of samples with pancreatic cancers (PC) contain a causative mutation in the known susceptibility genes, suggesting the importance of identifying cancer-associated genes that carry the causative mutations in high-risk individuals for early detection of PC. In this study, we develop a statistical pipeline using a new concept, called gene-motif, that utilizes both mutated genes and mutational processes to identify 4,211 3-nucleotide PC-associated gene-motifs within 203 significantly mutated genes in PC. Using these gene-motifs as distinguishable features for pancreatic cancer subtyping results in identifying five PC subtypes with distinguishable phenotypes and genotypes. Our comprehensive biological characterization reveals that these PC subtypes are associated with different molecular mechanisms including unique cancer related signaling pathways, in which for most of the subtypes targeted treatment options are currently available. Some of the pathways we identified in all five PC subtypes, including cell cycle and the Axon guidance pathway are frequently seen and mutated in cancer. We also identified Protein kinase C, EGFR (epidermal growth factor receptor) signaling pathway and P53 signaling pathways as potential targets for treatment of the PC subtypes. Altogether, our results uncover the importance of considering both the mutation type and mutated genes in the identification of cancer subtypes and biomarkers.

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

The use of high strength to weight ratio laminated composites is emerging in marine industry and applications as a very efficient solution for improving productivity. Nevertheless, delamination between the layers is a limiting factor for the wider application of laminated composites, as it reduces the stiffness and strengths of the structure. Interleaving nanofibrous mats between layers of composite laminates has been proved to be an effective method for improving composites delamination resistance. This paper aims to characterize the effect of interleaved nanofiber on mode I interlaminar properties and failure mechanisms when subjected to static and fatigue loadings. For this purpose, virgin and nanomodified woven laminates were subjected to Double Cantilever Beam (DCB) specimens. Static and fatigue tests were performed and the tests were monitored by acoustic emission technique. The mechanical results showed a 130% increase of delamination toughness for nanomodified specimens in the static loadings and more crack growth resistance in the fatigue loading. The AE results revealed that different type of failure mechanisms was the cause of these improvements for the modified specimens compared with the virgin ones.

Dielectric barrier discharge (DBD) plasma has several applications in different fields. One of these fundamental applications is medical usages, where various methods are employed to improve the plasma treatment process. The combination of different gases is one of the important strategies to improve the performance of plasma in treatment. In this paper, the optimized plasma parameters for one-dimensional radiofrequency discharge produced at low pressures in a helium gas combination is studied. In this research, the optimal combination of H₂O and He is identified to attain the highest amount of reactive oxygen species (ROS). Considered mixture are 5,10,15 and 20 precent of H₂O for one dimensional helium gas discharge. The results show that the parameters of the output plasma are highly dependent on the composition of the input gases. It is found that the greatest concentrations of H+, He+, Hes (excited helium), and OH/ densities are observed when the H₂O percentage is 10%. Moreover, the density distributions of various species and the temperatures of electrons are numerically calculated during the electrical discharge process. These findings provide useful knowledge on how to optimize plasma parameters for biomedical applications, which may lead to improved treatment results in several therapeutic areas.

We present a finite element model updating technique for soil-structure system identification of the Millikan Library building using the seismic data recorded during the 2002 Yorba Linda earthquake. A detailed finite element (FE) model of the Millikan Library building is developed in OpenSees and updated using a sequential Bayesian estimation approach for joint parameter and input identification. A two-step system identification approach is devised. First, the fixed-base structural model is updated to estimate the structural model parameters (including effective elastic modulus of structural components, distributed floor mass, and Rayleigh damping parameters) and some uncertain components of the foundation-level motion. Then, the identified structural model is used for soil-structure model updating wherein the Rayleigh damping parameters, the stiffness and viscosity of the soil subsystem (modeled using a substructure approach), and the foundation input motions (FIMs) are estimated. The identified model parameters are compared with state-of-practice recommendations. While a specific application is made for the Millikan Library, the present work offers a framework for integrating large-scale FE models with measurement data for model inversion. By utilizing this framework for different civil structures and earthquake records, key structural model parameters can be estimated from the real-world recorded data, which can subsequently be used for assessing and improving, as necessary, state-of-the-art seismic analysis and structural modeling techniques. This paper presents an effort towards using real-world measurements for large-scale FE model updating in the soil and structure, uniform soil time domain for joint parameter and input estimation, and thus paves the way for future applications in system identification, health monitoring and diagnosis of civil structures.

Real estate market analysis and place-based decision-making can both benefit from understanding house price development. Although considerable amounts of interest have been devoted to housing price modelling, the assessment of house price fluctuation still requires further comparing studies. Housing price prediction is challenging as contributing factors are quite dynamic and subject to a variety of regulating elements. The future understanding of the housing market trends not only provides sufficient customers’ investment trust potential but also enables the financial support to progress more realistic in advance. In this study, a comprehensive data-informed framework is developed to investigate and anticipate real estate house prices using historical data by combining explanatory features. We examined about 500 houses in the Boston area as a case study and discussed how the increase in housing prices could vary by each of the contributing components. Fourteen Machine Learning (ML) regressors imply to the dataset and lead to a comparative study of the accuracy of all the models. ML-based regressors forecast real estate home prices as a function of thirteen influencing factors. The most informative features were also selected by conducting the Permutation Feature Importance technique on all the features The study provides a comprehensive tool for evaluating the robustness and efficiency of ML models for housing price predictions. The results highlighted Random Forest as the best model has an R2 equals to 0.88 and Voting Regressor as the second highest rated model has R2 equals to 0.87. Results of multivariate exploratory data analysis also implied that the average number of rooms and percentage of the lower status of the population have the most significant impact on the price range predictions.

Denture wearers often complain about jeopardized function and reduced quality of life due to lack of prosthesis’ retention. Implant retained mandibular overdentures, on 2 non-connected implants (2IOD) are well-proven solutions to overcome these issues. We prospectively assessed 69 patients and scrutinized clinical records until at least 7 years of function. Thirty-six were retained on Locator ® Abutments (LA) and thirty-tree on Ball Abutments (BA). Both systems were compared regarding the type, amount and total cost of required maintenance. One implants was lost, yielding 98.7% survival after 7 years. In total 438 technical issues occurred: 121 (27.35%) in BA and 317 (72.4%) in LA. Out of these, 343 events (78%) were solved chairside: 191 (43.6%) were replacements of retention caps, 113 (25.8%) were minor acrylic repairs, in 26 (5.9%) pressure ulcers had to be relieved and 13 (3%) related to abutments. LA required 179 insert replacements compared to 12 in the BA group. The overall initial treatment cost was 3850 euro. The average total maintenance cost in relation to the initial cost for the LA and BA groups was 19.11% (range 0% - 82.24%) and 18.91% (range 0% - 113.26%) respectively (P=0.540). Conclusions: The 7-years maintenance costs for a 2IOD is acceptable when the patient is regularly checked and professionally maintained. Most events are easily solvable chairside, but a few patients required more expensive interventions, regardless of the type of attachment used.

Prediction of stock groups values has always been attractive and challenging for shareholders. This paper concentrates on the future prediction of stock market groups. Four groups named diversified financials, petroleum, non-metallic minerals and basic metals from Tehran stock exchange are chosen for experimental evaluations. Data are collected for the groups based on ten years of historical records. The values predictions are created for 1, 2, 5, 10, 15, 20 and 30 days in advance. The machine learning algorithms utilized for prediction of future values of stock market groups. We employed Decision Tree, Bagging, Random Forest, Adaptive Boosting (Adaboost), Gradient Boosting and eXtreme Gradient Boosting (XGBoost), and Artificial neural network (ANN), Recurrent Neural Network (RNN) and Long short-term memory (LSTM). Ten technical indicators are selected as the inputs into each of the prediction models. Finally, the result of predictions is presented for each technique based on three metrics. Among all algorithms used in this paper, LSTM shows more accurate results with the highest model fitting ability. Also, for tree_based models, there is often an intense competition between Adaboost, Gradient Boosting and XGBoost.

Purpose: To investigate the relationship of lamina cribrosa displacement to corneal biomechanical properties and visual function after mitomycin C-augmented trabeculectomy. Method: Eighty-one primary open angle eyes were imaged before and after trabeculectomy using an enhanced depth spectral-domain optical coherence tomography (SDOCT). Corneal biomechanical properties were measured with the Ocular Response Analyser before the surgery. The anterior lamina cribrosa (LC) was marked at several points in each of six radial scans to evaluate LC displacement in response to Intraocular pressure (IOP) reduction. A Humphrey visual field test (HVF) was performed before the surgery as well as three and six months postoperatively. Results: Factors associated with a deeper baseline anterior lamina cribrosa depth (ALD) were cup-disc ratio (P=0.04), baseline IOP (P= 0.01), corneal hysteresis (P= 0.001), and corneal resistance factor (P= 0.001). After the surgery, the position of LC became more anterior (negative), posterior (positive) or remained unchanged. The mean LC displacement was -42 μm (P= 0.001) and was positively correlated with the magnitude of IOP reduction (regression coefficient: 0.251, P=0.02), and negatively correlated with age (regression coefficient: - 0.224, P= 0.04) as well as baseline cup-disk ratio (Regression coefficient: -0.212,P= 0.05) Eyes with a larger negative LC displacement were more likely to experience an HVF improvement of more than 3 dB gain in mean deviation (P= 0.002). Conclusion: A lower SDOCT cup-disc ratio, younger age, and a larger IOP reduction were correlated with a larger negative LC displacement and improving HVF. Corneal biomechanics did not predict LC displacement.

Due to the upward trend in the globalization of sustainability issues and the intense competitive environment, it is evident that higher education institutions need new strategic approaches to succeed. To this end, the inquiry for this paper has been made into the debate about student relationship management. Going through the literature indicates that institutions have mainly perceived the concept as a technological initiative for solving the problems in individual domains, accompanied by uncoordinated efforts. Thus, the aims of this study are to theoretically present critical success factors of this strategic approach and to empirically examine the recognized factors. To do so, confirmatory factor analysis that is a quantitative analytic method was performed. The results and analyses revealed that there has been a significant correlation between the four critical success factors including knowledge management, student relationship management technology, student orientation, and employees’ involvement. It was also found that these factors are significantly correlated with the construct of student relationship management success. The findings have consequently highlighted that in addition to the technological tool, the role of knowledge management, employees’ involvement, and student orientation appeared to be particularly important for the implementation of the application.

Purpose: To investigate the extent of anterior chamber angle circumference needed to maintain a physiological outflow facility (C). Methods: Twenty anterior segments of porcine eyes were assigned to 5 groups, each with a different degree of cyanoacrylate-mediated angle closure: 90° (n = 4), 180° (n = 4), 270° (n = 4), 360° (n = 4) and four unoccluded control eyes. The outflow facility was measured at baseline, 3, 12, 24, and 36 hours after angle closure. Outflow patterns were evaluated with canalograms and the histomorphology was compared. Results: Baseline outflow facilities of the five groups were similar (F = 0.922, P = 0.477). Complete, circumferential occlusion over 360° induced a significant decrease in facility from baseline at all time-points (P ≤ 0.023 at 3, 12, 24 and 36 hours). However, no difference from baseline was found in any of the partially-occluded (0–270°) groups (F ≥ 0.067, P ≥ 0.296 at 3, 12, 24 and 36 hours). The canalograms confirmed the extent of occlusion with flow through the unblocked regions. Histology revealed no adverse effects of blockage on the TM or aqueous plexus in the unoccluded angle portions. The unoccluded TM appeared normal. Conclusion: Cyanoacrylate-mediated angle occlusion created a reproducible angle closure model. 90° of unoccluded anterior chamber angle circumference was sufficient to maintain physiological outflow. This model may help understand how outflow can be regulated in healthy, nonglaucomatous TM.

Due to their wide range of uses in modern technology, nanomaterial’s of silver metallic (Ag-NPs) have engaged considerable concern, driving significant research with development efforts. This study focuses on investigating the antimicrobial properties of Ag-NPs, encompassing their production, characterization, and biological aspects. The eco-friendly extracellular biosynthetic method employed in this work utilized extracts from the marine fungus Aspergillus flavus MK4 as reducing agents for nanoparticle synthesis. Analyzing the colloidal Ag-NPs using UV-visible spectroscopy revealed a peak in absorbance at 460 nm, indicating the presence of plasmonic properties. The electrons imaging of internal structure (TEM) elucidated spherical shape of the Ag-NPs by 15 nm size. Testing against bacterial diversities (Gr-ve & Gr+ve) revealed potent antibacterial and antifungal efficacy. Cytotoxicity versus HepG-2 cell line was assessed using the MTT assay, and antioxidant properties were examined through the radical scavenging (DPPH) assay. In wise of their conceivable applications in the fields of antibacterial, anticancer, and wound-healing, the Ag-NPs that were synthesized exhibit promising features. With its powerful ability to synthesize silver nanoparticles (Ag-NP), Aspergillus flavus MK4 Aspergillus flavus MK4 can generate nanoparticles extensively characterized, demonstrating their antibacterial, antioxidant, anticancer, and wound-healing properties.

Nonlinear loads, a crucial component of power systems grids, pose a challenge due to harmonics injection. Our research tackles this issue with a novel approach - a modified green plug / switched filter compensation scheme using fuzzy logic controllers. This innovative scheme, presented in the paper, utilizes dual action pulse width modulation to ensure switching functions from harmonic reduction and capacitive compensation for inrush nonlinear type AC loads.  The scheme's multi-loop regulations and online switching effectively handle dynamic type slow acting inrush, motorized and other rectifier type-nonlinear loads, enhancing power factor, power quality at source and load buses, and reducing total harmonic distortion at key source and sensitive nonlinear load buses. A simulation model in the MATLAB/SIMULINK software environment demonstrates the efficiency of the proposed FACTS technique. The results show that voltage, current, active power, reactive power, power factor regulation, and effective energy utilization are achievable with the designed FACTS-MFCC-SFC. The switched modulated AC green plug filter significantly improves power quality and enhances power factor in the case of inrush and nonlinear loads.

Gas turbine power plants have important roles in the global power generation market. The examines thermodynamically the impact of steam injection for a combined cycle including a gas turbine cycle with a two-stage turbine and carbon dioxide recompression. The combined cycle is compared with the simple case without steam injection. Steam injection’s impact is observed on important parameters such as energy efficiency, exergy efficiency, and output power. It is revealed that steam injection reduces exergy destruction in components compared to the simple case. The efficiencies for both cases are obtained. The energy and exergy efficiencies respectively are found to be 30.4% and 29.4% for the simple case, and 35.3% and 34.1% for the case with steam injection. Also, incorporating steam injection reduces emissions of carbon dioxide.

This paper presents a computational approach designed to construct and query a literature-based knowledge graph for predicting novel drug therapeutics. The main objective is to offer a platform that discovers drug combinations from FDA-approved drugs and accelerates their investigations by domain scientists. Specifically, the paper introduced the following algorithms: (1) an algorithm for constructing the knowledge graph from drug, gene, and disease mentions in the biomedical literature; (2) an algorithm for vetting the knowledge graph from drug combinations that may pose a risk of drug interaction; (3) and two querying algorithms for searching the knowledge graph by a single drug or a combination of drugs. The resulting knowledge graph had 844 drugs, 306 gene/protein features, and 19 disease mentions. The original number of drug combinations generated was 2,001. We queried the knowledge graph to eliminate noise generated from chemicals that are not drugs. This step resulted in 614 drug combinations. When vetting the knowledge graph to eliminate the potentially risky drug combinations, it resulted in predicting 200 combinations. Our domain expert manually eliminated extra 54 combinations which left only 146 combination candidates. Our three-layered knowledge graph, empowered by our algorithms, offered a tool that predicted drug combination therapeutics for scientists who can further investigate from the viewpoint of drug targets and side effects.

In this study, we focus on the effects of fuel bed representation and fire heat and smoke distribution in a coupled fire-atmosphere simulation platform for two landscape-scale fires: the 2018 Camp Fire and the 2021 Caldor Fire. The fuel bed representation in the coupled fire-atmosphere simulation platform WRF-Fire currently includes only surface fuels. Thus, we enhance the model by adding canopy fuel characteristics and heat release, for which a method to calculate the heat generated from canopy fuel consumption is developed and implemented in WRF-Fire. Furthermore, the current WRF-Fire heat and smoke distribution in the atmosphere is replaced with a heat-conserving Truncated Gaussian (TG) function and its effects are evaluated. The simulated fire perimeters of case studies are validated against semi-continuous, high-resolution fire perimeters derived from NEXRAD radar observations. Furthermore, simulated plumes of the two fire cases are compared to NEXRAD radar reflectivity observations followed by buoyancy analysis using simulated temperature and vertical velocity fields. The results show that while the improved fuel bed and the TG heat release scheme have small effects on the simulated fire perimeters of the wind-driven Camp Fire, they affect the propagation direction of the plume-driven Caldor Fire, leading to better-matching fire perimeters with the observations. However, the improved fuel bed representation together with the TG heat smoke release scheme leads to more realistic plume structure in comparison to the observations in both fires. The buoyancy analysis also depicts more realistic fire-induced temperature anomalies and atmospheric circulation when the fuel bed is improved.