As a new type of high-performance material, gradient structural steel is widely used in engineering fields due to its unique microstructure and excellent mechanical properties. For the prevalent fatigue failure problem, the rate of change of the local grain size gradients along the structure (referred to as the gradient rate) is a key parameter in the design of gradient structures, which significantly affects the fatigue performance of gradient structural steel. In this work, a method of ‘Voronoi primary + secondary modeling’ is adopted to successfully establish three typical high-strength steel models corresponding to the convex, linear, and concave type gradient rates for gradient structures, focusing on the stress-strain response and crack propagation in structural steel with different gradient rates under cyclic loading. It is found that the concave gradient rate structural model is dominated by finer grains with larger volume fraction, which is conducive to hindering fatigue crack propagation and has the longest fatigue life; the linear structure is the second one, and the convex structure is the shortest one. The simulation results in this work are consistent with the relevant experimental phenomena. Therefore, when regulating the gradient rate, priority should be given to increasing the volume fraction of fine grains and designing a gradient rate structure dominated by fine grains to improve the fatigue life of the material.

Carboxymethyl cellulose (CMC) can act as a nonpermeating cryoprotectant, reducing the toxicity of glycerol. The dog spermatozoa were diluted and frozen in freezing extender supplemented with 0 (control), 0.1, 0.25, 0.5, or 0.75 % (w/v) CMC. Post-thaw, the sperm kinematic parameters (CASA), viability (SYBR-14/PI), and acrosome integrity (FITC-PSA) were evaluated. The expression of genes related to apoptosis (BCL2, BAX, and ANAXA), motility (BACTIN), cold shock proteins (YBX3), and ROS (CAT, ROMO1, and SMOX) was evaluated using qPCR. The total motility was higher in the 0.1 % CMC group compared to the other groups (p<0.05). In addition, the expression of the antiapoptotic gene BCL2 was upregulated in 0.1 % CMC group compared to the control, whereas the gene expression of BAX and SMOX exhibited lower levels in 0.1 and 0.25 % CMC groups than the control group (p<0.05). The expression of YBX3 genes was significantly decreased with 0.25 % CMC supplementation. No significant differences were found in viability, acrosome integrity, or the relative expression of ANAXA, BACTIN, CAT, and ROMO1 genes between the groups. In conclusion, the addition of CMC to a freezing extender improved sperm motility and alleviated cryostress by regulating the gene expression of BCL2, BAX, SMOX, and YBX3.

Dissemination of resistant pathogens via food supply chains is a notable public health danger from farm to fork. The proposed work analysed the distribution of E. coli among various sources of animal-based food supply chain. Overall, 500 samples were heaped up from livestock, poultry sectors, environment, fisheries, and dairy. Standard microbiological procedures were used to isolate and identify E. coli. Further, isolates were confirmed by using MALDI-TOF and virulence associated genes (VAGs). The phenotypic resistance pattern of the isolates was determined by molecular identification of antibiotic resistance genes (ARGs) through PCR using specific primers. Shiga toxin producing E. coli (STEC) were subjected to PCR based O typing using specific primers against different O types. Overall results revealed that 154 (30.5%) samples were confirmed as E. coli, in which77 (50%) isolates were MDR E. coli. Among these, total 52 (67.53%) isolates showed array of VAGs in which 21 (40.38%) were confirmed as STEC based on stx1 and stx2. Furthermore, 12/52 (23.07%) isolates were detected as Non-O157 STEC co-harboring mcr-1 and blaNMD-1. Overall, O26 STEC was found to be the most prevalent among different non-O157 types. Herein, results proposed that detection of such STEC MDR E. coli causing serious public health consequences in developing countries.

As a result of increased energy consumption, global trade has surged, and serious environmental problems have arisen. The problem is made more complicated by the fact that either environmental damage or economic advancement cannot be compromised. The elements contributing to environmental contamination must be identified and dealt with accordingly if we are to have a healthier future. In this study, what causes CO₂ emissions in Malaysia are examined using the Autoregressive distributed lag (ARDL) model using time series data from 1995 to 2020. The paper also examines the impact of energy use and economic complexity on CO2 emissions. Firstly, it is noted that the Environmental Kuznets Curve (EKC) theory holds in Malaysia. Furthermore, energy consumption has a detrimental impact on reducing CO2 emissions. Furthermore, it has been noted that a greater degree of economic complexity leads to an elevated amount of CO2 emissions over an extended period. The findings suggest significant environmental policy implications for reducing CO2 emissions in Malaysia.

This paper analyses the viability of different solutions to passively augment the axial stiffness of a horizontal axis radial levitation passive magnetic bearing (PMB) with topology previously studied. The zero-field cooling (ZFC) of high-temperature superconductor (HTS) bulks promotes higher magnetic impulsion and levitation forces and lower electromagnetic losses than with the field-cooling (FC), but on the other hand, the guiding stability is much lower than with FC. Because of stability reasons, FC was adopted in most superconducting maglev systems. The trend of this research group has been to develop an HTS ZFC horizontal axis radial levitation PMB presenting notable levitation forces with reduced electromagnetic losses, defined by a topology that creates guiding stability. Previous work has shown that even optimizing the bearing geometry to maximize magnetic guidance forces, could not be enough to guarantee the axial stiffness required for many applications. First, the extent to which guidance forces are augmented by increasing the number of HTS bulks in the stator is evaluated. Then, the axial stiffness augmentation by passively adding two limiting permanent magnet (PM) rings is evaluated. Results show that the axial stiffness is highly augmented by adding limiting PM rings with no significant additional investment. This change enables using the studied ZFC superconducting PMB in high-precision axial stability applications, such as precision gyroscopes, horizontal axis propellers and turbines.

This study explored the incidence of metaphorical virtual classrooms and interactive learning objects in the interaction of students in online mode. The main objective was to analyze how these digital tools, driven by a set of strategies to promote their use, affect the interaction of students in the virtual classroom system and their derived effects. To this end, the latest version of Moodle was implemented in conjunction with gamification plugins and interactive tools in the higher education institution used as a case study. The methodology consisted of data collection through ordinal instruments applied to the teachers and student performance metrics gathered using a plugin developed to extract accurate metrics of each student's usage and performance through direct queries to the Moodle database and its processing through a neural network. This facilitated the collection of standardized data on the actual metrics of each virtual classroom at the end of the teaching of each subject from both the previous LMS and the newly implemented one. This data was then analyzed using advanced statistical techniques, including Mahalanobis distances, confirmatory factor analysis, and the Wilcoxon signed-rank test. These methods provided a compelling comparison between the old and new systems, revealing significant improvements in the metrics and factors evaluated. The results showed a significant improvement in teachers' perceptions of the usability of the virtual classroom system and an increase in students' academic performance, interaction, progress, and time spent learning in virtual contexts. These results provide solid empirical evidence of the added value of these educational tools as effective strategies for improving student interaction, performance, and motivation in online education.

Electrochemotherapy (ECT) is one of the newest therapeutic strategies employed as medical procedure for skin neoplasms’ treatment, especially for Classic Kaposi’s Sarcoma (CKS). The aim of this study was to demonstrate ECT clinical response and local control of CKS disease. Primary endpoint was to value the worth and efficacy of this local therapy in CKS skin lesions’ treatment. Nineteen CKS patients were enrolled, 14 males and 5 females with median age at diagnosis of 72. Complete response (CR) has been gained in 12 patients after first ECT attempt, meanwhile 3 and 4 out of 19 patients obtained a Partial response (PR), so that they underwent to a second and a third ECT treatment, each other. Clinical response was evaluated during all the time of the follow-up, which ranged between 3 months and 4 years with a median of 18 months. CKS skin lesions control still represents a challenge for surgeons and oncologists. Nevertheless, according to this and other authors’ recent experiences, ECT could be considered the gold standard strategy for early-stage patients, but at the same time it could be considered as a valid option in controlling Kaposi’s Sarcoma locally advanced lesions.

The intricate functionality of the vertebrate retina relies on the interplay between neurotransmitter activity and calcium dynamics, offering important insights into de-velopmental processes, physiological functioning, and disease progression. Neuro-transmitters orchestrate cellular processes to shape the behavior of the retina under diverse circumstances. Despite research to elucidate the roles of individual neuro-transmitters in the visual system, there remains a gap in our understanding of the holistic integration of their interplay with calcium dynamics in the broader context of neuronal development, health, and disease. To address this gap, the present review explores the mechanisms used by the neurotransmitters glutamate, GABA, glycine, dopamine, and acetylcholine and their interplay with calcium dynamics. This con-ceptual outline is intended to inform and guide future research, underpinning novel therapeutic avenues for retinal-associated disorders.

Polyphenols, long-used components of medicinal plants, have drawn great interest in recent years as potential therapeutic agents because of their safety, efficacy, and wide range of biological effects. Approximately 74% of the world's population still uses plant-based medicines, indicating the ongoing significance of phytochemicals for human health. The study emphasizes the growing body of research examining the role of polyphenols in uncovering their anti-adipogenic and anti-obesity properties. Functions of polyphenols including phenylpropanoids, flavonoids, terpenoids, alkaloids, glycosides, and phenolic acids are distinct due to changes in chemical diversity, and structural characteristics. This review methodically investigates the mechanism of how naturally occurring polyphenols mediate obesity and metabolic function. To this end, hormonal control of hunger has the potential to inhibit pro-obesity enzymes such as pancreatic lipase, the promotion of energy expenditure, and the modulation of adipocytokine production. Specifically, polyphenols affect insulin, a hormone that is essential for regulating blood sugar, and they also play a role in part in a complex web of factors that affect the progression of obesity. This review also explores the immunomodulatory properties of polyphenols, providing insight into their ability to improve immune function. The effects of polyphenols on gut health, improving the number of commensal bacteria, cytokine production suppression, and immune cell mediation, including natural killer cells and macrophages. Taken together, continuous studies are required for prudent and precise mechanisms underlying polyphenols' therapeutic potential in obesity and immunomodulation. In the interim, this review emphasizes the holistic approach to health and promotes the consumption of a wide range of foods and drinks high in polyphenols. This review lays the groundwork for future developments, indicating that the components of polyphenols and their derivatives may provide the answer to urgent worldwide health issues. This compilation of the body of knowledge paves the way for future discoveries in the global treatment of pressing health concerns in obesity and metabolic diseases.

This study explores the cocrystallization between the drug praziquantel (PZQ) and the nutraceutical curcumin (CU). The investigation revealed two novel solid forms: a cocrystal solvate with ethyl acetate and a non-solvated cocrystal. This novel drug-nutraceutical cocrystal is identi-fied as “generally recognized as safe” (GRAS) praziquantel-curcumin-ethyl acetate (2:1:1) co-crystal solvate. This latter undergoes desolvation, leading to the formation of the praziquan-tel-curcumin (2:1) cocrystal. The cocrystal solvate has ethyl acetate molecules occupying the voids with minimal interactions within the crystal lattice. The application of heat treatment induces solvent removal and prompts the transition to the non-solvated cocrystal, as highlighted by VT-XRD. Thermal analyses demonstrate the stability of the cocrystal solvate up to approximately 100°C, beyond which it transforms into the non-solvated phase, which eventually melts at 130°C.

Recently, Electric Two-Wheelers ETW are changing the face of the global automotive market. This study focused on selecting proper material and mechanical isolation gap to design a protective enclosure for the battery pack of ETW. The integration of the Failure, Modes, Mechanism and Effect Analysis (FMMEA) method is utilized to develop the interface matrix and the severity index of different components of the enclosure. By analyzing different forces from the road conditions, dynamics during turn, acceleration, and deceleration with the enclosure, it becomes a crucial load-bearing element. Employing Finite Element Modeling (FEM), structural strength using materials like AL6061, Q235, C22000, DC 01 and Teflon are assessed under varying static, dynamic and thermal conditions. Modal analysis is conducted to observe the excitation frequencies where the maximum deformation for the metal enclosure is observed beyond 500Hz. AL6061 material that can withstand the stresses and deformations that are under allowable stress limits with negligible deformation is most preferable material based on the results. Minimal of 2.5 mm gap to be provided in case of metal casing and 10mm in case of Teflon is proven.

Legacy IPv4 networks are strenuous to manage and operate. Network operators are in need to minimize the capital and operational expenditure of running network infrastructure. The implementation of Software-defined networking (SDN) addresses those issues by minimizing the expenditures in the long run. Legacy networks need to integrate with the SDN networks for the smooth migration towards the fully functional SDN environment. In this paper, we compare the network performance of the legacy network with the SDN network for IP routing in order to determine the feasibility of the SDN deployment in the Internet Service provider (ISP) network. The simulation of the network is performed in the Mininet test-bed and the network traffic is generated using distributed Internet traffic generator. Open network operating system is used as a controller for the SDN network in which SDN-IP application is used for IP routing. Round trip time, bandwidth, and packet transmission rate from both SDN and legacy networks are first collected and then the comparison is done. We found that SDN-IP performs better in terms of bandwidth and latency as compared to legacy routing. The experimental analysis of interoperability between SDN and legacy networks shows that SDN implementation in production level carrier-grade ISP network is viable and progressive.

Epigenetic alterations are known contributors to cancer development and aggressiveness. Additional to alterations in cancer cells, aberrant epigenetic marks are present in cells of the tumor microenvironment, including lymphocytes and tumor-associated macrophages, which are often overlooked but known to be a contributing factor to a favorable environment for tumor growth. Therefore, the main aim of this review is to give an overview of the epigenetic alterations affecting immune cells in the tumor microenvironment to provoke an immunosuppressive function and contribute to cancer development. Moreover, immunotherapy is briefly discussed in the context of epigenetics, describing both its combination with epigenetic drugs and the need for epigenetic biomarkers to predict response to immune checkpoint blockage. Combining both topics, epigenetic machinery plays a central role in generating an immunosuppressive environment for cancer growth, which creates a barrier for immunotherapy to be successful. Furthermore, epigenetic-directed compounds may not only affect cancer cells but also immune cells in the tumor microenvironment, which could be beneficial for the clinical response to immunotherapy. Thus, modulating epigenetics in combination with immunotherapy might be a promising therapeutic option to improve the success of this therapy. Further studies are necessary to (1) understand in-depth the impact of the epigenetic machinery in the tumor microenvironment; (2) how the epigenetic machinery can be modulated according to tumor type to increase response to immunotherapy and (3) find reliable biomarkers for a better selection of patients eligible to immunotherapy.

Urban cities in sub-Saharan Africa (SSA) are faced with ambient air pollution. This is an important public health problem with models and limited monitoring data indicating high concentrations of pollutants such as fine particulate matter (PM_2.5). Going through most global air quality index maps, however, information about ambient pollution from SSA is scarce. We evaluated the feasibility and practicality of longitudinal measurements of ambient PM_2.5 using low-cost air quality sensors (Purple Air-II-SD) across thirteen locations in seven countries in SSA. Devices were used to gather data over a 30-day period with the aim of assessing the efficiency of its data recovery rate and identifying challenges experienced by users in each location. The median data recovery rate was 94% (range: 72% to 100%). The mean 24-hour concentration measured across all sites was 38 µg/m³ with the highest PM_2.5 period average concentration of 91 µg/m³ measured in Kampala, Uganda and lowest concentrations of 15 µg/m³ measured in Faraja, The Gambia. Kampala-Uganda and Nnewi-Nigeria recorded the longest periods with concentrations>250µg/m³. Power outages, SD memory card issues, internet connectivity problems and device safety concerns were important challenges experienced when using Purple Air-II-SD sensors. Despite some operational challenges, this study demonstrated that it is reasonably practicable and feasible to establish a network of low-cost devices to provide data on local PM_2.5 concentrations in SSA countries. Such data are crucially needed to raise public-, societal and policymaker awareness about air pollution across SSA.

HspA1A, a molecular chaperone, translocates to the plasma membrane (PM) of stressed and cancer cells. This translocation results in HspA1A’s cell-surface presentation, which renders tumors radiation insensitive. To specifically inhibit the lipid-driven HspA1A’s PM translocation and devise new therapeutics it is imperative to characterize the unknown HspA1A’s lipid-binding regions and determine the relationship between the chaperone and lipid-binding functions. To elucidate this relationship, we determined the effect of phosphatidylserine (PS)-binding on the secondary structure and chaperone functions of HspA1A. Circular dichroism revealed that binding to PS resulted in minimal modification on HspA1A’s secondary structure. Measuring the release of inorganic phosphate revealed that PS-binding had no effect on HspA1A’s ATPase activity. In contrast, PS-binding showed subtle but consistent increases in HspA1A’s refolding activities. Furthermore, using a Lysine-71-Arginine mutation (K71A; a null-ATPase mutant) of HspA1A we show that although K71A binds to PS with affinities similar to the WT, the kinetics of the binding are significantly different, probably because of the mutant’s inability to achieve specific conformations. These observations suggest a two-step binding model that includes conformational changes and strongly support the notion that the chaperone and lipid-binding activities of HspA1A are dependent but the regions mediating these functions do not overlap. These findings provide the basis for future interventions to inhibit HspA1A’s PM-translocation in tumor cells, making them sensitive to radiation therapy.

The paper presents construction, laboratory tests as well as the first field application of a new fiber-optic rotational seismograph. The system based on fiber-optic gyroscope (FOG) with determined Angle Random Walk of the order of 10-8 rad/Sqrt(s) and a few rad/s maximum detectable amplitude of rotation in the frequency range from DC to 328.12 Hz. It has been designed for rotational seismology area of interest. This work also presents exemplary relevant measurements which were conducted using a set of two devices installed in the geophysical observatory in Książ, Poland.

The explosive emergence of wireless technologies and standards, covering licensed and unlicensed spectrum bands has triggered the appearance of a huge amount of wireless technologies, with many of them coexisting in the same band. Unfortunately, the wireless spectrum is a scarce resource, and the available frequency bands will not scale with the foreseen demand for new capacity. Certain parts of the spectrum, in particular the license-free ISM bands, are overcrowded, while other parts, mostly licensed bands, may be significantly underutilized. As such, there is a need to introduce more advanced techniques to access and share the wireless medium, either to improve the coordination within a given band, or to explore the possibilities of intelligently using unused spectrum in underutilized (licensed) bands. Therefore, in this paper, we present a SDR based framework that can be employed to devise disruptive techniques to optimize the sub-optimal use of radio spectrum that exists today. Additionally, we describe two use cases for the proposed framework.

The Wind Energy Conversion System (WECS) plays an inevitable role across the world. In particular, the attention for Permanent Magnet Synchronous Generators (PMSGs) connected with wind farm is popular. This paper deals with the literature review that describes the recent advances, progresses and innovatory trends on PMSGs for WECS. Comparison between geared and direct-driven conversion systems and the classification of electrical machines used in WECS are discussed. A detailed analysis on the design aspects considering various topologies of PMSGs are encompassed in the literature. The PMSG design and optimization problems are solved by field computation techniques and optimized by using Soft Computing (SC) techniques .The three-dimensional, finite element software platform for the analysis and design of PMSGs is discussed. This paper also deals with the interdisciplinary modeling, analysis, and optimization of PMSG using Finite Element Analysis (FEM) and SC techniques. Finally, PMSGs are reviewed and compared for further exploration.

Lithium-metal batteries offer high energy density, but are challenged by dendrite growth, which can lead to short circuits and battery failure. Various models with varying degrees of accuracy and computational cost have been developed to understand and predict dendrite growth. In this study, a simple simulation model to predict macroscale dendrite growth on lithium metal electrodes is presented. The model uses a 3D single-particle diffusion-limited aggregation (DLA) algorithm, biased by the electric field, to simulate dendrite growth. The model was validated using 3D MRI of dendrite growth in a lithium symmetric cell. It was used to simulate deposition between consecutive measured dendrite structures. The model also introduces an important parameter, namely the biasing factor c, which determines the balance between diffusion and electric field effects. This parameter was calibrated using the overlap and displacement of deposition sites metrics, achieving optimal results at c = 0.7. The model predictions were in good agreement with experimental data confirmed by fractal dimension analysis. This model provides a useful tool for predicting and studying dendrite formation in lithium and other metal batteries.

This paper explores the role of open-source large language models in IoT cybersecurity world. The threats of malicious activity on the Internet and the loss of private information are very real and lead to serious consequences. The purpose of this paper is to investigate how open source-large language models can help to defend against the growing threat of cyber-crimes. We conducted our experiments in two directions. The first one is a security assistant that helps with cybersecurity best practices advices. The second one is a how large language model can simulate a vulnerable IoT system. For both types of experiments, the interactive mode of operation of the language model is used. In the context of the cybersecurity research, a major advantage of the locally installed open-sourced large language models is that they do not share sensitive data with a remote system in a cloud. The paper concludes by discussing the potential impact of open-source large language models on cybersecurity research and recommends future research directions.

Tunnel-interchange connecting sections pose significant safety challenges on mountainous expressways due to their high incidence of accidents. Improving road safety necessitates a comprehensive understanding of driver behavior in such areas. This study explores the influences of road characteristics, signage information volume, and traffic conditions on drivers' car-following and lane-changing behavior in tunnel-interchange diverging areas. Utilizing driving data from 25 subjects of 72 simulated road models, driving performance is assessed using Friedman rank test and multivariate variance analysis. The results highlight the significant influence of both connection distance and signage information load on driving behavior. In tunnel-interchange scenarios, the reduction of velocity increased by 62.61%, and speed variability surged by 61.11%, indicating potential adverse effects on driving stability due to the environmental transitions. Decreased connection distances are associated with reduced lane-changing durations, larger steering angles, and increased failure rates. Furthermore, every two units increase in signage information leads to a 13.16% rise in maximum deceleration and a 5% increase in time headway. Notably, the signage information volume shows a significant interaction with connection distance (F=1.60, P=0.045) for most car-following indicators. Hence, the study recommends a maximum connection distance of 700 m and signage information not exceeding nine units for optimal safety and stability.

The medium-voltage DC (MVDC) technology has gained increasing interests in recent years. Power electronics devices dominate those grids. Accurate simulation of such a grid, with detailed models of switching semiconductors, can quickly became very time-consuming, according to the number of connected devices to be simulated. A simulation approach based on interactions on a continuous time model can be very interesting, especially for developing a system level control model of such a modern MVDC distribution grid. Aim of this paper is to present all the steps required for obtaining a continuous time modelling of a +/- 10 kV MVDC grid case study, including Solid State Transformer (SST) and Modular Multilevel Converter (MMC) based Active Front End (AFE). Additional aim of this paper is to supply an educational content about the use of the continuous time simulation approach, thanks to a detailed description of the various devices modelled into the presented MVDC grid. The results of a certain number of simulation scenarios are eventually presented.

This research paper presents a comprehensive study on the gasification of wheat straw pellets in a 10-kW fixed bed gasifier through a combination of Computational Fluid Dynamics (CFD) simulation and experimental validation. The developed 2D CFD model in ANSYS meshing simulates the gasification process in ANSYS Fluent software. The investigation evaluates key parameters such as equivalence ratio, higher heating value, and temperature distribution within the gasifier to enhance syngas production efficiency. The simulated results exhibit a noteworthy agreement with experimental data obtained from gasification. The impact of the equivalence ratio on gas production and lower heating value (LHV) is systematically explored, revealing that an equivalence ratio of 0.35 is optimal for syngas production. The resulting producer gas composition at this optimum condition includes CO (~27.67%), CH4 (~3.29%), CO2 (~11.09%), H2 (~11.09%), and N2 (~51%). The study further investigates the positive influence of reactor temperature on increasing syngas quantity while considering the uneven shape of the reactor and various thermal reactions affecting temperature, static pressure, velocity, and density. The findings contribute valuable insights to improve the efficiency of fixed-bed gasifiers, offering essential information on performance parameters for sustainable and optimized syngas production.

The first paper of this two-part series paper discussed the development of a novel lightweight 3D wood dowel-reinforced glass epoxy hybrid composite material (3DdrFRP) and its manufacturing procedures. It also experimentally compared the performance of scaled utility poles made from conventional 2D E-glass epoxy and 3DdrFRP materials. In the second part, we developed robust finite element (FE) models in LS-DYNA, calibrated them based on experimental results, and used them to simulate the performance of equivalent 2D and 3D poles, proving the integrity of the numerical model. Additionally, we developed a simplified analytical calculation method for engi-neers to determine the stiffness of 3D-DrFRP poles accurately and quickly.

Understanding the infiltration and solidification processes of liquid 5083Al alloy into Al2O3 three-dimensional reticulated porous ceramic (Al2O3(3D) RPC) is essential for optimizing the microstructure and properties of Al2O3(3D)/5083Al interpenetrating phase composites (IPCs) prepared by low-pressure infiltration process (LPIP). This study employs ProCAST software to simulate the infiltration and solidification processes of liquid 5083Al with pouring velocities (PV) of 0.4 m/s infiltrating into Al2O3(3D) RPC preforms with varying porosities at different pouring temperatures (PT) to prepare Al2O3(3D)/5083Al IPCs using LPIP. The results demonstrate that pore diameter of Al2O3(3D) RPC preforms and PT of liquid 5083Al significantly influence the of the infiltration. Solidification process analysis reveals that the Al2O3(3D) RPC preform with smaller pore diameters allows the lower pouring velocity of 5083Al to solidify faster compared to the preform with larger pore diameters. Al2O3(3D)/5083Al IPCs were prepared successfully from Al2O3(3D) RPC porosity of 15 PPI with liquid 5083Al at PV 0.4 m/s and PT 800 °C using LPIP, resulting in nearly fully dense composites, where both Al2O3(3D) RPCs and 5083Al interpenetrate throughout the microstructure. The infiltration and solidification defects were reduced under air pressure of 0.3 MPa (corresponding to PV of 0.4 m/s) during LPIP. Finite volume method simulations are in good agreement with experimental data, validating the suitability of the simplified model for Al2O3(3D) RPCs in the infiltration simulation.

Chipless radio frequency identification (RFID) technology is expected to replace barcode technology due to its ability to read in non-line-of-sight (NLOS) situations, long reading range, and low cost. Currently, there is extensive research being conducted on frequency-coded (FC) co-polarized radar cross-section (RCS)-based tags, which are widely used. However, detecting co-polarized chipless RFID tags in cluttered environments is still a challenge, as confirmed by measuring two co-polarized tags in front of a perfect metal reflector (30.5cm×22.5cm). To address this challenge, a realistic mathematical model for a chipless RFID system has been developed that takes into account the characteristics of the reader and the tag, as well as reflections from cluttered objects. This model has been simulated and verified with measurement results by placing a single flat metal reflector behind two co-polarized one-bit designs: a dipole array tag and a square patch tag. The results showed that the interfering signal completely overlaps the ID of the co-polarized tag, severely limiting its detectability. To solve this issue, the proposed solution involves reading the tag in cross-polarization mode by etching a diagonal slot in the square patch tag. This proposed tag provides high immunity to the environment and can be detected in front of both dielectric and metallic objects.

Nitrile butadiene rubber (NBR) and its various composites are extensively studied as matrix materials for oil-free lubricated mechanical friction surfaces. While numerous macroscopic friction experiments can compare the frictional performance of different NBR composites, the complex material preparation and low experimental efficiency pose challenges. Nanoscale molecular dynamics (MD) simulations can help develop materials with improved friction properties, but they cannot directly provide the friction coefficients of actual macroscopic friction surfaces. Therefore, it is important to integrate the advantages of macroscopic and nanoscale friction studies and perform a synergistic analysis to modify the friction properties of composite materials. In this study, a multi-scale approach is proposed to simulate the frictional characteristics of carbon nanotube (CNT)-reinforced NBR by combining the MD friction simulation， micromechanical bridging model, and macroscopic finite element analysis (FEA) methods. The results of the multi-scale friction simulation of copper (Cu)-CNT/NBR composites show that the addition of CNTs significantly improves the frictional properties of NBR, and the extent of the improvement is related to the mass fraction of CNTs. As the mass fraction of CNTs increases (0%, 1.25%, 2.5%, 5%), the coefficient of friction decreases from 0.50 to 0.38. The results are validated by ring-block friction experiments at the same scale as the simulations.

The coronaviral pandemic has led to a shift in traditional teaching methods to more innovative approaches, such as high-fidelity patient simulation (HFPS), which can improve students’ clinical judgement and decision-making for quality patient care. A modified guideline was introduced to enhance students’ satisfaction and self-confidence in learning through HFPS. The study involved 189 baccalaureate nursing students, with 92 in the intervention group and 97 in the control group. The intervention group received the modified HFPS guideline, while the control group received standard treatment with basic instruction. After the HFPS debriefing session, students provided narrative feedback on their learning experience. The quantitative results showed that students in the intervention group reported a significant improvement in satisfaction and self-confidence in learning compared to the control group. The modified HFPS guideline provided clear guidance for students to learn and apply knowledge and skills more effectively, leading to increased engagement during interactive simulation sessions. The results suggest that the HFPS guideline should be added to the curriculum to enhance students’ satisfaction and self-confidence in learning, even in junior students. Innovative teaching methods, such as HFPS, can be necessary and beneficial for healthcare professional training, after the pandemic.

Elevated indoor CO2 levels might have adverse effects on human health. However, the introduction of outdoor air to lower indoor CO2 concentrations results in significant HVAC energy consumption. Aligning with office hours and the natural light cycle, the utilization of photosynthesis in living walls offers an energy-efficient and sustainable solution for the mitigation of high CO2 levels in office spaces. This study experimentally investigates the impacts of the carbon fixation pathways, light intensity, and substrate moisture content on the CO2 removal rate of living walls at the room scale. Furthermore, the fresh air energy-saving effects of living walls under different scenarios are accurately simulated in EnergyPlus. The results demonstrate that choosing C3 plants over CAM plants in living walls yields higher CO2 removal efficiency. In a 30-m2 office room accommodating 2-3 occupants, living walls can reduce the demand for fresh air by 12.3%-27.8% and decrease fresh air energy consumption by 11.2%-28.2%. The city with the highest energy savings has energy savings that are 4.5 times greater than those of the city with the lowest energy savings. The findings of this research promote the application and development of living walls, thus providing a viable solution for improving indoor air quality.

The paper presents the results of modeling the problem of injecting liquid sulfur dioxide into a porous reservoir initially saturated with methane and ice. The model presented in the paper assumes the formation of three different regions, namely, the near one, saturated with liquid SO2 and its hydrate; the far one, containing methane and ice, and the intermediate one, saturated with methane and water. The effects of various parameters of the porous medium and injected SO2 on the nature of the course of the hydrate formation process have been studied. It is shown that with a decrease in reservoir permeability or injection pressure, the length of the intermediate region decreases, which in the limiting case means the formation of SO2 hydrate in the mode without the formation of an extended region saturated with methane and water. It is shown that such a regime is also typical for the case of high initial injection pressures, as well as low values of the initial reservoir temperature and injection temperature.

Copper indium gallium diselenide, Cu(In,Ga)Se2, thin-film of about 1.2 μm has been deposited on Mo coated soda lime glass by thermal co-evaporation of Cu, In, Ga, and Se sources. The thin film CIGS was characterized by energy dispersive spectroscopy (EDS) and identified the elemental composition of Cu, In, Ga and Se in the film. A CIGS device was completed by the successive deposition of multiple layers; SLG/Mo/CIGS/CdS/ZnO/ITO which yielded the efficiency of 11%. External spectroscopic ellipsometry (ex-situ SE) was performed on the completed device and the device analysis was performed. Starting with the parameterized complex dielectric functions of the individual component layers, SE analysis was performed using a step-wise procedure that ranks the fitting parameters according to their ability to reduce the mean square error (MSE) of the fit. The resulting layer thicknesses and dielectric functions were used to simulate the external quantum efficiency (EQE) of the device assuming complete active layer collection. Electronic losses were identified by comparison of simulated EQE with the measured EQE. The ultimate goal of this work is the optimization of narrow band-gap CIGS-related solar cells for the bottom layer in tandem devices.

The problem of optimal scheduling in a system with parallel queues and a single server has been extensively studied in the queueing theory. However, such systems have mostly been studied by assuming homogeneous attributes of arrival and service processes, or in heterogeneous case a Markov random process was usually assumed. In this paper, we explore the possibility of combining simulation and neural network paradigms for optimal scheduling in a heterogeneous system with an arbitrary inter-arrival and service time distributions. The service of any queue by the server is exhaustive. Further routing of the server after the queue has been emptied is as instructed by the trained neural network. Any change in the queue that currently has a server is accompanied by a switching cost. The simulated annealing optimization algorithm which is used to optimize the parameters of the neural network was adapted for numerically evaluated average cost function. A Markov decision problem was formulated for the corresponding markovian queueing system to verify the results of solving the optimization problem. In addition to comparing the actual values of the average cost, the parameter values of the neural network trained on the optimal control policy of the Markov model are also used to determine the domains of parameters required for the simulated annealing defined on a finite discrete domain. Numerical analysis shows the effectiveness of this approach. Moreover, a comparison of the results for different distributions illustrates the insensitivity of the optimal control policy to the shape of the distributions for the same two first moments. Thus, the optimal control policy for an exponential model can be used as a suboptimal one for the general system.

Advanced manufacturing techniques, aimed at implants with high dependability, flexibility, and low manufacturing costs, are crucial in meeting the growing demand for high-quality products like biomedical implants. Incremental sheet forming is a promising flexible manufacturing approach for rapidly prototyping sheet metal components using low-cost tools. Titanium and its alloys are used to shape most biomedical implants because of its superior mechanical qualities, biocompatibility, low weight, and great structural strength. The poor formability of titanium sheets at room temperature, however, limits their widespread use. The goal of this research is to show that gradual sheet formation of a titanium biomedical implant is possible. The possibility of creative and cost-effective concepts for the manufacturing of such complicated shapes with significant wall angles is explored in this study. A numerical simulation based on finite element modeling as well as a design process tailored to metal forming is used to complete the development. The mean of uniaxial tensile tests with a constant strain rate was used to study the flow behavior of the studied material. To forecast the crack, the obtained flow behavior was modeled using the behavior model and failure model.

The construction field uses up over one-third of the global energy consumption and contribute to 40% of CO2 emissions according to the International Energy Agency (IEA) and the 2020 annual reporter of United Nation, Goal 11 (Make cities inclusive, safe, resilient and sustainable) which discusses sustainable, safe and efficient buildings. Therefore, Morocco has a commitment to this program by publishing the law 47-09 of energy efficiency. This work aims to study the energy efficiency of two types of building, a conventional and a natural building. Conventional building is constructed using concrete, while the natural one uses sand clay and straws. As for the technique of making the natural building, it perpetually follows the same approach accustomed in rural zones of Atlas Mountains in Morocco. In this research we also simulate, temperature and humidity variation inside these buildings using TRNSYS software. Sketch Up software was employed to design these houses. The weather database is used for a typical meteorological year (TMY). In the case of natural building, many building configurations were simulated: roof insulation, floor insulation, different types of glazing and sun protection. What's more, the thermal comfort is revealed to be more conspicuous in the case of natural building.

Fibre reinforced concrete (FRC) has become an alternative for structural applications due its outstanding mechanical properties. The appearance of new types of fibres and the fibre cocktails that can be configured mixing them has created FRC that clearly exceed the minimum mechanical properties required in the standards. Consequently, in order to take full advantage of the contribution of the fibres in construction projects, it is of great interest to have constitutive models that simulate the behaviour of the materials. This study aimed to simulate the fracture behaviour of five types of FRC, three with steel hooked fibres, one with a combination of two types of steel fibres and one with a combination of polyolefin fibres and two types of steel fibres, by means of an inverse analysis based on the cohesive crack approach. The results of the numerical simulations defined the softening functions of each FRC formulation and have pointed out the synergies that are created through use of fibre cocktails. The information obtained might suppose a remarkable advance for designers using high-performance FRC in structural elements.

Electrochemical-mechanical interactions, in particular pressure-induced ones, have been identified to be a cause for lithium-plating in lithium-ion cells. Mechanically-induced porosity inhomogeneities in the separator layers due to electrode expansion during charging especially lead to cell internal balancing currents and can cause localized plating. To identify cell-format and cell-material dependent mechanical weak spots, a layer-resolved mechanical simulation of different cell types and cell-material combinations is presented in this work. The simulation results show distinctive layer strain patterns for different cell-types that coincide with localized lithium-plating found in post-mortem cells. Additionally, the effects of cell bracing in battery modules is investigated and a method to mitigate the increased layer strain due to bracing counterforces is proposed that also increases cell energy density for hardcase-type automotive cells.

The rising world population has made it imperative to get rid of time-consuming and non-economical agricultural practices. Thus, advances in Internet of Things (IoT) technology have recently propelled significant advancements in agriculture, similar to several other sectors. Through the combination of IoT and Farm Management Information Systems (FMIS), field data can be automatically collected, stored, analysed and accessed by farmers in real-time. In this paper, we present Proof-of-Concept (PoC) IoT circuit and FMIS web app for precision agriculture. The IoT circuit incorporates sensors, microcontroller and Wi-Fi module, which acquire and transfer field data to the FMIS web app. We designed and simulated the IoT circuit using Proteus, Arduino Uno, and Arduino Integrated Development Environment (IDE). The FMIS PoC web app was modelled with relevant Unified Modeling Language (UML) diagrams and Django (a Python framework) was employed to implement the app. Our simulation result illustrates how essentila field parameters can be monitored remotely and seamlessly by agriculture practitioners. It further provides a blueprint for real-time precision agriculture at scale and could serve as a learning aid for students in engineering and agricultural science.

The translocator protein (TSPO) is a transmembrane protein present in the three domains of life. Its functional quaternary structure consists of one or more subunits. In mouse, the dimer-to-monomer equilibrium is shifted in vitro towards the monomer by adding cholesterol, a natural component of mammalian membranes. Here, we present a coarse-grained molecular dynamics study on the mouse protein in the presence of a physiological content and of an excess of cholesterol. The latter turns out to weaken the interfaces of the dimer by clusterizing mostly at the inter-monomeric space and pushing the contact residues apart. It also increases the compactness and the rigidity of the monomer. These two factors might play a role for the experimentally observed incremented stability of the monomeric form with increased content of cholesterol. Comparison with simulations on bacterial proteins suggests that the effect of cholesterol is much less pronounced for the latter than for the mouse protein.

Development of rovers and development of infrastructure which enables them to probe other planets (such as Mars) have sparked a lot of interest recently specially with increasing public attention in Moon and Mars program by National Aeronautics and Space Administration. This is designed to be achieved by various means such as advanced spectroscopy and artificial intelligent techniques such as deep learning and transfer learning to enable the rover to not only map the surface of planet but to get a detailed information about its chemical makeup in layers beneath (deep learning) and in areas around point of observation (transfer learning). In this work, which is part of a proposal, later approach is explored. A systematic strategy is presented which make use of aforementioned techniques developed for metallic glass matrix composites as benchmark and helps develop algorithms for chemistry mapping of actual Martian surface on Perseverance Rover launching shortly.

SARS-CoV2 RNA depended RNA polymerase is an essential enzyme for the survival of the virus in hosts as it helps in the replication of viral RNA. There are no human polymerases that share either sequence or structural homology with viral RNA depended RNA polymerase. These make it a good target for inhibitor discovery, as a specific inhibitor cannot cross-react with the human polymerases. We have used virtual screening, docking, binding energy calculation and simulation to show that valproic acid Co-A, a metabolite from prodrug valproic acid, forms stable interaction with nsP12 of CoV. Our results suggest valproic acid Co-A could be a potential inhibitor of nsP12 of SARS-CoV2.

Histone Lysine Specific Demethylase 1 (LSD1) is overexpressed in many cancers and become a new target for anticancer drugs. In recent years, the small molecule inhibitors with various structures targeting LSD1 have been reported. Here we report the binding interaction modes of a series of thieno[3,2-b]pyrrole-5-carboxamides LSD1 inhibitors using molecular docking, three dimensional quantitative structure-activity relationship (3D-QSAR). Comparative molecular field analysis (CoMFA q²=0.783, r²=0.944, r²_pred=0.851) and Comparative molecular similarity indices analysis (CoMSIA q²=0.728, r²=0.982, r²_pred=0.814) were used to establish 3D-QSAR models, which had good verification and prediction capabilities. Based on the contour maps and the information of molecular docking, 8 novel small molecules were designed in silico, among which compounds D4, D5 and D8 with high predictive activity were subjected to further molecular dynamics simulations (MD), and their possible binding modes were explored. It was found that Asn535 plays a crucial role in stabilizing the inhibitors. Furthermore, the ADME and bioavailability prediction for D4, D5 and D8 were carried out. The results would provide valuable guidance for designing new reversible LSD1 inhibitors in the future.

The details of antigen-antibody interactions and the identification of epitopes are critical for the development of monoclonal antibody drugs. Ab42 is a native human-derived anti-CFH monoclonal antibody. In this study, the interaction between antigen pCFH and antibody (Ab42) was theoretically demonstrated by molecular docking and MD simulation, combined with free energy calculation and computational alanine scanning (CAS), and key amino acids and epitopes were identified. Experimental alanine scanning (EAS) was then carried out to verify the results of the calculation, and our results indicated that Ab42 antibody forms hydrogen bonds and interacts hydrophobically with pCFH through the Tyr315, Ser100, Gly33, and Tyr53 residues on its CDR, while the main pCFH epitopes are located at the six sites of Pro441, Ile442, Asp443, Asn444, Ile447, and Thr448. In conclusion, this study has explored the mechanism of antigen-antibody interaction from both theoretical and experimental aspects, and our results have important theoretical significance for the design and development of relevant antibody drugs.

Although, promotion of urbanization culture in recent decades has made inevitable development of cities in the world, however, the development can be guided in a direction that leave, to the extent possible, minimum socioeconomic and environmental impacts. For this, it is required to first forecast auto-spreading orientation of cities and suburbs in rural areas over time and then avoid shapeless growth of cities. This paper is an attempt to develop a dynamic hybrid model based on logistic regression (LR), Markov chain (MC), and cellular automata (CA) for prediction of future urban sprawl in fast-growing cities. The model was developed using 12 widely-used urban development criteria, whose significant coefficient was determined by logistic regression, and validated by relative operating characteristic (ROC) analysis. The validated model was run in Guilan, a tourist province in northern Iran with a very high rate of urban development. For this, changes in the area of urban land use were detected over the period of 1989 to 2013 and then, future sprawl of the province was forecasted by the years 2025 and 2037. The analysis results revealed that the area of urban land use was increased by more than 1.7 % from 36012.5 ha in 1989 to 59754.8 ha in 2013, and the area of Caspian Hyrcanian forestland was reduced by 31628 ha. The results also predicted an alarming increase in the rate of urban development in the province by the years 2025 and 2037, during which urban land use is predicted to develop 0.9 % and 1.38 %, respectively. The development pattern is expected to be uneven and scattered, without following any particular direction. The development will occur close to the existing or newly-formed urban basements as well as around major roads and commercial areas. This development, if not controlled, will lead to the loss of 13863 ha of Hyrcanian forests and if the trend continues, 21013 ha of Hyrcanian forests and 20208 ha of Barren/open lands are expected to be destroyed by the year 2037. In general, the proposed model is an efficient tool for the support of urban planning decisions and facilitates the process of sustainable development of cities by providing decision-makers with an overview on future development of cities where the growth rate is very fast.

Global wastes accumulate at an accelerating rate especially food and slaughterhouse waste constituting a major proportion owing to inefficient management. The high protein concentration of fish waste and keratinaceous byproducts is hazardous, harbouring pathogens, and emitting toxins. Transforming proteinaceous waste into amino acids (AAs) reduces waste bulk and offers a smarter nutrient approach for agriculture. However, this resource remains underutilized triggering eco-system pollution, diseases spreading, and climate change. Currently, small quantities of proteinaceous waste are valorized as animal feed, fertilizer, and in medical, and biotechnological sectors, which cannot solve bulk waste accumulation further, natural AA sources in agriculture are less recognized meanwhile researchers focus on synthetic AAs. Introducing various plant-nutrient formulas enriching fish AA with keratinaceous waste hydrolysate has not been broadly discussed. With this novel approach, using proteinaceous waste resources in agriculture would solve environmental pollution while minimizing land degradation by synthetic fertilizer. In this review, we illustrate the potential and benefits of valorizing proteinaceous waste in bulk through bioconversion. More importantly, we spotlight the biocompatibility of natural AAs and propose an enrichment, to use the ultimate product as catalysts for crops. Consequently, this approach reduces hazardous waste while paving a paradigm change in agriculture.

Oxidoreductase (OXR) enzymes are in high demand for biocatalytic applications in food industry and cosmetics: glucose oxidase (GOx), cellobiose dehydrogenase (CBDH), bioremediation: horseradish peroxidase (HRP), laccase (LAC), and medicine for biosensors and miniature biofuel cells (GOx, CBDH, LAC, HRP). They can be used in a soluble form and/or within the yeast cell walls expressed as chimeras on the surface of yeast cells (YSD), such as P. pastoris and S. cerevisiae. However, most of the current studies suffer from either low yield for soluble enzyme expression or low enzyme activity when expressed as chimeric proteins using YSD. This is always the case in studies dealing with heterologous expression of oxidoreductase enzymes, since there is requirement not only for multiple OXR genes integrations into the yeast genome (super transformations), and codon optimization, but also very careful design of fermentation media composition, and fermentation conditions during expression due to need for adding transition metals (copper, iron), and metabolic precursors of FAD and heme. Therefore, scientists are still trying to find optimal formula using above mentioned approaches, and most recently also using protein engineering and directed evolution for additional increase in the yield of recombinant enzyme production. In this review article we will cover all the current state of the art technologies and most recent advances in the field that yielded high expression level for some of these enzymes in specially designed expression/fermentation systems. We will also tackle and discuss new possibilities for further increase in fermentation yield by the cutting-edge technologies such are directed evolution, protein and strain engineering, high-throughput screening methods based on in vitro compartmentalization, flow cytometry and microfluidics.

Implementing dietary screening tools into clinical practice has been challenging, including in Nigeria. This study evaluated the impact of the Nigerian dietary screening tool (NiDST) on patient-clinician communication and barriers to and facilitators of implementation. Mixed methods approach was used to collect data from patients (n=151) and clinicians (n=20) from outpatient clinics in Nigeria. Patients completed the validated 25-item NiDST prior to outpatient consultations. Both patients and clinicians completed the Measurement Instrument for Determinants of Innovations (MIDI) questionnaire to assess implementation determinants post-consultation. Semi-structured interviews were conducted for in-depth feedback. Fidelity of implementation was 92% for NiDST-reported dietary discussion, with a mean completion time of < 6 minutes and an accepted marginal increase in consultation time (< 10 mins). For clinicians, 25% reported time constraints and their additional nutritional knowledge as barriers, while facilitators of NiDST implementation were the clarity and completeness of the NiDST, clinical relevance and improved patient-clinician communication, as reported by all the clinicians. Over 96% of patients reported the NiDST as quick to complete, with 90.7% reporting self-reflection on dietary intake. This study demonstrated the NiDST's potential to enhance patient-clinician communication and highlighted major facilitators of implementation in clinical practice to improve dietary discussion in Nigeria.

In this paper, we investigate the Green measure for a class of non-Gaussian processes in $\mathbb{R}^{d}$. These measures are associated with the family of generalized grey Brownian motions $B_{\beta,\alpha}$, $0

The widespread use of hydrophilic intraocular lenses (IOL) in eye surgery, fabricated by poly-2- (hydroxyethylmethacrylic acid) (PHEMA), has highlighted their calcification as a serious problem, which implies their surgical explantation a rather risky process. The field of biomaterials has been developing rapidly in recent years with research interest turning to the development of novel ma-terials which involve either copolymers of PHEMA or protective functional coatings. Graphene coatings are particularly attractive because of the respective unique properties. In the present work we present the results of the investigation of the development of graphene coatings on hydrophilic IOLs and their subsequent performance with respect to calcification opacification. Hydrophilic IOL with water content of 18% by weight, were coated with graphene oxide (GO) by equilibration with GO suspensions in water. The concentrations of the suspensions ranged 1x10-4 to 20x10-4 % w/v. The GO suspensions were equilibrated with the IOLs for 5 days at constant temperature, 37°C, rotated in 30 mL tubes end over end. This treatment, resulted in the formation of a uniform coating of GO on the IOLs verified by scanning electron microscopy (SEM) and other physicochemical methods. The contact angle of the GO coated IOLs decreased significantly in comparison with the uncoated. The GO-coated IOLs exhibited higher tendency to calcify in supersaturated solutions simulating aqueous humor (SAH). The growth rate of hydroxyapatite (Ca5(PO4)3OH, HAP) on GO-coated IOLs was higher, in comparison with the respective untreated IOLs. The conversion of the GO coating by reduction with phenyl hydrazine resulted in the formation of reduced graphene (rGO) surface film, identified by Raman and XPS spectroscopy. of the rGO film was hydrophobic (contact angle 100 o) and did not calcify in supersaturated calcium phosphate solutions.

Adipose stem cells (ASCs) have multilineage differentiation capacity and hold great potential for regenerative medicine. Compared to bone-marrow-derived mesenchymal stem cells (bmMSCs), ASCs are easier to isolate from abundant sources with significantly higher yields. It is generally accepted that bmMSCs show age-related changes in their proliferation and differentiation potentials, whereas this aspect is still controversial in the case of ASCs. In this review, we evaluated the existing data on the effect of donor age on the osteogenic potential of human ASCs. Overall, a poor agreement has been achieved because of inconsistent findings in the previous studies. Finally, we attempted to delineate the possible reasons behind the lack of agreements reported in the literature. ASCs represent a heterogeneous cell population and the osteogenic potential of ASCs can be influenced by donor-related factors such as age, but also gender, lifestyle, and the underlying health and metabolic state of donors. Furthermore, future studies should consider experimental factors in in-vitro conditions, including passaging, cryopreservation, culture conditions, variations in differentiation protocols, and readout methods.

Transposable elements (TEs) are repetitive elements which make up around 45% of the human genome. A class of TEs known as SINE-VNTR-Alu (SVA) demonstrate the capacity to mobilise throughout the genome, resulting in SVA polymorphisms for presence or absence within the population. Although studies have previously highlighted the involvement of TEs within neurodegenerative diseases, such as Parkinson’s disease and amyotrophic lateral sclerosis (ALS), however the exact mechanism has yet to be identified. In this study we used whole genome sequencing and RNA sequencing data of ALS patients and healthy controls from the New York Genome Center ALS Consortium, to elucidate the influence of reference SVA elements on gene expression genome-wide within central nervous system (CNS) tissues. To investigate this, we applied matrix expression quantitative trait loci analysis and demonstrated that reference SVA insertion polymorphisms can significantly modulate the expression of numerous genes, preferentially in the trans position, and in a tissue-specific manner. We also highlight that SVAs significantly regulate mitochondrial genes as well as genes within the HLA and MAPT loci, previously associated within neurodegenerative disease. In conclusion, this study continues to bring to light the effects of polymorphic SVAs on gene regulation and further highlights the importance of TEs within disease pathology.

Peptide-based opioid ligands are important candidates for the development of novel, safer, and more effective analgesics to treat pain. To develop peptide-based safer analgesics, we synthesized a mixture-based cyclic pentapeptide library containing a total of 24,624 pentapeptides and screened the mixture-based library samples using a 55 °C warm-water tail-withdrawal assay. Using this phenotypic screening approach, we deconvoluted the mixture-based samples to identify a novel cyclic peptide Tyr-[D-Lys-Dap(Ant)-Thr-Gly] (CycloAnt), which produced dose- and time-dependent antinociception with an ED50 (and 95% confidence interval) of 0.70 (0.52-0.97) mg/kg i.p. mediated by the mu-opioid receptor (MOR). Additionally, higher doses (≥ 3 mg/kg, i.p.) of CycloAnt antagonized delta-opioid receptors (DOR) at least 3 hours. Pharmacological characterization of CycloAnt showed the cyclic peptide did not reduce breathing rate in mice at doses up to 15 times the analgesic ED50 value, and dramatically less hyperlocomotion than the MOR agonist, morphine. While chronic administration of CycloAnt resulted in antinociceptive tolerance, it was without opioid-induced hyperalgesia and significantly reduced signs of naloxone-precipitated withdrawal suggestive of reduced physical dependence as compared to morphine. Collectively, the results suggest this dual MOR/DOR multifunctional ligand is an excellent lead for the development of peptide-based safer analgesics.

More and more people quantify their sleep using wearables and are getting obsessed in their pursuit of optimal sleep (“orthosomnia”). However, it is criticized that many of these wearables are giving inaccurate feedback and even can lead to negative daytime consequences. Acknowledging these facts, we here aim to extend previous findings [] in a new sample of 136 self-reported poor sleepers by implementing optimization procedures to minimize erroneous classification when ambulatory sensing sleep. Firstly, here, we introduce an advanced interbeat-interval (IBI) quality control using a random forest method to account for wearable recordings in naturalistic and more noisy settings. We further aim to improve sleep classification by opting for a loss function model instead of overall epoch-by-epoch accuracy to avoid model biases towards the majority class (i.e., “light sleep”). Using these implementations, we compare the classification performance between the optimized (loss function model) and the accuracy model. We use signals derived from PSG, one-channel ECG, and two consumer wearables: the ECG breast belt Polar® H10 (H10) and the Polar® Verity Sense (VS), an optical Photoplethysmography (PPG) heart-rate sensor. Results reveal high overall accuracy for the new model for ECG (86.3 %, κ=.79), H10 (84.4%, κ=.76), and VS (84.2%, κ=.75) with intended improvements in deep sleep and wake. In addition, the new optimized model displays moderate to high correlations and agreement with PSG on primary sleep parameters, while measures of reliability, expressed in intra-class correlations, suggest excellent reliability for most sleep parameters. Finally, it is demonstrated that the new model is still classifying sleep accurately in 4-classes in users taking heart-affecting and/or psychoactive medication which can be considered a prerequisite for use also in high age groups and/or with common disorders. Further improving and validating sleep stage classification algorithms with affordable wearables may resolve existing scepticism and open the door for such approaches in clinical practice.

Water deficit greatly affects global crop growth and productivity, particularly in water-limited environments like upland rice cultivation, leading to reduced grain yield. Plants activate various defense mechanisms during water deficit, involving numerous genes and complex metabolic pathways. Exploring orthologous genes, linked to enhanced drought tolerance, can aid functional validation in target species using genomic data from model organisms. Here, we studied the OsCPK5 gene in upland rice, an AtCPK6 ortholog from A. thaliana, by overexpressing it in the BRSMG Curinga cultivar. Transformants were assessed under two conditions: water deficit applied 79 days after seeding, lasting 14 days, followed by 7 days of irrigation at 80% field capacity. Physiological data and samples were collected at R3, R6, and R8 stages. GM plants consistently exhibited higher OsCPK5 gene expression across stages, peaking during grain filling. During this phase, GM plants displayed reduced stomatal conductance, photosynthetic rate, and increased water use efficiency compared to non-GM plants under drought. GM plants also exhibited higher filled grain percentage in both irrigation conditions. Their drought susceptibility index was 0.9 times lower than NGM plants, and they maintained a higher chlorophyll a/b index, indicating sustained photosynthesis. NGM plants under water deficit exhibited more leaf senescence, while OsCPK5-overexpressing plants retained green leaves. Overall, OsCPK5 overexpression induced diverse drought tolerance mechanisms, hinting at potential for future development of more drought-tolerant rice cultivars.

Soluble cell adhesion molecules (sCAMs) are secreted ectodomain fragments of surface adhesion molecules, ICAM1 and VCAM1. sCAMs have diverse immune functions beyond their primary function, impacting immune cell recruitment and activation. Elevated sVCAM1 levels have been associated with poor cardiovascular disease (CVD) outcomes, supporting its role as a potential diagnostic marker and therapeutic target. Inhibiting sVCAM1 release or interaction with immune cells could offer cardioprotection in conditions such as diabetes. Membrane bound surface adhesion molecules are widely expressed in a wide variety of cell types with higher expression in endothelial cells (ECs), but the source of sCAMs in the circulation is not clear. Hypothesizing that endothelial cells (ECs) could be a potential source of sCAMs, this study investigated whether dysfunctional EC signaling mechanisms during diabetes cause VCAM1 ectodomain shedding. Our results from samples from a inducible diabetic mouse model revealed increased sVCAM1 plasma levels in diabetes. Protein analysis indicated upregulated VCAM1 expression and metalloproteases ADAM10 and ADAM17 in diabetic ECs. ADAMs are known for proteolytic cleavage of adhesion molecules, contributing to inflammation. GSK3β, implicated in EC VCAM1 expression, was found to be activated in diabetic ECs. GSK3β overexpression in control ECs increased ADAM10/17 and VCAM1. A GSK3β inhibitor reduced active GSK3β and VCAM1 ectodomain shedding. These findings suggest that diabetic ECs with elevated GSK3β activity led to VCAM1 upregulation and ADAM10/17-mediated sVCAM1 shedding. This mechanism underscores the potential therapeutic role of GSK3β inhibition in reducing the levels of circulating sVCAM1. The complex roles of sCAMs extend well beyond CVD. Thus, unraveling the intricate involvement of sCAMs in the initiation and progression of vascular disease particularly in diabetes, holds significant therapeutic potential.

An experiment was conducted in 2016 & 2017 for development of novel rice hybrids for aerobic ecology with lesser yield penalties in comparison to irrigated ecosystem, further with added advantage of reduced methane emissions and water budget as witnessed in irrigated system. Based on the restorer / maintainer reaction and spikelet fertility (%), 10 restorer lines were selected to cross with 3 CMS lines in the line by tester fashion in Yasangi (summer) season 2016-17. Resulting 30 experimental hybrids besides 13 parental lines (10 Restorer lines and 3 B - lines of akin CMS lines) and checks (GK 5022, CR Dhan 201) assessed during Vankalam (rainy) season 2017 at three different places/locations viz., Rajendranagar, Warangal and Kampasagar. The outcome of experiment were two experimental hybrids viz., APMS-6A × HRSV – 7 and IR-79156A × ATR – 372, were categorized as stable hybrids with desirable sca effects, heterosis (ranging from 7% to 13%) over best check GK 5022 along with an in-essence performance for yield and other yield attributing characters.

Vegetation classifications on large geographic scales are necessary to inform conservation decisions and monitor keystone, invasive, and endangered species. These classifications are often effectively achieved by applying models to imaging spectroscopy, a type of remote sensing, data, but such undertakings are often limited in spatial extent. Here we provide accurate, high-resolution spatial data on the keystone species Metrosideros polymorpha, a highly polymorphic tree species distributed across bioclimatic zones and environmental gradients on Hawai'i island, using airborne imaging spectroscopy and LiDAR. We compare two tree species classification techniques, support vector machine (SVM) and spectral mixture analysis (SMA), to assess their ability to map M. polymorpha over 28,000 square kilometers where differences in topography, background vegetation, sun angle relative to the aircraft, and day of data collection, among others, challenge accurate classification. To capture spatial variability in model performance, we applied gaussian process classification (GPC) to estimate the spatial probability density of M. polymorpha occurrence using only training sample locations. We found that, while SVM and SMA models exhibit similar raw score accuracy over the test set (96.0% and 93.4%, respectively), SVM better reproduces the spatial distribution of M. polymorpha than SMA. We developed a final 2 m x 2 m M. polymorpha presence dataset and a 30 m x 30 m M. polymorpha density dataset using SVM classifications that have been made publicly available for use in conservation applications. Accurate, large-scale species classifications are achievable, but metrics for model performance assessments must account for spatial variation of model accuracy.

Airway epithelial cells (AECs) play a key role in maintaining lung homeostasis, epithelium regeneration and the initiation of pulmonary immune responses. To isolate and study murine AECs investigators have classically used short and hot (1h 37°C) digestion protocols. Here, we present a workflow for efficient AECs isolation and culture, utilizing long and cold (20h 4°C) dispase II digestion of murine lungs. This protocol yields a greater number of viable AECs compared to an established 1h 37°C dispase II digestion. Using a combination of flow cytometry and immunofluorescent microscopy, we demonstrate that compared to the established method, the cold digestion allows for recovery of a 3-fold higher number of CD45-CD31-EpCAM+ cells from murine lungs. Their viability is increased compared to established protocols, they can be isolated in larger numbers by magnetic-activated cell sorting (MACS), and they result in greater numbers of KRT5+p63+ colonies in vitro. Our findings demonstrate that temperature and duration of murine lung enzymatic digestion have a considerable impact on AEC yield, viability, and ability to form colonies in vitro. We believe this workflow will be helpful for studying lung AECs and their role in the biology of lung.

Snow-covered regions are the main source of reflection of incident shortwave radiation (ISR) on Earth’s surface. The deposition of light-absorbing particles (LAPs) on these regions increases the capacity of snow to absorb ISR and decreases surface snow albedo (SSA), which intensifies the radiative forcing leading to accelerated snowmelt and modifications of the hydrologic cycle. In this work we investigate changes in SSA and radiative forcing (RF) induced by LAPs in the Upper Aconcagua River Basin (Chilean central Andes) using remote sensing satellite data (MODIS), in-situ spectral SSA measurements, and the ISR (Chilean Solar Explorer platform) during the austral-winter months (May to August) for the 2004-2016 period. To estimate the changes in SSA and RF, we define two spectral ranges: i) an enclosed range (Ecr) between 841-876 nm, which isolates effects of Black-Carbon, an important LAP derived from anthropogenic activities, and ii) a broadband range (Bbr) between 300-2500 nm. Our results show that percent variations in SSA in the Ecr are higher than in the Bbr, regardless of the total amount of radiation received, which may be attributed to the presence of LAPs as these particles have a greater impact on SSA at wavelengths in the Ecr band than in the Bbr band.

Turmeric, Curcuma longa L., is a type of medicinal plant characterized by its perennial nature and rhizomatous growth. It is a member of the Zingiberaceae family and is distributed across the world’s tropical and subtropical climates, especially in South Asia. Its rhizomes are highly valued for food supplements, spices, flavoring agents, and yellow dye in South Asia since ancient times. It exhibits a diverse array of therapeutic qualities that encompass its ability to combat diabetes, reduce inflammation, act as an antioxidant, exhibit anticancer properties, and promote anti-aging effects. In this study, organic extracts of C. longa rhizomes were subjected to HPLC separation followed by mass spectrometry analysis. The Global Natural Product Social Molecular Networking (GNPS) approach was utilized for the first time in this ethnobotanically important species to conduct an in-depth analysis of its metabolomes based on their fragments. A total of 30 metabolites including 16 diarylheptanoids, 1 diarylpentanoid, 3 bisabolocurcumin ethers, 4 sesquiterpenoids, 4 cinnamic acid derivatives, and 2 fatty acid derivatives were identified. Among 16 diarylheptanoids identified in this study, five of them are reported for the first time in this species.

The location of Israel at the junction of three continents leads to a unique faunal combination of Palearctic and Afrotropic zoogeographic origins. Following systematic revisions over the past sixty years and the discovery of new species, the only available key to the pseudoscorpions (Arachnida: Pseudoscorpiones) of Israel (Beier 1963) has become outdated. We provide here an up-to-date checklist of the pseudoscorpion species of Israel including distribution maps, and the first illustrated identification key of the Israeli fauna based on morphological characters. Prior to our study this fauna comprised twelve families, 26 genera and 52 morphospecies, including several “subspecies”. We increase this number and list 61 pseudoscorpion morphospecies that belong to 28 genera and fourteen families. Most species are Palearctic and Mediterranean, and only a few are Afrotropic. Two families new to Israel are reported here for the first time: Syarinidae and Cheiridiidae. Both families are cosmopolitan and have representatives in the Mediterranean region. The putative new species are presented here at a genus level and will be described separately elsewhere.

Research for novel vaccine adjuvants remains a critical need to improve the immune responses to a recombinant vaccine antigen. Emulsion adjuvants such as AS03 and MF59, particularly in the area of Influenza vaccines, have shown antigen dose sparing and allowed reduced immunizations. It was previously demonstrated that these emulsion adjuvants can be formulated using a simpler, low shear process of self-emulsification. The role of alpha tocopherol as an immunomodulator in emulsion adjuvants is evident from the success of AS03 in the on-going covid-19 pandemic. Although, it was a challenge to formulate alpha tocopherol with a low shear process to get closer to the AS03 composition, the self-emulsified version referred as self-emulsifying adjuvant systems (SE-AS) showed comparable immune responses to AS03 when co-administered with a Quadrivalent influenza virus (QIV) vaccine. In this paper, we first optimized the SE-AS with alpha tocopherol referred to as SE-AS44 to allow sterile filtration. We compared the in vitro cytokine profile with self-emulsifying adjuvant 160 (SEA160), a squalene-only self-emulsified adjuvant with composition similar to MF59. We compared SE-AS44 and SEA160 co-administered with a recombinant cytomegalovirus (CMV) pentamer antigen, which is a less immunogenic antigen, in vivo to compare the antibody and T-cell responses, in different adjuvanted groups, in C57BL/6 mice.

Renal clear cell carcinoma (ccRCC) comprises over 75% of all renal tumors and arises in the epithelial cells of the proximal convoluted tubule. Molecularly ccRCC is characterized by copy number alterations (CNAs) such as the loss of chromosome 3p and VHL inactivation. Additional driver mutations (SETD2, PBRM1, BAP1, and others) promote genomic instability and tumor cell metastasis through the dysregulation of various metabolic and immune-response pathways. Many researchers identified mutation, gene expression, and proteomic signatures for early diagnosis and prognostics for ccRCC. Despite a tremendous influx of data regarding DNA alterations, gene expression, and protein expression, the incorporation of these analyses for diagnosis and prognosis of RCC into the clinical application has not been implemented yet. In this review, we focused on the molecular changes associated with ccRCC development, along with gene expression and protein signatures, to emphasize the utilization of these molecular profiles in clinical practice. These findings, in the context of machine learning and precision medicine, may help to overcome some of the barriers encountered for implementing molecular profiles of tumors into the diagnosis and treatment of ccRCC.

Quantitative dynamic strain measurements of the ground would be useful for engineering scale problems such as monitoring for natural hazards, soil-structure interaction studies and non-invasive site investigation using full waveform inversion (FWI). Distributed Acoustic Sensing (DAS), a promising technology for these purposes, needs to be better understood in terms of its directional sensitivity, spatial position, and amplitude for application to engineering-scale problems. This study investigates whether the physical measurements made using DAS are consistent with the theoretical reception patterns and experimental measurements of ground strain made by geophones. Results show that DAS and geophone measurements are consistent in both phase and amplitude for broadband (10s of Hz), high amplitude (10s of microstrain) and complex wavefields originating from different positions around the array when: (1) the DAS channels and geophone locations are properly aligned, (2) the DAS cable provides good deformation coupling to the internal optical fiber, (3) the cable is coupled to the ground through direct burial and compaction, and (4) laser phase noise is mitigated in the DAS measurements. The theoretical relationship between DAS-measured and point-wise strain for vertical and horizontal active sources is introduced using 3D elastic finite-difference simulations. The implications of using DAS strain measurements are discussed including directionality and magnitude differences between the actual and DAS-measured strain fields. A method for spatially aligning the DAS channels with the geophone locations at tolerances less than the spatial resolution of the DAS system is proposed.

Background: Conclusions of meta-analyses of clinical studies may substantially influence opinions of perspective patients and stakeholders in health care. Nineteen meta-analyses of clinical studies on the management of primary knee osteoarthritis (pkOA) with stem cells, published between January 2020 and July 2021, came to inconsistent conclusions regarding the efficacy of this treatment modality. It is possible that a separate meta-analysis based on an independent, systematic assessment of clinical studies on the management of pkOA with stem cells may reach a different conclusion. Methods: PubMed, Web of Science and the Cochrane library were systematically searched for clinical studies and meta-analyses of clinical studies on the management of pkOA with stem cells. All clinical studies and meta-analyses identified were evaluated in detail, as were all sub-analyses included in the meta-analyses. Results: The inconsistent conclusions regarding the efficacy of treating pkOA with stem cells in the 19 assessed meta-analyses were most probably based on substantial differences in literature search strategies among different authors, misconceptions about meta-analyses themselves, and misconceptions about the biology of stem cells. An independent, systematic review of the literature yielded a total of 183 studies, of which 33 were randomized clinical trials, including a total of 6860 patients with pkOA. However, it was not possible to perform a scientifically sound meta-analysis. Conclusion: Clinicians should interpret the results of the 19 assessed meta-analyses of clinical studies on the management of pkOA with stem cells with caution, and should be cautious of the conclusions drawn therein. Clinicians and researchers should strive to participate in FDA and/or EMA reviewed and approved clinical trials to provide clinically and statistically valid efficacy.

Possible alterations of maternal immune function due to psychological stress may reflect immunoactive factor levels in breast milk. This study aimed to assess the association between maternal distress and breast milk levels of secretory IgA (SIgA), IgM, IgG, and lactoferrin (LF). We hypothesized this association is moderated by maternal social support achieved from others during lactation. The study group included 103 lactating mothers and their healthy 5-months-old infants. Maternal distress was determined based on the State Anxiety Inventory and the level of salivary cortisol. Social support was assessed using Berlin Social Support Scales. Breast milk samples were collected to test for SIgA, IgM, IgG, and LF using the ELISA method. Milk immunoactive factors were regressed against maternal anxiety, social support, salivary cortisol, and infant gestational age using the general regression model. Maternal anxiety was negatively associated with milk levels of LF (β=-0.22, p<0.05) and SIgA (β=-0.29, p<0.01), while social support was positively associated with milk IgG (β=0.25, p<0.05). Neither anxiety nor social support was related to milk IgM. No association was found between the level of maternal salivary cortisol and immunoactive factors in milk. Our results suggest that maternal psychological well-being and social support may affect milk immune properties.

Some emergent SARS-CoV-2 variants raise concerns due to their altered biological properties. For both B.1.1.7 and B.1351 variants, named as variants of concern (VOC), increased transmissibility was reported, whereas B.1.351 was more resistant to multiple monoclonal antibodies (mAbs), as well as convalescent and vaccination sera. To test this hypothesis, we examined the proportion of VOC over time across different geographic areas where the two VOC, B.1.1.7 and B.1.351, co-circulate. Our comparative analysis was based on the number of SARS-CoV-2 sequences on GISAID database. We report that B.1.1.7 dominates over B.1.351 in geographic areas where both variants co-circulate and the B.1.1.7 was the first variant introduced in the population. The only areas where B.1.351 was detected at higher proportion were South Africa and Mayotte in Africa, where this strain was associated with increased community transmission before the detection of B.1.1.7. The dominance of B.1.1.7 over B.1.351 could be important since B.1.351 was more resistant to certain mAbs, as well as heterologous convalescent and vaccination sera, thus suggesting that it may be transmitted more effectively in people with pre-existing immunity to other VOC. This scenario would lessen the effectiveness of vaccine and urge the need to update them with new strains.

The work focused on the development of high-temperature electrical insulation coatings for film photovoltaics. The idea was into replacing the electroconductive metal dispersed phase in siloxane high-temperature coating to ceramic particles with phonon thermal conductivity. The slurry of industrial composition based on polysiloxane lacquer and thermally conductive paste containing zinc oxide was centrifuged to obtain a thin, optically transparent coating with the destruction temperature of over 600 °C. Topology, electrical properties, and thermal conductivity of the resulting film were investigated. The mathematical model of thermal processes in films in the course of heating was figured out. Quantitatively the relation of thermal conductivities of a control sample and a sample with a heat-conducting filler was established. The effectiveness of using this technology is shown.

The impact of metals bioaccumulation on marine organisms is under investigation. This study was designed to determine the association of oxidative stress in mussels Mytilus galloprovincialis induced by seawater enriched with trace metals with protein synthesis. Mussels were exposed to 40 μg/L Cu, 30 μg/L Hg, or 100 μg/L Cd for 5 and 15 days, and the pollution effect was evaluated by measuring established oxidative biomarkers. The results showed damage on the protein synthesis machine integrity and specifically, on translation factors and ribosomal proteins expression and modifications. Exposure of mussels to all metals caused oxidative damage that was milder in the cases of Cu and Hg, and more pronounced for Cd. However, after prolonged exposure of mussels to Cd (15 days), the effects receded. These changes that perturb protein biosynthesis can serve as a great tool for elucidating the mechanisms of toxicity and could be integrated in biomonitoring programs.

Wireless Sensor Networks (WSN) are the core of Internet of Things and require cryptographic protection due to the increase number of attacks. Cryptographic methods for WSN should be fast and consume low power as these networks consist of battery-powered devices and constrained microcontrollers. NTRU, the fastest and secure public key cryptosystem, uses high degree polynomials, and is susceptible to the lattice basis reduction attack (LBRA). CPKC, proposed by NTRU authors, works on integers modulo $q$ and is easily attackable by LBRA since it uses small numbers for the sake of the correct decryption. Herein, RCPKC, a random congruential public key cryptosystem working on integers modulo $q$ is proposed, such that the norm of a two-dimensional vector formed by its private key is greater than $\sqrt{q}$. RCPKC works similar to NTRU, and it is a secure version of insecure CPKC. RCPKC specifies a range from which the random numbers shall be selected, and it provides correct decryption for valid users and incorrect decryption for an attacker using LBRA by Gaussian lattice reduction. Because of its resistance to LBRA, RCPKC is more secure. Simultaneously, due to the use of big numbers instead of high degree polynomials, RCPKC is about 24 (7) times faster in encryption (decryption) than NTRU. Also, RCPKC is more three times faster than the most effective known NTRU variant, BQTRU. Compared to NTRU, RCPKC reduces energy consumption at least seven times that allows increasing life-time of unattended WSN more than seven times.

Baggage overweight is a significant phenomenon in the air travel in- dustry which is both regulated by the governments and highly profitable for the airlines. The recent years increase in airlines' profits is attributed to so-called ancillary payments – all consumer costs excluding the actual transportation fare. Baggage overweight fees, being a part of such pay- ments, have high profit-generation potential. Is the pricing for baggage overweight positioned correctly? The rationality of policies currently used by airlines is questionable, as they lack a customer-oriented marketing ap- proach. The general purpose of this research is to explore the possibilities of rational baggage overweight pricing for airlines by developing a multi- level price optimization model aimed to maximize company revenue. The modeling is based on determining consumer Willingness-To- Pay values for baggage overweight, and providing an optimal ticket price for them to maximize company expected revenue. The total profit value is forecasted too, based on the company market share and revenue level.

There are different treatment options in modern dentistry for the replacement of lost dentition. Of these the most upcoming and acceptable treatment option is Dental implants. The common problem usually with immediate implant placement in the anterior region is the post-operative soft tissue contour as a part of the bone modelling during healing. Hurzeler et al in 2010 introduced a new technique called the “socket shield technique”. This technique has been used as an alternative treatment modality for immediate implant placement in the aesthetic zone.This review articles provides a detailed information regarding the clinical concept of Root membrane technique.

This paper describes a novel methodology of data management in materials characterisation, which has as starting point the creation and usage of Data Management Plan (DMP) for scientific data in the field of materials science and engineering, followed by the development and exploitation of ontologies for the harnessing of data created through experimental techniques. The case study that is discussed here is nanoindentation, a widely used method for the determination and/or modelling of mechanical properties on a small scale.The same methodology can be applicable to a large number of techniques that produce big amount of raw data, while at the same time it can be invaluable tool for big data analysis and for the creation of an open innovation environment, where data can be accessed freely and efficiently.Aspects covered include the taxonomy and curation of data, the creation of ontology and classification about characterization techniques, the harnessing of data in open innovation environments via database construction along with the retrieval of information via algorithms. The issues of harmonization and standardization of such novel approaches are also critically discussed.Finally, the possible implications for nanomaterial design and the potential industrial impact of the new approach are described and a critical outlook is given.

Background: The present study compared corticospinal excitability to the biceps brachii muscle during arm cycling at a self-selected and a fixed cadence (SSC and FC, respectively). We hypothesized that corticospinal excitability would not be different between the two conditions. Methods: The SSC was initially performed and the cycling cadence was recorded every 5 seconds for one minute. The average cadence of the SSC cycling trial was then used as a target for FC of cycling that the participants were instructed to maintain. Motor evoked potentials (MEPs) elicited via transcranial magnetic stimulation (TMS) of the motor cortex were recorded from the biceps brachii during each trial of SSC and FC arm cycling. Results: Corticospinal excitability as assessed via normalized MEP amplitudes (MEPs were made relative to a maximal compound muscle action potential) were not different between groups. Conclusions: Focusing on maintaining a FC cadence during arm cycling does not influence corticospinal excitability as assessed via TMS-evoked MEPs.

Molecular dynamics(MD) simulations are playing an increasingly important role in structure-based drug discovery (SBDD). Here we review the use of MD for proteins in aqueous solvation, organic/aqueous mixed solvents (MDmix) and with small ligands, to the classic SBDD problems: binding mode and binding free energy predictions. The simulation of proteins in their condensed state reveals the solvent structure and preferential interaction sites (hot spots) on the protein surface. This information is largely transferable across all classes of protein ligands (from water to drugs) and can be used very effectively to understand ligand recognition and improve the predictive capability of well-established methods such as molecular docking. MD simulations for protein and drug or drug-like compounds are now being used but are still computationally expensive and can only be applied to specific cases. On the other hand, MDmix simulations can now be used in SBDD and we will describe the latest developments and implementations. We expect to see an increase in the application of these techniques to a plethora of protein targets to identify new drug candidates with the advent of new tools and faster computers.

The oil and gas industry faces increasing pressure to remove people from dangerous offshore environments. Robots present a cost-effective and safe method for inspection, repair and maintenance of topside and marine offshore infrastructure. In this work, we introduce a new immobile multi-sensing robot, the Limpet, which is designed to be low-cost and highly manufacturable, and thus can be deployed in huge collectives for monitoring offshore platforms. The Limpet can be considered an instrument, where in abstract terms, an instrument is a device that transforms a physical variable of interest (measurand) into a form that is suitable for recording (measurement). The Limpet is designed to be part of the ORCA (Offshore Robotics for Certification of Assets) Hub System, which consists of the offshore assets and all the robots (UAVs, drones, mobile legged robots etc.) interacting with them. The Limpet comprises the sensing aspect of the ORCA Hub System. We integrated the Limpet with Robot Operating System (ROS), which allows it to interact with other robots in the ORCA Hub System. In this work, we demonstrate how the Limpet can be used to achieve real-time condition monitoring for offshore structures, by combining remote sensing with signal processing techniques. We show an example of this approach for monitoring offshore wind turbines. We demonstrate the use of four different communication systems (WiFi, serial, LoRa and optical communication) for the condition monitoring process. By processing the sensor data on-board, we reduce the information density of our transmissions, which allows us to substitute short-range high-bandwidth communication systems with low-bandwidth long-range communication systems. We train our classifier offline and transfer its parameters to the Limpet for online classification, where it makes an autonomous decision based on the condition of the monitored structure.

In this study, we evaluated chlorophyll fluorescence (CF) under two extreme illumination conditions at plant scale with a passive spectroradiometer. Fluorescence (F) was estimated by reading directly from radiance spectra of a variety of plants illuminated with light-emitting diode (LED) grow lights in the laboratory. Solar-induced fluorescence (SIF) was estimated from spectral measurements of the same plants under sunlight using the Fraunhofer Line Depth (FLD) method. Chlorophyll fluorescence yield (F_yield) and solar-induced fluorescence yield (SIF_yield) were calculated by normalizing F and SIF with absorbed photosynthetically active radiation (APAR). Two approaches to estimating APAR were compared: utilizing white reference spectra and reflected spectra versus white reference spectra combined with the fraction of absorbed photosynthetically active radiation (fPAR) derived from literature. Average F and SIF were different by a factor of approximately twenty-four (F = 0.110 ± 0.038 Wm⁻²μm⁻¹sr⁻¹ versus SIF = 2.60 ± 1.87 Wm⁻²μm⁻¹sr⁻¹). In contrast, the average normalized values F_yield and SIF_yield were within the margin of error of one another (F_yield = 0.022 ± 0.008 μm⁻¹sr⁻¹ and SIF_yield = 0.030 ± 0.020 μm⁻¹sr⁻¹). This study highlights the influence of APAR on CF and the importance of properly accounting for it when estimating yield and demonstrates the ability of two simple and portable experimental setups with a passive instrument to obtain fluorescence metrics.

Within the genus Macrolophus (Heteroptera: Miridae), the species M. costalis (Fieber), M. melanotoma (Costa) and M. pygmaeus (Rambur) are present in the Mediterranean region on a wide variety of plant species. While M. costalis can easily be separated from the other two by the black tip at the scutellum, M. pygmaeus and M. melanotoma are cryptic species, extremely similar to one another in external traits, which have resulted in misidentifications. M. pygmaeus is an efficient biological control agent, both in greenhouse and field crops. The misidentification of these cryptic species could limit the effectiveness of biological control programs. Although morphology of the left paramere of the male genitalia has been used as a character for identification of these two cryptic species, there is controversy on the reliability of this character as a taxonomic tool for these species. Using geometric morphometric techniques, which are a powerful approach in detecting slight shape variations, the left parameres from these three Macrolophus species were compared. The paramere of M. costalis was larger and had a different shape than that of M. melanotoma and M. pygmaeus; however, no differences in size or shape were found between the left paramere of M. melanotoma and that of M. pygmaeus. Therefore, our results confirm that this character is too similar and it cannot be used to discriminate between these two cryptic species.

Substituted bis(iminophosphoranyl)methanes are CH acidic compounds that can form complexes with formally dianionic central carbon centres. The reaction of H₂C(Ph₂P=NDip)₂ (≡ H₂L), Dip = 2,6-diisopropylphenyl, with one equivalent of di-n-butylmagnesium afforded the methanide complex [HLMgnBu] 1. Treatment of complex 1 with phenylsilane in aromatic solvents at elevated temperatures afforded the methanediide complex [(LMg)₂] 2 presumably via the MgH intermediate [(HLMgH)_n] (n = 1 or 2). The reaction of 1 with LiAlH₄ in diethyl ether yielded the AlH complex [HLAlH₂] 3. Alternatively, this complex was also obtained from the reaction of H₂L with AlH₃∙NMe₃. The molecular structures of [HLMgnBu] 1, [(LMg)₂] 2, and [HLAlH₂] 3 are reported. Complex 3 shows no sign of H₂ elimination to a methanediide species at elevated temperatures in contrast to the facile elimination of the putative reaction intermediate [(HLMgH)_n] (n = 1 or 2) to form [(LMg)₂] 2. The chemical properties of complex 2 were investigated and this complex appears to be stable against coordination with strong donor molecules.

Pulmonary hypertension (PH) is a serious condition linked to immune-system dysfunction. Myositis-specific/associated antibodies (MSA/MAA) play a role in idiopathic inflammatory myopathy (IIM) and interstitial lung disease (ILD), but their significance in PH remains unclear. We believe the presence of these antibodies, may be underestimated. This study analyzed adult PH patients without pre-existing IIM for MSA/MAA prevalence using a line-blot assay. We compared PH patients with and without ILD signs to a cohort clinically suspected of IIM/ILD (n=558). Our PH cohort (n=121) showed a significantly higher prevalence of overall weak positive MSA/MAAs and positive overlap syndrome-associated MAAs than the suspected IIM/ILD group (p

The transcriptional response of human blood leukocytes to SARS-CoV-2 infection was investigated focusing on the differences between mild and severe cases and between age subgroups. Weighted gene co-expression network analysis and comparative gene expression analysis were used. Three transcriptional modules positively associated with the traits of interest and their respective high hierarchy genes were identified. Enrichment analyses showed that the yellow module, associated with severe cases and older patients, had an overrepresentation of genes involved in inflammatory and innate immune responses, and neutrophil activation. The magenta and black modules, associated with disease severity and younger patients, contained genes related to innate immunity and inflammation and genes that regulate those responses. Subnetworks for these modules were constructed using genes enriched for innate immunity, inflammation, immunoregulation and differentially expressed genes (severe vs. mild). Their analysis evidenced that immunoregulatory functions are more activated in the modules associated with younger patients, what may help to explain the better disease course and faster recovery observed in younger COVID-19 patients. Comparative gene expression analysis between severe and mild groups, followed by gene enrichment and normalized gene expression analyses, revelated a set of 23 potential biomarkers for COVID-19 severity, of which 13 are newly described.

The irregular timing and spatial variation in zoonotic arbovirus spillover from vertebrate hosts to humans and livestock present challenges to predicting their occurrence from year to year and within their broader geographic range, compromising effective prevention and control strategies. The objective of this study was to quantify effects of landscape composition and configuration and dynamic temperature and precipitation values on the 2018 spatiotemporal distribution of eastern equine encephalitis virus (EEEV) (Togaviridae, Alphavirus) and West Nile virus (WNV) (Flaviviridae, Flavivirus) sentinel chicken seroconversion in northeastern Florida using Earth Observation (EO) data and a modeling framework that incorporated joint spatial and temporal effects. We investigated environmental effects using Bernoulli generalized linear mixed effects models (GLMMs) including a site level random effect, and then added spatial random effects and spatiotemporal random effects in subsequent runs. Models were executed using integrated nested Laplace approximation (INLA) and a stochastic partial differential equation (SPDE) approach in R-INLA. GLMMs that included a spatiotemporal random effect performed better relative to models that included only spatial random effects and better than non-spatial models. Results indicated strong spatiotemporal structure in seroconversion for both viruses, but EEEV exhibited more punctuated and compact structure at the beginning of the sampling season, while WNV exhibited more gradual and diffuse structure across the study area toward the end of the sampling season. Percentage of cypress/tupelo wetland land cover within 3500 m of coop sites and edge density of forest land cover within 500 m had a strong positive effect on EEEV seroconversion, while the best fitting model for WNV was the intercept only model with spatiotemporal random effects. Lagged temperature and precipitation variables included in our study did not have a strong effect on seroconversion for either virus when accounting for temporal autocorrelation, demonstrating the utility of capturing this structure to avoid Type I errors. Predictive accuracy on out-of-sample data for EEEV seroconversion demonstrates the potential to develop a temporally dynamic framework to predict arbovirus transmission.

Currently available COVID-19 mRNA vaccines have demonstrated high efficacy in clinical trials.1-3 However, cancer patients, including those with hematological malignancies, were largely excluded from these trials. In this prospective, observational study we measured anti-S protein IgG titers as well as avidity in lymphoma patients (n=67) vaccinated with a COVID-19 mRNA vaccine. Serological response rates in lymphoma patients who were treatment naïve (100% in CLL, 88.9% in other, non-CLL non-Hodgkin lymphoma patients), or who were last treated more than 24 months prior to vaccination (100% in CLL, 90% in other-NHL), were similar to healthy controls (100%). Patients on active therapy, however, had a diminished response rate (40% in CLL, 21.0% in other-NHL). No patient who received anti-CD20 monoclonal antibodies (mAb) within six months of vaccination responded. Thus, the utility of testing anti-S titers should be explored in patients on active therapy or with recent anti-CD20 mAb exposure, to assess their response to vaccination. We also propose studying passive protection with S-protein mAbs as an alternative prophylactic strategy for patients who respond poorly to vaccination.

Despite the many advantages of lung ultrasound (LUS) in the diagnosis and management of patients with dyspnea, adoption among hospitalists has been slow. We performed semi-structured interviews of hospitalists from 4 diverse health systems in the US to understand determinants of adoption within a range of clinical settings. We used the Diffusion of Innovation Theory to guide a framework analysis of the data. Of 27 hospitalists invited, we performed in-terviews of 22 from 4 hospitals of diverse types. Median years post-residency of interviewees was 10.5 [IQR:5-15]. Four main themes emerged: 1) There are important clinical advantages to LUS despite operator dependence, 2) LUS enhances patient and clinician experience, 3) Investment of clinician time to learn and perform LUS is a barrier to adoption but yields improved efficiency for the health system and 4) Mandated training and use may be necessary to achieve broad adoption as monetary incentives are less effective. Despite perceived benefits of LUS for patients, clinicians and health systems, an important barrier to broad LUS adoption is the experience of time scarcity by hospitalists. Future implementation strategies should focus on changes to the clinical environment that address clinician barriers to learning and adoption of new skills.

Meta-heuristic algorithms are methods that try to solve problems by imitating events and systems existing in nature. It can be very difficult to solve nonlinear problems with the help of known classical algorithms. In this study, a new meta-heuristic method, the Pi algorithm, is proposed. Pi is a special number that is not periodic and contains many different number combinations in its digits calculated so far. The most important part of meta-heuristic methods is the equations and parameters used to ensure the diversity of the created population. In this study, a specific Pi coefficient for each attribute was calculated with the help of the Monte-Carlo method. These coefficients were used in the Pi algorithm in other parts of the study. In the application for data clustering, experiments were made on 5 different data sets. The best accuracy rate of 95.5% was achieved. In this respect, it has been seen that the Pi algorithm works successfully as a meta-heuristic method. It can be studied on a population-based data clustering, classification, prediction, etc. It can be easily applied in all areas.

Developing new antibiotics poses a significant challenge in the fight against antimicrobial resistance (AMR), a critical global health threat responsible for approximately 5 million deaths annually. Finding new classes of antibiotics that are safe, have acceptable pharmacokinetic properties, and are appropriately active against pathogens is a lengthy and expensive process. Therefore, high-throughput platforms are needed to screen large libraries of synthetic and natural compounds. In this review, we present bacterial cytological profiling (BCP) as a rapid, scalable, and cost-effective method for identifying the mechanisms of action of antibiotics, offering a promising tool for combating AMR and drug discovery. We present the application of BCP for different bacterial organisms and different classes of antibiotics and discuss BCP's advantages, limitations, and potential improvements. Furthermore, we highlight the studies that have utilized BCP to investigate pathogens listed in the Bacterial Priority Pathogens List 2024 and we identify the pathogens whose cytological profiles are missing. Lastly, we explore the most recent artificial intelligence and deep learning techniques that could enhance the analysis of data generated by BCP, potentially advancing our understanding of antibiotic resistance mechanisms and the discovery of novel druggable pathways.

In this paper, aiming at the safety evaluation of ultimate bearing safety capacity of composite pile bridge foundation, the symmetrical reliability method with forward and inverse analysis con-sidering the randomness of parameters is proposed to evaluate the safety performance of com-posite pile foundation. The proposed method, considering the randomness of parameters, was used to calculate the safety factor of ultimate bearing capacity of composite pile foundation to meet the prescribed target reliability requirements. Through the example calculation of the safety coefficient of pile foundation, the results show that the statistical characteristics of random vari-ables that affect the safety coefficient of pile foundation have a significant impact. The degree of the safety coefficient depends on the probability distribution type and distribution parameters of the random parameters, among which the more important is the variation characteristics of the random variables. The stronger the parameter variability, the lower the safety level of the struc-ture. During design and evaluation, it is necessary to measure the safety reserve of pile founda-tions in accordance with engineering practice to ensure the safe construction and well operation of the pile.

Chip pad alignment inspection is of great importance in the industrial field. However, due to the fact that chip pads are usually small, problems such as misdetection and missed detection often occur. When applying deep learning methods for chip pad detection, it is necessary to ensure accurate detection of small target chips while meeting the requirements of lightweight detection models for industrial needs. To solve the above problems, this paper proposes a lightweight model based on improved YOLOv5s. Firstly, the feature extraction part is improved to increase the network's focus on the target. Secondly, the feature fusion layer is improved to double the resolution of the prediction head, and the context-aware network is designed to enhance the context-capture ability of key features of small targets. Finally, SIoU is adopted as the loss function to improve the speed and accuracy of the regression frame. The experimental results show that the improved YOLOv5s algorithm improves the detection accuracy by 2.3% and reduces the network parameters by 81.8% compared to the original algorithm. The improved algorithm is combined with image processing techniques to design correction methods for alignment anomalies and realize real-time alignment anomaly correction in industry.

This work presents the study of a reproducible acoustic emission method based on the launching of a metallic sphere and low-cost piezoelectric diaphragm. For this purpose, tests were first conducted on a carbon fiber-reinforced polymer structure, and then on an aluminum structure for comparative analysis. The Pencil Lead Break (PLB) tests were also conducted for comparisons with the proposed method. Different launching heights and elastic deformations of the structures were investigated. The results show higher repeatability for the sphere impact method, as the PLB is more affected by human inaccuracy, and it was also effective in damage detection.

In recent years, artificial intelligence technology has seen increasingly widespread application in the field of intelligent manufacturing, particularly with deep learning offering novel methods for recognizing geometric shapes with specific features. In traditional CNC machining, computer-aided manufacturing (CAM) typically generates G-code for specific machine tools based on existing models. However, the tool paths for most CNC machines consist of a series of collinear motion commands (G01), often resulting in discontinuities in the curvature of adjacent tool paths, leading to machining defects. To address these issues, this paper proposes a method for CNC system machining trajectory feature recognition and path optimization based on intelligent agents. This method employs intelligent agents to construct models, analyze the key geometric information in the G-code generated during CNC machining, and uses the MCRL deep learning model incorporating linear attention mechanisms and multiple neural networks for recognition and classification. Path optimization is then carried out using mean filtering, Bezier curve fitting, and an improved novel adaptive coati optimization algorithm (NACOA) according to the degree of unsmoothness of the path. Taking the optimization of a gear model’s process file as an example, the effectiveness of the proposed method is verified. The research results indicate that the CNC system machining trajectory feature recognition and path optimization method based on intelligent agents can significantly enhance the smoothness of CNC machining paths, reduce machining defects, and has substantial application value.

Induction motors are the most numerous and significant dynamic loads in power systems. In transient voltage stability analysis, dynamic equivalent models are commonly used to simplify the representation of a group of induction motors. This paper presents an method for the grouping and aggregation of induction motors at a common bus. Firstly, grouping rules were provided for clustering induction motors into several subgroups based on the mechanical principles of rotor force and motion, and Aggregation Rules were provided for aggregating a motor subgroup into a single-unit model based on the relationship between voltage drop and power transmission in distribution networks. Secondly, guided by the grouping rules, high-speed remaining electromagnetic torque and low-speed remaining electromagnetic torque were defined as new clustering indicators, and an adaptive K-means clustering method using silhouette coefficient verification was provided to obtain the optimal motor subgroups. Thirdly, guided by the Aggregation Rules, a dynamic equivalent method was further provided to obtain the equivalent single-unit model from a motor subgroup. Lastly, a transient voltage stability simulation in a typical distribution network was presented to illustrate that the proposed clustering and equivalent methods are more reasonable, accurate, and effective than traditional methods, as the obtained model has better dynamic characteristics and can more accurately reproduce the process of voltage collapse.

This paper presents machine learning methods for approximate solutions of reaction-diffusion equations with multivalued interaction functions. This approach addresses the challenge of finding all possible solutions for such equations, which often lack uniqueness. The proposed method utilizes physics-informed neural networks (PINNs) to approximate generalized solutions.

In order to reduce the environmental impact of energy enterprises, it is important to promptly identify cases of possible changes in the quality of wastewater generated at power plants, that is cases of exceeding the maximum permissible concentrations of pollutants in wastewater. The goal of the method for detecting complex anomalies in multidimensional time series obtained from smart energy station’s sensor channels is to increase the accuracy of detecting pollution levels in industrial wastewater. To achieve this goal, the following tasks were solved: methods for detecting time series anomalies were analyzed, the method for detecting complex anomalies was developed, software implementation of the algorithm was carried out and experiments were conducted. The developed method is proposed to be used in an smart energy monitoring system.

Vibration problems have become one of the most important factors affecting robot performance. To this end, a terminal residual vibration suppression method based on joint trajectory optimization is proposed to improve the accuracy and stability of robot motion. Firstly, based on the characteristics of the friction nonlinearity due to joint coupling and physical feasibility of dynamic parameters, a semidefinite programming method is used to identify dynamic parameters with actual physical meaning, thereby obtaining an accurate dynamic model. Then, based on the result of the residual vibration time domain analysis, a joint trajectory optimization model with the goal of minimizing joint tracking error is established. The Chebyshev collocation method is used to discretize the optimization model. The dynamic model is used as the optimization constraint, and barycentric interpolation is used to obtain the optimized joint motion trajectory. Finally, industrial robot experiments prove that the vibration suppression method proposed in this article can reduce the maximum acceleration amplitude of residual vibration by 62% and the vibration duration by 71%. Compared with the input shaping method, the method proposed in this paper can reduce the terminal residual vibration more effectively and ensure the consistency of running time and trajectory.

This research is concentrated on the physical technique of carbon activation from carbon-based material such as bamboo. In addition, we impregnated the activated carbon by use of experimental methods. In the first case, the results from various parameters on the carbon activation process, such as kiln rotation speed, steaming time, calcination temperature and heating rate at the different levels has been investigated on iodine number of activated carbon. Using the Taguchi method and ANOVA analysis, the optimal test results were obtained which are as follows: steaming time and calcination temperature were the most influential factors and heating rate and kiln rotation speed were the less influential factors in the process. In the second case, palladium-impregnated activated carbon (Pa/AC) was prepared at different conditions of the experiment and effects of optimum ratios on the responses have been studied.

Vehicular ad hoc networks (VANET) are a subset of mobile ad hoc networks used to communicate between vehicles and infrastructure. During vehicular congestion communication, nodes compete to acquire channels, causing the channels to become congested. The congestion on the vehicle network results in increased delay and packet loss, resulting in reduced VANET performance. To address this problem, we developed a load-aware and priority adaptive traffic congestion control method in vehicular ad hoc networks (VANETs). The proposed scheme identifies less-congested road segments based on the network's load and reduces traffic congestion by suggesting other routes between nearby roadside units (RSUs). This research aims to improve the efficiency of the vehicular environment by utilizing the movement of vehicles with (RSUs) and sharing the traffic load between them. Simulation results demonstrate the effectiveness of the proposed protocol in reducing congestion and enhancing the overall performance of VANETs. To validate the proposed algorithm, we have implemented and tested the proposed algorithm using Network Simulator 3 (NS3) for Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication scenarios and computed the performance of the algorithm on different parameters of the network. The simulation result of the proposed load aware and priority adaptive traffic congestion control method in VANET improved the packet delivery ratio, packet lost ratio, and end-to-end delay by 96%, 4.1%, and 1102 milliseconds, when compared to TDCCA value of 92%, 5.7%, and 1154 milliseconds, respectively

This paper proposes a methodology for the development and its Verification and Validation(V&amp;V) of safety-rated software. The development methodology is based on the structured analysis and structured design methodology, which has been proven in the field of safety software development during long period. The suggested the development and the V&amp;V methodology additionally focuses on the quality attributes 3C+T (completeness, Correctness, Consistency and traceability), which are important criteria in IEC international standards. The software development life cycle is compiled from IEC international standardization, which are of the requirements analysis phase, design phase, implementation phase, testing phase, installation/commissioning phase, and operation &amp; maintenance phases. The activities of the development process method were defined from requirements analysis to implementation, and the activities of the verification and validation were defined to include the scope of the developer's activities and then to include the test phase, installation/commissioning phase, and operation phases additionally. In other words, testing phase performed by the developer was defined as informal testing, and testing performed by the verifier according to test criteria, test plans, and test procedures was defined as formal testing. According to proposed methodology, the requirements phase defines the sequence of Entity Relationship Diagram (ERD)-Context Diagram-Data Flow Diagram (DFD), functional decomposition process and defines operational scenario. In the design phase, module design such as task analysis and task allocation were defined. In the implementation phase, we listed the coding guidelines that must be followed for safety-related system. The test phase defined the test criteria, test plan, test procedure, and test result report activities. In the installation/commissioning and operation phases, recommendations for improving reliability, quality, and safety were proposed for deliverables such as user documentation and technical documentation.

Berberine (BBR), as a natural isoquinoline alkaloid, has been demonstrated various pharmacological activities, and widely applied in the treatment of diseases. The quantitative analysis of BBR is important for its pharmacological researches and clinical applications. In the work, utilizing the specific interaction between BBR and triplex DNA, a sensitive and selective fluorescent detecting method was established with DNA-templated silver nanoclusters (DNA-AgNCs). After binding with the triplex structure in the template of DNA-AgNCs, BBR quenched the fluorescence of DNA-AgNCs and formed BBR-triplex complex with yellow-green fluorescence. The ratiometric fluorescence signal showed a linear relationship with BBR concentration in a range between 10 nM to 1000 nM, with a detect limit of 10 nM. Our method exhibited excellent sensitivity and selectivity, and it was further applied in BBR detection in real samples.

The design flood estimation should consider the regulation of upstream cascade reservoirs which has significantly altered downstream hydrologic regime. The current flood regional composition (FRC) methods do not consider the unfavorable reservoir operation conditions for flood prevention. In this paper, a novel framework, the most unfavorable flood regional composition (MUFRC) method, was proposed based on the flood risk analysis to estimate design flood in cascade reservoir operation period. The results of case study in Yalong River basin indicated that: (1) The proposed MUFRC method would allocate more flood volume into downstream uncontrolled sub-basin, and the precise definition of flood loss could make great difference on MUFRC method for rational estimation of design flood. (2) The 1000-year design flood peak, 3-day and 7-day flood volume of the outlet section estimated by MUFRC method are 15400 m3/s, 3.91, and 8.42 billion m3 respectively, which are all greater than values estimated by other FRC methods; (3) The reduction of design floods in reservoir operation period result the adjustment of flood control water level in downstream reservoirs, which could additionally generate 460 million kW·h (+1.82%) hydropower during flood season. Comparison study and sensitivity analysis further proves that the MUFRC method could rationally allocate flood volume and meanwhile balance the flood prevention risk and comprehensive utilization benefits, which is worth for further study and practical application.

Fluorescence microscopic images of cells contain a large number of morphological features that are used as an unbiased source of quantitative information about cell status, through which researchers can extract quantitative information about cells and study the biological phenomena of cells through statistical and analytical analysis. As an important research object of phenotypic analysis, images have a great influence on the research results. Saturation artifacts present in the image result in a loss of grayscale information that does not reveal the true value of fluorescence intensity. From the perspective of data post-processing, we propose a two-stage cell image recovery model based on generative adversarial network to solve the problem of phenotypic feature loss caused by saturation artifacts. The model is capable of restoring large areas of missing phenotypic features. In the experiment, we adopt the strategy of progressive restore to improve the robustness of the training effect, and add the contextual attention structure to enhance the stability of the restore effect. We hope to use deep learning methods to mitigate the effects of saturation artifacts to reveal how chemical, genetic, and environmental factors affect cell state, providing an effective tool for studying the field of biological variability and improving image quality in analysis.

Demand for environmentally friendly types of energy is growing all over the world, which naturally increases the intensity of work on the production of hydrogen-containing fuel mixtures. Steam reforming of methanol is one of the most effective areas of research, as it is a process with low energy consumption. The results of steam reforming of methanol on the catalysts prepared by solution combustion synthesis (SCS) and impregnation by moisture capacity are presented. Investigation of the activity of Cu-Ce-Al catalysts of different composition was carried out for production of hydrogen and comparison of SCS with traditional impregnation method. Methanol conversion reaches 99%, while the selectivity of H2 was 88%, at 500 °C. It was found that CuAl2O4 spinel formation is due to substitution of Al3+ with Cu2+ Ce3+ cations. A consequence CuAl2O4 and CeAlO3 lattice parameters increase because of difference in ionic radius of Al3+ (0.53 Å) and Cu2+ (0.73 Å), Ce3+ (1.07 Å). Advantages of SCS catalysts in steam reforming of methanol were shown. The aim of this research is to develop a new catalyst for conversion of methanol into hydrogen containing fuel mixtures, which will further promote the organization of a new era of environmentally friendly energy-saving production methods.