The development of hybrid rockets offers excellent opportunities for the practical education of students at universities due to a high safety and a relatively low complexity of the rocket propulsion system. During the German educational program STERN, students of the Technische Universität Braunschweig obtain the possibility to design and launch a sounding rocket with a hybrid engine. The design of engine HYDRA 4X is presented and results of first engine tests are discussed. Results for measured regression rates are compared to results from literature. Furthermore, the impact of the lightweight casing material CFRP on the hybrid engine mass and flight apogee altitude is examined for rockets with different total impulse classes (10 to 50 kNs). It is shown that the benefit of a lightweight casing material on engine mass decreases with an increasing total impulse. However, a higher gain on apogee altitude, especially for bigger rockets with a comparable high total impulse is shown.

Rice blast and brown planthopper (BPH) are two of the most destructive diseases and pests in rice (Oryza sativa L.) production in China. The development of multi-resistant varieties is widely recognized as the most efficient and environmentally friendly approach to controlling crop diseases and pests. Functional molecular markers (FMMs) have been developed from functional variants in the genic region associated with trait variation, greatly enhancing the efficiency of identifying and pyramiding valuable genes in crop breeding. In this study, two FMMs and a multiplex PCR amplification system were developed for two major broad-spectrum BPH resistance genes, Bph6 and Bph9. By assistance of FMMs in multi-resistant rice restorer line development pipeline, two lines (Huahui7713 and Huahui3006) with blast and BPH resistance were developed by pyramiding three resistance genes Pigm, Bph6 and Bph9. Huahui7713 and Huahui3006 as male lines, two new hybrid rice varieties (WeiLiangYou7713 and Xuanliangyou3006) were certificated and commercial released in China. WeiLiangYou7713 and Xuanliangyou3006 showed improved blast and BPH resistance, and no penalty on yield and grain-quality. Annual promotion area of WeiLiangYou7713 has exceeded 53.3 thousand hectares, indicating its widespread adoption and success in the market. Huahui7713 and Huahui3006 could have great potential for utilization in blast and BPH resistance rice breeding.

To achieve low-cost and on-demand launches of micro satellites, the authors have been researching and developing a micro hybrid rocket since 2014. In 2018, a ballistic launch experiment was performed using the developed hybrid rocket, where it reached an altitude of about 6.2 km. The rocket engine had a 3D-printed solid fuel grain made of acrylonitrile butadiene styrene (ABS) resin in combination with a nitrous-oxide oxidizer. The fuel grain port had a star-fractal swirl geometry in order to increase the surface area of the port, to promote the laminar-turbulent transition by increasing the friction resistance, and to give a swirling velocity component to the oxidizer flow. This overcame the hybrid rocket’s drawback of low fuel regression rate; i.e., it achieved a higher fuel-gas generation rate compared with a classical port geometry. In 2021, the hybrid rocket engine was scaled up and its total impulse was increased to over 50 kNs for reaching an altitude of 15 km. In addition to the engine, other components were also improved, such as through incorporation of lightweight structures, low-shock separation devices, a high-reliability telemetry device and data logger, while keeping costs low. The rocket was launched and reached an altitude of about 10.1 km, which broke the previous Japanese altitude record of 8.3 km for hybrid rockets. The presentation will report on the developed components from the viewpoint of system design and the results of the ballistic launch experiments.

Autonomous aircraft are the key enablers of future urban services, such as postal and transportation systems. Digital Twins (DT) are promising cutting-edge technologies that can transform the future transport ecosystem into an autonomous and resilient system. However, since DT is a data-driven solution based on AI, proper data management is essential in implementing DT as a Service (DTaaS). One of the challenges in DT development is the availability of real-life data, particularly for training algorithms and verifying the functionality of DT. The current article focuses on data augmentation through synthetic data generation. This approach can facilitate the development of DT in case the developers do not have enough data to train the Machine Learning (ML) algorithm. The current twinning approach provides a prospective ideal state of the engine used for proactive monitoring of the engine’s health as an anomaly detection service. In line with the track of Unmanned Aircraft Vehicles (UAV) for Urban Air Mobility in Smart Cities applications, this paper focuses specifically on the common Hybrid turbo-shaft in drones/helicopters. However, there is a significant gap in real-life similar synthetic data generation in the UAV domain literature. Therefore, Rolling Linear Regression and Kalman Filter algorithms were implemented on noise-added data, which simulate the data measured from the engine in a real-life operational lifecycle. For both thermal and hybrid models, the corresponding DT model has shown high efficiency in noise filtration and a certain amount of predictions with a lower error rate on all engine parameters, except the engine torque.

This study investigates the influence of adding hydrogen as an additive to gasoline in a four-stroke engine, utilizing a comprehensive thermodynamic comparative analysis conducted with self-developed engine model. The research aims to assess the performance, emissions, and efficiency of the engine when using gasoline-hydrogen blends, and to provide insights into the potential benefits of this approach. First the engine performance and emissions under different hydrogen blending levels were examined. A range of different air-to-fuel ratios (rich to lean) and varying percentages of hydrogen were considered. This systematic variation allowed for a detailed evaluation of the influence of hydrogen content on combustion efficiency, power out-put, and emissions characteristics. The analysis results included key parameters such as indicated specific fuel consumption and mean effective pressure. Additionally, the study focused on range prediction of nitrogen oxides (NOx) emissions, which are a critical environmental concern asso-ciated with internal combustion engines. The analysis of pressure and temperature profiles throughout the engine cycle shed light on the combustion characteristics and efficiency im-provements associated with hydrogen addition. In terms of emissions, the study projected that all emissions were reduced except NOx, which is highly dependent on hydrogen percentage, and it might be reduced in some cases, but with higher temperatures and pressures associated with hydrogen addition in most cases there is actually NOx increase, especially at higher engine loads.

Hybrid power plants have recently emerged as reliable and flexible electricity generation stations by combining multiple renewable energy sources, energy storage systems (ESS), and fossil-based output. However, the effective operation of the hybrid power plants to ensure continuous energy dispatch under challenging conditions is a complex task. This paper proposes a dispatch engine (DE) based on mixed-integer linear programming (MILP) for the planning and management of hybrid power plants. To maintain the committed electricity output, the dispatch engine will provide schedules for operation over extended time periods as well as monitor and reschedule the operation in real-time. Through precise prediction of the PV and wind power output, the proposed approach guarantees optimum scheduling. The precise prediction of the PV and wind power are achieved by employing a predictor of Feed-Forward Neural Network (FFNN) type. With such a dispatch engine, the operational cost of the hybrid power plants, and the use of diesel generators (DGs) are both minimized. A case study is carried out to assess the proposed dispatch engine feasibility. A real-time measurement data pertaining to load, wind, and PV power outputs is obtained from different locations in the Sultanate of Oman. The real-time data is utilized to predict the future output power from PV and the Wind farm over 24 hours. The predicted powers are then used in combination with a PV-Wind-DG-ESS-Grid hybrid plant to evaluate the performance of the proposed dispatch engine. The proposed approach is implemented and simulated using MATLAB. The results of the simulation reveal the proposed Feed-Forward Neural Network’s (FFNN) powerful forecasting abilities. In addition, the results demonstrate that adopting the proposed DE can minimize the use of DG units and reduce plant running expenses.

This study aims to examine the non-volatile Particle Number (PN) emissions of a retrofitted Heavy-Duty Spark-Ignition (HD-SI) engine powered by liquefied petroleum gas (LPG) under both steady-state and transient conditions. The engine was tested under seven steady-state operating points to investigate the PN behavior and Particle Size Distribution (PSD) upstream and down-stream of the Three-Way Catalyst (TWC). This analysis intends to assess the impact of including particles with diameters ranging from 10 nm to 23 nm in the total particle count, a consideration for future regulations. The study employed the World Harmonized Transient Cycle (WHTC) for transient conditions to encompass the same engine working region used in the steady-state analysis. A Dekati FPS-4000 diluted the exhaust sample to measure the PSD and PN for particle diameters between 5.6 nm and 560 nm using the TSI-Engine Exhaust Particle Sizer (EEPS) 3090. Findings indicate that PN levels tend to increase downstream of the TWC under steady-state conditions in operating points with low exhaust gas temperatures and flows (equal to or less than 500 °C and 120 kg/h). Furthermore, the inclusion of particles with diameters between 10 nm and 23 nm leads to an increase in PN emissions by 17.70% to 40.84% under steady conditions and by an average of 40.06% under transient conditions, compared to measurements that only consider particles larger than 23 nm. Notably, in transient conditions, most PN emissions occur during the final 600 seconds of the cycle, linked to the most intense phase of the WHTC.

During the launch and return of a spacecraft, the intense combustion of propellants generates strong electromagnetic radiation, which interferes with the operation of electronic equipment in the spacecraft. To improve the electromagnetic compatibility of electronic equipment in spacecraft, it is necessary to study the electromagnetic radiation characteristics of rocket fuel. An electromagnetic radiation measurement system based on antennas is designed to measure the electromagnetic radiation generated by rocket fuel, and the electromagnetic radiation characteristics are obtained through data analysis. The mechanism of the electromagnetic radiation generated by rocket fuel is comprehensively analysed through the spatial, time-domain, frequency-domain, and energy-domain characteristics. A characterization model is established to provide a reliable scheme for evaluating the influence of rocket fuel electromagnetic radiation on electronic equipment in spacecraft.

A concept of guided artillery rockets with reusable first stage otherwise called a rocket launcher aircraft (RLA) is presented. RLA raises the second stage rockets (SSR) to an altitude of up to 20 km. While RLA is moving at low supersonic speed, it releases one or more SSRs. RLA returns to the base within a few minutes of it’s launch. SSRs use slow burning motors to gain altitude and velocity. At the apogee of their flight SSRs release projectiles which fly to the target and strike it at high impact velocity. The projectiles reach a target at ranges of up to 374 km and impact velocity up to 1.35 km/s. We show that a rocket launched at high altitude and high initial velocity does not need expensive thermal protection to survive ascent. Delivery of munitions to target by the system described should be much less expensive than delivery by a conventional rocket. All parameters of RLA, SSR, and their trajectories are calculated based on theoretical (mechanical and thermo-dynamical) analysis and on several MatLab programs.

Hybrid electric vehicles (HEVs) are perceived as a first step towards the future of sustainable transport. Of course, battery electric vehicles (EVs) are currently ideal for what is wanted to stand for transport in the future. The lack of infrastructure for these vehicles makes many potential users choose hybrid vehicles in these conditions. This paper analyzes the influence of the engine and electric motor of hybrid vehicles on their performances. Three engines with slightly different power and close characteristics were considered for configured models. Also, two electric motors with very different power but having a very close maximum continuous torque have been used in models. One is an induction motor, and the other is a permanent magnet synchronous motor. ADVISOR software has been used for vehicles configuration and simulation. Series and parallel hybrid vehicles have been considered. The main dynamic performances and the fuel consumption in two driving cycles were compared for the configured models. Three conventional models with the same engines used in HEVs have also been simulated for reference. The results highlight that in the case of HEVs, the choice of combination engine/electric motor is crucial in obtaining the best report between dynamic performances and low fuel consumption, and implicitly a low negative impact on the environment.

In order to overcome the limitation of the traditional adaptive Unscented Kalman Filtering (UKF) algorithm in noise covariance estimation for statement and measurement, we propose a hybrid adaptive UKF algorithm based on combining Maximum a posteriori (MAP) criterion and Maximum likelihood (ML) criterion, in this paper. First, to prevent the actual noise covariance deviating from the true value which can lead to the state estimation error and arouse the filtering divergence, a real-time covariance matrices estimation algorithm based on hybrid MAP and ML is proposed for obtaining the statement and measurement noises covariance, respectively; and then, a balance equation the two kinds of covariance matrix is structured in this proposed to minimize the statement estimation error. Compared with the UFK based MAP and based ML, the proposed algorithm provides better convergence and stability.

In times of war, civilians experience a heightened risk of exposure to traumatic experiences. This can lead to a greater risk of developing mental health conditions such as post-traumatic stress disorder (PTSD), which can impact individuals and communities. Research suggests that the severity of the reactions to traumatic events is likely to be influenced by the level of exposure to the traumatic events and personal characteristics. This paper examines the correlation between symptoms of Post-Traumatic Stress Disorder (PTSD) and various factors such as the extent of exposure to missile alarms, personal traits, and socioeconomic elements among Israeli civilians impacted by rocket attacks from Hamas. Our specific focus is on the influence of these rocket attacks on Israel's populace during the military operation conducted by the Israeli army, "Guardian of the Walls," in May 2021.Our findings suggest that, typically, individuals who have experienced less exposure to missile attacks and alarms, those belonging to larger families, and those with a higher level of education and income are less prone to experiencing post-traumatic symptoms. The study was carried out using surveys that gathered demographic information about the participants, as well as a specific questionnaire designed to evaluate the presence and extent of post-trauma symptoms.These findings could aid in devising preventative strategies to mitigate the PTSD risk among vulnerable groups. They indicate the importance of considering socio-economic elements when delivering mental health services to those impacted by violent incidents and trauma.

Determining the vibration environment is crucial to analyzing a design of any mechanical system, especially such dynamic systems as sounding rockets. Accuracy of measurement using accelerometers could be improved by application of mechanical vibration filtering and amplifying devices. This work presents a theoretical description of a tunable filter and amplifier. Principle of work is provided as well as results from application of the device on a sounding rocket are provided. It is shown that implementation of such devices allowed for enhancing the accuracy of acceleration measurements. Conclusions on future implementations are also provided.

Attribute reduction is a core technique in the rough set domain and an important step in data preprocessing. Researchers have proposed numerous innovative methods to enhance the capability of attribute reduction, such as the emergence of multi-granularity rough set models, which can effectively process distributed and multi-granularity data. However, these innovative methods still have many shortcomings, such as how to deal with complex constraints and how to perform multi-angle effectiveness evaluations. Based on the multi-granularity model, this study proposes a new method of attribute reduction, namely using multi-granularity neighborhood information gain ratio as the measurement criterion. This method combines both supervised and unsupervised perspectives, and by integrating multi-granularity technology with neighborhood rough set theory, constructs a model that can adapt to multi-level data features. This model can select the optimal granularity level or attribute set according to the requirements. Finally, the proposed method is experimented with on 15 UCI datasets against six other attribute reduction algorithms. The experimental outcomes, upon analysis and comparative review, substantiate the reliability and consistency of the attribute reduction approach introduced in this study.

This work presents a novel approach for the study of the movement of droplets on inclined surfaces under the influence of gravity and chemical heterogeneities. The developed numerical methodology uses data-driven modeling to extend the applicability limits of an analytically derived reduced-order model for the contact line velocity. More specifically, while the reduced-order model is able to capture the effects of the chemical heterogeneities to a satisfactory degree, it does not account for gravity. To alleviate this shortcoming, datasets generated from direct numerical simulations are used to train a data-driven model for the contact line velocity, which is based on the Fourier neural operator and corrects the reduced-order model predictions to match the reference solutions. This hybrid surrogate model, which comprises of both analytical and data-driven components, is then integrated in time to simulate the droplet movement, offering a speedup of five orders of magnitude compared to direct numerical simulations. The performance of this hybrid model is quantified and assessed in different wetting scenarios, by considering various inclination angles and values for the Bond number, demonstrating the accuracy of the predictions as long as the adopted parameters lie within the ranges considered in the training dataset.

As the natural resources are getting exhausted, the concept of sustainable development of region has received increasing attentions, especially for resource-dependent cities. In this paper an innovative method that emergy analysis and IPAT (Human Impact Population Affluence Technology) model were combined in order to analyze the quantitative relationship of economic growth and energy consumption and further to evaluate its overall sustainability level. Taiyuan, a traditional, resource-dependent city in China, is selected as the case study region. The main results show that total emergy of Taiyuan increased from 9.023× 1023 sej in 2007 to 9.116× 1023sej in 2014, with 38% reduction on non-renewable emergy and 125% growth on imported emergy. Regional emergy money ratio (RMB) was reduced by 48% from 5.31× 1013sej/$ in 2007 to 2.74× 1013sej/$ in 2014, indicating that the increasing speed of consuming resources and energy was faster than the increase of GDP, and that Taiyuan’s money purchasing power declined. The lower emergy sustainability index (ESI) indicates that Taiyuan was explored and produced large quantities of mineral resources, which puts more stress on the environment as a consequence, and that this is not sustainable in the long run. The IPAT analysis demonstrates that Taiyuan sticks to the efforts of energy conservation and environmental protection, in order to promote regional sustainable development, it is necessary to take an integrated effort. Policy insights suggest that resourceful regions should improve include energy and resource efficiency, optimizing energy and resourceful structure.

In this paper, a novel permanent magnet-assisted synchronous reluctance machine (PMASynRM) with rare-earth PMs and ferrite magnets is proposed. The performance of PMASynRM is discussed with respected to the different magnet ratio of rare-earth PMs and ferrite magnets. Some characteristics including the flux density, output torque, cogging torque, output power, power factor, torque ripple, loss, efficiency, and demagnetization are calculated by 2-D finite element analysis (FEA). The analysis results show that the excellent performance can be obtained by using hybrid magnet of rare-earth PMs and ferrite magnets with the suitable magnet ratio, and provide some desirable cost-performance trade-off.

Graphene hybrid-filler polymer composites have emerged as prominent materials that revolutionize heavy industries. This study encapsulates the in-depth analysis of different influential factors such as filler/graphene type, aspect ratios, dispersion methods, filler-matrix compatibility, fiber orientation, synergistic effects, different processing techniques, and post-curing conditions that affect the processing, properties and determine the resultant applications of graphene hybrid polymer composites. Additionally, it discusses the substantial role of graphene collaboration with other fillers such as carbon nanotubes, silica, nano-clays and metal oxides to produce functionalized hybrid polymer composites with synergistically enhanced tailored properties to offer solutions for heavy industries like aerospace, automotive, electronics, and energy harvesting. The review concludes with some suggestions and outlook on the future of these composite materials by emphasizing the need for continued research to fully optimize their potential.

A low-cost bio-mass-derived carbon substrate has been employed to synthesize MoS₂@carbon composites through a hydrothermal method. Carbon fibers derived from natural cotton provide a three-dimensional and open framework for the uniform growth of MoS₂ nanosheets, thus constructing hierarchically coaxial architecture. The unique structure could synergistically benefit fast Li-ion and electron transport from the conductive carbon scaffold and porous MoS₂ nanostructures. As a result, the MoS₂@carbon composites, when served as anodes for Li-ion batteries, exhibit a high reversible specific capacity of 820 mAh g^-1, high-rate capability (457 mAh g^-1 at 2 A g^-1), and excellent cycling stability. The superior electrochemical performance makes the MoS₂@carbon composites to be low-cost and promising anode materials for Li-ion batteries.

This research presents a comprehensive framework for efficiently generating forest fire datasets from Google Earth Engine data sources. The primary contribution of this work lies in providing a methodology to swiftly extract forest fire factors without the need for permissions or access to private datasets, rendering the dataset openly accessible and shared without barriers. Furthermore, given that the remote sensing data used is a global dataset, it can be applied in any region without restrictions. In this study, Peninsular Malaysia is chosen as a case study to demonstrate the framework's effectiveness. The generated dataset includes essential variables including the climate and environment, landcover, topography, and anthropogenic factors facilitating the analysis of fire occurrences. The methodology empowers data scientists, enabling them to leverage their analytical skills on the extracted dataset without requiring specialized remote sensing knowledge. Additionally, this study also showcases the adoption of large language models, specifically GPT-4 with the Noteable plugin, as a tool for conducting preliminary analyses on the generated dataset. Sample analyses reveal that several key features, including the KBDI, LST, PDSI, climate water deficit, and precipitation, significantly impact forest fire occurrences in Peninsular Malaysia. Despite the successful application of the GPT-4 with Noteable plugin, certain limitations and challenges are identified, highlighting the necessity for further validation of the tool's applicability and limitations. This study encourages future research to (1) adopt the proposed framework in other regions, (2) explore more detailed analyses encompassing all variables, and (3) leverage machine learning for advanced forecasting.

Lily tepals have a short lifespan. Once the tepals senesce, the ornamental value of the flower is lost. Some cultivars have attractive purple ovaries and fruits which greatly enhance the ornamental value of Asiatic hybrid lilies. However, little is known about the molecular mechanisms of anthocyanin biosynthesis in Asiatic hybrid lily ovaries. To investigate the transcriptional network that governs purple ovary coloration in Asiatic hybrid lilies, we obtained transcriptome data from green ovaries (S1) and purple ovaries (S2) of Asiatic ‘Tiny Padhye’. Comparative transcriptome analysis revealed 4228 differentially expressed genes. Differential expression analysis revealed that nine unigenes including four CHS genes, one CHI gene, one F3H gene, one F3′H gene, one DFR gene, and one UFGT gene were significantly up-regulated in purple ovaries. One MYB gene, LhMYB12-Lat, was identified as a key transcription factor determining the distribution of anthocyanins in Asiatic hybrid lily ovaries. Further qPCR results showed unigenes related to anthocyanin biosynthesis were highly expressed in purple ovaries of Asiatic ‘Tiny Padhye’ at stages 2 and 3, while they showed an extremely low level of expression in ovaries of Asiatic ‘Yellow Pixels’ during all developmental stages. In addition, shading treatment significantly decreased pigment accumulation by suppressing the expression of several unigenes related to anthocyanin biosynthesis in ovaries of Asiatic ‘Tiny Padhye’. These results could further our understanding of the molecular mechanisms of anthocyanin biosynthesis in Asiatic hybrid lily ovaries.

This study investigates the potential of a hybrid nanofluid composed of MoS2 and ZnO nanoparticles dispersed in engine oil, aiming to enhance the properties of lubricant's chemical reaction with the Soret effect on stretching sheet under the influence of applied magnetic field. With the growing demand for efficient lubrication systems in various industrial applications, including automotive engines, the development of novel nanofluid-based lubricants presents a promising avenue for improving engine performance and longevity. However, the synergistic effects of hybrid nanoparticles in engine oil remain relatively unexplored. This research addresses this gap by examining the thermal conductivity, viscosity, and wear resistance of the hybrid nanofluid, shedding light on its potential as an advanced lubrication solution. By analyzing the dispersion stability and interaction mechanisms between nanoparticles and base oil, valuable insights are gained into the fundamental aspects governing the entertainment of hybrid nano lubricants. Overall, the objectives of studying hybrid nano lubricant MoS2+ZnO with engine oil aim to advance the development of more efficient and durable lubrication solutions for automotive engines, contributing to improved reliability, fuel efficiency, and environmental sustainability. Governing non-linear partial differential equations are simplified as ordinary differential equations by utilizing similarity variables. MATLAB Bvp4c technique is used to solve the obtained linear ODE equations. The outputs are presented through graphs and tables for various state variables. The comparative survey in all the graphs is presented for the nanofluid (MoS2/Engine oil) with the hybrid nanofluid (MoS2+ZnO/Engine oil). These graphs show the effect of various parameters M, Q, β, Pr, Ec, Sc, Sr, Kp, Kr and (hybrid nano lubricant parameter). The velocity profile diminished against the values of M, Kp, and β. Temperature profile rising with Ec and Q. reciprocally goes down to Pr. The concentration profile incremented with the value of Sr and decremented with the value of Sc, and Kr. With the increasing value of both f′ (η) and θ(η) increases. But in θ(η) hybrid nanofluid slows down with the higher value of .

As marine traffic is contributing to pollution and mostly have predictable route with repetitive load profiles, to reduce their impact on environment, hybrid system with proton exchange membrane fuel cells (PEMFC-s) and battery pack, is promising replacement. For this purpose, the numerical model of repowered existing vessel that have two diesel engines with 300 kWe is developed. The goal of this paper is to develop numerical model which analyzes and determine equivalent hybrid ship propulsion system for known traveling route. The developed numerical model consists of an interconnected system with PEMFC stack and battery pack as power sources. Numerical model is developed and optimized to meet minimal required power demand for successful route demand, which has variable loads and daily sails six-time same voyage- in total 54 nautical miles. The results have shown that equivalent hybrid power system consists of 300 kWe PEMFC stack and battery pack with 424 kWh battery and state of charge varying between 20 and 87%. To power this new hybrid power system, hydrogen tank of 7200 l holding 284.7 kg at pressure of 700 bar is needed, compared to previous system that spend 1524 kg of diesel and generate 4886 kg of CO2.

Hybrid materials are a recent addition in the field of Restorative Dentistry for computer-assisted design/ computer-assisted manufacturing (CAD/CAM) indirect restorations. The long–term clinical success of modern dental restorative materials follows a multifactorial pattern. Among the characteristics, affecting the longevity of a restoration, mechanical properties and physicο – chemical interactions are of utmost importance. While numerous researchers constantly evaluate the mechanical properties, the biological background of resin–based CAD/CAM biomaterials is scarcely investigated and, therefore, less described in the literature. This review aims to analyze the biofilm formation on the surfaces of novel hybrid, resin–based CAD/CAM materials and evaluate the methodological protocols followed to assess microbial growth. It is demonstrated that the surface structure, the composition and the finishing and polishing procedures on the surface of a dental restorative material influence the initial bacterial adhesion; however most studies focus on in vitro protocols, whereas in vivo and/or in situ research of microbiomics in CAD/CAM restorative materials is lacking, obstructing in that manner the accurate understanding of the bioadhesion phenomenon in the oral cavity.

This article introduces a novel payload structure, which has been experimentally proven to have smaller impact force while the carried internal electronics remains intact. The study describes in details how materials and geometric structures were chosen based on information analysis. It is clarified how possible geometric structures and materials can be chosen to yield optimal efficiency. Finite element method simulations were carried out to investigate the mechanical properties of several geometric structures, which has led to the dodecahedron form. For the frame, Polylactic Acid were chosen as an optimal material. A low air pressure test for parts of the structure, and also the whole structure were carried out with successful results. Drop tests, with force measuring sensors and high speed videos, were carried out for measuring and comparing the maximal impact force of classical payloads and the novel one. Laboratory and real life tests demonstrated that the developed new structure is safer and it is applicable.

Recent efforts to conceptually design a technologically meaningful electromagnetic retarded engine indicated that this can only be done using the immense charge and current densities which exist in the atomic scale. However, this scale cannot be described by Newtonian physics, and only a quantum description will suffice to describe the dynamics of an electron on this scale properly. Here we study the retarded field quantum engine and highlight the differences between the quantum and the classical retarded engines.

Preventive Maintenance (PM) is a periodic maintenance strategy, which has great results for devices in extending their life, increasing productivity, and most importantly, help avoid unexpected breakdowns and their costly consequence. A preventive maintenance scheduling (PMS) is determining the time for carrying out PM, and it represents a sensitive issue in terms of impact on production if the time for the PM process is not optimally distributed. In this study, hybrid heuristic methods were used to solve the PMS problem, as genetic algorithm and Tabu research were adopted. The optimal solution was reached in a short time compared to previous studies in which optimal methods were used, integer programming and nonlinear integer programming. Also, sensitivity analysis was applied to measure the robustness and strength of the method used, where an optimal solution was obtained for all experiments and in record time. This method can be used for power plants in privet or public sectors, to generate an optimal PMS, to save money, and to avoid water or electricity cuts.

A hybrid material, consisting of commercially available nitrocellulose (NC) membrane non-covalently modified with amino-polyethylene glycol functionalized reduced graphene oxide (NH2-PEG-rGO) nanoparticles, was successfully synthesized for oligonucleotide extraction. Fourier Transform Infrared Spectroscopy (FTIR) confirmed the modification of the NC membrane, revealing characteristic peaks of both compounds NC and NH2-PEG-rGO. Scanning Electron Microscopy (SEM) exhibited morphological changes in the NC/NH2-PEG-rGO hybrid membrane, marked by the introduction of NH2-PEG-rGO particles, resulting in a distinctly smothered surface compared to the porous surface of the NC control membrane. Wettability assays revealed hydrophobic behavior for the NC/NH2-PEG-rGO hybrid membrane, with a water contact angle exceeding 90°, contrasting with the hydrophilic behavior characterized by a 16.7° contact angle in the NC membrane. The performance of the NC/NH2-PEG-rGO hybrid membrane was evaluated for the extraction of ssDNA with fewer than 50 nucleotides from solutions containing various ionic species (MnCl2, MgCl2, and MnCl2/MgCl2). The NC/NH2-PEG-rGO hybrid membrane exhibited optimal performance when incubated in MgCl2, presenting the highest fluorescence emission at 525 relative fluorescence units (r.f.u.). This corresponds to the extraction of approximately 610 pg (≈13%) of the total oligo-DNA, underscoring the efficacy of the pristine material, which extracts 286 pg (≈6%) of oligo-DNA in complex solutions.

In the era of mounting water scarcity, particularly in arid regions, the agricultural sector is striving to optimize the water resource management practices. Treated wastewater (TWW) has emerged as a viable alternative to freshwater for irrigation, offering a sustainable solution to water conservation. However, ensuring the safety and quality of TWW is paramount to safeguard for both crop health and environmental integrity. Electrical conductivity (EC), a critical factor in assessing TWW suitability for irrigation, can significantly impact crop yields and soil health since it is directly linked to the concentration of dissolved salts and ions. Therefore, this study delves into the development of advanced predictive models for EC assessment by employing supervised machine learning (ML) techniques like the Support Vector Regression (SVR), Adaptive Network-based Fuzzy Inference System (ANFIS), and Multiple Linear Regression (MLR). The TWW samples collected from Al-Qatif area, Saudi Arabia were assessed in the lab through a sensor-based real-time monitoring system integrated with Internet of Things (IoT) network and a multiprobe device. The key water quality parameters assessed involved, temperature (T), pH, Oxidation Reduction Potential (ORP), Resistivity (RES), Total Dissolved Solids (TDS), Salinity and Turbidity which served as the foundation for these models, given their profound influence on and their anticipated impact on EC levels. Following feature selection, two distinct model combinations were identified for the SVR, ANFIS and MLR models. Furthermore, to evaluate prediction performance, four statistical criteria, including coefficient of determination (R²), correlation coefficient (R), mean square error (MSE), and root mean square error (RMSE), were employed. Overall, the comparative performance results proved that ANFIS-M2 indicated the strongest predictive capability with the R² value of 0.99572 and 0.75145 and the RMSE values of 0.00965 and 0.01134, respectively in both calibration and verification stages.

A multiscale model that enables quantitative understanding and prediction of the size effect on scattering properties of micro- and nanostructures is crucial for the design of LED surface textures optimized for high light extraction efficiency (LEE). In this paper, a hybrid process for combining full-wave finite-difference time-domain simulation and a ray-tracing technique based on a bidirectional scattering distribution function model is proposed. We apply this method to study the influence of different pattern sizes of a patterned sapphire substrate on GaN-based LED light extraction from the microscale to the nanoscale. The results show that near-wavelength–scaled patterns with strong diffraction are not expected to enhance LEE. By contrast, microscaled patterns with optical diffusion behavior have the highest LEE at a specific aspect ratio, and subwavelength-scaled patterns that have antireflection properties show marked enhancement of LEE for a wide range of aspect ratios.

Hybrid reinforcement in a composite material (CM), in many situations, allows better govern material mechanical properties. Polymer matrix CM plate is reinforced with small particles and macro-fibers. Particles are oil shale ash (OSA) powder, macro-fibers – basalt fiber threads im-pregnated by matrix material. With the goal to find out elastic properties of the matrix- polymer with OSA, samples with its different concentrations were experimentally fabricated and their elastic properties were experimentally measured. Obtained data were used in numerical model-ling. Was observed polymer matrix composite slab, reinforced with OSA and three-dimensional (3D) basalt fiber fabric. Fabric are forming threads impregnated by matrix (polymer resin mixed with OSA particles). Damage accumulation in the stretched slab was modelled. Impregnated thread, in the modelling, is observed as a macro-fiber (MF) with averaged elastic properties. With the load increase MF in the material began to break. At first, appeared isolated single breaks. A probabilistic sequential MF rupture accumulation model was accepted and numerical-ly realized. The geometry of the reinforcing knitted fabric was modeled using the Leaf and Glaskin approach. In the knitted fabric highly, stressed MF cross-sections are observed as po-tential location where thread breakage can occur. If one thread breaks, the cross-sections of the adjacent threads experience the overload caused by this rupture. More overloaded crossection in the adjacent overloaded thread breaks and we obtain two adjacent broken MF designated as a defect with the size j=2. The probabilities of obtaining defect consisting of at least 2, 3, … j adjacent broken MF in the plate were calculated. The calculations continued until the rupture process becomes unstable (j→∞). The overstresses on adjacent threads in the CM were calculat-ed using the FEM three-dimensional numerical approach. Analytical and stochastic methods were used for the load-carrying capacity determination and different size defects accumulation in the hybrid composite material (HCM) plate. Also, was calculated the probability of the plate rupture.

This paper presents a two-stage ADC based on pseudo-differential operational transconductance amplifier (OTA), which is designed for the readout circuit of X-ray linear array sensor. This hybrid ADC employs an incremental sigma-delta ADC and a cyclic ADC, achieving a good trade-off between accuracy and conversion speed. The two stages share the same hardware to reduce power consumption and die area. A common-mood feedback module is used to suppress the influence of charge injection, and the effectiveness is demonstrated by detailed theoretical analysis. A test chip of 14-bit ADC is fabricated in 0.35μm CMOS technology. The measured root mean square (RMS) value of DNL is 0.254 LSB, and the maximum value of INL is -0.776/+1.56 LSB. The measured effective number of bits (ENOB) is 13.43 bits.

This research investigates the fatigue crack propagation on the lower surface of rocket deflector troughs of offshore rocket launch platforms. Initially, a numerical model of the offshore rocket launch platform is established using ABAQUS based on the Extended Finite Element Method (XFEM). Subsequently, two variable parameters—namely, the initial crack length and initial tilt angle—are introduced. This research systematically analyzes the impact of these parameters on fatigue crack propagation patterns in both the maximum stress and maximum deformation regions of the deflector channels under the combined conditions of high temperature and impact. Finally, the research indicates that the propagation length of surface cracks in the deflector trough exhibits a trend of initial increase followed by a subsequent decrease with the increase in the preset inclination angle. Notably, the stable propagation rate of the crack in the region of maximum deformation surpasses that observed in the region of maximum stress. Through meticulous comparative analysis, it is evident that temperature loading significantly fosters the initiation and propagation of cracks, particularly in the upper region of the deflector channel's lower surface.

This investigation mainly focused on the microstructural analysis of Al based hybrid metal matrix composites. Aluminium as base metal and reinforced with 2% B4 C, ZrO2 (2%,4%,6%), SiC (4%,8%,12%). Specimens were manufactured by using conventional stir casting technique. Hybrid metal matrix composites were examined by using Scanning electron microscope. This research explained XRD, EDS and Fractography analysis. This investigation shows the reinforced material has given better results in all aspects than pure Al6061 alloy and homogeneous dispersion of particles inside the matrix. Porosity, delamination, cracks, particle distributions were observed through microstructural analysis. Additionally determined the crystal size of pure Aluminium 6061 alloy and hybrid composites.

Hybrid Renewable Energy Systems (HRES) integrate renewable sources, storage, and optionally conventional energies, offering an eco-friendly solution to fossil fuels. Microgrids (MGs) bolster HRES integration, enhancing energy management, resilience, and reliability at various levels. This study, emphasizing the need for refined optimization methods, investigates three themes: renewable energy, microgrid, and multiobjective optimization (MOO), through a bibliometric analysis of 470 Scopus documents from 2010-2023, analyzed with SciMAT software. It segments the research into two periods, 2010-2019 and 2020-2023, revealing a surge in MOO focus, especially in the latter period, with a 35% increase in MOO-related research. This indicates a shift towards com-prehensive energy ecosystem management that balances environmental, technical, and economic elements. The initial focus on MOO, genetic algorithms, and energy management systems has expanded to include smart grids and electric power systems, with MOO remaining a primary theme in the second period. The increased application of Artificial Intelligence (AI) in optimizing HMGS within the MOO framework signals a move towards more sustainable, intelligent energy solutions. Despite progress, challenges remain, including high battery costs, the need for reliable MOO data, the intermittency of renewable energy sources, and HMGS network scalability issues, highlighting directions for future research.

: The crystallization conditions from the solution play an important role in determining the mor-phology, phase composition, and photovoltaic properties of perovskite films. Post-processing of the obtained films can have a crucial role in increasing the grain size of perovskite and enhancing its crystallinity. It has been shown that the formation of crystal nuclei can be utilized to accelerate crystallization. In this case, crystallization occurs through the growth of seed crystals created in the solution, enabling the formation of relatively large crystals. For the deposition of CH3NH3PbI3 hybrid halide perovskite films from a solution of the perovskite in dimethylformamide the spin coating technique was employed. Pre-crystallization was achieved by annealing the films at a temperature of 100 or 110 °C. The dissolution process involved adding a drop of dimethylformamide onto the substrate surface and allowing it to partially dissolve the perovskite film. Subsequently, residual solvent was removed through spin coating. The mor-phological analysis of the perovskite film surface after recrystallization at temperatures ranging from 80 to 130 °C was performed. The infrared transmission spectra of the obtained perovskite films were investigated, and their light absorption characteristics were studied through transmission spectra. The perovskite structure in the obtained films was confirmed by the peaks observed in the X-ray diffraction patterns.

This paper describes the load capacity of short fiber-reinforced polymer lugs with Ti-6Al-4V selective laser-melted bushings with the original embedded element surface roughness and after sandblasting and vibratory finishing. Lugs are an important part of pin-joints. Adhesion between PA6 and SLM produced titanium is not fully studied. The cohesive zone material Mode II model parameters were calibrated experimentally by extruding a cylindrical bushing along the lug axis. The values of the maximum equivalent tangential contact stress were fitted for cases with investigated embed element roughness. The critical fracture energy for tangential slip was also estimated. Validation of the constructed CZM Mode II models with the results of strength experiments was performed. In both the experiment and the calculation, greater bushing roughness provides greater lug load-bearing capacity. The presented models can be used for the preliminary evaluation of SFRP PA6 parts with titanium embedded elements bearing capacity.

The strategy of any organization is based on the growth of its customer base, and one of 5 its principles is that selling a product to an existing customer is much more profitable than acquiring 6 a new customer. However, this approach has several opportunities for improvement, since it usu- 7 ally has a totally reactive approach, which does not give opportunity to the areas specialized in 8 customer experience and recovery, to give an effective response for that moment, since the customer 9 is gone at the time of the intervention. This happens because usually a diagnostic analysis of cus- 10 tomers who have stopped buying products or services in a defined period, commonly three (3) pe- 11 riods or months, is performed. This thesis work challenges the way to face this problem, and pro- 12 poses the development of a complete solution, which does not focus exclusively on the prediction 13 of churn, as is usually done in the state of the art research, but to intervene in different interactions 14 that can be carried out with customers. The above focused not only to prevent customer churn, but 15 to generate an added value of continuous improvement in sales processes, increase customer pene- 16 tration, leading to an improvement in customer experience and consequently, an increase in cus- 17 tomer loyalty.

In this paper, we introduce hybrid numbers with Fibonacci and Lucas hybrid number coefficients. We give the Binet formulas, generating functions, exponential generating functions for these numbers. Then we define an associate matrix for these numbers. In addition, using this matrix, we present two different versions of Cassini identitiy of these numbers.

Gas turbine engines are widely used in various industries such as aviation, power generation, and marine propulsion due to their high efficiency and reliability. The combustion chamber of a gas turbine engine design contains a number of elements, including as the chamber's size and form, the fuel injection system, the ignition system, and the cooling system and it plays a critical role in the performance and efficiency of the engine. This research discusses the fabrication of a combustion chamber model for a gas turbine engine. The aim of this research is to develop a combustion chamber that is cost-effective, easily available, and can be fabricated in less time. The research covers the different types of combustion chambers used in gas turbine engines and compares their advantages and disadvantages. It then selects the best type of combustion chamber for the gas turbine engine based on its cost, availability, and fabrication time, Efficiency and high power to fuel ratio. The report also describes the fabrication process involved, including the techniques and materials used, along with safety precautions and environmental protection measures. Lastly, the research suggests future improvements and potential applications for the fabricated combustion chamber model.

Graphene is perhaps the most prominent “Dirac material,” a class of systems whose electronic structure gives rise to charge carriers that behave as relativistic fermions. In multilayer graphene several crystal sheets are stacked such that the honeycomb lattice of each layer is displaced along one of the lattice edges. When subject to an external magnetic field, the scaling of the multilayer energy spectrum with the magnetic field, and thus the system’s thermodynamic behavior, depends strongly on the number of layers. With this in mind, we examine the performance of a finite-time endoreversible Otto cycle with multilayer graphene as its working medium. We show that there exists a simple relationship between the engine efficiency and the number of layers, and that the efficiency at maximum power can exceed that of a classical endoreversible Otto cycle.

Accurate classification of objects in 3D point clouds is a significant problem in several applications, such as autonomous navigation and augmented/virtual reality scenarios, which has become a research hot spot. In this paper, we presented a deep learning strategy for 3D object classification in augmented reality. The proposed approach is a combination of the GRU and LSTM. LSTM networks learn longer dependencies well, but due to the number of gates, it takes longer to train; on the other hand, GRU networks have a weaker performance than LSTM, but their training speed is much higher than GRU, which is The speed is due to its fewer gates. The proposed approach used the combination of speed and accuracy of these two networks. The proposed approach achieved an accuracy of 0.99 in the 4,499,0641 points dataset, which includes eight classes (unlabeled, man-made terrain, natural terrain, high vegetation, low vegetation, buildings, hardscape, scanning artifacts, cars). Meanwhile, the traditional machine learning approaches could achieve a maximum accuracy of 0.9489 in the best case.

Bonding agents (BA) are key compounding ingredients for the correct formulation of composite solid rocket propellants (CSRP). In particular, the addition of BA is essential to achieve suitable mechanical properties of CSRP in terms of adequate tensile strength and elongation at break. It is shown that the polarity of each conventional BA as well as new potential BA can be measured through the Reichardt’s ET(30) polarity scale. Using this criteria it was possible to find a substitute for MAPO (tris-(methylaziridinyl)-phosphine oxide), a conventional BA with the drawback of high toxicity and high reactivity, with TTPT (tris-(pyrrolidine)-phosphine oxide), a completely safe and effective BA. In this work, many other potential BA were evaluated through the Reichardt’s ET(30) polarity scale but only a selection of the potential BA were effectively tested in a standard CSRP. Particular interesting was the evaluation of TTPT vs MAPO showing the ability of the former BA to match the mechanical properties of the latter BA.

Background: The commonly known assumption that combinations of several herbs in one formulation can have better efficacy due to additive or synergistic effects has not been systematically studied despite some evidence supporting the synergy concept. Study aim: The study aimed to reveal the molecular interactions in situ of host cells in response to the intervention of BHP and justify the benefits of implementing BHP in clinical practice. Results: This overview provides the results of recent clinical and network pharmacology studies of botanical herbal preparations (BHP) of Rhodiola with other plants, including Ashwagandha, Green Tea, Eleutherococcus, Schisandra, Eleutherococcus, Leuzea, Caffeine, Cordyceps, Gingko, Black Cohosh, saffron, and L-carnosine. Conclusions: The most important finding from network pharmacology studies of BHP was the evidence supporting the synergistic interaction of BHP ingredients, revealing unexpected new pharmacological activities unique and specific to the new BHP. Some studies show the superior efficacy of BHP compared with mono-drugs. At the same time, some a priori-designed combinations can fail, presumably due to antagonistic interactions and crosstalk between molecular targets within molecular networks involved in the cellular and overall response of organisms on the intervention. Network pharmacology studies help predict the results of studies to discover new indications and unpredicted adverse events.

The purpose of this project was to demonstrate the potential of converting an existing internal combustion engine for hydrogen operation at relatively low cost and to optimize the combustion process for ultra-low NOx emissions. The conversion has been carried out on a 450cc naturally aspirated single cylinder engine. The first baseline measurements and setup have shown lower NOx emissions than the baseline gasoline engine (without catalytic converter) for similar power output and engine efficiency. Further optimization of the combustion process allowed to achieve Euro VI and (latest European Commission proposal) Euro VII heavy-duty NOx limits without exhaust gas aftertreatment system (EATS) in steady state conditions. The concept is very promising and the knowledge gained should allow for further optimization to achieve near zero NOx emissions at high engine efficiency and power output with supercharging. It should also allow an efficient conversion of other engine types for different applications. In particular, a project with the aim to convert a heavy duty 6 cylinder engine intended for public transport buses has started. It follows a feasibility study which has shown the competitiveness of the hydrogen engine for this application.

This paper reports the efforts made and experiences gained in developing the Flood Prevention and Emergency Response System (FPERS) powered by Google Earth Engine, with focus on its applications at the three stages of floods. At the post-flood stage, FPERS integrates various remote sensing imageries, including Formosat-2 optical imagery, to detect and monitor barrier lakes, synthetic aperture radar imagery to derive an inundation map, and high-spatial-resolution photographs taken by unmanned aerial vehicles to evaluate damage to river channels and structures. At the pre-flood stage, a huge amount of geospatial data are integrated in FPERS and are categorized as typhoon forecast and archive, disaster prevention and warning, disaster events and analysis, or basic data and layers. At the during-flood stage, three strategies are implemented to facilitate the access of the real-time data: presenting the key information, making a sound recommendation, and supporting the decision-making. The example of Typhoon Soudelor in August of 2015 is used to demonstrate how FPERS was employed to support the work of flood prevention and emergency response from 2013 to 2016. The capability of switching among different topographic models and the flexibility of managing and searching data through a geospatial database are also explained, and suggestions are made for future works.

Translation of basic animal research to find effective methods of diagnosing and treating human neurological disorders requires parallel analysis infrastructures. Small animals such as mice provide exploratory animal disease models. However, many interventions developed using small animal models fail to translate to human use due to physical or biological differences. Recently, large-animal minipigs have emerged in neuroscience due to both brain similarity and economic advantages. Medical image processing is a crucial part of research as it allows researchers to monitor their experiments and understand disease development. However, although many algorithms are created and optimized for MR analysis of human data, those tools are not directly applicable or sufficiently sensitive to measure minipig data. In this work, we propose PigSNIPE - a pipeline for the automated handling, processing, and analyzing of large-scale data sets of minipig MR images. The pipeline allows for image registration, AC-PC alignment, landmark detection, skull stripping, brainmasks and intracranial volume segmentation (DICE 0.98), tissue segmentation (DICE 0.82), and caudate-putamen brain segmentation (DICE 0.8) in under two minutes. To the best of our knowledge, this is the first automated pipeline tool aimed at large animal images.

This present work explores the performance of a thermal-magnetic engine of Otto type, considering as a working substance an effective interacting spin model corresponding to the q− state clock model. We obtain all the thermodynamic quantities for the q = 2, 4, 6, 8 cases in a small lattice size (3×3 with free boundary conditions) by using the exact partition function calculated from the energies of all the accessible microstates of the system. The extension to bigger lattices was performed using the mean-field approximation. Our results indicate that the total work extraction of the cycle is highest for the q=4 case, while the performance for the Ising model (q=2) is the lowest of all cases studied. These results are strongly linked with the phase diagram of the working substance and the location of the cycle in the different magnetic phases present, where we find that the transition from a ferromagnetic to a paramagnetic phase extracts more work than one of the Berezinskii–Kosterlitz–Thouless to paramagnetic type. Additionally, as the size of the lattice increases, the extraction work is lower than smaller lattices for all values of q presented in this study.

The demands for high power and low emissions put focus on energy efficiency when developing new engines. An important part is the UAV engine cooling system, which cools the engine structure below damaging temperatures. For high temperatures in the cooling system there is a risk that boiling occurs and while the initial nucleate boiling enhances the cooling effect, the subsequent Film boiling decreases the heat transfer drastically. It is important to understand the benefits and limitations of various fluids when designing a two-phase cooling system. This paper proposes an alternative organic coolant. Its normal application range is 15°C to 400°C, and its pressure range is from atmospheric to 10.6 bar. DOWTHERM A fluid possesses unsurpassed thermal stability at temperatures of 400°C. This fluid, in both the liquid and vapor form, is noncorrosive toward common metals and alloys. Even at the high temperatures involved, the equipment usually exhibits excellent service life. Original equipment in many systems is still being used after 30 years of continuous service. The numerical results, described in this paper, show the potential of DOWTHERM A as a good alternative for cooling a UAV engine. It will extend the working temperature range of the Diesel engine and will maintain the structural integrity of the piston, cylinder and the piston rings.

We study the effect of the degeneracy factor in the energy levels of the well-known Landau problem for a magnetic engine. The scheme of the cycle is composed of two adiabatic processes and two isomagnetic processes driven by a quasi-static modulation of external magnetic field intensity. We derive the analytical expression of the relation between the magnetic field and temperature along the adiabatic process and, in particular, reproduce the expression for the efficiency as a function of the compression ratio.

This paper presents an operational approach for detecting floods and establishing flood extent using Sentinel-1 radar imagery with Google Earth Engine. Flooded areas are identified using a change-detection method based on the normalized difference. The HAND algorithm is used to delineate zones for processing. The approach was tested and calibrated at small scale to identify optimal parameters for flood detection. It was then applied to the whole of the island of Madagascar after the cyclone Batsirai in 2022. The proposed method is enabled by the computing power and data availability of Google Earth Engine and Google Colab. The results show satisfactory accuracy in delineating flooded areas. The advantages of this approach are its rapidity, online availability and ability to detect floods over a wide area. The approach relying on Google tools thus offers an effective solution for generating a large-scale synoptic picture to inform hazard management decision-making. However, one of the method’s drawbacks is that it depends to a large extent on frequent radar imagery being available at the time of flood events and on free access to the platform. These drawbacks will need to be taken into account in an operational scenario.

Crop type and field boundary mapping enable cost-efficient crop management on the field scale and serve as the basis for yield forecasts. Our study uses a data set with crop types and corresponding field borders from the federal state of Bavaria, Germany, as documented by farmers from 2016 to 2018. The study classified corn, winter wheat, barley, sugar beet, potato, and rapeseed as the main crops grown in Upper Bavaria. Corresponding Sentinel-2 data sets include the normalised difference vegetation index (NDVI) and raw band data from 2016 to 2018 for each selected field. The influences of clouds, raw bands, and NDVI on crop type classification are analysed, and the classification algorithms, i.e., support vector machine (SVM) and random forest (RF), are compared. Field boundary detection and extraction are based on non-iterative clustering and a newly developed procedure based on Canny edge detection. The results emphasise the application of Sentinel’s raw bands (B1–B12) and RF, which outperforms SVM with an accuracy of up to 94%. Furthermore, we forecast data for an unknown year, which slightly reduces the classification accuracy. The results demonstrate the usefulness of the proof-of-concept and its readiness for use in real applications.

The aim of this work is to present a design approach of a Shock-Induced Combustion Ramjet (Shcramjet) inlet, and present its optimization in terms of the flow and geometrical parameters. The flow properties of mixed compression type inlet of a Shcramjet are examined using analytical and numerical techniques. The geometries obtained with variations in the wedge angles, length, height and cowl lip positions are used to study the flow characteristics of inlet, identifying bow shock temperature ratio as the optimization parameter. The two-dimensional geometries of two-shock and three-shock inlet models designed analytically for shock-on-lip condition at Mach 12.5 and an altitude of 32.5 km are numerically simulated in OpenFOAM CFD Toolbox. A density based compressible CFD solver based on central upwind schemes of Kurganov and Tadmore is used to solve 2D inviscid Euler equations. The inlet total pressure recovery is expressed as a function of temperature ratios of compression shocks, and is found to have a maxima at a bow shock temperature ratio at the design condition when the bow and external shocks have equal strengths. The effect of considering the flow to be calorically imperfect is studied numerically, and the deviation from the analytical design is presented.

Wild rocket plants were cultivated in a plastic greenhouse into two independent sectors, one for soil-bound (SbS) cultivation and another equipped for soilless (ScS) cultivation systems. In both SbS and ScS, the crop was subjected to treatments consisting in a high-compared to a low-input fertilization program (HF and LF treatment, respectively). Water use efficiency (WUE) and partial factor productivity (PFP) for nutrients (N, P, K, Ca and Mg for ScS, and N for SbS) were measured. Rocket leaves, separated for cultivation system and fertilization program, collected at different cuts during the growing cycle, were cold stored at 10 °C until 16 d. At each sampling day (at harvest and during the storage), sensory parameters, respiration rate, dry matter, colour, electrolyte leakage, antioxidant activity, total phenols, total chlorophyll and ammonia content were evaluated. In ScS, the PFP for all nutrients supplied as fertilizers showed a significant increase with the LF treatment, with values higher than 30 % for N, K and Ca. As for the postharvest performance, rocket leaves cultivated in ScS showed better qualitative traits than those cultivated in SbS, as suggested by the lower values of ammonia content and electrolyte leakage recorded at the end of storage in samples grown in ScS. Moreover, in ScS, data showed a lower membrane damage in LF than HF rocket leaves. Finally, regarding total chlorophyll content, even if no effect of treatments was recorded in SbS, rocket cultivated in ScS, showed a better retention of this parameter applying LF than HF treatment. In addition to this, a PLS model (R2 = 0.7) able to predict the cultivation system, using as variables total chlorophyll content no-destructively measured, was implemented.

Aseptic implant loosening after total joint replacement is partially influenced by material-specific factors when cobalt-chromium alloys are used, including osteolysis induced by wear- and corrosion products, and stress shielding. Here, we aim to characterize a hybrid material consisting of alumina-toughened zirconia ceramics (ATZ) and additively manufactured Ti-35Nb-6Ta (TiNbTa), joined by a glass solder. The structure of the joint, static and fatigue shear strength, the influence of accelerated aging, and the cytotoxicity using an elution assay with human osteoblasts is characterized. Furthermore, the biomechanical properties of functional demonstrators of a femoral component for total knee replacements are evaluated. TiNbTa-ATZ specimens showed a homogenous joint with statistical distributed micro-pores and a slight accumulation of Al-rich compounds at the glass solder-TiNbTa interface. The static shear strength was 26.4 ± 4.2 MPa (Ti-ATZ reference: 38.2 ± 14.4 MPa, p = 0.117), all specimens survived 107 cycles of shear loading to 10 MPa, and aging did not reduce the strength. TiNbTa-ATZ did not impair the proliferation and metabolic activity of human osteoblasts. Functional demonstrators of TiNbTa-ATZ provided a maximum bearable extension-flexion moment of 40.7 ± 2.2 Nm. Biomechanical and biological properties of TiNbTa-ATZ demonstrate potential application for endoprosthetic implants.

Hybrid forecasting methods have emerged as a solution surpassing the limitations of both statistical and deep learning approaches. While the first emphasize the significance of variables, they often produce worse forecasting results when compared to newer techniques. In contrast, deep learning models remain enigmatic "black boxes" in terms of interpretability, although achieving better results in forecasting. This article introduces the Comprehensive Cross-Correlation and Lagged Linear Regression Deep Learning (CCLR-DL) framework, designed to harness the best of both approaches, enhancing forecasting accuracy while retaining model interpretability through a feature selection process. CCLR-DL blens cross-correlation analysis, lagged multiple linear regression and granger's causality procedures with deep learning architectures based on LSTM. In a practical demonstration, CCLR-DL was applied to a real database of clinical visits associated to diagnoses in Catalonia, Spain (tracking a population of 6.3 million patients during 10 years). Predicting visits enables the healthcare managers to be ready for future demand shifts. Results demonstrate a consistent and substantial improvement over standalone statistical and deep learning methods when predicting healthcare demand. This hybrid approach not only showcases its efficacy but also offers a promising solution to the challenge of balancing predictive accuracy with model explicability. In this context, this work aims to design and validate a method for feature selection and forecasting of multivariate high dimensional time series datasets not only to improve prediction accuracy but also to model transparency by identifying a subset of variables that improve predictions and G-cause the target variable.

The rising importance of Big Data in modern information analysis is supported by vast quantities of user data, but it is only possible to collect sufficient data for all tasks within certain data-gathering contexts. There are many cases where a domain is too novel, too niche, or too sparsely collected to adequately support Big Data tasks. To remedy this, we have created ADG Engine that allows for the generation of additional data that follows the trends and patterns of the data that’s already been collected. Using a database structure that tracks users across different activity types, ADG Engine can use all available information to maximize the authenticity of the generated data. Our efforts are particularly geared towards data analytics by identifying abnormalities in the data and allowing the user to generate normal and abnormal data at custom ratios. In situations where it would be impractical or impossible to expand the available dataset by collecting more data, it can still be possible to move forward with algorithmically expanded data datasets.

\textsc{Background} Often, in biology, we are faced with the problem of exploring relevant unknown biological hypotheses in the form of myriads of combination of factors that might be affecting the pathway under certain conditions. Currently, a major problem in biology is to cherry pick the combinations based on expert advice, literature survey or guesses for investigation. The search and wet lab testing of these combinations costs a lot in terms of time, investment and energy. In a recent development of the PORCN-WNT inhibitor ETC-1922159 for colorectal cancer, a list of down-regulated genes were recorded in a time buffer after the administration of the drug. The regulation of the genes were recorded individually but for a majority, it is still not known which higher ($\geq 2$) order combinations might be playing a greater role in the pathway. \textsc{Results} The pipeline provides a prioritised list of important $2^{nd}$ order combinations of a range of family of genes involved in the Wnt pathway. More specifically, it reveals the various unexplored FZD-WNT combinations that have been untested till now in the pathway. In relation to ETC-1922159 affected combinations, the down-regulation of LGR-RNF family after the drug treatment is evident in these rankings as it takes bottom priorities for LGR5-RNF43 combination. The LGR6-RNF43 takes higher ranking than LGR5-RNF43, indicating that it might not be playing a greater role as LGR5 during the Wnt enhancing signals. These rankings confirm the efficacy of the proposed search engine design. \textsc{Conclusion} A pipeline has been developed to prioritise an $n^{th}$ order combination of factors that affect a signaling pathway. It takes into account the sensitivity indices computed from variance based (SOBOL) and density-kernel based (HSIC) methods to estimate the influence of each factor or combination of factors. These are then fed as feature vectors into a powerful support vector ranking algorithm that produces a ranked list of the interactions/combinations.

The COVID-19 pandemic has not only affected public health but has also significantly impacted the economy. Bandung, a bustling city in Indonesia serving as a satellite to the capital, has been hit hard due to its high population density, mobility, and reliance on the tourism, trade, and transportation sectors. Using a Computable General Equilibrium (CGE) model developed at the inter-regional level of Indonesia, this study investigates the microeconomic indicators of several economic activities in Bandung to assess the impact of the pandemic. Additionally, the study examines the role of private sector actors in contributing to the sustainable recovery efforts toward achieving Sustainable Development Goals (SDGs) amidst the pandemic. The findings reveal that Bandung's transportation, accommodation, food and beverage, water supply, and trade sectors experienced a significant decline in economic activity. However, there was a gradual recovery with increased economic activity between 2019 and 2021. Private sector actors and the health sector were the main drivers of economic recovery, with other sectors also contributing to the efforts.

Alternative fuels have the potential to reduce exhaust emissions in the transportation sector. In this study, the effects of oxygenated fuels on the performance and emissions of a gasoline single-cylinder spark-ignition engine have been investigated experimentally. Experiments were conducted using a DIDACTA-T85 testbed under full load conditions and variable engine speed. Performance tests were performed by measuring the brake torque, brake power, brake mean effective pressure (BMEP), brake specific fuel consumption (BSFC), and brake thermal efficiency (BTE). The tested fuel blends were G0, E10, A10, and M10. G0 represented the base fuel and pure gasoline. E10, A10, and M10 were represented by a 10% volume of ethanol, acetone, and methanol in gasoline respectively. Results showed that M10 produced better engine performance in terms of brake torque, brake power, and BMEP, while E10 performed better results in terms of BSFC and BTE. A10 gave the lowest value in NOx emission at all engine speeds. On the other hand, it increased the NOX emission for E10 and M10 blends. This study showed that oxygenated fuel blends significantly reduced carbon monoxide emission at all engine speeds whereas carbon dioxide emission was higher at the highest engine speed.

Accurate inundation maps for flooded wetlands and rivers are a critical resource for their management and conservation. In this paper we automate a method (thresholding of the short-wave infrared band) for classifying inundation, using Landsat imagery and Google Earth Engine. We demonstrate the method in the Okavango Delta, northern Botswana, a complex case study due to the spectral overlap between inundated areas covered with aquatic vegetation and dryland vegetation classes on satellite imagery. Inundation classifications in the Okavango Delta have predominately been implemented on broad spatial resolution images. We present the longest time series to date (1990-2019) of inundation maps at high spatial resolution (30m) for the Okavango Delta. We validated the maps using image-based and in situ data accuracy assessments, with accuracy ranging from 91.5 - 98.1%. Use of Landsat imagery resulted in consistently lower estimates of inundation extent than previous studies, likely due to the increased number of mixed pixels that occur when using broad spatial resolution imagery, which can lead to overestimations of the size of inundated areas. We provide the inundation maps and Google Earth Engine code for public use.

In this study, we address the formidable challenge of solving large-scale Max-Cut problems (MCP). We introduce a rapid computational procedure utilizing a hybrid 1-flip/r-flip local search heuristic. This innovative strategy significantly reduces the computational time required for MCP problems while consistently generating solutions of exceptional quality. The paper presents substantial computational insights, showcasing the effectiveness of our approach on very-large-scale Max-Cut instances with varying densities. Our proposed heuristic is rigorously evaluated by comparing its performance against a quantum annealing solver, leveraging a multi-start Tabu Search framework. The results underscore the potency of this unique combination as an efficient and effective solution for large-scale QUBO problems. Notably, our hybrid heuristic consistently delivers high-quality solutions within the stringent CPU time limits of 600 seconds, demonstrating its efficacy across Max-Cut instances ranging from 10,000 to 40,000 variables. This research contributes to advancing the state-of-the-art in large-scale QUBO problem-solving, offering a powerful and time-efficient approach with broad applicability.

In the present-day digital landscape, websites have increasingly relied on digital marketing practices, notably search engine optimization (SEO), as a vital component in promoting sustainable growth. The traffic a website receives directly determines its development and success. As such, website owners frequently engage the services of SEO experts to enhance their website's visibility and increase traffic. These specialists employ premium SEO audit tools that crawl the website's source code to identify structural changes necessary to comply with specific ranking criteria, commonly called SEO factors. Working collaboratively with developers, SEO specialists implement technical changes to the source code and await the results. The cost of purchasing premium SEO audit tools or hiring an SEO specialist typically ranges in the thousands of dollars per year. Against this backdrop, this research endeavors to provide an open-source Python-based Machine Learning SEO software tool to the general public, catering to the needs of both website owners and SEO specialists. The tool analyzes the top-ranking websites for a given search term, assessing their on-page and off-page SEO strategies, and provides recommendations to enhance a website's performance to surpass its competition. The tool yields remarkable results, boosting average daily organic traffic from 10 to 143 visitors.

Engine Overheating is a condition where the engine temperature has increased above the working temperature. This analysis aims to analyze the damage, determine the factors causing the damage, identify corrective steps and prevent engine overheating. The inspection procedure is carried out by checking the cooling system's components, cylinder head, and cylinder block. Further analysis using a fishbone diagram to analyze the causes of engine overheating troubleshooting. Damage analysis results Engine overheating occurs because the thermostat is damaged, namely, the thermostat valve which cannot open when the temperature reaches the standard temperature (71oC), and also due to damage to the cylinder gasket which causes bubbles due to compression leaks and mixing of radiator coolant and oil. Repair steps are taken in the form of replacing damaged components with new ones. Preventive measures are taken by not using the unit on an over-running basis, checking coolant levels, carrying out daily checks properly and correctly, and carrying out preventive maintenance regularly.

The ACC feature when activated augments the engine performance in real-time. This article presents a novel methodology to predict the optimal adaptive cruise control set speed profile (ACCSSP) by considering all the effecting parameters. This paper investigates engine operating conditions (EOC) criteria to develop a predictive model of ACCSSP in real-time. We developed a deep learning (DL) model using the NARX method to predict engine operating point (EOP) mapping the vehicle-level vectors (VLV). We used real-world field data obtained from Cadillac test vehicles driven by activating the ACC feature for developing the DL model. We used a realistic set of assumptions to estimate the VLV for the future time steps for the range of allowable speed values and applied them at the input of the developed DL model to generate multiple sets of EOP’s. We imposed the defined EOC criteria on these EOPs, and the top three modes of speeds satisfying all the requirements are derived for each second. Thus three eligible speed values are estimated for each second, and an additional criterion is defined to generate a unique ACCSSP for future time steps. Performance comparison between predicted and constant ACCSSPs indicates that the predictive model outperforms constant ACCSSP.

Often, in biology, we are faced with the problem of exploring relevant unknown biological hypotheses in the form of myriads of combination of factors that might be affecting the pathway under certain conditions. Currently, a major persisting problem is to cherry pick the combinations based on expert advice, literature survey or guesses for investigation. This entails investment in time, energy and expenses at various levels of research. To address these issues, a search engine design was recently been developed, which showed promise by revealing existing confirmatory published wet lab results. Additionally and of import, the engine mined up a range of unexplored/untested/unknown combinations of genetic factors in the Wnt pathway that were affected by ETC-1922159 enantiomer, a PORCN-WNT inhibitor, after the colorectal cancer cells were treated with the inhibitor drug. As an example, MYC is known to upregulate PRC2 complex. PRC2 complex contains EZH2, which suppresses tumor suppressor genes via epigenetic modifications. MYC and HOXB8 are up regulated in colorectal cancer, however, the dual working mechanism of the same is not known. The in silico engine showed positioning which correctly approximates and assigns to this 3rd order combination of MYC-HOXB8-EZH2, pointing to the in vitro/in vivo down regulation by ETC-1922159. If the protein interaction of MYC-HOXB8 can be established and a study be done apropos EZH2, it will establish at in vitro/in vivo level, the in silico ranking also. The potential of this engine is immense given the problem faced in biology and other fields. Here we elucidate the R code to understand the mechanics of the search engine in a fluid manner for systems biologists. Though the search engine is in the developmental stage, we share the detailed mechanism of the working principles of the same as it can be generalized to problems in other fields.

Energy storage systems play a crucial role in the overall performance of hybrid electric vehicles. Therefore, the state of the art in energy storage systems for hybrid electric vehicles has been discussed in this paper along with appropriate background information for facilitating future research in this domain. Specifically, we have compared the key parameters such as cost, power density, energy density, cycle life, and response time for various energy storage systems. For the energy storage systems employing ultra capacitors, we have presented the characteristics such as cell voltage, cycle life, power density and energy density. Furthermore, we have discussed and evaluated the interconnection topologies for the existing energy storage systems. We have also discussed the hybrid battery-flywheel energy storage system as well as the mathematical modeling of the battery-ultracapacitor energy storage system. Toward the end, we have discussed energy efficient powertrain for hybrid electric vehicles.

Gedanken experiments illustrating exemplifications of the Landauer principle in the free falling Einstein elevator are treated. Double-well simplest information system embedded into the free falling elevator is addressed. Infinitesimal horizontal force applied to the particle m transfers it from position “0” to position “1”, emerging from the free falling double-well system confining mass m. When thermal noise is considered, the potential barrier of kBT should be surmounted for the erasing of one bit of information. Entropic forces arising in the free falling elevator are considered. The maximal change in the entropy of free-joint polymer chain attached to the free falling elevator is estimated as ΔSmax≅kB, and it is remarkably independent of the mass attached to the chain and the parameters of the chain itself. Free falling minimal Szilard engine is treated. The informational re-interpretation of the minimal Szilard process is shaped as follows: the energy kBTln2 necessary for erasing of 1 bit of information is spent for lifting up mass, whatever, is the value of this mass. Appropriate choice of frames enables elimination of gravity in the considered system; however elimination of the thermal noise (dissipation processes) by the same procedure is impossible.

We study the performance of a classical and quantum magnetic Otto cycle with a quantum dot as a working substance using the Fock-Darwin model with the inclusion of the Zeeman interaction. Modulating an external/perpendicular magnetic field, we found in the classical approach an oscillating behavior in the total work that is not perceptible under the quantum formulation. Also, we compare the work and efficiency of this system for different regions of the Entropy, $S(T,B)$, diagram where we found that the quantum version of this engine always shows a reduced performance in comparison to his classical counterpart.

The escalating demand for multifunctional and advanced materials for growing industries has turned scientific exploration to graphene-based hybrid filler polymer composites. The introduction of graphene with other fillers into polymer matrices results in astonishing properties and thus has gained significant attention in recent years. This study addresses the significance of graphene-based hybrid filler polymer composites in progressive materials science and engineering. The paper encloses some traditional and current preparation techniques employed in the fabrication of composites and a comprehensive review of successfully prepared graphene hybrid polymer composites with enhanced mechanical, thermal, and electrical properties reported in published research. The role of different factors, e.g., filler type, aspect ratios, filler-matrix compatibility, dispersion effects, homogeneity, filler orientation through polymer matrices, and curing conditions, are summarized. Finally, the applications in heavy industries, e.g., aerospace, electronics, energy storage, and automotive sectors were also analyzed. We hope this study will serve as a practical resource for new researchers and practitioners to understand and harness the full potential of these composites in various scientific and industrial fields.

The article studies and analyzes a hybrid power source system, lithium battery, and fuel cell assisted by flexible photovoltaic panels for hybrid electric vehicles (HEVs). The new configuration operates based on optimizing the performance of the dual power source to maximize the available energy and operating time. The operational mode simulates specific driving conditions in urban, peripheral, and intercity routes under variable driving patterns and traffic conditions. The simulation results show that the new configuration increases the hybrid electric vehicle performance, providing higher reliability despite the more complex design. The proposed system reduces the external power supply dependence and avoids unexpected sudden stops due to energy exhaustion. The new configuration also extends the vehicle driving range depending on driving mode and route conditions. The vehicle driving range extension may reach up to 276.3 km. The hybridization of fuel cells with lithium battery and PV panel assistance enhances battery management, reducing battery servicing and increasing lifespan. The proposed topology is cheaper than a single fuel cell power system by 17.2%, reducing the total cost related to the single battery power system if we consider the battery recharge for its entire lifespan. The cost saving is 2.6% for the American market and 14.8% for the European one.

The structural and strength analysis of the material used to construct such an important engine element as the turbine is of great significance, both at the design stage as well as during tests and expertises related to emergency situations. Bearing in mind the conditions above mentioned, the paper presents the results of research on the chemical composition, morphology and phased structure of the metallic construction material used to produce the blades of the high and low pressure turbine of the RD-33 jet engine, which is the propulsion unit of the MiG-29 aircraft. The data obtained as a result of the material tests of the blades allowed, on the basis of the analysis of chemical composition and phased structure, to determine the grade of the alloy used to construct the tested elements of the jet engine turbine. The structural stability of the material was found to be lower in comparison with engine operating conditions, which manifested itself as a clear decrease in the resistance properties of the blade material. The results obtained can be used as a basis for analyzing the life span of an object or a selection of material replacements, which enable to produce the analyzed engine element.

This article focuses on the issue of motor oils used in the engines of off-road mobile machinery (NRMM), more specifically tractors. The primary goal of the paper is to determine the appropriate replacement interval for these oils. The physical properties of the examined samples were first determined by conventional instruments. Furthermore, the concentration of abrasive metals, contaminants, and additive elements were measured using an optical emission spectrometer. Lastly, the content of water, fuel, glycol, and the products of oxidation, nitration, and sulphation were determined by using infrared spectrometry. The measured values were compared to the limit values. Based on the processing and evaluation of these analyses, the overall condition of the oils was assessed and subsequently the optimal exchange interval of the examined oils was determined. In addition, a risk analysis of the outage was performed. Due to the high yields of crops, farmers can lose a significant amount of product when a tractor is not functioning during the harvest period. This loss for calculated in the paper.

Farm activities continued sand winning operations and the allocation of plots of land to prospective developers in Ghana pose a serious threat to the forest covers and lifespan of the Forest and game reserves. With all the positive add ups to the country from forests, Ghana has lost more than 33.7%(equivalent to 2,500,000 hectares) of its forest, since the early 1990s between 2005 and 2010, the rate of deforestation in Ghana was estimated at 2.19% per annum; the sixth highest deforestation rate globally for that period. This shows how important forest monitoring can be to the forestry commission in Ghana. Despite the frameworks which have been developed to help Ghana to protect and restore its forest resources, inadequate monitoring systems remain a barrier to effective implementation. In this study, Google earth engine was used to map and analyze the structural changes of forest cover using JavaScript to query and compute Landsat, MODIS and NOAA AVHRR satellite imageries of the study area (Ghana) with spatial resolutions 30m, 250m and 7km respectively. A supervised classification was performed on three multi-temporal satellite imageries and a total of six major land use and land cover classes were identified and mapped. By using random Forest-classification technique, from 1985 to 2018 recorded by NOAA AVHRR, forest cover has decreased by 66% and 2000 to 2018 recorded by Landsat and MODIS 61% and 47% respectively. A decrease in the forest has been as a result of anthropogenic activities in Ghana. A change detection analysis was performed on these images and it was noted that Ghana is losing forest reserves in every 5years. Overlay of the reserved forest of the 2000 and the classified map of 2018 shows vegetation changed during 2000-2018 remarkably. Therefore, forest-related institutions like the Forestry Commission can employ and use this monitoring system on Google Earth Engine for processing satellite images particularly Landsat, MODIS and NOAA AVHRR for forest cover monitoring and analysis for fast, efficient and reliable results.

In this paper, a multibody calculation methodology has been applied to the vibration analysis of a 4-cylinder, 4-stroke, turbocharged diesel engine, with a simulation driven study of the angular speed variation of a crankshaft under consideration of different modeling assumptions. Moreover, time dependent simulation results, evaluated at the engine supports, are condensed to a vibration index and compared with experimental results, obtaining satisfactory outcomes. The modal analysis also considers the damping aspects and has been conducted using a multibody model created with the software AVL/EXCITE. The influence of crankshaft torsional frequencies on the rotational speed behavior has been evaluated in order to reduce the vibration phenomena.

Nowadays, microfluidics has become an anarchist interdisciplinary topic with considerable attention in a wide range of biotechnology applications. In this research work, numerical investigation of microfluidic micro-mixer is carried out using hybrid actuation approach with different micropillar shapes and gaps. For this purpose, COMSOL Multiphysics is used with three different physics such as thermoviscous acoustic to solve acoustic governing equations, laminar physics is used to solve fluid flow governing equations and diluted transport species is used to solve mixing governing equations. The simulations were carried out with flow velocity of 200 µm/s at both inlets with an oscillation frequency of 15kHz. The outcomes were in the form of acoustic characteristics such as acoustic pressure, acoustic velocity, acoustic streaming and mixing index. The results revealed that the inclusion of micropillars enhanced the mixing performance and strength of acoustic field resulting in improvement of mixing performance compared to the case without micropillars. In addition, it was also investigated that blade shape micropillars with 0.150 mm gaps deliver best results compared to the other cases with maximum and minimum values of the mixing index 0.95 and 0.72 respectively. The obtained results can be extremely helpful for design and modifications of hybrid microfluidics micro-mixer.

Metastasis is the leading cause of cancer death and can be realized through the phenomenon of tumor cell fusion. The fusion of tumor cells with other tumor or normal cells leads to the appearance of tumor hybrid cells (THCs) exhibiting novel properties such as increased proliferation and migration, drug resistance, decreased apoptosis rate and avoiding immune surveillance. Experimental studies showed the association of THCs with a high frequency of cancer metastasis; however, the underlying mechanisms remain unclear. Many other questions also remain to be answered: the role of genetic alterations in tumor cell fusion, the molecular landscape of cells after fusion, the lifetime and fate of different THCs, and the specific markers of THCs, and their correlation with various cancers and clinicopathological parameters. In this review, we discuss the factors and potential mechanisms involved in the occurrence of THCs, the types of THCs, and their role in cancer drug resistance and metastasis, as well as potential therapeutic approaches for the prevention and targeting of tumor cell fusion. In conclusion, we emphasize the current knowledge gaps in the biology of THCs that should be addressed to develop highly effective therapeutics and strategies for metastasis suppression.

This study addresses the pressing need for flood extent and exposure information in data-scarce and vulnerable regions, with a specific focus on West Africa, particularly Senegal. Leveraging the Google Earth Engine (GEE) platform and integrating data from Sentinel-1 SAR, Global Surface Water, HydroSHEDS, Global Human Layer Settlement, and MODIS Land Cover, our primary ob-jective is to delineate flood extents and compare them with centennial flood-prone areas, offering a comprehensive assessment of exposure during the period from July to October 2022 across Sen-egal's 14 regions. The findings underscore a total inundation area of 2 951 square kilometers, impacting 297 142 people, 175 square kilometers of urban and 16 square kilometers of crops. Notably, August wit-nessed the largest flooded areas, reaching 780 square kilometers, constituting 0.40% of the coun-try's surface. Subsequent regions, including Saint-Louis, Ziguinchor, Fatick, and Matam, experi-enced varying extents of flooding, with August data showing a 1.34% overall overlap compared to centennial flood-prone areas derived from hydrological and hydraulic modeling. This low per-centage reveals the distinct purposes and natures of the two approaches (remote sensing and modeling), as well as their complementarity. Turning to flood exposure, August emerges as the most critical month, affecting 76 595 people (0.43% of the total population). Dakar, Diourbel, Thiès, and Saint-Louis regions bore substantial impacts, affecting 100 707, 57 648, 31 579, and 26 581 people, respectively. These findings emphasize the imperative for comprehensive disaster preparedness and mitigation efforts. The study provides a crucial national-scale perspective to guide Senegal's authorities in formulating effective flood management, intervention, and adaptation strategies.

This study entailed the design and analysis of a 400 m-class underwater glider operated by a bladder-type buoyancy engine. The underwater glider was designed for high-speed movement with a maximum velocity of 2 knots. The shape of the hull was designed to reduce water resistance using the Myring hull profile equation. The reliability was verified by performing simulations using resistance coefficients. The relationship between the control value of the ballast discharged from the buoyancy engine and the glider's speed according to the path angle was analyzed. Further, the relationship between the optimal glide angle and the design control value of the ballast was derived, and the optimal glider speed was estimated accordingly. Based on the analysis results, a bladder-type buoyancy engine was developed, and the maximum speed of the tested underwater glider was measured via sea trials.

Diesel engines, as power equipment, are widely used in the fields of automobile industry, ship and power equipment. Due to wear or faulty adjustment, the valve train clearance abnormal fault is a typical failure of diesel engines, which may result in the performance degradation, even valve fracture and cylinder hit fault. However, the failure mechanism features mainly in time domain and angular domain, on which the current diagnosis methods based, are easily affected by working conditions or hard to extract accurate enough, as the diesel engine keeps running in transient and non-stationary process. This work arms at diagnosing this fault mainly based on frequency band features which would change when the valve clearance fault occurs. For the purpose of extracting a series of frequency band features adaptively，a decomposition technique based on improved variational mode decomposition is investigated in this work. As the connection between the features and the fault is fuzzy, the random forest algorithm is used to analyze the correspondence between features and faults. In addition, the feature dimension is reduced to improve the operation efficiency according to importance score. The experimental results under variable speed condition show that the method based on variational mode decomposition and random forest is capable to detect valve clearance fault effectively.

This article presents an experimental study focused on the impact of supplementing pure gasoline (E0) with bioethanol, gradually increasing the biofuel concentration up to 70% (E10, E50, and E70). The research was conducted in two phases: In the first phase (Part I), the engine's operation involved varying engine speeds (n = 2000 rpm, n = 2500 rpm) and adjusting throttle openings (15%, 20%, 25%) to analyze changes in brake torque, thermal efficiency, and ecological indicators. The second phase (Part II) aimed to complement and clarify the study data. Here, the engine was maintained at a constant speed (n = 2000 rpm) and brake torque (MB = 80 Nm) while altering ignition timing. The findings revealed that increasing the bioethanol concentration up to 70% led to modest enhancements in engine brake torque and thermal efficiency. However, the most significant impact was observed in the reduction of greenhouse gas emissions and incomplete combustion byproducts. The study also established that achieving higher thermal efficiency requires compensating for the extended ignition delay phase resulting from bioethanol addition by advancing ignition timing. Nevertheless, this approach is constrained by the escalating emissions of carbon dioxide and hydrocarbons. These findings provide valuable insights into optimizing the balance between bioethanol supplementation, engine performance, and environmental sustainability in spark ignition engines.

Considering the concerns for emissions reduction in the maritime sector, the present paper evaluates, through modeling and simulation, the oxy-fuel combustion in a two-stroke ship engine (2SE) and the best production system configuration to obtain the required oxygen (O2). An initial model of a ship engine is calibrated with the engine manufacturer’s data, and then adapted to work with O2 as the oxidant to eliminate nitrogen oxides (NOx) emissions and with exhaust gas recirculation (EGR) to control the in-cylinder combustion temperature. Mixed Ionic-Electronic Conducting (MIEC) membranes produce the necessary O2 from the ambient air, which is heated up and pressurized by a heat exchanger and turbocharging coupled system to provide the air conditions required for the proper operation of the MIEC. Several layouts of this system are evaluated for the full load engine operating point to find the optimum O2 production system configuration. Results reveal that the engine operating under oxy-fuel combustion conditions avoids NOx emissions at the expense of higher brake specific fuel consumption (BSFC) to obtain the original brake torque, and also expels a stream composed exclusively of CO2 and H2O, what facilitates the separation of CO2 from exhaust gases.

The vibration signal of the engine contains strong background noise and many kinds of modulating components, which is difficult to diagnose. Variational mode decomposition (VMD) is a recently introduced adaptive signal decomposition algorithm with a solid theoretical foundation and good noise robustness compared with empirical mode decomposition (EMD). VMD can effectively avoid endpoint effect and modal aliasing. However, VMD cannot effectively eliminate the random noise in the signal, so the random decrement technique is introduced to solve the problem. Based on the crankshaft bearing fault simulation experiment, the four kinds of wear state vibration signals are decomposed by VMD, and the modal components with smaller permutation entropy are selected as fault components. Then the fault component is processed by the random decrement technique, and the Hilbert envelope spectrum of the fault component is obtained. Compared with the fault feature extraction method based on EMD and EEMD, the feature extraction results of the proposed method are better than those of the above two methods. The simulation analysis and the simulation test of the crankshaft bearing fault verify the effectiveness of the proposed method.

To accurately predict the matching relationships between the various components and the engine performance in the whole aero-engine environment, this study introduces a two-dimensional throughflow simulation method for the whole aero-engine. This method is based on individual throughflow solvers for the turbo-machinery and the combustor. It establishes a throughflow simulation model for the whole engine by integrating with the compressor-turbine co-operating equations and boundary conditions. The turbo-machinery throughflow solver employs a circumferentially averaged form of the time-dependent Navier-Stokes equations (N-S) as the governing equation. The combustor solver uses the Reynolds Average Navier-Stokes (RANS) method to solve flow and chemical reaction processes by constructing turbulence, combustion, and radiation models. The accuracy of the component solver is validated using Pratt & Whitney’s three-stage axial compressor (P&W3S1) and General Electric’s high-pressure turbine (GE-EEE HPT), and the predicted results are consistent with the experimental data. Finally, the developed throughflow method is applied to simulate the throttling characteristics of the WZ-X turboshaft engine. The results predicted by the throughflow program are consistent with the GasTurb calculations, including the trends of shaft power delivered, specific fuel consumption (SFC), inlet airflow, and total pressure ratio of the compressor. The developed method to perform throughflow simulation of the whole aero-engine eliminates the dependence on a general component map. It can quickly obtain the meridian flow field parameters and overall engine characteristics, which is expected to guide the design and modification of the engine in the future.

The biological conservation measures factor (B) in the Chinese Soil loss Equation (CSLE) model is one of the main components in evaluating soil erosion, and the accurate calculation of B factor at the regional scale is fundamental in predicting regional soil erosion and the dynamic changes. In this study, we developed an optimal computational procedure for estimating and mapping the B factor in the Google Earth Engine (GEE) cloud computing environment using multiple data sources through data suitability assessment and image fusion. Taking the Yanhe River Basin in the Loess Plateau of China as an example, we evaluated the availability of daily precipitation data (CHIRPS, ERA5, and PERSIANN-CDR data) against the data at national meteorological stations. We estimated the B factor from Sentinel-2 data and proposed a new method, namely trend migration method, to patch the missing values in Sentinel-2 data using three other remote sensing data (MOD09GA, Landsat-7, Landsat-8). We then calculated and mapped the B-factor in Yanhe River Basin based on rainfall erosivity, vegetation coverage, and land use types. The results show that the ERA5 precipitation dataset outperforms the CHIRPS and PERSIANN-CDR data in estimating rainfall erosivity and rainfall, and it can be utilized as an alternative data source for meteorological stations in soil erosion modeling. Compared to the harmonic analysis of time series (HANTS), the trend migration method proposed in this study is more suitable for patching the missing parts of Sentinel-2 data. The restored high-resolution Sentinel-2 data fit nicely with the 10-m resolution land use data, enhancing the B factor calculation accuracy from the region to the spot level. The B factor computation procedure developed in this study is applicable to various river basin and regional scales for regional soil erosion monitoring.

The article presents development of the more electric turbofan engine in distributed architecture with a design thrust in the range from 3 to 7.5 and from 7.5 to 30 kN for small and medium-sized unmanned aerial vehicles. The engine subsystems are considered as separate smart modules with a built-in control system, exchanging data via a digital channel with the central engine control and diagnostics unit. The key smart engine units are combined in the following subsystems: starter and turbine generators, oil pumps, actuator of guide vanes, fuel pumps, fuel metering unit, control and diagnostic unit. All pumps and guide vane actuator are electrically driven. Control and monitoring signals are transmitted via a digital bus. Functional and reliability analysis, technical configuration design of each subsystem are presented. Based on analysis of the architecture of distributed control system for gearbox-free more electric engine, different configurations of described subsystems are proposed.

This report is considered different aspects of the concept of the networked distributed engine control system (DECS) of future air vehicles. These aspects include the following: the structure of multiple networks similar to NATO Generic Vehicle Architecture (NGVA), the role of Artificial Intelligence (AI) in DECS, and the use Augmented Reality (AR) as Human-Machine Interface between AI and pilots. Deployment of AI solutions for monitoring equipment in on-board infrastructure can be provided on physical or virtual servers and in the clouds. In this case, it is possible to use various methods of alerting the pilot and ground personnel on the basis of AR. The use of AI allows covering an unlimited set of scenarios, to provide an assessment of the likelihood of equipment failure, classification alarm is normal, and recognition of the development of defects. To collect Big Data from sensors and the pre-processing of this data before a machine learning (ML) procedure it is proposed to form data sets with the help of the face-splitting matrix product. To decrease the time of reaction of Neural Networks it has been suggested the implementation of advanced tensor-matrix theory on the basis of penetrating face product of matrices. Other important results of the report are a possible version of the AR data format for DECS and a proposal about the use of non-orthogonal frequency discrete multiplexing (N-OFDM) signals to data transfer via fibre optics.

The application of thermoelectric generators (TEG) on the nozzle of an aviation jet engine was studied by finite element TEG-simulations. Against the background of system-level requirements of the reference aircraft this work reports the resulting requirements on the TEG design with respect to applied thermoelectric (TE) element lengths and fill factors (F) within the TE modules in order to maintain a positive effect on the specific fuel consumption. Assuming a virtual optimized TE material and varying the convective heat transfer coefficients at the nozzle surfaces this work reports the achievable power output. System-level requirement on the gravimetric power density (> 100 Wkg-1) can only be met for F ≤ 21%. Extrapolating TEG coverage to the full nozzle surface, the power output reaches 1.65 kW per engine. Assessment of further potential is demonstrated by a parametric study on the fill factor, convective heat transfer coefficients, and materials performance. This study confirms a feasible design range for TEG installation on the aircraft nozzle with a positive impact on the fuel consumption. This application translates into a reduction of operational costs, allowing for an economically efficient installation of TEG in consideration of the cost-specific power output of modern thermoelectric materials.

The limited velocity in a geometry of Lorentzian signature, seem to prevent the universe to reach thermodynamic equilibrium as suggested by the cosmic microwave background. Thus it was suggested that the universe which was initially minuscule has reached a more considerable radius in a short duration by a process known as cosmic inflation. However, to drive such a process have led to the suggestion of an ad-hoc scalar field the inflaton, which has no purpose in nature other than driving the cosmic inflation field and then stopping it once the universe reached the right size. In a recent paper it was shown that rapid expansion can occur without postulating an inflation by following Hawking's suggestion and assuming that primordially the metric of the universe had an Euclidean signature, in which case velocity is not limited and thermalization and rapid expansion are derived without the need to assume an ad-hoc field. However, while in the previous work emphasis was put on the dynamics and physical statistics of the particles in a Euclidean space vs. Lorentzian space in which both spaces were given. No mathematical model was given regarding the development of current Lorentzian space-time from the early Euclidean space-time, and how fundamental problems such as the space-time singularity and the homogeneity of the CMB can be solved in the hybrid Euclidean-Lorentzian picture. This lacuna is to be rectified in the current paper.

With the rapid surge in technological advancements, an equal amount of investment in technology-embedded teaching has become vital to pace up with the ongoing educational needs. Distance education has evolved from the era of postal services to the use of ICT tools in current times. With the aid of globally updated content across the board, technology usage ensures all students receive equal attention without any discrimination. Importantly, web-based teaching allows all kind of students to learn at their own pace, without the fear of being judged, including professionals who can learn remotely without disturbing their job schedules. Having web-based content allows low-cost and robust implementation of the content upgradation. An improved, yet effective, version of the education using such tools is Hybrid Learning (HL). This learning mode aims to provide luxurious reinforcement to its legitimate candidates while maintaining the quality standards of various elements. Incorporated with both traditional and distance learning methods, along with exploiting social media tools for increased comfort level and peer-to-peer collaboration, HL ultimately facilitates the end user and educational setup. The structure of such a hybrid model is realized by delivering the study material via a learning management system (LMS) designed in compliance with quality standards, which is one of the fundamental tackling techniques for controlling quality constraints. In this paper, we present the recently piloted project by COMSATS University Islamabad (previously known as COMSATS Institute of Information Technology) which is driven by technology-embedded teaching model. This model is an amalgam of the traditional class room model with the aid of state-of-the-art online learning technologies. The students are enrolled as full-time students, with all the courses in traditional classroom mode, except one course offered as hybrid course. This globally adapted model helps the students to benefit from both face-to-face learning as well as gaining hands-on experience on technology-enriched education model providing flexibility of timings, learning pace, and boundaries. Our HL model is equipped with two major synchronous and asynchronous blocks. The synchronous block delivers real-time live interaction scenarios using discussion boards, thereby providing a face-to-face environment. Interactions via social network has witnessed equally surging improvement in the output performance. The asynchronous block refers to the lecture videos, slides and handouts, prepared by imminent professors, available 24/7 for students. To ensure quality output, our HL model follows the course learning outcomes (CLOs), and program learning outcomes (PLOs) as per international standards. As a proof of concept, we have deployed a mechanism at the end of each semester to verify the effectiveness of our model. This mechanism fundamentally surveys the satisfaction levels of all the students enrolled in the HL courses. With the surveys already conducted, a significant level of satisfaction has been noted. Extensive results from these surveys are presented in the paper to further validate the efficiency and robustness of our proposed HL model.

Garlic, as an important economic crop in China, still has room for improvement in terms of identification using remote sensing technology, Among them, high-precision classification of garlic has become an important subject. The Erhai Lake is an important freshwater lake in China. Under the influence of technology and policies, significant changes have occurred in the cultivation of garlic crops. this study constructed multidimensional features for crop classification suitable for Google Earth Engine, and Propose a method for identifying garlic crops using sample and feature datasets under limited conditions. The results indicate that: 1) In the land-use classification of the Erhai Lake Basin, the importance ranking of characteristic bands, from high to low, is as follows: spectral features, vegetation features, texture features, and terrain features. 2) The Random Forest method based on feature selection demonstrates high classification accuracy in land-use classification within the Erhai Lake Basin in Yunnan Province. The overall classification accuracy reaches 95.79%, with a Kappa coefficient of 0.9481. 3) The expansion direction of garlic cultivation in the Erhai Lake Basin initially strengthened and then weakened from 1999 to 2023. The vertical development of garlic cultivation reached saturation, showing a slow trend towards horizontal expansion between 2005 and 2018. The planting distribution in various townships in the Erhai Lake Basin gradually shifted from a relatively uniform distribution to an upstream development in the basin. This study utilizes the Google Earth Engine (GEE) cloud computing platform and machine learning algorithms to compensate for the lack of statistical data on garlic cultivation in the Erhai Lake Basin. Simultaneously, it accurately, rapidly, and efficiently extracts planting information, demonstrating significant potential for practical applications.

The performance analysis of the internal/external flows around a high speed vehicle in near-space is fundamental and critical to the configuration and structure design for high speed aircraft. In this paper, the two-dimensional coupled implicit Reynolds Average Navier-Stokes (RANS) solver and RNG k–ε turbulence model is employed to numerically simulate the flow field of an integrated high speed vehicle. The flow field are divided three parts to solve separately and compared with that solved by integrated simulation. The flow field are decomposed to save computer storage and simulation time. The first part is inlet/isolator, the second part is combustion chamber and the third is nozzle. In the computation process, the outlet condition of isolator is endowed to the inlet boundary of combustion chamber and the outlet boundary of combustion chamber is endowed to the inlet boundary of nozzle, then the whole flow field of integrated high speed vehicle is solved. Aerodynamic characteristic of fragment-computation and integration-computation is comparatively analyzed at different angles of attack, the results show that the maximum deviation of lift coefficient obtained by fragment-computation is less than 3% compared with that by integration, the maximum deviation of drag coefficient fragment-computation is less than 5% compared with that by integration, while pitching moment coefficient deviation less than 12%. However, for simulation time-consumption, the time used by fragment-computation reduces 45% compared with integration-computation. Meanwhile, by components force analysis, it can be concluded that the aerodynamic property of inlet/isolator can been obtained separately and hardly effected by other parts.

Forest damage has become more frequent in Europe in the last decades, and remote sensing offers a powerful tool for monitoring them rapidly and cost-effectively. A novel approach was developed to utilize high-resolution ESA Sentinel-2 satellite imagery and Google Earth Engine cloud computing. The processing, analysing, and visualization of vegetation and water index (NDVI, NDVIch, Z NDVI, EVI, NDWI) maps and charts derived from satellite images took place in the cloud to ensure the detection of forest disturbances in the Hungarian study site for the period 2017-2020. The index maps were classified to reveal forest disturbances, and the cloud-based method successfully showed drought and frost damage in the oak-dominated Nagyerdő forest of Debrecen. Differences in the reaction to damage between tree species were visible in the index values, therefore a Random Forest Machine Learning classifier was applied to show the spatial distribution of dominant species. Accuracy assessment was accomplished with confusion matrices that compared classified index maps to field-surveyed data, demonstrating 71% Total Accuracy for forest damage and 76% for tree species. Based on the results of this study and the resilience of Google Earth Engine, the presented method has the potential to be extended to monitor the entire Hungary.

After long years of development, the technology of analyzing the working condition of power units based on vibration signals has had relatively stable applications, but the accuracy and the degree of automation and intelligence for fault diagnosis are still inadequate due to the limitations of the current development of key technologies. With the development of big data and artificial intelligence technology, the involvement of new technologies will be an important boost to the development of this field. To support the subsequent research, bibliometrics is used as a tool to sort out the development of the technology in this field at the macro level; at the micro level, the classical and key literature is studied to grasp the development status at the technical level and prepare for the selection of entry points to continue in-depth innovation afterwards.

A quantum Otto engine with a single ion harmonic oscillator as its working substance is studied for an adiabatic operation at high and low temperature limits. Using the universal optimization method, the heat engine is effectively optimized and found to yield better working conditions in some ranges of temperature ratios. Accordingly, the figure of merit, ψ, is found to be greater than unity in the range: 0<β2β1≤0.12; showing that the heat engine performs better in the optimized condition than in the maximum power working condition. ψ is determined to be less than one in the range 0.12≤β2β1≤0.35 under the same temperature limit ; depicting that the maximum power working condition is preferred to the optimized working condition. On the other hand, in the low temperature limit, the figure of merit, ψ, is found to be greater than unity in the range 3.7<ω1ω2≤18; revealing that optimized working condition is better than the maximum power working condition for the heat engine. In the same temperature limit, ψ is found to be less than one in the range 0≤ω1ω2≤3.7; showing that maximum power working condition is preferred to the optimized one. For the model of heat engine studied, in some ranges of temperature ratio, it is found to work better in the optimized condition, whereas in the other ranges it performs better under the maximum power working condition. So, it is possible to switch the engine between the two conditions depending on one’s need.

Data from ocean color (OC) remote sensing are considered a cost-effective tool for the study of biogeochemical processes globally. Satellite-derived chlorophyll, for instance, is considered an Essential Climate Variable since it is helpful in detecting climate change impacts. Google Earth Engine (GEE) is a planetary scale tool for remote sensing data analysis. Along with OC data, such tools allow an unprecedented spatial and temporal scale analysis of water quality monitoring in a way that has never been done before. Although OC data have been routinely collected at medium (~1 km) and more recently at high (~250 m) spatial resolution, only coarse resolution (≥4 km) data are available in GEE, making them unattractive for applications in the coastal regions. Data reprojection is needed prior to making OC data readily available in the GEE. In this paper, we introduce a simple but practical procedure to reproject and ingest OC data into GEE. The procedure is applicable to OC swath (Level-2) data and is easily adaptable to higher-level products. The results showed consistent distributions between swath and reprojected data, building confidence in the introduced framework. The study aims to start a discussion on making high resolution OC data readily available in GEE.

Sea-level rise is a problem increasingly affecting coastal areas worldwide. The existence of Free and Open-Source Models to estimate the sea-level impact can contribute to better coastal man-agement. This study aims to develop and to validate two different models to predict the sea-level rise impact supported by Google Earth Engine (GEE) – a cloud-based platform for planetary-scale environmental data analysis. The first model is a Bathtub Model based on the uncertainty of projections of the Sea-level Rise Impact Module of TerrSet - Geospatial Monitoring and Modeling System software. The validation process performed in the Rio Grande do Sul coastal plain (S Brazil) resulted in correlations from 0.75 to 1.00. The second model uses Bruun Rule formula implemented in GEE and is capable to determine the coastline retreat of a profile through the creation of a simple vector line from topo-bathymetric data. The model shows a very high cor-relation (0.97) with a classical Bruun Rule study performed in Aveiro coast (NW Portugal). The GEE platform seems to be an important tool for coastal management. The models developed have been openly shared, enabling the continuous improvement of the code by the scientific commu-nity.

Sea-level rise is a problem increasingly affecting coastal areas worldwide. The existence 15 of Free and Open-Source Models to estimate the sea-level impact can contribute to better coastal 16 management. This study aims to develop and to validate two different models to predict the 17 sea-level rise impact supported by Google Earth Engine (GEE) – a cloud-based platform for plan-18 etary-scale environmental data analysis. The first model is a Bathtub Model based on the uncer-19 tainty of projections of the Sea-level Rise Impact Module of TerrSet - Geospatial Monitoring and 20 Modeling System software. The validation process performed in the Rio Grande do Sul coastal 21 plain (S Brazil) resulted in correlations from 0.75 to 1.00. The second model uses Bruun Rule for-22 mula implemented in GEE and is capable to determine the coastline retreat of a profile through the 23 creation of a simple vector line from topo-bathymetric data. The model shows a very high correla-24 tion (0.97) with a classical Bruun Rule study performed in Aveiro coast (NW Portugal). The GEE 25 platform seems to be an important tool for coastal management. The models developed have been 26 openly shared, enabling the continuous improvement of the code by the scientific community.

The present study examines the effect of SiO2 nano-additives on the performance and emission characteristics of a diesel engine fuelled with soybean biodiesel. Soybean biofuel was prepared using the transesterification process. Nano-additives characterisations were done using different tests such as FESEM, XRD, EDS, etc., to study the morphology of nano-additives. For proper blending of nano-additives with biodiesel, the ultrasonication process was used. Surfactant was used for the stabilisation of nano-additives. After making all the combinations of nano fuel blends, physicochemical properties were measured as per ASTM standards. Performance and emissions readings were taken at different load conditions. It was found that with the addition of SiO2 nano-additives, brake thermal efficiency (BTE) and brake specific fuel consumption (BSFC) was increased by 3.48-6.39% and 5.81-9.88%, respectively. Significant reduction of CO, CO2, NOx, and smoke emissions were also observed compared to baseline fule due to better combustion efficiency with the use of SiO2 nano-additive.

The increasing concern regarding climate change continues to motivate research in the Arctic. Within the delicate cycle of the cryosphere, leads are an important kinematic feature that regulates heat balances and gaseous exchanges in the Arctic. Therefore, it is necessary to quantify the genesis of leads over time to identify when and where changes are occurring. The use of learning techniques is one such tool that is used to identify the characteristics of seasonal ice variability. This paper utilizes the Sentinel-1 RADAR imagery with a support vector machine learning scheme through Google Earth Engine to classify lead types in the Beaufort Sea.