Agricultural greenhouse is largely answered in the agricultural sphere, despite the shortcomings it has, including overheating during the day and night cooling which sometimes results in the thermal inversion mainly due to its low inertia. The glasshouse dressed chapel is relatively more efficient than the conventional tunnel greenhouse. Its proliferation on the ground is more or less timid because of its relatively high cost[14-22]. Agricultural greenhouse aims to create a favorable microclimate to the requirements of growth and development of culture, from the surrounding weather conditions, produce according to the cropping calendars fruits, vegetables and flower species out of season and widely available along the year. It is defined by its structural and functional architecture, the quality thermal, mechanical and optical of its wall, with its sealing level and the technical and technological accompanying[12-13]. The greenhouse is a very confined environment, where multiple components are exchanged between key stakeholders and them factors are light, temperature and relative humidity[8]. This state of thermal evolution is the level sealing of the cover of its physical characteristics to be transparent to solar, absorbent and reflective of infrared radiation emitted by the enclosure where the solar radiation trapping effect otherwise called "greenhouse effect" and its technical and technological means of air that accompany. The socio-economic analysis of populations in the world leaves appear especially the last two decades of rapid and profound transformations These changes are accompanied by changes in eating habits, mainly characterized by rising consumption spread along the year[14]. To effectively meet this demand, greenhouse-systems have evolved, particularly towards greater control of production conditions (climate, irrigation, ventilation techniques, CO2 supply, etc ...). Technological progress has allowed the development of greenhouses so that they become increasingly sophisticated and of an industrial nature (heating, air conditioning, control, computer, regulation, etc ...). New climate driving techniques have emerged, including the use of control devices from the classic to the use of artificial intelligence[10-11] such as neural networks and / or fuzzy logic, etc... As a result, the greenhouse growers prefer these new technologies while optimizing the investment in the field to effectively meet the supply and demand of these fresh products cheaply and widely available throughout the year.

The iron roughneck is an automated equipment utilized for the connection and removal of drilling tools. This paper presents the design of an integrated iron roughneck, providing a detailed introduction to its clamp body structure, along with an analysis of its structural characteristics and performance requirements. The study delves into the integration mode and working characteristics of the clamping mechanism and spin buckle mechanism for the integrated upper clamp body structure of the iron roughneck. Additionally, this paper conducts an in-depth theoretical study on the spin buckle mechanism. Firstly, it analyzes the actual working condition of the spin buckle roller from two perspectives: contact theory and rolling friction theory, determining the structural form of the spin buckle roller. Secondly, it investigates the relative displacement between the spin buckle mechanism and the drilling tool, proposing a design method for the floating device mounted on the spin buckle roller and establishing the kinematic equation of the spin buckle roller under the influence of the floating device. Furthermore, the kinematic equations of the spin buckle roller under the influence of the floating device are established. Finally, a dynamics simulation experiment is performed to simulate the working process of the spin buckle mechanism under actual working conditions, analyzing the dynamics and kinematics of the spin buckle mechanism and obtaining the relevant parameter curves of the spin buckle mechanism and drilling tools. Through data comparison and analysis, the correctness of the theoretical analysis results and the rationality of the performance and structure of the spin buckle mechanism are verified.

Digital Twin (DT) technology is a pivotal innovation within the built environment industry, facilitating digital transformation through advanced data integration and analytics. DTs have demonstrated significant benefits in building design, construction, and asset management, including optimizing lifecycle energy use, enhancing operational efficiency, enabling predictive maintenance, and improving user adaptability. By integrating real-time data from IoT sensors with advanced analytics, DTs provide dynamic and actionable insights for better decision-making and resource management. Despite these promising benefits, several challenges impede the widespread adoption of DT technology, such as technological integration, data consistency, organisational adaptation, and cybersecurity concerns. Addressing these challenges requires inter-disciplinary collaboration, standardization of data formats, and the development of universal design and development platforms for DTs. This paper provides a comprehensive review of DT definitions, applications, capabilities, and challenges within the Architecture, Engineering, and Construction (AEC) industries. This paper provides important insights for researchers and professionals, helping them gain a more comprehensive and detailed view of DT. The findings also demonstrate the significant impact that DTs can have on this sector, contributing to advancing DT implementations and promoting sustainable and efficient building management practices. Ultimately, DT technology is set to revolutionize the AEC industries by enabling autonomous, data-driven decision-making and optimizing building operations for enhanced productivity and performance.

This work offers a new log generalized odd log-logistic exponential regression model for analyzing weekly dengue fever cases in 2022 with a location-systematic component. To achieve this, a data set of 49 observations of dengue fever cases in the Federal District of Brazil is employed. A review of the mathematical properties of the generalized odd log-logistic exponential distribution is provided, the maximum likelihood method is used to estimate the parameters, and, through Monte Carlo simulations, the accuracy of the estimators is investigated. The model's fit is assessed using global influence metrics and residual analysis. For the time scenario studied, the proposed regression identified factors that have an impact on dengue fever cases, which may contribute to improve disease control. Finally, several interpretations are addressed, and a discussion presents results that aid in better understanding the data set and future research on different data.

Petri nets are a graphical notation for describing a class of discrete event dynamic systems whose behaviours are charaterised by concurrency, synchronisation, mutual exclusion and conflict. They have been used over the years for the modelling of various distributed systems applications. With the advent of pervasive systems and the Internet of Things, the Calculus of Context-aware Ambients (CCA) emerged as a suitable formal notation for analysing the behaviours of these systems. In this paper, we are interested in comparing the expressive power of Petri nets to that of CCA. That is, can the class of systems represented by Petri nets be modelled in CCA? To answer this question, an algorithm is proposed that maps any Petri net onto a CCA process. We show that a Petri net and its corresponding CCA process behave the same way through experiments. It follows that CCA is at least as expressive as Petri nets, i.e. any system that can be specified in Petri nets can also be specified in CCA. Moreover, tools developed for CCA can also be used to analyse Petri nets.

With the continuous increase in ship size combined with the generally slower increase in the size of waterways, the need for the prediction of ship maneuvering in shallow waterways continues to attract attention from the international scientific community. Ship behavior in shallow water is relevant in seabed effects that result in changing the hydrodynamic forces acting on the ship. In this study, the maneuvering characteristics of container ship with four degrees of freedom in shallow water are analyzed. The Reynolds-averaged Navier Stokes approach in Ansys Fluent code is used to produce the maneuvering coefficients through the simulations of forward running, static drift, static heel, circular motion, the combined motions, and the pure roll motion of the Kriso container ship. The maneuvering characteristics of the ship are estimated for evaluating the ship behaviors in shallow water condition. The obtained results show that the roll has a significant decrease and the ship’s turning diameter has a significant increase when the ship operates in the shallow waterway. The prediction of maneuvering characteristics of the ship are in good agreement with those of free running model tests, indicating that the numerical simulation based on the Computational Fluid Dynamics method has compromising capability to predict the maneuvering derivatives and the 4-DOF ship maneuvering motion in shallow water as well.

In this study, a Traveling-Wave Mach-Zehnder intensity Modulator (TW-MZM) was designed and optimized for six different electro-optical (EO) crystals: Lithium Niobate (LNB), Potassium Niobate (KNB), Lithium Titanate (LTO), Beta Barium Borate (BBO), Cadmium Telluride (CdTe), and Indium phosphide (InP). The performance of each EO crystal, including optical and radio frequency (RF) loss, applied voltage, and modulation bandwidth, was estimated and compared. The results suggested that, in theory, KNB, LTO, BBO, and CdTe have the potential to outperform LNB. However, it should be noted that the loss associated with KNB and LTO is comparable to that of LNB. The findings demonstrated that BBO and CdTe exhibit a modulation bandwidth exceeding 100 GHz and demonstrate the lowest loss among the considered crystals based on the assumed geometry.

Dynamic virtual testing by Hardware in the Loop (HIL) real time simulation systems is common to minimize physical tests to validate and verify control algorithms, logics and software, under normal and emergency conditions. The accurate but costly HIL models setup activities could have an extended use to simulate faulty conditions that are seldom observable in the real system, yet they must be detected in advance and recognized. Data driven diagnostic methods have reached a mature development, in various sectors, proving effective in early detecting slowly varying deviations from nominal patterns of signals, even without a priori knowledge of their nature and behavior. These methods rely on vast amount of data logged from real systems that are ever and ever richer of monitoring telemetries that are worth being exploited. However, due to the usually reliable and safe systems being managed, it is often difficult to collect enough data on anomalous situations to validate fault detection and isolation and limit false positives. HIL model-based simulations allow generating and observing operational domains or transients rarely observed in real operations. This allows identifying the signals that are most correlated to faults and that could be selected to setup data-based diagnostic models of parts or subsystems, which are more suitable than the whole HIL model for online diagnostics, as well as to identify the features and trends that are most symptomatic of anomalous conditions.

In this paper, low circumferential reciprocating load foot-scale tests were performed on two truncated PHC B 600 130 tubular piles with bearing nodes to characterize the damage process and morphology of the specimens and to investigate the load-carrying performance of the members. The test results reveal that under the action of tensile-bending-shear loading, the bearing concrete in the node area buckles and is damaged, the anchored reinforcement in the node area yields, the constraint is weakened, an articulation point is formed, and the node rotational capacity increases. When the embedment depth increases from 200 mm to 300 mm, the ultimate bearing capacities of the positive and negative nodes increase by 57.60% and 54.60%, respectively. A numerical simulation is used to verify the test results. Considering the three types of piles with truncated nodes, the numerical simulation is used to analyze the node-bearing capacity at different embedment depths. Finally, two preferred node types are proposed as follows: pipe pile core-filled longitudinal reinforcement anchored into the bearing node and pipe pile body longitudinal reinforcement anchored into the bearing node + pipe pile core-filled longitudinal reinforcement anchored into the bearing node, with preferred embedment depths of 350 mm and 200 mm, respectively.

This paper proposes a lightweight joint with bamboo culm cross connection, which is constructed by screwing one side of the screw into the other side of the wooden plug, its simple construction makes it easy to manufacture and cost-effective. This form connects adjacent bamboo culms to form corner joints, and bolt connects adjacent bamboo culm frames to form edge joints, to explore the mechanical properties of the combination joints formed by corner joints and edge joints, by combining experiments and numerical simulations, the method discovered and explained the relationship and mechanism between the number and position of bolts and the stiffness of edge joints. A calculation model for frame-unit bamboo culm members considering semi-stiffness of joints is proposed, and the conversion equations between linear stiffness of corner and edge joint and their rotational stiffnesses are derived. By establishing the series and parallel relationship between the corner and edge joints in the assembled members, a generalized relational equation between the total stiffness of the assembled joint and the stiffness of the corner and edge joints is derived. A numerical simulation method for frame-unit bamboo culm members with semi-rigid joints is proposed, which can obtain the stiffness calculation formula of edge joints and the average rotational stiffness value of semi-rigid corner joints. A finite element analysis method based on semi-rigid joints is proposed for frame-unit bamboo culm members, and the internal force sketches and calculation methods for bamboo culms and joints are presented, which can be used for the internal force analysis and design of frame-unit bamboo culm lattice structures.

Transport electrification is essential for reducing CO2 emissions, and technologies such as hybrid and range-extended electric vehicles will play a crucial transitional role. Such vehicles employ an internal combustion engine for on-board chemical energy conversion. The Wankel rotary engine should be an excellent candidate for this purpose, offering high power-to-weight ratio, simplicity, compactness, perfect balance, and low cost. Until recently, however, it has not been in production in the automotive market, due, in part, to relatively low combustion efficiency and high fuel consumption and unburnt hydrocarbon emissions, which can be traced to constraints on flame speed, an elongated combustion chamber, and relatively low compression ratios. This work uses large-eddy simulation to study the in-chamber flow in a peripherally ported 225cc Wankel rotary engine, providing insight into these limitations. Flow structures created during the intake phase play a key role in turbulence production but presence of the pinch point inherent to Wankel engine combustion chambers inhibits flame propagation. Two efficiency-enhancement technologies are introduced as disruptive solutions: (i) pre-chamber jet ignition and (ii) the two-stage rotary engine. These concepts overcome the traditional efficiency limitations and show that Wankel rotary engine design can be further enhanced for its role as a range extender in electrified vehicles.

An existing simplified simulation model of global climate, population, economy, and governance is extended, through statistical and other data analysis, to include several measures of human well-being and population displacement. The revised model is used to explore (in testing to the year 2060) vicious cycles and causal cascades that, some have warned, could lead to acceleration of climate change in the coming decades. Model scenario testing addresses two uncertainties, namely the strength of the effect of climate change on governance erosion, and the strength of the “heat trap” effect from physical tipping points such as ice loss and permafrost thaw. This testing indicates that a strong heat trap effect could very well accelerate climate change, but that even a strong governance erosion effect is unlikely to do so. Governance erosion would hurt human well-being but is probably not much of a climate threat per se. These results are tentative but point to the possibility of formally modeling causal cascades beyond what the larger climate models have done to date.

For both conventional and renewable energy conversion processes, computational fluid dynamics (CFD) has been used to address more energy-related challenges in recent decades. Using CFD to investigate vertical-axis wind turbines has become more common in recent years. The main goals of this application have been to more accurately predict the turbine's performance and to comprehend the complicated nature of the complex turbulent flow. The Vertical Axis Wind Turbine (VAWT) simulation for energy-generating applications has several intricate components. One of them is being aware of the chaotic flow that occurs during the first stages of the starting process, which influences overall effectiveness. In this article, the performance of the wind turbine was increased by using a passive flow control approach. The numerical research was carried out using Large Eddy Simulation for four alternative tip speed ratios in both cases, the classic and the optimized, equipped with a vortex trap on the exterior of the blades. The power and torque coefficient variations, as well as the velocity magnitude contours, show that the starting process may begin with a significant improvement in efficiency when flow control is used.

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

Zinc, being a part of many proteins and enzymes, plays an important role in the vital processes of the body. Racemic α-lipoic acid (LA) has a wide range of biological activities: antioxidant, antidiabetic, anticancer, and it is also used in diseases such as Parkinson's and Alzheimer's. Nowadays, the synthesis and structural studies of new zinc complexes with biologically active compounds are of great importance for drug discovery. This work shows the studies carried out on the synthesis, structure, identification, also biological activity using in silico methods of a complex compound based on LA and zinc. A complex compound of zinc with LA (ZnLA) was synthesized in the presence of LA and zinc acetate in ethanol. The identification of ZnLA by (FT-IR, XRD and TGA-DTA), also DFT calculations at the B3LYP/def2-TZVPP level of theory was performed. The molecular docking investigation involved docking ZnLA with two different protein receptors such us Glycogen phosphorylase (2GPA) and Kv potassium channel beta subunit (1ZSX) using CB-Dock 2 server and Auto-Dock Vina program to determine the mechanism of action and biological activity. This study reveals potential ligand-protein interactions at the atomic level and suggests that the treatment of diabetes (non-insulin dependent) with ZnLA.

The resistance of Candida spp to fluconazole is a serious problem; Therefore, it must be investigated to expand the understanding of the phenomenon with a view to its clinical applicability. The aim was to evaluate the expression of ERG3, ERG11, CDR1, and SNQ2 genes involving computational analysis. Relative expression was quantified by RT-qPCR. Metrics were obtained by molecular docking and through Fisher's discriminant functions, a predictive classification against susceptibility to fluconazole was performed. The expression of ERG3, CDR1, and SNQ2 genes was higher in resistant strains than in dose-dependent sensitive strains. The gene with the highest expression in strains exposed to fluconazole was CDR1 and, in both resistant and dose-dependent sensitive strains exposed to fluconazole, the ERG11 gene had lower expression. The molecular docking model generated lower median fluconazole-ERG11 contacts than the median fluconazole-ERG3, -CDR1, and -SNQ2 contacts. The predicted classification through the multivariate model for fluconazole sensitivity obtained an accuracy of 73.5%. Resistant strains had a significant expression of CDR1, SNQ2 and ERG3 genes, compared to a comparatively low expression of ERG11. Molecular analysis identified a low affinity between fluconazole and its drug target, which may explain a lower intrinsic susceptibility of the fungus.

Large language models (LLMs) excel in providing natural language responses that sound authoritative, reflect knowledge of the context area, and can present from a range of varied perspectives. Agent Based Models and Simulation consist of simulated agents that interact within a simulated environment to explore societal, social, and ethical, among other, problems. Agents generate large volumes of data over time and discerning useful and relevant content is an onerous task. LLMs can help in communicating agents’ perspectives on key events by providing natural language narratives. However, these narratives need to be factual, transparent, and reproducible. To this end, we present a structured narrative prompt for sending queries to LLMs. Chi-square tests and Fisher’s Exact tests are applied to assess statistically significant difference in sentiment scores of the narrative messages between simulation generated narratives, ChatGPT-generated narratives, and real tweets. The narrative prompt structure effectively yields narratives with the desired components from ChatGPT. This structure is expected to be extensible across LLMs. In 14 out of 44 categories, ChatGPT generated narratives which has sentiment scores that were not discernibly different, in terms of statistical significance (alpha level of 0.05), from the sentiment expressed in real tweets. Three outcomes are provided: (1) a list of benefits and challenges for LLMs in narrative generation; (2) a structured prompt for requesting narratives of a LLM based on simulated agents’ information; and (3) an assessment of statistical significance in the sentiment prevalence of the generated narratives compared to real tweets. This indicates significant promise in the utilization of LLMs for helping to connect simulated agent’s experiences with real people.

Bistatic scattering coefficient have important application of ocean active microwave remote sensing. In this paper, a general expression of the composite scattering coefficient for multiple dielectric targets of one-dimensional fractal sea surface is derived by MOM method. Secondly, the bistatic scattering coefficient is computed, there are two class missile targets and one class ship target of one-dimensional fractal sea surface, and the influence of the water dielectric constant, fractal dimension, wind speed, the target dielectric constant and spatial position is discussed, and the corresponding conclusions are given. The proposed algorithm is able to solve some composite scattering problems, such as “dielectric or conductive object + rough surface”, “dielectric or conductive floating object +rough ”and it has certain generalization and reference.

Glioblastoma multiforme (GBM) is a highly heterogeneous brain tumor with limited treatment options and a poor prognosis. Cancer stem cells (CSCs) have emerged as a critical factor in GBM resistance and management, contributing to tumor growth, heterogeneity, and immunosuppression. The transcription factor FOXM1 has been identified as a key player in the progression, spread, and therapy resistance of various cancers, including GBM. In this study, researchers conducted structure-based in silico screening to identify natural compounds that could target the DNA-binding domain (DBD) of the FOXM1 protein. Through molecular docking analyses, identified Silybin B as a potential inhibitor of FOXM1, exhibiting strong interaction with the protein. MD simulations were performed to validate the binding stability of the FOXM1-Silybin B complex. The study provides valuable insights into the potential of Silybin B as a FOXM1 inhibitor and its ability to induce senescence in GBM stem cells. These findings contribute to the development of structure-based design strategies for FOXM1 inhibitors and innovative therapeutic approaches for the treatment of Glioblastoma.

Breaching and liquefaction are two types of coastal flow slides proposed in the literature. While their final results are similar, liquefaction and breaching failure are two different processes, with breaching failure characterized by a different progression and sand movement mechanisms. In densely packed sand, breaching causes delayed sand grain discharge and negative excess pore pressures. Liquefaction, when a mass of soil suddenly behaves like a liquid, flow out over overly gentle slopes. Flow slides typically go unnoticed until the slope fails above ground because they start below the water. Without modern technology, diving equipment, risk assessment, and probabilistic and sensitivity assessments, flow slides inherently challenging. The authors developed a new sensitivity index to detect the susceptibility of coastal flow slides. It was established a sophisticated hybrid model that accommodates flow slides in sync with unpredictable variables employed in this new sensitivity index. Innovative hybrid model has three distinctive models. The Hybrid Hydrodynamic Model addresses wave, wind, current, climate change, and sediment transfer. The Monte Carlo Simulation analyses sensitivity, and the Bayesian Network calculates joint probability of coastal flow slide parameters of this new index that includes environmental characteristics, including climate change. With the assistance of these three models, researchers aim at a) presenting a methodology for coupling coastal flow slide projections with outcomes; b) distinguishing two flow slides; c) examining variables influencing flow slides. The use of such a hybrid model and risk index offers a robust and computationally efficient approach to evaluating the critical angle slope for breaching failure.

Lamb waves occur in thin-walled structures in two wave modes, the symmetric and antisymmetric mode. Their oscillation on the structures‘ surfaces is either in phase (symmetric) or shifted by a phase angle of π (antisymmetric). In this work, a method is developed to compare the surfaces‘ oscillation phase relation. It is based on the evaluation of time signals regarding the instantaneous phase angle using the continuous wavelet transformation and as a comparative method the short-time Fourier transformation. For this purpose, numerical simulations utilizing the finite element method provide time signals from the top and bottom surface of different thin-walled structures. They differ with respect to their material settings and laminate configurations. The numerically obtained time signals are evaluated by the developed methods. The occurring oscillation phase differences on the top and bottom surface are studied and both methods are compared. Subsequently, the oscillation phase is evaluated experimentally for the wave propagation in a fiber metal laminate. It is shown that the method based on the continuous wavelet transformation is suitable for the evaluation of oscillation phase relations in time signals. Additionally, it is proven that fiber metal laminates show only two phase relations which indicates the occurrence of Lamb waves.

The most widely spread and neglected zoonotic disease – Leptospirosis, with an estimated 1.03 million cases and 58,900 deaths occurring annually, is a concern and can be prevented only by effective immunization. Due to the urgent need for a novel vaccine, various research organizations have been evaluating the protective immune response elicited by recombinant vaccines. In this study amino-acid sequences of Outermembrane protein (OMPL) 1, LipL 32, 41 and 46 from ten pathogenic serovars of Leptospira were selected to obtain epitopes that induce CD4+ and CD8+ T cell responses by binding to the MHC molecules. These B cell and T cell epitopes were used to design multi-epitope vaccine to which an adjuvant sequence was added at the N-terminal end and appropriate linkers were used to join the different epitopes to increase the efficiency of the vaccine constructs. Using an immuno-informatics approach, constructs were analyzed for the physiochemical properties, secondary and tertiary structure and its validation, docking with different receptors and prediction of binding affinity for each docked complex, followed by molecular dynamics simulation. Taking in consideration all the results, multi-epitope constructs designed may prove to be promising vaccine candidates against Leptospirosis and warrants experimental validation.

We study in this work a dilute granular gas immersed in a thermal bath made of smaller particles with nonnegligible masses as compared with the granular ones. The kinetic theory for this system is developed and described by an Enskog--Fokker--Planck equation for the one-particle velocity distribution function. Granular particles are assumed to have inelastic and hard interactions, losing energy in collisions as accounted by a constant coefficient of normal restitution. The interaction with the thermal bath is based on a nonlinear drag force plus a white-noise stochastic force. To get explicit results of the temperature aging and steady states, Maxwellian and first Sonine approximations are developed. The latter takes into account the coupling of the excess kurtosis with the temperature. Theoretical predictions are compared with direct simulation Monte Carlo and event-driven molecular dynamics simulations. While good results for the granular temperature are obtained from the Maxwellian approximation, a much better agreement, especially as inelasticity and drag nonlinearity increase, is found when using the first Sonine approximation. The latter approximation is, additionally, crucial to account for memory effects like Mpemba and Kovacs-like ones.

This work investigates how integrated polyimide inlays with applied sensor bodies influence the guided ultrasonic wave propagation in narrow glass fiber-reinforced polymer specimens. Preliminary numerical simulations indicate that in a damping-free specimen, the inlays show reflections for the S0-mode propagation. Hence, an air-coupled ultrasonic technique and a 3D laser Doppler vibrometer measurement are used to measure different parts of the propagating waves’ displacement field after burst excitation at different frequencies. No significant reflections on the inlay can be seen in the experiments. However, it is shown that the reflections from the strip specimen’s narrow width cause periodical reflections that superimpose with the excited wave fronts. A continuous wavelet transformation in the time-frequency domain filters discontinuities from the measurement signal and is used for reconstruction of the time signals. The reconstructed signals are used in a spatial continuous wavelet transformation to identify the occurring wavelengths and hence to prove the assumption of reflections from the narrow edges. Since the amplitude of the reflections identified in the numerical data at the polyimide inlays are an order of magnitude smaller than the excited wave packages, it is concluded that material damping of the epoxy resin matrix extincts possible reflections from the inlays.

Nuclear technologies have the potential to play a unique role delivering low carbon energy for a future net-zero society. However, for the long-term success, nuclear will need to deliver innovative solutions as proposed in iMAGINE. One of the key challenges for the envisaged highly integrated nuclear energy system is the need for a demand driven salt clean-up system. The work described provides an insight into the interplay between a potential salt clean-up system and the reactor operation in a dynamic approach. The results provided will help to optimize the parameters for the salt clean-up process by delivering a dynamically calculated priority list identifying the elements with high influence on reactor operation. The integrated model is used to investigate the ideal time for the initiation of the clean-up as well as the effect of different throughput through the clean-up system on criticality as well as on the concentration of the elements in the reactor salt. Finally, a staggered approach is proposed with the idea to phase in the chemical clean-up processes step by step to keep the reactor critical. The results provide an essential step for the progress of iMAGINE as well as the basis for the inter disciplinary work required to bring iMAGINE into real operation.

During mechanical ventilation, a disparity between flow, pressure or volume demands of the patient and the assistance delivered by the mechanical ventilator often occurs. Asynchrony effect and ventilator performance are frequently studied from ICU datasets or using commercially available lung simulators and test lungs. This paper introduces an alternative approach of simulating and evaluating patient-ventilator interactions with high fidelity using the electro-mechanical lung simulator xPULM™ under selected conditions. The xPULM™ approximates respiratory activities of a patient during alternating phases of spontaneous breathing and apnoea intervals while connected to a mechanical ventilator. Focusing on different triggering events, volume assist-controlled (V/A-C) and pressure support ventilation (PSV) modes were chosen to test patient-ventilator interactions. In V/A-C mode a double-triggering was detected every third breathing cycle leading to an asynchrony index of 16.67%, being classified as severe. This asynchrony causes a major increase of Peak Inspiratory Pressure PIP = 12.80 ± 1.39 cmH2O and Peak Expiratory Flow PEF = -18.33 ± 1.13 L/min when compared to synchronous phases of the breathing simulation. Additionally, events of premature cycling were observed during PSV mode. In this mode, the peak delivered volume during simulated spontaneous breathing phases almost doubles compared to apnoea phases. The presented approach demonstrates the possibility of simulating and evaluating disparities in fundamental ventilation characteristics caused by double-triggering and premature cycling under V/A-C and PSV ventilation modes. Various dynamic clinical situations can be approximated and could help to identify undesired patient-ventilation interactions in the future. Rapidly manufactured ventilator systems could also be tested using this approach.

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

In Differentiated Services (DiffServ) architecture, Priority Service System (PSS) plays a vital responsibility to provide Quality-of-Service (QoS) for the applications based on networks. Priority queuing systems has always been a subject of interest for analytical modelling and evaluating the performance. However, previous works have mainly focused on performing priority queuing under range dependent traffics, namely Short Range Dependent (SRD) or Long Range Dependent (LRD). Recent studies revealed that realistic traffic demonstrates a heterogeneous nature for the modern networks that provide multiple services. In this paper, the results of analytical and simulation models of dynamic priority service systems is reviewed to study the heterogeneous traffic impact on designing and performance of the systems based on networks. In this paper, the results of analytical and simulation models of dynamic priority service systems is reviewed to investigate the impact of heterogeneous traffic on the design and performance of network-based systems. Here, the time of presence of requests in limited and unlimited buffer system is restricted. If the restriction is violated, the requests will be lost. The results of experiments conducted on both models are compared and it is identified that they differ in the allowable limit (2-9) %.

The work presents an approach to instrument the load sensing bearings for automotive applications for estimation of the loads acting on the wheels. The system comprises fiber-optic sensors based on addressed fiber Bragg structures (AFBS) with two symmetrical phase shifts. A mathematical model for load-deformation relation is presented, and the AFBS interrogation principle is described. The simulation includes (i) modeling of vehicle dynamics in a split-mu braking test, during which the longitudinal wheel loads are obtained, (ii) the subsequent estimation of bearing outer ring deformation using a beam model with simply supported boundary conditions, (iii) the conversion of strain into central wavelength shift of AFBS, and (iv) modeling of the beating signal at the photodetector. The simulation results show that the estimation error of the longitudinal wheel force from the strain data acquired from a single measurement point was 5.44% with root-mean-square error of 113.64 N. A prototype load sensing bearing was instrumented with a single AFBS sensor and mounted in a front right wheel hub of an experimental vehicle. The experimental setup demonstrated comparable results with the simulation during the braking test. The proposed system with load-sensing bearings is aimed at estimation of the loads acting on the wheels, which serve as input parameters for active safety systems, such as automatic braking, adaptive cruise control, or fully automated driving, in order to enhance their effectiveness and safety of the vehicle.

Bus operators around the world are facing the transformation of their fleets from fossil-fuelled to electric buses. Two technologies prevail: Depot charging and opportunity charging at terminal stops. Total cost of ownership (TCO) is an important metric for the decision between the two technologies, however, most TCO studies for electric bus systems rely on generalised route data and simplifying assumptions that may not reflect local conditions. In particular, the need to re-schedule vehicle operations to satisfy electric buses’ range and charging time constraints is commonly disregarded. We present a simulation tool based on discrete-event simulation to determine the vehicle, charging infrastructure, energy and staff demand required to electrify real-world bus networks. These results are then passed to a TCO model. A greedy scheduling algorithm is developed to plan vehicle schedules suitable for electric buses. Scheduling and simulation are coupled with a genetic algorithm to determine cost-optimised charging locations for opportunity charging. A case study is carried out in which we analyse the electrification of a metropolitan bus network consisting of 39 lines with 4748 passenger trips per day. The results generally favour opportunity charging over depot charging in terms of TCO, however, under some circumstances, the technologies are on par. This emphasises the need for detailed analysis of the local bus network in order to make an informed procurement decision.

The study of co-fusion was one of the essential topics in the history of metallurgy in China. Simulation experiments had been an essential concept in the study of the co-fusion steelmaking process. This paper mainly studied the simulation experiments of co-fusion from two aspects: the replication of co-fusion swords by three different methods, and the micro-analysis of the co-fusion samples. The experimental results indicated that several co-fusion swords could be made by different processes, but the carbon content and surface hardness were quite different. During repeated forging welded, the microstructure of the samples transformed from laminated to homogenized, finally the steel with a uniform carbon content was obtained. It was challenging to find the characteristics of co-fusion from the homogenized samples. The results prompted people to rethink the microstructure characteristics of ancient co-fusion artifacts.

Polydimethylsiloxane (PDMS) is one of the most popular elastomers and has been used in different fields, especially in biomechanics research. Among the many interesting features of this material, its hyperelastic behaviour stands out, which allows the use of PDMS in various applications, like the ones that mimic soft tissues. However, the hyperelastic behaviour is not linear and needs detailed analysis, especially the characterization of shear strain. In this work, two approaches, numerical and experimental, were proposed to characterize the effect of shear strain on PDMS. The experimental method was implemented as a simple shear testing associated with 3D digital image correlation and was made using two specimens with two thicknesses of PDMS (2 and 4 mm). A finite element software was used to implement the numerical simulations, in which four different simulations using the Mooney-Rivlin, Yeoh, Gent, and Polynomial hyperelastic constitutive models were performed. These approaches showed that the maximum value of shear strain occurred in the central region of the PDMS, and higher values emerged for the 2 mm PDMS thickness. Qualitatively, in the central area of the specimen, the numerical and experimental results have similar behaviours and the values of shear strain are close. For higher values of displacement and thicknesses, the numerical simulation results move further away from experimental values.

In the steeply inclined and extremely thick coal seams (SIETCS) mined using the horizontal sublevel top-coal caving (HSTCC) method, the uncertainty of gas emission is a safety threat to the mining operations. In order to reduce the occurrence of accidents, the determination of gas emission is crucial. In this paper, we first proposed a prediction model for workers at the floor coalbed to calculate gas emission on site. We then put forward a finite element numerical simulation for researchers to predict gas emission from the floor coalbed. At last, we measured gas emitted from the floor coalbed of SIETCS in Wudong Coal Mine in a specific mining period and used the data to verify the applicability and accuracy of these two gas-emission prediction methods. The results showed that the gas emission from Wudong Coal Mine was 1.08 m³/min calculated based on the prediction model and 1.07 m³/min obtained using the user-defined integration method. Both methods have their own advantages, disadvantages and applicable objects, and are important in predicting gas emission from SIETCS mined using HSTCC method.

Natural calamities observation, study and simulation has always been a prime concern for disaster management agencies. Billions of dollars are spent annually to explore geo-seismic movements especially earthquakes but it has always been a unique accident. The real-time study of seismic waves, ground motions, and earthquakes always needed a programmable mechanical structure capable of physically producing the identical geo-seismic motions with seismology domain definitions. A programmable multi-parametric five degrees of freedom electromechanical seismic wave events simulation platform to study and experiment seismic waves and earthquakes realization in the form of geo-mechanic ground motions is exhibited in this work. The proposed platform was programmed and interfaced through an IoT cloud-based Web application. The geo-mechanics was tested in the range of i) frequencies of extreme seismic waves from 0.1Hz to 178Hz; ii) terrestrial inclinations from -10.000° to 10.000°; iii) velocities of 1km/s to 25km/s iv) variable arrival times 1us to 3000ms; v) magnitudes M1.0 to M10.0 earthquake; vi) epi-central and hypo-central distances of 290+ and 350+ kilometers. Wadati and triangulation methods have been used for entire platform dynamics design and implementation as one of key contributions in this work. This platform is as an enabler for a variety of applications such as training self-balancing and calibrating seismic-resistant designs and structures in addition to studying and testing seismic detection devices as well as motion detection sensors. Nevertheless, it serves as an adequate training colossus for machine learning algorithms and event management expert systems.

Single-molecule fluorescence detection (SMFD) experiments are useful in distinguishing sub-populations of molecular species when measuring heterogeneous samples. One experimental platform for SMFD is based on a confocal microscope, where molecules randomly traverse an effective detection volume. The non-uniformity of the excitation profile and the random nature of Brownian motion, produce fluctuating fluorescence signals. For these signals to be distinguished from the background, burst analysis is frequently used. Yet, the relation between the results of burst analyses and the underlying information of the diffusing molecules is still obscure and requires systematic assessment. In this work we performed three-dimensional Brownian motion simulations of SMFD, and tested the positions at which molecules emitted photons that passed the burst analysis criteria for different values of burst analysis parameters. The results of this work verify which of the burst analysis parameters and experimental conditions influence both the position of molecules in space when fluorescence is detected and taken into account, and whether these bursts of photons arise purely from single molecules, or not entirely. Finally, we show, as an example, the effect of bursts that are not purely from a single molecule on the accuracy in single-molecule Förster resonance energy transfer measurements.

We quantitatively investigated the statistical behaviors of semiflexible polymer chains, which are simultaneously subjected to force stretching and rectangular tube confinement. Based on the wormlike chain model and Odijk deflection theory, we derived a new deflection length, by which new compact formulas are obtained for the confinement free energy and force-confinement-extension relation. These newly derived formulas have been justified by numerical solutions of an eigenvalue problem associated with the Fokker-Planck governing equation and extensive Brownian dynamics simulations based on the so-called Generalized Bead-Rod (GBR) model. We found that, comparing to the classical deflection theory, these new formulas are valid for a much extended range of the confinement-size /persistence-length ratio, and have no adjustable fitting parameters for sufficient long semiflexible chains in the whole deflection regime.

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

Fluctuations of protein three-dimensional structures and large-scale conformational transitions are crucial for the biological function of proteins and their complexes. Experimental studies of such phenomena remain very challenging and therefore molecular modeling can be a good alternative or a valuable supporting tool for the investigation of large molecular systems and long-time events. In this mini-review, we present two alternative approaches to the coarse-grained (CG) modeling of dynamic properties of protein systems. We discuss two CG representations of polypeptide chains used for Monte Carlo dynamics simulations of protein local dynamics and conformational transitions and, on other hand, highly simplified structure-based Elastic Network Models of protein flexibility. In contrast to classical Molecular Dynamics the modeling strategies discussed here allow quite accurate modeling of much larger systems and longer time dynamic phenomena. We briefly describe the main features of these models and outline some of their applications, including modeling of near-native structure fluctuations, sampling of large regions of the protein conformational space, or possible support for the structure prediction of large proteins and their complexes.

With the increasing adoption of building integrated photovoltaics (BIPV), concerns arise about potential glare. While recommended criteria to assess glare exist, it is challenging to apply these in the spatial and temporal domains and communicate the complex data to planning authorities and clients. This paper presents a new computational workflow using annual daylight simulation, material modelling using bi-directional scattering distribution functions (BSDFs) and image-based postprocessing to obtain 3-dimensional renderings of cumulative annual irradiance and glare duration on the built environment. The annual daylight simulation considers relevant sun positions in high temporal resolution (15-minute timesteps) and measured BSDFs to model different PV materials. The postprocessing includes a relative irradiance visualisation comparing the impact of a proposed PV proportional to a reference material. It also includes a new spatio-temporal workflow to assess the glare duration based on recommended thresholds. This workflow is demonstrated with a case study of a proposed PV roof for a church, assessing the glare potential of two different PV materials. The visualisations indicate glare durations well below the thresholds with satinated PVs, and in noncritical zones outside observer positions with standard PVs. Thus the proposed PV roof does not cause any disturbing glare.

The aim of this work is to introduce new ways to model the I-V characteristic of a PV cell or PV module using straight lines and Bézier curves. This is a complete novel approach, Bézier curves being previously used mainly for computer graphics. The I-V characteristic is divided in three sections, modeled with lines and a quadratic Bézier curve in the first case and with three cubic Bézier curves in the second case. The result proves to be accurate and relies on the fundamental points usually present in the PV cell datasheets: Voc (the open circuit voltage), Isc (the short circuit current), Vmp (the maximum power corresponding voltage) and Imp (the maximum power corresponding current) and the parasitic resistances Rsh0 (shunt resistance at Isc) and Rs0 (series resistance at Voc). The proposed algorithm completely defines all the implied control points and the error is analyzed. The temperature and irradiance influence is also analyzed. The model is also compared using the least squares fitting method. The final validation shows how to use Bézier cubic curves to accurately represent the I-V curves of an extensive range of PV cells and arrays.

This qualitative research explores the transformative role of Artificial Intelligence (AI) in Supplier Relationship Management (SRM) within the e-commerce sector. SRM is critical for e-commerce platforms to maintain efficient supply chains, optimize supplier interactions, and ensure competitive advantage in a dynamic marketplace. AI technologies offer advanced capabilities such as predictive analytics, machine learning algorithms, and natural language processing, which revolutionize traditional SRM practices by enhancing decision-making accuracy, mitigating supply chain risks, and fostering personalized supplier relationships. Through semi-structured interviews with 20 e-commerce professionals and industry experts, this study investigates AI's impact on supplier selection, operational efficiencies, and strategic supplier relationships. Findings highlight AI's ability to streamline supplier evaluation processes, improve demand forecasting accuracy, and optimize inventory management strategies. AI also facilitates personalized supplier engagement through sentiment analysis and real-time insights, promoting collaboration and trust. Ethical considerations, including algorithmic bias and data privacy, emerge as significant concerns in AI adoption for SRM. Addressing these challenges is crucial to maintaining stakeholder trust and ensuring responsible AI deployment. Furthermore, technological integration barriers and organizational readiness are identified as critical factors influencing successful AI implementation. Looking forward, the study underscores the potential of AI to drive innovation and competitiveness in e-commerce SRM, emphasizing the importance of ethical AI practices, technological infrastructure investments, and organizational preparedness for sustainable growth.

For quantitative determination of honokiol (HNK) and metformin hydrochloride (M-HCl) by HPLC from formulations and from dissolution studies, a QbD approach using three factors - three levels design was implemented to optimize the chromatographic conditions. To study the impact of each variable on the peak area of both compounds and retention time of last compound eluted, the 3D response surface plots were developed. HNK and M-HCl were separated on a Thermoscientific ODS Hypersyl TM (250 x 4.6 mm, 5 μm) column mantained at 30 C, using 0.02 M acetate buffer (pH = 3) / methanol (15 / 85, v / v) as mobile phase with a flow rate of 1 mL/min. The injected volume was 20 L, and the detection wavelengths were 256 nm for HNK and 236 nm for M-HCl. The validation of proposed method showed a linearity range of 4 – 1200 μg/mL for M-HCl (r2 = 0.9992) and 1 – 300 μg/mL for HNK (r2 = 0.9998), good precision (RSD < 2% for both HNK and M-HCl, in intra- and inter-day studies) and accuracy (mean recovery = 100.09% in the range 99.80 – 100.43% for M-HCl and mean recovery = 100.12% in the range 99.70 – 100.38% for HNK). The environmentally friendly nature of the method was confirmed by the evaluation of the method’s greenness. The proposed method was applied for the determination of HNK and M-HCl from oral dosage forms and in dissolution studies.

In the fast-evolving landscape of web services, success relies on optimizing efficiency, reliability, and user satisfaction. Decision-making in real-world applications demands a blend of quantitative (numerical values of quality of Service factors) and qualitative data of experts’ ratings of the quality of service factors. This paper proposes an optimization technique that integrates Hesitant and Intuitionistic Fuzzy Linguistic Terms with TOPSIS, leveraging Entropy Objective weights. There is limited research on Hesitant Intuitionistic Fuzzy Linguistic terms and the need to model fuzziness, haste and intuition in data precisely from experts rating. Thus, this article introduces an Entropy-based model tailored for web service optimization. Using the WS-DREAM dataset, quantitative data was extracted and objective weights were determined through the Entropy algorithm and qualitative evaluations from five experts, employing Hesitant Intuitionistic Fuzzy Linguistic Terms, were integrated. The process involved computing criteria weights and experts' assessment of Quality of Service factors for four services (S1, S2, S3, and S4) to form the Fuzzy Decision Matrix using modified Online output software. Applying the proposed technique to a web service optimization problem validated its effectiveness. The results indicated optimal selection of S1 as the best web service, supported by Coefficient Index values (0.785). This study introduces a novel approach, Entrop – Hesitationistic - Fuzz-TOPSIS, for web service optimization, integrating quantitative and qualitative data through an Entropy-based model. The effectiveness of the proposed technique, validated through application on the WS-DREAM dataset, underscores its potential in addressing real-world decision-making challenges. The optimal selection of S1 as the best web service suggests practical utility. The study encourages further exploration of this technique's applicability to diverse Multiple Criteria Decision Making (MCDM) problems, opening avenues for future research and application.

This study explores the intricacies of supplier relationships and inventory optimization within high-technology industries, offering a comprehensive analysis of the strategic imperatives and operational challenges faced by firms in these sectors. By adopting a qualitative research methodology involving semi-structured interviews with supply chain professionals and documentary analysis, the study provides in-depth insights into the evolving dynamics of supply chain management. Findings reveal a marked shift towards collaborative and strategic supplier relationships, characterized by trust, transparency, and joint innovation initiatives. These partnerships are essential for enhancing operational efficiency, driving product development, and navigating market uncertainties. Additionally, the research underscores the critical role of advanced technologies, such as artificial intelligence and the Internet of Things, in optimizing inventory levels and improving supply chain visibility. The integration of these technologies facilitates precise demand forecasting and proactive inventory management, thereby reducing costs and enhancing service levels. However, the study also highlights significant challenges, including geopolitical uncertainties, supply chain disruptions, and cultural barriers, which necessitate robust risk management strategies and adaptive supply chain practices. Strategic implications for organizational leaders and policymakers include the need to invest in supplier development, embrace digital transformation, and enhance risk management frameworks to build resilient and agile supply chains. This study contributes valuable insights into the complex interplay between supplier relationships, inventory optimization, and technological integration in high-technology industries, offering actionable recommendations for practitioners and researchers seeking to drive innovation and maintain competitiveness in a dynamic global marketplace.

Lead optimization in drug discovery is a crucial phase where initial hits are refined into compounds with improved pharmacological properties. While traditional methods rely on manual experimentation and modifications, AI-driven techniques have revolutionized this process by leveraging big data and predictive modeling. This review explores how AI-driven approaches accelerate lead optimization, showcasing examples like deep neural networks and reinforcement learning. Integration of multi-omic data and experimental validation further enhances AI-driven strategies. The future lies in refining these AI methods, democratizing tools, and interdisciplinary collaboration to streamline drug discovery and address medical needs efficiently.

The insufficient flexibility of the hybrid renewable integrated energy system (HRIES) makes it difficult to accommodate renewable energy with a high proportion. It is thus necessary to excavate its potential flexibility to improve its operational performance and consume renewable energy. This paper unlocks the adjustment potential of flexible buildings and builds a flexible thermal load regulation model according to the dynamic thermal characteristics and thermal comfort elastic interval of the buildings and a regulation model of the flexible electrical load based on its transferability, resectability, and rigidity. An operation optimization model, which incorporates multiple flexible load regulations and the variable load of devices, is then developed. A case study is presented to analyze the regulation and synergy mechanism in different types of flexible loads. Its results show a saturation effect between the flexible thermal and electrical loads in increasing renewable energy consumption and a synergy effect in decreasing the total cost. This synergy can reduce the total cost by 0.73%. Besides, the total cost can be reduced by 15.13%, and the renewable energy curtailment rate can decrease by 12.08% if the flexible electric and thermal loads are integrated into the operation optimization of HRIES.

Rotational jumps are crucial techniques in sports competitions. Estimating ground reaction forces (GRFs), one of the components constituting jumps, through a biomechanical model-based approach enables analysis even in environments where force plates or machine learning training data cannot be utilized. In this study, rotational jump movements involving twists on land were measured using inertial measurement units (IMUs) and estimated GRFs and body loads using a 3D forward dynamics model. Our estimation method, based on forward dynamics and optimization calculations, generated and optimized body movements using cost functions defined by motion measurements and internal body loads. To reduce the influence of the dynamic acceleration in the optimization calculation, the 3D orientation using sensor fusion composed of acceleration and angular velocity data obtained from IMUs and an extended Kalman filter was estimated. As a result, by generating movements based on the cost function, it was possible to calculate biomechanically valid GRFs while following the measured movements even if not all joints are covered by IMUs. This estimation method allows for 3D motion analysis independent of measurement conditions or training data.

To address the large hysteresis and susceptibility to instability in high-speed deposition fusion moulding (FDM) 3D printing’s temperature control, this paper proposes an improved ant-lion optimization algorithm (OCALO) to optimize the PID parameters.The improvement aims to solve the problem that the traditional ant-lion optimization algorithm (ALO) reduces the population diversity and tends to fall into the local optimal solution when it is close to the global optimal solution.The generation of the initial solution is enhanced by the introduction of a new Tent-Logistic-Cotangent composite chaotic mapping. This integration into Elite Opposition-Based Learning aids in optimizing population diversity. Concurrently, the incorporation of cosine factors of a specified parameter into the elitist formula of the traditional ALO algorithm is aimed at reducing the tendency of ants to randomly gravitate towards ant-lions with lower fitness values. This adjustment helps in balancing the exploration-exploitation trade-off in the algorithmic process. Compared with two existing classical algorithms and three improved ALO algorithms, the algorithm improves the convergence speed, global search ability and the ability to jump out of the local optimal solution. The improved algorithm was combined with PID control to design an OCALO-PID temperature controller and simulated on a high-speed 3D printing temperature model identified by modelling. The results show that the method improves the transient and steady-state performance of reactor temperature control with good control accuracy and robustness. Finally, the proposed algorithm is applied to a physical experimental platform to verify the feasibility of the algorithm.

Recent advancements have revolutionized interplanetary spacecraft missions by combining low-thrust propulsion systems with planetary Gravity-Assist (GA) maneuvers. This study investigates/improved a novel dual-step efficient-robust homotopy algorithm, grounded in indirect optimal control principles, to analyze fuel-optimal bang-bang control problems. The methodology integrates efficient techniques, including the homotopic method, compatible normalization, and improved switching function detection, aimed at enhancing optimal convergence in solving low-thrust gravity-assist spacecraft missions. To illustrate practical application, an Earth-to-Jupiter interplanetary scenario utilizing Mars-GA is examined. Results demonstrate the algorithm's superior efficacy in achieving fuel efficiency, robust convergence, and remarkable accuracy compared to existing solutions.

Interior permanent magnets motors are widely used in applications requiring high power den-sity and high efficiency due to their high torque generating capabilities. Recently, given the re-cent price fluctuations and unstable supply of rare-earth permanent magnets, alternative config-urations with reduced use of permanent magnets are being sought. Among the various candi-dates related to this, the consequent-pole type rotor structure can halve the number of perma-nent magnets used compared with conventional structures. However, in the no-load analysis, the waveform of the back electromotive force becomes asymmetric, generating a harmonic component. As a result, there is a disadvantage that the torque ripple increases. To overcome these shortcomings, we propose a novel rotor structure that applies a consequent-pole structure to an embedded permanent-magnet motor structure, wherein a number of permanent magnets are arranged in a flared structure to constitute a single polarity. In the proposed flared-structured magnet arrangement, it is possible to adjust the angle of the permanent magnet and the polar angle to mitigate the asymmetry of the back EMF waveform. The proposed struc-ture was optimized with a genetic algorithm and a prototype of the optimal model was con-structed and experimentally evaluated to verify its validity. Finally, the performance improve-ment and validity of the proposed structure were verified by comparing the analysis results of the optimal model with the experimental results.

Utilizing unmanned aerial vehicles (UAVs) to facilitate wireless communication has emerged as a viable and promising strategy aimed at enhancing both current and prospective wireless systems. This approach offers many advantages by establishing line-of-sight connections, optimizing operational efficiency, and enabling flexible deployment capabilities in various terrains. Thus, in this paper, we investigate UAV communication in a relaying network in which a UAV helps communication between a source and two destination users while flying to a location. To have a complete view of our proposed system, we consider both orthogonal multiple access as OFDM and non-orthogonal multiple access (NOMA) scenarios. Moreover, we apply successive convex optimization (SCO) and the Block-coordinate gradient descent (BCGD) for the sum-rate optimization problems to improve the system performance under constraints of total bandwidth and total power at the ground base station and UAV. The experimental results validate that the achievable secrecy rates are notably enhanced by using our proposed algorithms and show optimal trends for critical parameters, such as transmit powers, the flight trajectory and speed of UAV, and resource allocation of OFDM and NOMA.

The oxidative coupling of methane (OCM) to produce ethane and ethylene (C2 compounds) as platform chemicals involves complex chemistry with reactions both in the gas phase and on the catalyst surface, resulting in a distribution of products at the expense of C2 selectivity. This work uses experimental data from a variety of mixed metal oxides on supports at different reaction conditions (temperature, contact time, and reactant flow rates) to train a random forest regressor that predicts methane conversion and C2 selectivity (key performance indicators (KPIs)), and deploys it to locate optimal conditions that maximize C2 yield for a catalyst. Investigating the regressor interpretability via feature importance reveals that the choice of metals and support are crucial to C2 selectivity predictions, while the predictions of methane conversion are driven by the reaction conditions. The machine learning (ML) regressor is used as a surrogate to develop performance curves for each of the catalysts via a multi-objective optimization routine that seeks to maximize the KPIs in the decision space of reaction conditions, is seen to locate optimal conditions at which the maximum C2 yields for catalysts are predicted to be 15%, higher on average. Analyzing the catalysts in the space of their performance curves with respect to a popular OCM catalyst, Mn-Na2WO4/SiO2, reveals distinct patterns based on intrinsic properties of metals and supports. Further, the decision space with catalyst descriptors and reaction conditions is optimized for high C2 yields using the ML surrogate, in a static multi-objective optimization routine, and an adaptive Bayesian routine, where the latter was found to have a wider field focus in proposing catalyst formulations and conditions. Transition metal oxides on a variety of supports were proposed but not their lanthanide oxide counterparts.

This work explores five falling film evaporator (FFE) simulation approaches combined with energy consumption minimization strategies, namely Mechanical Vapor Recompression and Thermal Vapor Recompression (MVR and TVR respectively). Global system analysis and advanced dynamic optimization strategies are then investigated to minimize steam consumption, the cost of steam, the total annualized cost and maximizing product yield. The results indicate that a higher TVR discharge pressure, or MVR compression ratios, along with higher feed temperatures enhance evaporation with but increase operational costs. The most economic option includes three evaporator effects with TVR for achieving 50% product dry mass content. However, for a 35% dry mass content, MVR becomes cost-effective with an 11% reduction in unit electricity prices or a simultaneous 7% drop in electricity prices and a 5% increase in gas-based steam prices. Furthermore, switching from milk powder production to milk concentrates, an annual cost reduction ranging from 10.75 to 44% is achieved. Also, a forecasted 20% (or more) reduction of biomass-based steam cost can lead to lower annual expenditure comparing with the nominal NG-based steam Case. Regarding the total annualized cost, for a new plant design, optimization strategies lead to a 9 - 45% reduction in the total cost depending on the Case under consideration.

Deep learning is based on matrix computing with a large amount of hidden parameters that is not visible outside the computing module. Deep learning is a non-linear system and a linear approach is not possible. It is natural for people to visualize the algorithm and to follow some hidden parameters. In this paper, we propose a simple graphical programming of a nonlinear system based on drawing the simplest unit, such as a single neuron model. A more complex scheme of a classical neural network is obtained by commands copy-move-place of one neuron. The number of neurons and layers can be chosen arbitrarily. Once the scheme is complete, the implementation code is evaluated with symbolic parameters and nonlinear activation functions. This cannot be done manually. With the symbolic expression of outputs in terms of inputs and symbolic parameters, including symbolic activation pure functions, some other properties can be derived in closed form. This unique original approach can help scientists and developers and design numerical algorithms for machine learning and to understand how deep learning algorithms work.

The power generation structure and load characteristics in different provinces are quite different in China, and the distribution of peak regulating resources and demands is extremely unbalanced. Due to many factors such as low power marketization degree, shortage of peak regulating resources and block of transmission channels, the consumption of new energy is facing greater pressure. In order to improve the mutual aids in regional power grid, this paper proposes a two-layer optimization dispatching model considering the mutual aids of peak regulation resources between each province, and determines the optimal units startup mode and power output in each provinces, thus obtaining all the power supply output arrangements and the technical and economic indicators. Finally, take the actual regional power grid in China as an example, the simulation results show that the proposed model can significantly improve the new energy utilization rate, which verified the effectiveness and feasibility of the proposed model and methods in this paper.

Melanoma is widely recognized as one of the most lethal forms of skin cancer, with its incidence showing an upward trend in recent years. Nonetheless, the timely detection of this malignancy substantially enhances the likelihood of patients’ long-term survival. Several computer-based methods have recently been proposed in the pursuit of diagnosing skin lesions at their early stages. Despite achieving some level of success, there still remains a margin of error that the machine learning community considers to be an unresolved research challenge. This study presents a novel framework for the classification of skin lesions. The framework incorporates deep features to generate a highly discriminant feature vector, while also maintaining the integrity of the original feature space. Recent deep models including Darknet53, DenseNet201, InceptionV3, and InceptionResNetV2 are employed in our study for the purpose of feature extraction. Additionally, transfer learning is leveraged to enhance the performance of our approach. In the subsequent phase, the extracted feature information from the chosen pre-existing models is combined, with the aim of preserving maximum information, prior to undergoing the process of feature selection using a novel entropy-controlled grey wolf optimization (ECGWO) algorithm. The integration of fusion and selection techniques is employed to initially incorporate the feature vector with a high level of information and subsequently eliminate redundant and irrelevant feature information. The efficacy of our design is substantiated through the evaluation on three benchmark dermoscopic datasets, namely PH2, ISIC-MSK, and ISIC-UDA. In order to validate the proposed methodology, a comprehensive evaluation is conducted, including a rigorous comparison with established techniques in the field.

Energy demand and consumption in recent times have witnessed a rapid proliferation influenced by technological developments, increased population and economic growth. This has fuelled research trends in the domain of energy management employing tri-generation systems such as the combined cooling, heating and power (CCHP) systems. Furthermore, the incorporation of renewable energy, especially solar energy, to complement the thermal input by fossil fuels has facilitated the effectiveness and sustainability of CCHP systems. This study proposes a new approach to improve the overall efficiency of CCHP systems and compute the optimal design parameters in order to assist decision makers to identify the best geometrical configuration. A multi-objective optimization formulation of a solar-assisted CCHP system was adopted to maximize the net power, the exergy efficiency and minimize the CO2 emission using the greywolf optimization technique. In addition, the effects of the decision variables on the objective functions were analysed. The proposed optimization approach yielded 100 set of Pareto optimal solutions which would serve as options to the decision maker to make a selection in order to improve the performance of a solar-assisted CCHP system. This study demonstrates that the proposed approach is potentially suitable for the optimization of a solar-assisted CCHP system.

One-Dimensional Solar Cell Capacitance Simulator (1D-SCAPS) and n-i-p planar configuration have been employed for the simulation of the caesium tin iodide (CsSnI₃)-based perovskite solar cell (PSC) with titanium (IV) oxide (TiO₂) and copper thiocyanate (CuSCN) as the electron and hole transport materials respectively. The results obtained for the initially modeled PSC compared well with similar devices in the literature but marred with high recombination rate. As such, the defect density in the CsSnI₃ absorber was varied from 10¹³ cm^-3 to 10¹⁷ cm^-3 and enriched with SnCl₂ (additive) and Br⁻(dopant) respectively. This strategy ensured a reduced concentration of Sn⁴⁺ vacancy (V_Sn) in the CsSnI₃ absorber with improved carrier lifetime and diffusion length beyond 0.05 ns and 1.1µm by a magnitude of the order of 10³ and 10² respectively for the enriched CsSnI₃ –based PSC. For the optimized PSC, we recorded V_OC=1.289 V, J_SC=32.60 mA.cm^-2, FF=83.56% and PCE=35.12%.

In this paper, stochastic multi-objective intelligent framework (MOIF) is performed for distribution network reconfiguration to minimize power losses, the number of voltage sags, the system's average RMS fluctuation, the average system interruption frequency (ASIFI), the momentary average interruption frequency (MAIFI), and the system average interruption frequency (SAIFI) considering the network uncertainty. The unscented transformation (UT) approach is applied to model the demand uncertainty due to simplicity to implement and no presumptions to simplify. A human-inspired intelligent method named improved mountaineering team-based optimization (IMTBO) is applied to determine the decision variables defined as the network's optimal configuration. The conventional MTBO is improved using a quasi-opposition-based learning strategy to overcome premature convergence and achieve the optimal solution. The simulation results showed that in single- and double-objective optimization, some objectives are weakened compared to their base value, while the results of the MOIF indicated a fair compromise between different objectives, and all objectives are improved. The results of MOIF based on the IMTBO cleared that the losses are reduced by 30.94%, the voltage sag numbers and average RMS fluctuation are reduced by 33.68% and 33.65%, and also ASIFI, MAIFI, and SAIFI are improved by 6.80%, 44.61%, and 0.73%, respectively. Also, the superior capability of the MOIF based on the IMTBO is proved compared to the conventional MTBO, particle swarm optimization, and artificial electric field algorithm. Moreover, the results of the stochastic MOIF based on the UT showed the power loss increased by 7.62%, voltage sag, and SARFI increased by 5.39% and 5.31%, and ASIFI, MAIFI, and SAIFI weakened by 2.28%, 6.61%, and 1.48%, respectively compared to the deterministic MOIF model.

Floating Offshore Wind Turbine (FOWT) is an innovative technology with little industry guidance for its hull design. Various FOWT floaters with different hull shapes claim to support the same turbines. Structural integrity and material expense analyses of different pontoon shapes were conducted, and it was found that some configurations, such as those with every two columns connected by both pontoon and bracing, have advantages over others. However, it is important to note that the choice of pontoon shape should be based on the wave loading conditions the floater will be exposed to. While a T-shaped pontoon provides a cost-effective solution under certain wave loading scenarios, it may not be the best option for all conditions. Specifically, ring pontoon designs with full bracing were found to be necessary for withstanding certain wave loads. Therefore, it is important to consider different Dominant Load Parameters (DLP) and ensure that a FOWT floater can withstand all applicable DLPs. An uneven hexahedral column shape, which combines the best attributes of square and round shapes, is proposed as a better alternative to cylindrical columns. It offers ease of manufacture and reasonably low drag. Bracing is found to be necessary for withstanding the wind turbine’s incurred moment and forces. The conclusion is that platform design should prioritize manufacturing costs and strength over maximizing hydrodynamic performance.

This paper proposes the optimization method for the minimum mass of permanent magnet (PM) excited homopolar inductor alternator (HIA) with air-gap winding used for high-speed driving system. The calculation results show that there is an optimal value for shell diameter and unit number of HIA to make the mass to be smallest. A 1 MW at 22000 rpm PM excited HIA is designed. The analytical results are verified by the FEM simulations. The losses, temperature rise, rotor stress and modal of the PM excited HIA are also analyzed. The calculation results show that the solid rotor eddy-current losses and the temperature rise of PM excited HIA are acceptable. Also the rotor strength is adequate, and the rotor system is a stiffness rotor, and it can safely operate. Finally, the comparison between PM excited HIA and permanent magnet rotor machine is discussed.

Since the 1990’s, studies and pilot tests have been conducted to reduce traffic, accidents, and pollution due to urban freight transport (UFT). These ended up in several policies, regulations, and restrictions for UFT, such as low emission zones, delivery time windows, and vehicle size and weight restrictions. However, issues in UFT under regulatory measures still persevere. This study introduces an optimization framework for deriving an optimal combination of various types of vehicles with different capacities for vehicle replacement in UFT. This framework allows an understanding of how an urban freight company with a limited budget efficiently satisfies its freight demand within an urban area in the presence of regulatory measures by urban administrators. The introduced formulation, which is a mixed-integer linear programming, will assist the operator in choosing the best investment strategy for introducing new vehicles of certain types and sizes, for operation in different zones, into its fleet while gaining economic benefits and having a positive impact on the liveability of the urban area. Furthermore, a sensitivity analysis is performed to consider the effects of specific uncertain parameters on the total cost. The numerical results show that the share of electric vehicles in the fleet increases, and they are more competitive than diesel vehicles.

The economical load dispatch problem is a critical concern in thermal power systems to ensure cost-effective operation. This paper conducts a comparative analysis between the Social Spider Algorithm (SSA) and Particle Swarm Optimization (PSO) for economical load dispatch in a 6-unit thermal power system with the integration of 11,000 plug-in electric vehicles (PEVs). Both SSA and PSO are population-based optimization techniques designed to minimize the overall fuel cost while satisfying the load demand by determining the optimal power output of each unit. SSA emulates the social behavior of spiders, while PSO simulates the collective intelligence of a swarm of particles. Performance evaluation of the algorithms considers power system data, such as unit fuel costs, minimum and maximum loads, and total load demand. The objective is to minimize fuel cost while meeting the load demand. Comparative analysis of SSA and PSO includes convergence speed, solution quality, and computational efficiency. Experimental results indicate that SSA outperforms PSO in achieving a more optimal and economical load dispatch solution for the 6-unit thermal power system. SSA demonstrates faster convergence and provides superior-quality solutions compared to PSO. This paper contributes to the field by highlighting SSA's effectiveness in achieving cost-efficient operation of thermal power systems. The findings suggest that SSA holds promise as an optimization technique for similar power system optimization problems, enhancing overall operational efficiency and reducing costs for thermal power plants.

Centrifugal pumps are widely employed in the oil refinery industry due to their efficiency and effectiveness in fluid transfer applications. The reliability of pumps plays a pivotal role in ensuring uninterrupted plant productivity and safe operations. Analysis of failure history data shows that bearings have been identified as critical components in oil refinery pump groups. However, traditional reliability estimation theories may not apply when data is limited or subject to right censoring. This paper addresses the complexity of estimating the Weibull distribution parameters using the maximum-likelihood method under the abovementioned conditions. The likelihood equation lacks an explicit analytical solution, necessitating the use of numerical methods for resolution. The proposed approach presented in this article leverages the Expectation-Maximization (EM) algorithm for estimating the Weibull distribution parameters. This method provides more accurate estimates of failure rates and probabilities by accounting for limited and censored data. The findings are demonstrated through a case study, showcasing the practical application of the proposed approach.

As an emerging network paradigm, Space-Air-Ground integrated networks (SAGIN) has attracted the attentions from academia and industry. That is because SAGIN can implement seamless global coverage and connections among electronic devices in space, air, and ground spaces. Additionally, the shortages of computing and storage resources in mobile devices greatly affect the quality of experiences for intelligent applications. Hence, we devise to integrate SAGIN as an abundant resource pool into mobile edge computing environments (MEC). To facilitate efficient processing, we need to solve the optimal task offloading decisions. Different from the existing MEC task offloading solutions, we have to face some new challenges, such as the fluctuation of processing capability for an edge computing node, the uncertainty of the transmission latency caused by the heterogeneous network protocols, the uncertain amount of uploaded tasks during a period, and so on. In this paper, we firstly describe a task offloading decision problem in new challenge environments. But, we cannot use the standard robust optimization and stochastic optimization methods to obtain the optimal result under the uncertain network environments. In this paper, we propose the condition value at risk-aware distributionally robust optimization algorithm, named as CVAR-DRO, to solve the task offloading decision problem. The proposed CVAR-DRO method combines the distributionally robust optimization and the condition value at risk model for solving the optimal result. And then, We have evaluated our approach in simulation SAGIN environments with the confidence interval, the number of mobile task-offloading and the various parameters. We compare our proposed CVAR-DRO algorithm with the state-of-the-art algorithms, such as the standard robust optimization algorithm, the stochastic optimization algorithm, the DRO algorithm, and the brute algorithm. The experimental results show that CVAR-DRO can get a sub-optimal mobile task-offloading decision. Overall, CVAR-DRO is more robust than others to the new challenges mentioned above in SAGIN.

Keeping the bus voltage within acceptable limits depends on dispatching reactive power. Power quality improves as a result of creating an effective power flow system, which also helps to reduce power loss. Therefore, Optimal Reactive Power Dispatch (ORPD) studies aim at designing appropriate system configurations to enable a reliable operation of power systems. Establishment of such a configuration is handled through control variables in power systems. On the other hand, installing Distributed Generation Unit (DGU) into a power system can have an important effect on power quality and power loss minimization. In this paper, Grey Wolf Optimization (GWO method is applied to ORPD problem for the first time, to find optimal placement of newly installed DGUs at the power system. Active power loss and voltage minimization have been minimized by optimizing the control variables while keeping them within their upper and lower bounds, and by finding the optimal placement of a newly installed DGU in power system. On the basis of IEEE 30 bus and IEEE 118 bus systems, performance of the suggested approach is investigated. A comparison is also performed among the proposed method and some other methods. Test results demonstrate that the resulting configuration contributes to growing the rate of renewable integration to a power system. The results also indicate that the suggested optimization approach applied to ORPD problem has improved power system performance in terms of both on power loss and voltage deviation. The promising results highlight the potential of the GWO algorithm to facilitate the integration of renewable power sources, and its role in promoting sustainable energy solutions.

Wheat (Triticum aestivum L.) breeding programs can take over a decade to release a new variety. However, new methods of selection such as genomic selection (GS) must be integrated to decrease the time it takes to release new varieties to meet the demand of a growing population. The implementation of GS into breeding programs is still being explored, with many studies showing its potential to change wheat breeding through achieving higher genetic gain. In this review, we explore the integration of GS for a wheat breeding program by redesigning the traditional breeding pipeline to implement GS. We propose implementing a two-part breeding strategy by differentiating between population improvement and product development. The implementation of GS in the product development pipeline can be integrated into most stages and can predict within and across breeding cycles. Additionally, we explore optimizing the population improvement strategy through GS recurrent selection schemes to reduce crossing cycle time and significantly increase genetic gain. The recurrent selection schemes can be optimized for parental selection, maintenance of genetic variation, and optimal cross-prediction. Overall, we outline the ability to increase the genetic gain of a breeding program by implementing GS and a two-part breeding strategy.

Clustering algorithms for multi-database mining (MDM) rely on computing $(n^2-n)/2$ pairwise similarities between $n$ multiple databases to generate and evaluate $m\in[1, (n^2-n)/2]$ candidate clusterings in order to select the ideal partitioning which optimizes a predefined goodness measure. However, when these pairwise similarities are distributed around the mean value, the clustering algorithm becomes indecisive when choosing what database pairs are considered eligible to be grouped together. Consequently, a trivial result is produced by putting all the $n$ databases in one cluster or by returning $n$ singleton clusters. To tackle the latter problem, we propose a learning algorithm to reduce the fuzziness in the similarity matrix by minimizing a weighted binary entropy loss function via gradient descent and back-propagation. As a result, the learned model will improve the certainty of the clustering algorithm by correctly identifying the optimal database clusters. Additionally, in contrast to gradient-based clustering algorithms which are sensitive to the choice of the learning rate and require more iterations to converge, we propose a learning-rate-free algorithm to assess the candidate clusterings generated on the fly in a fewer upper-bounded iterations. Through a series of experiments on multiple database samples, we show that our algorithm outperforms the existing clustering algorithms for MDM.

In order to reach the theoretical efficiency limits of lead-based metal halide perovskite solar cells, the voltage should be enhanced because it suffers from nonradiative recombination. Perovskite materials contain intrinsic defects that can act as Shockley-Read-Hall recombination centers. Several experimental and computational studies have characterized such defect states within the band gap. We give a systematic overview of compositional engineering by distinguishing the different defect reducing mechanisms. Doping effects are divided into influences on: (1) Crystallization; (2) Lattice properties. Incorporation of dopant influences the lattice properties by: (a) Lattice strain relaxation; (b) Chemical bonding enhancement; (c) Band gap tuning. The intrinsic lattice strain in undoped perovskite was shown to induce vacancy formation. The incorporation of smaller ions, such as Cl, F and Cd, increases the energy for vacancy formation. Zn doping is reported to induce strain relaxation but also to enhance the chemical bonding. The combination of computational studies using (DFT) calculations quantifying and qualifying the defect reducing propensities of different dopants with experimental studies is essential for deeper understanding and unraveling insights, such as the dynamics of iodine vacancies and the photochemistry of the iodine interstitials, and can eventually lead to a more rational approach in the search for optimal photovoltaic materials.

Interval-valued functions have been widely used to accommodate data inexactness in optimization and decision theory. In this paper, we study interval-valued vector optimization problems, and derive their relationships to interval variational inequality problems, of both Stampacchia and Minty types. Using the concept of interval approximate convexity, we establish necessary and sufficient optimality conditions for local strong quasi and approximate $LU$-efficient solutions to nonsmooth optimization problems with interval-valued multiobjective functions.

In this study, the efficiency of an anaerobic treatment system for wastewater from a South African poultry slaughterhouse was evaluated using a lab-scale static granular bed reactor (SGBR). The down-flow SGBR (2 L) was operated continuously for 138 days under mesophilic conditions (35-37 ˚C), at hydraulic retention times (HRTs) ranging from 24 to 96 h and average organic loading rates (OLRs) of 0.78 to 5.74 g COD/L.day. The SGBR achieved an average chemical oxygen demand (COD) removal efficiency of 80% and the maximum COD removal achieved was 95%, at an HRT of 24 h and average OLR of 5.74 g COD/L.day. The optimization of the SGBR, with regard to a suitable HRT and OLR, was determined using response surface methodology (RSM). The optimal SGBR performance with regard to the maximum COD removal efficiency was predicted for an OLR of 12.49 g COD/L.day and a HRT of 24 h, resulting in a 95.5% COD removal efficiency. The model R2 of 0.9638 indicated that the model is a good fit and is suitable to predict the COD removal efficiency for the SGBR.

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

This work addresses the energy-based source localization problem in wireless sensors networks. Instead of circumventing the maximum likelihood (ML) problem by applying convex relaxations and approximations (like all existing approaches do), we here tackle it directly by the use of metaheuristics. To the best of our knowledge, this is the first time that metaheuristics is applied to this type of problems. More specifically an elephant herding optimization (EHO) algorithm is applied. Through extensive simulations, the key parameters of the EHO algorithm are optimized such that they match the energy decay model between two sensor nodes. A detailed analysis of the computational complexity is presented, as well as performance comparison between the proposed algorithm and existing non-metaheuristic ones. Simulation results show that the new approach significantly outperforms the existing solutions in noisy environments, encouraging further improvement and testing of metaheuristic methods.

This study presents application of demand response strategy in a standalone wind-solar-battery hybrid energy system (HES). Inputs for the designed HES are wind speed, solar radiation, temperature and load demand which is variable with time. In this study, hourly values of meteorological data and hourly load demand are considered in one year. An improved gravitational search algorithm (IGSA) is used to optimize the configuration of the standalone wind-solar-battery hybrid power system. The optimal objectives of the system are cost of the system in life cycle, the loss of power supply probability（LPSP）and the energy excess percentage（EXC）.The effect of demand response on economic benefit and energy storage allocation of the standalone wind-solar-battery system is studied. The obtained optimal configuration of the proposed HES can provide minimal energy cost with excellent performance and reduced waste and unmet load.

This paper considers a two-way relay network, where two source nodes exchange messages through several relays in the presence of an eavesdropper, and the channel state information (CSI) of the eavesdropper is imperfectly known. The amplify-and-forward relay protocol is used and the relay beamforming weights are designed. The model is built up to minimize the total relay transmit power while guaranteeing the quality of service at users and preventing the eavesdropper from decoding the signals. Due to the imperfect CSI, a semi-infinite programming problem is obtained. An algorithm is proposed to solve the problem, and the iterative points are updated through the linesearch technique, where the feasibility are preserved during iterations. The optimality property is analyzed. The obtained subproblems are quadratic constrained quadratic programming problems, either with less than $4$ constraints or with only one variable, which are solved optimally. Simulation results demonstrate the importance of the proposed model, and imply that the proposed algorithm is efficient and converges very fast, where more than 85% of the problems are solved optimally.

Background and Objectives: To assess femoral shaft bowing (FSB) in the coronal and sagittal planes and introduce the clinical implications of total knee arthroplasty (TKA) by analyzing a three-dimensional (3D) model with virtual implantation of the femoral component. Materials and Methods: Sixty-eight patients (average age: 69.1 years) underwent 3D model reconstruction, incorporating medullary canals, by importing computed tomography (CT) data into Mimics® software. A mechanical axis (MA) line was drawn from the midportion of the femoral head to the center of the intercondylar notch; proximal/distal straight centerlines (length, 60 mm; diameter, 1 mm) were placed in the center of the medulla canal. Acute angles between the two centerlines were measured as lateral and anterior bowing. The acute angle by distal centerline and MA line was measured as distal coronal and sagittal alignment in both anteroposterior (AP) and lateral views. The diameter of curve (DOC) of the medulla along the posterior border was measured. Results: The mean lateral bowing on the AP view was 3.71°, and the mean anterior bowing on the lateral view was 11.82°. The average DOC of the medullary canal was 1501.68 mm. The distal coronal alignment of all femurs was an average of 6.40°, and the distal sagittal alignment was 2.66°. Overall, 22 femurs had coronal bowing, 42 had sagittal bowing, and 15 had both. Conclusion: In Asians, FSB is present in coronal, sagittal, or both directions. Increased anterolateral FSB may lead to cortical abutment in the sagittal plane despite limited space in the coronal plane. During TKA, distal coronal alignment can guide distal femoral valgus cut angle, whereas distal sagittal alignment helps anticipate femoral component positioning, avoiding anterior notching. Osteotomy along the anterior cortical bone to prevent notching may result in outliers due to differences between distal sagittal alignment and distal anterior cortical axis.

3D-printed models with high anatomic fidelity are an increasingly viable tool in simulation-based medical education. One advantage of 3D models is they provide enhanced tactile and spatial understanding of complex anatomy to develop technical skills used in minimally invasive procedures. We propose that 3D anatomical models can improve the development of Interventional Radiology vascular access skills – first described in the 1950s as the Seldinger Technique – for pre-clerkship medical students. Early adoption of 3D-printed technology in pre-clinical medical education can lead to improved student engagement and satisfaction when learning procedural techniques. This study involved creating a 3D model of the upper extremity vasculature from an anonymized CT angiogram, using it as a medical education tool for 31 pre-clinical medical students practicing the Seldinger Technique on a prefabricated venipuncture arm, and assessing student satisfaction with this form of learning. Overall, attendees responded positively to the incorporation of the 3D model in medical education to improve their anatomic understanding and application to the Seldinger Technique. These results indicate that the use of 3D models in simulation-based medical education can provide benefits to acquiring technical skills and the potential to decrease training costs without harming a patient.

Newcastle disease (ND) is a viral infection that affects domestic and wild type birds especially poultry and establishing serious economic costs for poultry industry. Newcastle disease virus’ (NDV) HN membrane receptor is attractive therapeutic candidate, contributes to pathogenicity with various functions such as fusion and prevent viral self-agglutination to permit viral spread. No validated HN inhibitor has been reported yet. However, the purpose of current study was to determine potential compounds via pharmacophore-based screening of natural molecules to inhibit HN receptor of NDV. Physicochemical characteristics and phylogenetic analysis were determined to elucidate structural information and phylogeny of target protein across different species as well as members of gene family. Fill missing residues of HN target protein and evaluated via PROCHECK and VERIFY 3D. We employed shape and feature-based pharmacophore model and screen natural compound’s library through numerous scoring schemes. Top 48 hits with 0.8860 pharmacophore fit score were subjected towards structure-based molecular docking. Top 9 compounds was observed witihin the range of -8.9 to -7.5 kcal/mol binding score. Five best-fitting compounds in complex with HN receptor were subjected to predict biological activity and ADMET analysis. Top two hits were selected for MD simulations to validate binding modes and structural stability. Upon scrutinization, A1 (ZINC05223166) emerges as potential HN inhibitor to treat ND, necessitating further validation via clinical trials.

In the process of use and manufacture, carbon fiber foam sandwich structures were often damaged by low-energy impact, resulting in performance degradation. Therefore, it was necessary to study the damage caused by low-speed impact of composite sandwich structures. In this paper, based on Hashin failure criterion, an equivalent finite element model of carbon fiber foam sandwich panel under low velocity impact was established. The model was used to simulate the damage of foam sandwich panel with [±45°/±45°/ (core) /±45°/±45°] ply structure under the impact energy of 10.58J, 21.17J, 31.75J and 42.34J. The simulation results of impact damage depth were compared with the experimental results. The error was less than 10%, which proved the rationality of the impact equivalent model. The model was used to predict and analyze the damage of foam sandwich panels with [±45°/ (core) /±45°], [±45°/ (0°, 90°) / (core) /±45°] and [±45°/ (0°, 90°) (core) / (0°, 90°) /±45°] ply structures under 21.17J impact energy. By comparing and analyzing the damage situation, impact force response time and impact velocity response time, the low energy impact resistance was analyzed. The results showed that increasing the number of ply structure of [±45°] can reduce the impact damage degree and improve the bearing capacity of sandwich panels.

Computational rhinology is a specialized branch of biomechanics leveraging engineering techniques for mathematical modelling and simulation to complement the medical field of rhinology. Computational rhinology has already contributed significantly to advancing our understanding of the nasal function, including airflow patterns, mucosal cooling, particle deposition, and drug delivery, and is foreseen as a crucial element in e.g. the development of virtual surgery as a clinical, patient-specific decision support tool. The current paper delves into the field of computational rhinology from a nasal airflow perspective, highlighting the use of computational fluid dynamics to enhance diagnostics and treatment of breathing disorders. The paper consists of three distinct parts – an introduction to and review of the field of computational rhinology, a review of published literature on in vitro and in silico studies of nasal airflow, and the presentation and analysis of previously unpublished high fidelity CFD simulation data of in silico rhinomanometry. While the two first parts of the paper summarize the current status and challenges in the application of computational tools in rhinology, the last part addresses the gross disagreement commonly observed when comparing in silico and in vivo rhinomanometry results. It is concluded that this discrepancy cannot readily be explained by CFD model deficiencies caused by poor choice of turbulence model, insufficient spatial or temporal resolution, or neglecting transient effects. Hence, alternative explanations such as nasal cavity compliance or drag effects due to nasal hair should be investigated.

An approach based on the OpenFOAM library has been developed to solve a high-speed, multicomponent mixture of a reacting, compressible flow. This work presents comprehensive validation of the newly developed solver called compressibleCentralReactingFoam with different supersonic flows, including shocks, expansion waves, and turbulence-combustion interaction. The comparisons of the simulation results with experimental and computational data confirm the fidelity of this solver for problems involving multicomponent high-speed reactive flows. The gas dynamics of turbulence-chemistry interaction are modeled using a partially-stirred reactor formulation and provide promising results to better understand the complex physics involved in supersonic combustors. A time-scale analysis based on local Damköhler numbers reveals different regimes of turbulent combustion. In the core of the jet flow, the Damköhler number is relatively high indicating that the reaction time scale is smaller than the turbulent mixing time scale. This means that the combustion is controlled by turbulent mixing. In the shear layer, where the heat release rate and the scalar dissipation rate have the highest value, the flame is stabilized due to finite rate chemistry with small Damköhler numbers and a high fraction of fine structure. This solver allows three-dimensional gas dynamic simulation of high-speed multicomponent reactive flows relevant to practical combustion applications.

The research on the impact of excavation of underpass tunnels has already had certain results, but there is a lack of research cases of double-line highway tunnels with oblique angles underpass the existing railway tunnels, especially the method of determining the area of the impact of the dynamic and static loads of the new tunnels and existing trains to be enriched by considering them at the same time. In this paper, based on the tunnel project of the new double-line Shiqian Highway Tunnel with oblique angles underpass the Hurong Railway in Wanshoushan. By constructing a three-dimensional finite element model and simulating the application of static tunnel excavation load, dynamic blasting load and dynamic train operation load of the existing tunnel, the overall displacement of the existing tunnel and the settlement value of the bottom plate of the track surface by the static tunnel excavation load were analyzed. Then, the stresses, vibration speeds and displacements of the tunnel due to the dynamic blasting loads and the dynamic train operation loads were obtained. The results show that: the area of influence of the static loads of the new tunnel excavation on the existing tunnel is divided into three types of perturbations presenting strong, weak and slight; the area affected by the blasting dynamic load of new tunnel excavation is a circular domain with the datum point in the tunnel section as the center; the area affected by the dynamic load of train operation in the existing tunnel is an ellipse with the center of the track surface of the existing tunnel as the datum; there is an anomalous shape in the area affected by the blasting dynamic load of new tunnel excavation and the area affected by the dynamic load of train operation in the existing tunnel.

Intelligent ship technology is currently an international research hotspot, and model predictive control is widely used in the path-tracking control of intelligent vehicles. To construct an intelligent ship anti-disturbance path-tracking control method, firstly, an environmental disturbance model was constructed with the actual meteorological data of the target sea area. Secondly, the Fossen ship equation of motion is linearized and discretized as the ship motion model. Thirdly, the expression of the prediction equation is derived from the ship motion model. Fourthly, the cost function is constructed by using the polar diameter and polar angle values of the ship. Fifth, the power function in the cost function is replaced with an exponential function to obtain an improved cost function. Sixthly, according to the Lyapunov theory and the MPC terminal constraint theory, the stability of the improved cost function is verified. Seventh, different test paths are set up, the environmental disturbance model is taken as the external disturbance, the ship motion model, the prediction equation, and the improved cost function are used to design the anti-disturbance path-tracking control algorithm according to the model prediction control idea for simulation experiments. Finally, different MATLAB simulation results show that the improved cost function can resist disturbance of the external wave, current, and wind, and effectively track the target path. Therefore, this study provides a reference for improving the navigation safety of ship path-tracking.

We overview recent findings achieved in the field of model-driven development of additively manufactured porous materials for development of a new generation of bioactive implants for orthopedic applications. Porous structures produced of biocompatible titanium alloys by selective laser melting can present a promising material to design scaffolds with regulated mechanical properties and with capacity to be loaded with pharmaceutical products. Adjusting pore geometry, one could control elastic modulus and strength/fatigue properties of the engineered structures to be compatible with bone tissues, thus preventing the stress shield effect when replacing a diseased bone fragment. Adsorption of medicals by internal spaces would make it possible to emit the antibiotic and anti-tumor agents into surrounding tissues. We critically analyze the recent advances in the field featuring model design approaches, virtual testing of the designed structures, capabilities of additive printing of porous structures, biomedical issues of the engineered scaffolds and so on. A special attention is paid to highlight the current troubles in the field and the ways of their solutions.

Pyrochlore oxides (A2B2O7) are potential nuclear waste substrate material due to their superior radiation resistance properties. We performed molecular dynamics simulations to study the structural properties and displacement cascades in ytterbium titanate pyrochlore (Yb2Ti2O7). We computed threshold displacement energy ( Ed ) and lattice constant ( ao) of Yb2Ti2O7. The effect of displacement cascades in Yb2Ti2O7 of recoils of energies 1 keV, 2 keV, 5 keV, 10 keV in different crystallographic directions ([100], [110], [111]) were studied. The number of defects is found to be proportional to the energy of incident PKA. Additionally, the Ed of pyrochlore is computed and it exhibits anisotropy. Furthermore, radiation damage in high-entropy pyrochlore (HEPy) i.e., YbYTiZrO7, YbGdTiZrO7 , Yb0.5Y0.5Eu0.5Gd0.5TiZrO7 were investigated and compared with Yb2Ti2O7. It was found that HEPy have a larger Ed as compared with Yb2Ti2O7 which exhibits characteristic of lower radiation damage resistance than HEPy. Our simulation proposed that HEPy alloys are more radiation resistant than individual pyrochlore constituents . This work will provide atomic insights in developing substrate materials for nuclear waste applications.

Promoting the use of solar energy resources has always faced the challenges of instability and supply-demand mismatch. The key to solving these issues is to efficiently store and utilize solar energy resources using high-performance heat storage devices. This study designed a high-performance shell-and-tube phase change thermal storage device and established a numerical model using ANSYS software to summarize the device's dynamic melting law. To verify the accuracy of the numerical simulation, a performance testing platform for the phase change thermal storage device was built to investigate the impact of factors such as inlet water temperature, inlet water flow rate, type of heat storage, and initial temperature of the device, and to reveal the change law of the device's performance. The results show that the inlet water temperature has the most significant impact on the device's heat storage and release performance. When the device's heat storage or release is used for heating, changing the inlet water flow rate has a weak and limited effect on the device's performance. However, when the device's heat release is used to provide domestic hot water, increasing the makeup water temperature and reducing the inlet water flow rate can significantly improve the device's effective heat release. Furthermore, based on the experimental validation of the model's correctness, this study further simulated and studied the impact of different factors on the device's heat storage process to optimize its structural design and provide technical references for the device's actual operation and installation. The results show that the placement of fins has a negligible effect on the performance of the heat storage device while reducing the fin spacing and increasing the fin thickness can significantly improve the melting efficiency of the phase change material (PCM). Additionally, the heat storage characteristics of the device are significantly better in the vertical installation mode than in the horizontal installation mode. This study provides theoretical guidance and technical references for the design and use of phase change thermal storage devices.

Digital twins are the foundation of autonomous off-road vehicles. Dynamic reduction methods are one of several ways to develop digital twins for off-road vehicles. The article commences with a comprehensive overview of the most widely used dynamic reduction methods and then introduces performance metrics for assessing their efficacies in the context of digital twins. The paper additionally includes a detailed mathematical derivation of the state-space representation for reduced-order finite element models. The state-space representation of the reduced finite element models facilitates their export to problem-solving environments for dynamic analysis. The state-space models are eventually solved utilizing the built-in libraries of numerical solvers in textual and graphical programming platforms. In addition, the article identifies the set of solvers that best suit the simulation of virtual models for off-road vehicles. This article also includes an evaluation of the simulation results of digital models with modes ranging from 0 to 30 Hz. In addition, the article demonstrates the lower bound of the frequency range necessary and sufficient to be retained in off-road vehicle virtual models. Finally, the paper presents the simulation outcomes for digital models of commercial off-road vehicles with custom-built control, electrical, and control systems.

By 2020, over 100 countries expanded electric and plug-in hybrid electric vehicle (EV/PHEV) technologies, with global sales surpassing 7 million units. Governments are adopting cleaner vehicle technologies due to proven environmental and health implications of internal combustion engine vehicles (ICEVs), evidenced by the recent COP26 meeting. This article proposes an agent-based model of vehicle activity as a tool for quantifying energy consumption by simulating a fleet of EV/PHEVs within an urban street network at various spatio-temporal resolutions. Driver behaviour plays a significant role in fuel consumption, thus, simulating various levels of individual behaviour enhancing heterogeneity should provide more accurate results of potential energy demand in cities. The study found that 1) energy consumption is lowest when speed limit adherence increases (low variance in behaviour) and is highest when acceleration/deceleration patterns vary (high variance in behaviour) and 2) on average, for tested vehicles, EV/PHEVs were £116.33 cheaper to run than ICEVs across all experiment conditions. The difference in the average fuel costs (electricity and petrol) shrinks at the vehicle level as driver behaviour is less varied (more homogeneous). This research should allow policymakers to quantify the demand for energy and subsequent fuel costs in cities.

Whether the universe is a computer simulation, or whether we wish to efficiently model our universe in a computer simulation, there would be benefits to modeling it in a fashion analogous to computer spreadsheet, each lattice cell can be conceived as containing all the mathematical formula necessary to continuously compute its state relative to changes in all its neighboring cells, and by progression, in relation to all the cells of entire space-time lattice. Alternatively, the “real” universe may itself be built on a space cell lattice, an irregular foam of space cells, in which each cell may be conceived as a multidimensional cell of distortable space, the shape of which fully describes (a) the four basic forces (gravity, electromagnetic, strong, weak) observed at that cell of space, and (b) the probability (or weight distribution) of any quantum states overlapping the cell and its neighbors. At an appropriate scale, it would appear that this conceptual model would resolve apparent conflicts between general relativity and quantum physics. It would also provide a new interpretation of Planck’s constant as description of the number of space cell events associated with any set of observable events. If formulae operating at a lattice cell level can be improve our ability to understand and model larger scale phenomena, this would be strong evidence in favor of the theory that mathematics is not just a human invention but rather an inherent feature of space-time itself.

To enable purposeful design and implementation of automated concrete technologies, precise assessment and prediction of the complex material flow at various stages of the process chain are necessary. This paper investigates the intermediate stage of the extrusion and deposition processes in extrusion-based 3D-concrete-printing, using a numerical model based on the Particle Finite Element Method (PFEM). In PFEM, due to the Lagrangian description of motion, remeshing algorithms and the alpha shape method are used to track the free surface during large deformation scenarios. The Bingham constitutive model was used for describing the rheological behaviour of fresh concrete. This model is validated by comparing the numerically predicted layer geometries with those obtained from laboratory 3D printing experiments. Extensive parametric studies were then conducted using the numerical simulation, delineating the influence of process and material parameters on the layer geometries, the dynamic surface forces generated under the extrusion nozzle and the inter-layer interactions.

The transport sector in Germany causes one-quarter of energy-related greenhouse gas emissions. One potential solution to reduce these emissions is the use of battery electric vehicles. Although a number of life cycle assessments have been conducted for these vehicles, the influence of a transport system wide transition has not been researched sufficiently. Therefore, we developed a method which combines life cycle assessment with an agent-based transport simulation and synthetic electric, diesel and gasoline powered vehicle models. We use the transport simulation to obtain the number of vehicles, their lifetime mileage and road-specific consumption. Subsequently we analyze the product systems’ vehicle production, use phase and End-of-Life. The results are scaled depending on the covered distance, the vehicle weight and the consumption for the whole life cycle. The results indicate that the sole transition of drive trains is insufficient to significantly lower the greenhouse gas emissions. However, sensitivity analyses demonstrate that there is a considerable potential to reduce greenhouse gas emissions with higher shares of renewable energies, a different vehicle distribution and a higher lifetime mileage. The method facilitates the assessment of the ecological impacts of the complete car based transportation in urban agglomerations and is able to analyze different transport sectors.

X-ray fluorescence is largely employed in the measurement of the thickness of coatings. Despite of its diffusion, the task is not straightforward because of the complex physics involved that results in high dependence on matrix effects. Thickness quantification is in practice accomplished using the Fundamental Parameters approach, adjusted with empirical measurements of standards with known composition and thickness. This approach has two major drawbacks: i) there are no standards for any possible coating and coating architecture and ii) even relying on standards, the quantification of unknown samples requires the precise knowledge of the matrix nature (e.g., in case of multilayer coatings the thickness and the composition of each underlayer). In this work, we describe a semiquantitative approach to coatings thickness measurement based on the construction of calibration curves through simulated XRF spectra built with Monte Carlo simulations. Simulations have been performed with the freeware software XMI-MSIM. We have assessed the accuracy of the methods by comparing the results with those obtained by i) XRF thickness determination with standards and ii) FIB-SEM cross-sectioning. Then we evaluated which parameters are critical in this kind of indirect thickness measurements.

To understand functions of biomolecules such as proteins, not only structures but their conformational change and kinetics are important to be characterized but its atomistic details are hard to obtain both experimentally and computationally. We review our recent computational studies using novel enhanced sampling techniques for conformational sampling of biomolecules and calculations of their kinetics. For efficiently characterizing the free energy landscape of a biomolecule, we introduce the multiscale enhanced sampling method, which uses a combined system of atomistic and coarse-grained models. Based on the idea of Hamiltonian replica exchange, we can recover the statistical properties of the atomistic model without any biases. We next introduce the string method as a path search method to calculate the minimum free energy pathways along a multidimensional curve in high dimensional space. Finally we introduce novel methods to calculate kinetics of biomolecules based on the ideas of path sampling: One is the Onsager-Machlup action method, and the other is the weighted ensemble method. Some applications of above methods to biomolecular systems are also discussed and illustrated.

In this paper, we present a comprehensive 4D study of the early stage of plastic deformation in a polycrystalline binary AlLi alloy. The entire microstructure is mapped with X-ray diffraction contrast tomography and a set of bulk grains is further studied via X-ray topotomography during mechanical loading. The observed contrast is analyzed with respect to the slip system activation and the evolution of the orientation spread is measured as a function of applied strain. The experimental observations are augmented by the mechanical response predicted by crystal plasticity finite element simulations to analyze the onset of plasticity in details. Simulation results show a general agreement of the individual slip system activation during loading and that comparison with experiments at the length scale of the grains may be used to fine tune the constitutive model parameters.

The operation of electricity and natural gas transmission networks in the U.S. are increasingly interdependent, due to the growing number of installations of gas fired generators and the penetration of renewable energy sources. This development suggests the need for closer communication and coordination between gas and power transmission system operators in order to improve the efficiency and reliability of the combined energy system. In this paper, we present a co-simulation platform for examining the interdependence between natural gas and electricity transmission networks based on a direct current unit-commitment and economic dispatch model for the power system and a transient hydraulic gas model for the gas system. We analyze the value of day-ahead coordination of power and natural gas network operations and show the importance of considering gas system constraints when analyzing power systems operation with high penetration of gas generators and renewable energy sources. Results show that day-ahead coordination contributes to a reduction in curtailed gas during high stress periods (e.g., large gas offtake ramps) and a reduction in energy consumption of gas compressor stations.

The heavy demand of water resources from the Zarqa River Basin (ZRB) has resulted in a base-flow reduction of the River from 5m³/s to less than 1m³/s. This paper aims at predicting Curve Numbers (CN) as a baseline scenario and proposing restoration scenarios for ZRB. The method includes classifying the soil type and land use, predicting CNs, and proposing CN restoration scenarios. Prediction of existing CNs will be in parallel with the runoff prediction using the US Army Corps of Engineers HEC-1 Model, and Rainfall-Runoff Model (RRM). The models have been set up at the land use distribution of 0.3% water body, 9.3% forest and orchard, 71% mixture of grass, weeds, and desert shrubs, 7.0% crops, 4.0% urban areas, and 8.4% bare soil. The results show that CN under dry condition are 59, 78 under a normal condition and 89 under a wet condition. During vegetation period, CN are 52, 72 and 86 for the dry, normal and wet condition respectively. The restoration scenarios; CN decreases runoff, and increases soil moisture when using the contours, terraces and crop residues. Analyzing results of CN scenarios will be a fundamental tool to support end-users related to their targets to achieve watershed restoration.

The water cushion depth of stilling basin with shallow-water cushion is a key factor that affects the flow regime of hydraulic jump in the basin. However, the specific depth at which the water cushion is considered as “shallow” has not be stated clearly for now, and only conceptual description is provided. This paper attempts to specify the best water cushion depth based on the flow regime of hydraulic jump and underflow speed; namely, in case of critical hydraulic jump in the basin, the best water cushion depth is located where the minimum distance to the bottom plate of the stilling basin is 1/5~1/4 of the water cushion depth. The theoretical analysis indicates, at different inclinations of discharge chute (θ) and depth ratios of inlet (m), instead of monotonic change, the Froude number (Fr) at inlet of the stilling basin with shallow-water cushion firstly reduces and then increases as the flow velocity at discharge chute inlet (V) increases; the parameters of inflection point (critical flow velocity and critical Fr) increase as the inclinations of discharge chute (θ) and depth ratios of inlet (m) increase. Such regularity is the theoretical basis for selecting representative study cases. The reliability of the large eddy simulation calculation results are verified by a model test; in the paper, 30 cases including five different Froude numbers and six shallow-water cushion depths are selected, for calculating the hydraulic factors such as flow profile, flow regime and flow velocity in the stilling basin with shallow-water cushion; and the varying pattern between the best depth of stilling basin with shallow-water cushion (depth-to-length ratio) and the inflow Froude number is obtained which indicates that the best depth of stilling basin with shallow-water cushion varies little as the change of the Froude number before reaching the critical Froude number; however, the best depth-to-length ratio of stilling basin with shallow-water cushion increases as the Froude number increases after the critical Froude number is reached. The study results in this paper are of reference significance to design and calculation of the stilling basin with shallow-water cushion.

Arylamine N-acetyltransferases (NATs) are cytosolic enzymes, highly polymorphic, present in both eukaryotes and prokaryotes. These enzymes play an important role in the detoxification and activation of xenobiotics as well as in the synthesis of endogenous compounds. Specific NATs have been pointed out in the literature as possible therapeutic targets. In particular, the human NAT1, for the treatment of certain cancers, and the NAT from M. tuberculosis (TBNAT), for the treatment of tuberculosis. This paper describes an in silico approach to prospect and select potentially inhibitors of NAT1 and TBNAT from the Traditional Chinese Medicine (TCM) using free available tools. A library with ligands from TCM was previously screened in order to select only compounds with optimal pharmacological properties. The affinity of the selected ligands with respect to NAT enzymes was then evaluated by virtual screening (VS). Subsequently, the complexes with the best ligands were submitted to molecular dynamics (MD) simulations aiming to obtain better quality information on affinity and selectivity. The results for one specific ligand, ZINC14690579, indicated its potential for affinity and selectivity. ZINC14690579 structure may represent the discovery of a new scaffold for future development of NAT inhibitors.

The total enthalpies of the 16 different spin configurations that can be realized in the unit cell of the archetype spin crossover complex [Fe(phen)₂(NCS)₂] (phen=1,2-phenanthroline) were calculated applying periodic density functional theory combined with the Hubbard model and the Grimme-D2 dispersion correction (DFT+U/D2). The obtained enthalpy differences between the individual spin configurations were used to determine spin couplings of an Ising-like model and subsequent Monte Carlo simulations for this model allowed to estimate the phenomenological interaction parameter Г of the Slichter-Drickamer model, which is commonly used to describe the cooperativity of the spin transition. The calculational procedure described here, which led to an estimate of about 3 kJ/mol⁻¹ for Г, in good agreement with experiment, may be used to predict from first principles how modifications of spin crossover complexes can change the character of the spin transition from gradual to abrupt and vice versa.

In this paper we introduce a computational model for the simulation of hypervelocity impact (HVI) phenomena which is based on the Discrete Element Method (DEM). Our paper constitutes the first application of DEM to the modeling and simulating of impact events for velocities beyond 5 kms⁻¹. We present here the results of a systematic numerical study on HVI of solids. For modeling the solids, we use discrete spherical particles that interact with each other via potentials. In our numerical investigations we are particularly interested in the dynamics of material fragmentation upon impact. We model a typical HVI experiment configuration where a sphere strikes a thin plate and investigate the properties of the resulting debris cloud. We provide a quantitative computational analysis of the resulting debris cloud caused by impact and a comprehensive parameter study by varying key parameters of our model. We compare our findings from the simulations with recent HVI experiments performed at our institute. Our findings are that the DEM method leads to very stable, energy–conserving simulations of HVI scenarios that map the experimental setup where a sphere strikes a thin plate at hypervelocity speed. Our chosen interaction model works particularly well in the velocity range where the local stresses caused by impact shock waves markedly exceed the ultimate material strength.