This study proposes and validates a novel combustion control system for Oil-Fired Boilers aimed at reducing air pollutant emissions through flame image prediction. The proposed system is easily applicable to existing ships. Traditional proportional combustion control systems supply fuel and air at fixed ratios according to the set steam load, without considering the emission of air pollutants. To address this, a stable and immediate control system is proposed, which adjusts the air supply to modify the combustion state. The combustion control system utilizes oxygen concentration predictions from flame images via SEF+SVM as control inputs, and applies Internal Model Control (IMC)-based Proportional-Integral (PI) control for real-time combustion control. Due to the complexity of modeling the image-based system, IMC parameter tuning through experimentation is essential for achieving effective control performance. Experiments conducted on a 3000 kg/h marine oil-fired boiler in actual operation optimized the controller parameters for stability and responsiveness, and validated their effectiveness. The results demonstrate the potential of the proposed system to improve combustion efficiency and reduce emissions of air pollutants. This study provides a feasible and effective solution for enhancing the environmental performance of marine oil-fired boilers. Given its ease of application to existing ships, it is expected to contribute to sustainable air pollution reduction across the maritime environment.

This case study examines the impact of Machico City Airport expansion on sus-tainable tourism. It aims to identify the key effects on the environment, local economy, and community, and provides recommendations for mitigating negative impacts while promoting sustainable development. The study emphasizes adopting sustainable tourism practices to protect Machico's biodiversity and natural resources, drawing from the ECOS MACHICO project. It highlights the importance of environmental education for tourists, residents, and tour operators. Community engagement emerges as pivotal, advocating for inclusive plan-ning that incorporates local perspectives. The multifaceted implications of airport expansion on Machico's socio-economic fabric are explored, balancing positive and negative effects on residents and local businesses. Diversifying tourism offerings beyond mass tourism is advocated, with a focus on preserving local traditions and promoting authentic cultural experiences. In summary, the research provides a foundation for sustainable tourism devel-opment, offering strategies applicable to similar destinations. The study's implications extend to understanding broader impacts of airport expansions, presenting avenues for further research. The proposed strategies serve as a blueprint for destinations fac-ing analogous challenges, promoting enduring socio-economic and environmental well-being

High-precision displacement sensing has been widely used across from both scientific researches and industrial applications. The recent interests in developing micro-opto-electro-mechanical systems (MOEMS) have given rise to an excellent platform for miniaturized displacement sensors. Advancement in this field during past years is now yielding integrated high-precision sensors which show great potential in applications ranging from photoacoustic spectroscopy to high-precision positioning and automation. In this review, we briefly summarize different techniques for high-precision displacement sensing based on MOEMS, and discuss the challenges for future improvement.

The goal is the optimal synthesis of the parameters of the control law for a nonlinear satellite attitude control system under constraints related to the saturation effect of reaction wheels. The maximum stability degree of the control system is considered as a criterion for the optimality of the parameters of the control law, and the absolute values of the control torques and angular velocities of the reaction wheels, achievable within the limits of the physical characteristics of the drives, are considered as constraints. The solution to the optimal synthesis problem is based on the transformation of the nonlinear control system model into a linear model, within the framework of the problem of global asymptotic stability of the control system is also guaranteed. The problem is decomposed into a subproblems of obtaining the optimal transient process shape in the control system and considering the constraints. A method of synthesizing the optimal transient process shape based on the criterion of maximum stability degree had been developed. An analytical method of determining the optimal values of the control law parameters, based on decomposing the normalized characteristic polynomial in the linear model into three identical factors had been proposed. A method with regards to the constraints of the control torque and angular speed of the reaction wheels, based on transitioning from relative to real time is developed. An algorithm for calculation of the transition scale from relative to real time, ensuring the fulfillment of all constraints in the optimal synthesis problem, is proposed. An example demonstrating the efficiency and simplicity of the proposed methods and algorithm for targeted selection of technical characteristics of the control system, considering the maximum absolute values of angular speeds and control torques of the reaction wheels, is provided.

Non-toroidal shape primary pass through protection current transformers (CTs) are used to measure high currents. Their design provides them with a big airgap that allow passing several cables per phase though them, which is the main advantage versus toroidal types, as the number of CTs required to measure the whole phase current is drastically reduced. The cables pass through the transformer window can be in several positions. As the isolines of the magnetic field generated by the primary currents are centered in the cables, if these cables are not centered in the transformer window, the magnetic field will be non-uniform along the transformer core. Consequently, local saturations can appear if the cables are not properly disposed causing the malfunction of the CT. In this paper, the performance of a non-toroidal shape protection CT is studied. The research is focused on the cable position influence in possible partial saturations of the CT when it is operating near to its accuracy limit. Depending on the cable position, the ratio of the primary and secondary currents can depart from assigned ratio. The validation of this phenomenon has been carried out via Finite Element Analysis (FEA) showing that partial transformer core saturations appears in areas of the magnetic core close to the cable. Applying FEA, also the admissible accuracy region for cable positioning inside the CT is delimited. Finally, the simulation results are ratified with experimental tests performed in non-toroidal protection CTs varying the primary cables’ positions, which are subjected to currents up to 5 kA, achieving satisfactory results. From this analysis, installation recommendations are given.

Chitosan-based composites are interesting materials for dye adsorption. In this work, methyl orange (MO) adsorption using chitosan (CH) and chitosan-graphene oxide (CH-GO) aerogels produced by supercritical gel drying was assessed, by studying the effect of driving force (25-100 ppm) and adsorbent dosage (1-8 g/L). It was highlighted that the difference in the performance between the two adsorbents was non-negligible only at high concentrations, due to more intense interactions between the adsorbent active sites and MO molecules. Starting from a 10 ppm MO solution, using a dosage of 8 g/L it was possible to achieve adsorption efficiency of about 85%, meaning that small amounts of nanostructured devices can result in good process outcomes. Freundlich isotherm reliably describes the system behavior, leading to the consideration that adsorption between MO and CH-GO takes place between different active sites. Moreover, PSO kinetic describes the general trend of this system through time, unveiling heterogeneous chemisorption; lastly, the multi-linear IPD kinetic model confirms that in the case of nanostructured porous devices, there are different mass transfer phenomena that control molecule diffusion through the system.

In this paper, polyurethane concrete (PUC) is used as wire embedding material to form a new polyurethane concrete- prestressed steel wire (PUC-PSW) reinforcement method. The lightweight and high-strength PUC materials not only bond and anchor the prestressed steel wire (PSW), but also passively participate in the structural force. The problems caused by using mortar or composite mortar as the embedding material are avoided. Twelve reinforced concrete T-beams were tested for PUC-PSW flexural reinforcement. It consists of 1 unreinforced beam, 4 PSW reinforced beams, and 7 PUC-PSW reinforced beams. The wire embedding material, wire anchorage form, PUC material depth, amount of wire, and loading type were used as variables. The test results show that PUC-PSW reinforcement can obviously enhance the flexural load and ultimate load of the reinforced beams compared with PSW reinforcement. The ability of PUC-PSW reinforcement to limit crack development is better than that of PSW reinforcement beam, especially after the main beam steel yield. The strength, stiffness and crack limiting ability of the reinforced beam increase with the PUC thickness of the reinforced layer.

Delaying the onset of laminar-turbulent transition is an attractive method in reducing skin friction drag, especially on streamlined bodies where Tollmien-Schlichting instabilities are the dominating mechanism for transition. Miniature vortex generators (MVGs) offer an effective approach to attenuate these instabilities by generating counter-rotating vortex pairs. They are placed in pairs within an array and resemble small winglet-type elements. The conventional methodology involves adjusting MVG parameters and conducting computationally expensive DNS and/or downstream stability analyses to assess their effectiveness. However, analyzing the vortex parameters of MVG-generated vortices can potentially guide a more targeted approach in modifying MVG parameters and identifying critical factors for transition delay. Therefore, this study investigates changes in three primary MVG parameters, namely inner distance, periodicity and height, and utilizes computational fluid dynamics (CFD) analysis to create a dataset that examines the characteristics of the generated counter-rotating vortex pairs and their potential in drag reduction. The objective is to establish correlations among these parameters and their influence on delaying transition.

In this work, we propose an in-house simulation tool designed for the analysis and optimization of bifacial photovoltaic (PV) modules, which are currently under the spotlight in the renewable energy scenario. The tool is conceived to support researchers and engineers by providing fast and accurate predictions of the PV module yield under various operating and environmental conditions. For a chosen geographical site, the impact of module orientation, tilt, albedo, sky conditions, ambient temperature, and so on, can be effortlessly determined. In case of nonuniformity across the cells dictated by localized architectural shading, dirt, bird drops, defects, a circuit-based cell-level approach can be activated to compute the module production. An extensive simulation campaign is performed by assuming that the panels are installed in Naples without loss of generality. Results are shown to give detailed insights into the performance of bifacial modules, thus providing unambiguous guidelines for their correct installation. Further analyses are conducted to demonstrate the tool capability to quantify the detrimental influence of a poorly-irradiated cell on the backside, as well as of cracked cells.

In electromagnetic ultrasonic testing, it is difficult to recognize small-size bottom cracks by time of flight (ToF), and the lift-off fluctuation of the probe affects the accuracy and consistency of the inspection results. In order to overcome the difficulty, a novel EMAT (electromagnetic acoustic transducer)-PEC (pulse eddy current) composite sensor is designed. We use amplitude of bottom echo recorded by EMAT to identify the tiny bottom crack as well as the amplitude of PEC signals picked up by the integrated symmetric coils to measure the average lift-off of the probe in real time. Firstly, the effects of lift-off and bottom cracks on amplitude of bottom echo are distinguished combining the theoretical analysis and finite element method (FEM). And then an amplitude correction method based on the fusion of EMAT and PEC signals is proposed to reduce the impact of lift-off on the defect signal. The experimental results demonstrate that the designed composite sensor can effectively detect the bottom crack as small as 0.1mm* 0.3mm. The signal fusion method can accurately correct the amplitude of defect signal and the relative error is less than ±8%.

Runway Safety Assistant Foreseeing Excursions (RUN.S.A.F.E.) is a complete embedded system solution that predicts a potential runway overrun of a civil aviation aircraft, during the Take off and Landing. This work examines the feasibility of such a system, through the algorithms and computations that predict the overruns. The system executes both static and dynamic calculations, the former being dependent, while the latter independent to the user’s inputs. Both outcomes and the runway’s length continuously estimate in real time if the process will end up in an overrun. All inputs are specifically selected, to either be available to the pilots, or to be retrieved from the existing avionics systems of the cockpit. A performance evaluation is conducted on both static and dynamic calculations, and metrics unveil the accuracy of the predictions and the time needed to converge to a reliable result. The solution is adapted for a Boeing 737-800 aircraft, with CFM56-7B engines, yet the calculations also apply for similar aicrafts, equipped with a tricycle Landing gear and turbofan engines, namely the whole Boeing 737 family, the Airbus A320 family etc.. The system is aligned with current standards and certification specifications, where applicable.

This study focuses on the creation and implementation of a platform to improve personal financial management for scholarship students of the National Program of Scholarships and Educational Credit (Pronabec) in Peru. Using a mixed methodology that combines qualitative and quantitative techniques, the effectiveness and user satisfaction with the platform were evaluated. The results indicated that 50\% of users found the tool useful, 55\% considered it easy to use, 70\% reported a positive impact on their personal budget planning, and 85\% expressed high overall satisfaction. In addition to providing tools for financial recording and analysis, the platform promotes financial education among its users, significantly contributing to improving their financial management skills.

It is meaningful to monitor and identify the concrete cracks as they will seriously reduce the fatigue life of pavement. In this paper a new method based on distributed long-gauge optic fiber sensing is proposed to implement the crack identification. Firstly, the damage indicator, namely strain curve envelope area ratio (SCEAR), is proposed and calculated with the measured strain in time series when the plane goes across the pavement where the sensors are installed. Then the indicators with different sensors are normalization by the value of the indicator from one referenced sensor where damage normally will not happen. If the crack happens within the sensing gauge of the sensor, the value of damage indicator SCEAR from the sensor will increase. Therefore, the crack will be identified with the change of the damage indicator. The effect of the proposed method is first verified by some simulations of prefabricated pavement as the cracks can be accurately identified by the proposed method. Moreover, the influence from the aircraft, such as taxiing position, aircraft load and aircraft type, is not obvious enough to mislead the identification results. Lastly the static loading tests were implemented with one small-scale model of prefabricated concrete pavement in the lab. From the experimental results the prefabricated cracks inside the pavement slab can be identified accurately with the proposes method as the parameter SCEAR is increased by about 100% at the crack position, while it is only changed by about 3% at the undamaged position.

A comprehensive design framework is proposed for optimizing sparse MIMO (multiple-input, multiple-output) arrays to enhance multi-target detection. The framework emphasizes efficient utilization of antenna resources, including strategies for minimizing inter-element mutual coupling and exploring alternative grid-based sparse array (GSA) configurations by efficiently separating interacting elements. Alternative strategies are explored to enhance angular beamforming metrics, including beamwidth (BW), peak-to-sidelobe ratio (PSLR), and grating lobe limited field of view. Additionally, a set of performance metrics is introduced to evaluate virtual aperture effectiveness and beamwidth loss factors. The study employs the desirability function and explores optimization strategies for the partial sharing of antenna elements in multi-mode radar applications. A novel machine learning initialization approach is introduced for rapid convergence. Observations include the potential for peak-to-sidelobe ratio (PSLR) reduction in dense arrays and insights into GSA feasibility and performance compared to uniform arrays. The study validates the efficacy of the proposed framework through simulated and measured results. The study underscores the importance of effective sparse array processing in multi-target scenarios and highlights the advantages of the proposed design framework, offering clarity on sparse array efficiency and applicability in processing hardware providing clarity on its advantages over uniform and nonuniform arrays.

Autoencoders are neural networks that have applications in denoising processes. Their use is widely reported in imaging (2D), though 1D series can also benefit from this function. Here, three canonical waveforms are used to train a neural network and achieve a signal-to-noise reduction with curves whose noise energy was above that of the signals. A real-world test is carried out with the same autoencoder subjected to a set of time series corrupted by noise generated by a zener diode, biased on the avalanche region. Results showed that, observed some guidelines, the autoencoder can indeed denoise 1D waveforms usually observed in electronics, particularly square waves found in digital circuits.

A Physical Unclonable Framework (PUF) is a detached hardware component or, the structural arrangement of hardware that provides natural variations in the physical parts of semiconductor devices to generate unique and unpredictable identifiers based on challenging-to-replicate. In this case, PUFs are giving us a tool to utilise these variations to create an unclonable identifier for your device. PUFs generate a response or a unique pattern based on the physical characteristics of the device, and this response is difficult to predict or duplicate. This study aims to introduce a PUK device to provide security research on the Internet of Things technology. The elliptic curve cryptography algorithm was modified with an advanced equation to calculate a private key and K value. In this study, a PUF device will be employed with a modified elliptic curve cryptography algorithm on a novel security system to be used in current and future communication technology.

The extensive utilization of drones has led to numerous scenarios that encompass both advantageous and perilous outcomes. By using deep learning techniques, it is aimed to reduce the dangerous effects of drone use through early detection of drones.The state of the art of the study is evaluation of deep learning approaches such as pre trained YOLOv8 drone detection for security issues. In this study, dataset collected by Mehdi Özel for a UAV competition are sourced from GitHub. These images are labeled using Roboflow, and the model is trained on Google Colab. To enhance model performance, dataset augmentation techniques including rotation and blurring are implemented. Following these steps, a significant precision value of 0.946, a notable recall value of 0.9605 and a considerable precision-recall curve value of 0.978 are achieved, surpassing many popular models such as Mask CNN, CNN, and YOLOv5.

This paper is devoted to Petri net (PN) based models of Automated Manufacturing Systems (AMS) with resources in order to prevent deadlocks in them. The sustainability can be seen as the process of deadlock-freeness leading to correct and fluent production, because AMS with deadlocks work neither correctly nor fluently, need reconstruction and cause downtime in production. A class S3PR (Systems of Simple Sequential Processes with Resources) of such PN models is well known as to the deadlock prevention point of view. Here, Extended S3PR (ES3PR) will be explored as to modelling and deadlock prevention. While in case of S3PR Ordinary Petri Nets (OPN) were used for these aims, here, for ES3PR Generalized Petri Nets (GPN) are used. The reason for such a procedure is a possible presence of multiplex directed arcs in the structure of PN models of AMS. The significant alternation is that while in former case the elementary and dependent siphons were sufficient for the supervisor synthesis, here, in later case, the GPN and their siphons have to satisfy the max-cs property.

One of the relevant goals in forging technologies is simplifying the existing process and developing new processes that can retain the original or very close to the original dimensions and billet shape after the end of the deformation cycle. This study attempts to develop a forging equipment design that provides alternating cyclic loading; the alternate cyclic forging (ACF) method is proposed. To preliminary evaluate the ACF and study the plastic deformation behavior, numerical simulation via finite element modeling (FEM) was conducted using DEFORM-3D. The main attention was paid to the metal flow characteristics, effective strain and stress distribution, and the forging load. The ACF results in non-uniform metal flow with the formation of diagonal and vortex flows were demonstrated. Maximum effective strains and stresses zones are localized at two points of the billet contact with the surface of the tooling container and at two points of the interface between the punches. It was also assumed that reducing the contact area between the punch and the billet using separate semi-cylindrical punches can reduce the forging load. Preliminary finite element analysis (FEA) results of the proposed process after four successive steps of the deformation cycle confirmed the feasibility of its general implementation.

Recently, flares have been considered as a major source of air pollution from the petroleum refining industry. The United Nations have instigated an international effort related to the management of flare emissions to reduce the global warming impact related to flaring. Eliminating or removing the need for gas flares is difficult because these devices are generally used as safety devices to allow combustion of flammable gases in a controlled fashion which supports safe operation. However, reducing flaring is generally possible using well designed efficiently operated flare equipment. In general, flare performance can be enhanced following the API-521 methodology and using assist- media including air and steam to achieve smokeless operation. This present work will discuss flare emissions in petroleum refining industry, and a method to manage flare emissions. Moreover, this work will discuss flare Combustion Efficiency (CE) and Distraction and Removal Efficiency (DRE) in terms of efficient flare operation. This work uses actual operating flare data, published previously, will be used in this work together with the CFD Code C3d. This code, developed at the USDOE Sandia National Laboratory is based on a standard LES methodology to conduct transient flare analysis is used to simulate flare operation to estimate flame shape and emissions produced. In this work, a new air-assisted flare tip design which uses the Coanda effect to improve flare operation was analysed. This new flare design reduces the emission rate that demonstrates the design effectiveness. The analysis considers a flare 1m high and (6") diameter in the centre of a 4m*4m*4m domain. Boundary conditions considering no cross wind and an ambient temperature of 300K. The initial condition is a hydrostatic pressure profile across the computational domain. In the air assist simulation, stoichiometric ratio will be a variable and therefore more than one case was considered.

Cardiovascular disease is a major global health concern, with early detection being critical. This study assesses the effectiveness of the portable ECG device, based on Internet of Medical Things (IoMT) technology, for remote cardiovascular monitoring during daily activities. We conducted a clinical trial involving 2,000 participants who wore the HiCardi device while engaging in hiking activities. The device monitored their ECG, heart rate, respiration, and body temperature in real-time. If an abnormal signal was detected while a physician was remotely monitoring the ECG at the IoMT monitoring center, he notified the clinical research coordinator (CRC) at the empirical research site, and the CRC advised the participant to visit a hospital. Follow-up calls were made to determine compliance and outcomes. Of the 2,000 participants, 318 showed abnormal signals, and 182 were advised to visit a hospital. Follow-up revealed that 139 (76.37%) responded, and 30 (21.58% of those who responded) sought further medical examination. Most visits (80.00%) occurred within one month. Diagnostic approaches included ECG (56.67%), ECG and ultrasound (20.00%), ultrasound alone (16.67%), ECG and X-ray (3.33%), and general treatment (3.33%). Seven participants (23.33% of those who visited) were diagnosed with cardiovascular disease, including conditions such as arrhythmia, atrial fibrillation, and stent requirements. The portable ECG device using the patch-type electrocardiograph detected abnormal cardiovascular signals, leading to timely diagnoses and interventions, demonstrating its potential for broad application in preventative health care.

Accurate forecasting of available energy portion that corresponds to the reservoir inflow of the month(s) ahead provides important decision support for the hydropower plants in energy pro-duction planning for revenue maximization, as well as for the environmental impact prevention, and flood control at the upstream and downstream of a basin. Therefore, a reliable forecasting tool or model is deemed necessary and crucial. Considering the fluctuation and nonlinearity of data which significantly influence the forecasting results, this study develops an effective hybrid model by integrating Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO) called “PSO-ANN” model based on the hydrological and meteorological data pre-processed by cross-correlation function (CCF), autocorrelation function (AFC), and normalization techniques for predicting the available energy portion corresponding to the reservoir inflow mentioned above for a case study hydropower plant in Laos namely, Theun-Hinboun hydropower plant (THHP). The model was evaluated by using correlation coefficient (r), relative error (RE), root mean square error (RMSE), and Taylor diagram plots in comparison with the popular single algorithm approaches such as ANN, and NARX models. Results demonstrated the superiority of the proposed PSO-ANN approach over the other two models.

This paper presents a methodology based on deep learning models and metaheuristic algorithms for the optimization of airfoils for the design of aircraft wings with large endurance. The use of AZTLI-NN (a neural network with an architecture composed of a multilayer perceptron and a variational autoencoder) is implemented for the prediction of graphs of the aerodynamic coefficients of the airfoil as a function of the angle of attack. This neural network presents good predictions of the aerodynamic coefficients, similar to the coefficients obtained with computational fluid dynamics simulations. AZTLI-NN in combination of metaheuristic algorithms and the CST profile parameterization method show excellent performance in single-objective and multi-objective profile optimization tasks.

Photoplethysmography (PPG) signals, which measure blood volume changes through light absorption, are increasingly used for non-invasive Cardiovascular Disease (CVD) detection. Analyzing PPG signals can help identify irregular heart patterns and other indicators of CVD. This research involves a thorough analysis of CVD classification using the Capnobase dataset, which includes data from 20 CVD subjects and 21 normal subjects. In the initial stage, heuristic optimization algorithms, such as ABC-PSO, the Cuckoo Search algorithm (CSA), and the Dragonfly algorithm (DFA), were applied to reduce the dimension of the PPG data. Next, these Dimensionally Reduced (DR) PPG data are then fed into various classifiers such as Linear Regression(LR), Linear Regression with Bayesian Linear Discriminant Classifier (LR-BLDC), K-Nearest Neighbors (KNN), PCA-Firefly, Linear Discriminant Analysis (LDA), Kernel LDA (KLDA), Probabilistic LDA (ProbLDA), SVM-Linear, SVM-Polynomial, SVM-RBF, to identify CVD. Classifier performance is evaluated using Accuracy, Kappa, MCC, F1 Score, Good Detection Rate(GDR), Error rate, and Jaccard Index(JI). The SVM-RBF classifier for ABC PSO dimensionality reduced values outperforms other classifiers, achieving an accuracy of 95.12% with a MCC of 0.90.

Technological equipment in quarries that extract and deliver aggregates for different uses operates in a predetermined flow depending on the type of rocks exploited and the dimensional characteristics imposed on the final products. In this context, the interruptions in operation required to replace high-wear parts (such as the teeth of excavators and bucket loaders) must be limited as much as possible through technological solutions to increase their service life. The evolution of the wear of the teeth of the quarry equipment that come into direct contact with the rocks was concretely established in the production process, in parallel with the wear values obtained by simulating the wear phenomenon in laboratory conditions, in order to validate the data collected during the operation of the machines. Preventive-repetitive maintenance, within the activities of reconditioning the worn surfaces of the teeth, through the charging process by manual electric welding with covered electrodes, was applied directly to the machine, which led to the shortening of the interruptions in operation necessary to replace these spare parts.

Building Information Modeling (BIM) is undeniably the most important trend in the digitization of the construction sector in recent years. BIM models currently being built are extremely geometrically rich, that is, they are modeled at a high level of detail in terms of geometry. Thanks to object-oriented programming paradigms, BIM models include high-level relationships to ensure interactions between objects, rapid view generation and documentation. However, these models are not always equally rich in non-graphical data. This is true for parameters at the library object level, with which building object models are saturated, but also at the project, site, building or floor level according to the structure of the interoperable Industry Foundation Classes (IFC) format. For this reason, experimental work was undertaken on semantic enrichment in non-graphical data of a public building (Public Kindergarten, Secemin, Poland), which has its BIM model at a high level of geometric detail but is poor in non-graphical data. As a result of the research and development work, all levels of the IFC structure were saturated with non-graphical data, validated, and the possibilities of their use were shown from the perspective of the facility manager. The article contributes to the discussion on semantic enrichment from CAD3D to BIM by presenting a detailed process for entering non-graphical data into a BIM model.

This paper proposes the GD (Geometric Distribution) algorithm, a novel approach to enhance the default Adaptive Data Rate (ADR) mechanism in the Long Range Wide Area Network (LoRaWAN). By leveraging the Probability Mass Function (PMF) of the GD model, the GD algorithm effectively addresses massive device distribution challenges in real-life scenarios. To evaluate the algorithm's performance, the LoRaWAN simulations were conducted using the fixed node pattern derived from actual locations of dairy farms in Ratchaburi province, Thailand. The research established scenarios for assessing the network's performance namely, DER (Data Extraction Rate) and SF (Spreading Factor) assignment. Comparative analyses were performed against the uniform random node pattern and established algorithms, including the default ADR scheme, EXPLoRa, Quantile Classification of Variance from the Mean (QCVM), and Standard Deviation (SD). The GD algorithm demonstrated significant improvements over existing methodologies for both fixed and uniform random patterns. The fixed pattern exhibited an enhancement of 14.3%, while the uniform random pattern showed 4.8% enhancement over the default ADR scheme. Further assessments covered the coverage area, payload size, and energy consumption. The GD algorithm consistently achieved the optimal DER values with a coverage area and payload size, albeit often at the expense of increased energy consumption.

Virtual commerce has experienced significant growth in recent years, especially in emerging markets such as Peru. This boom has led to an increase in the number of online stores and with it a dispersion of prices which makes it difficult for consumers to make decisions. Comparing prices has become a crucial need to maximize savings and optimize online shopping. This platform uses web scraping techniques to collect price data of desktop products from various Peruvian online stores, presenting the information in a clear and accessible way for users. By centralizing and comparing prices, it not only facilitates purchasing decisions, but also promotes transparency and competitiveness in the market. For this according to the metrics used show 56% of success of the algorithm

Background: Monitoring the mobility of players during wheelchair sports is essential to support coaches in their understanding of the activity and in their training programming. However, the amount of information available from the monitoring tools, combined with a general approach to processing and a poor presentation of the data to the coaches, is not effective and remains unused. Thus, this study aimed to propose a simple and efficient algorithm for identifying locomotor tasks (static, forward/backward propulsion, pivot/tight/wide rotation) during wheelchair movements based on wheelchair kinematic data. Methods: A total of 36 wheelchair tennis and badminton players participated, completing at least one of three proposed tests: the star test, the figure-of-eight test, and the forward-backward test. Locomotor tasks were identified using a five steps procedure including data reduction, symbolic approximation and pattern logical search. Results: Using this method, 99% of locomotor tasks were properly identified for the star test, 95% for the figure-of-eight test and 100% for the forward-backward test. Conclusion: This method appears to be a valuable tool for a simple and clear identification and representation of locomotor tasks over extended periods of time. Future research should aim at applying this method to multiple wheelchair court sports matches or daily life declination.

The deposition of dust particles on solar panel modules constitutes a significant challenge, leading to a potential reduction in solar cell performance. This work tackles this issue by investigating the surface modification of a layer of transparent indium tin oxide (ITO), with a detailed analysis of the influence of its characteristics via different annealing conditions/processing. The ITO layer has been studied to enhance its characteristics for dust removal applications. Of particular note is that the developed ITO layer can be applied to dust removal using electrostatic or magnetic-based approaches. Thus, the correlation between annealing gas (using Argon, Oxygen, and Nitrogen), annealing temperature, and annealing ramping rate is explored with the obtained surface topography, as well as electrical and optical properties. The developed ITO layers annealed in the Argon medium at 600°C with an annealing ramping rate of 50°C/s showed smooth surface roughness, the highest electrical conductivity, and a relatively low refractive index. Such findings can play a crucial role in the future implementation of using the enhanced ITO layer for dust mitigation in solar panels, thus improving their long-term performance.

The reconstruction of standard at-grade intersection into roundabout is very often motivated by the need to improve the safety of a part of the road network. To assure the effect of the reconstruction before-after analyses based on traffic accidents or some other indirect traffic safety parameter is needed. This paper presents a detailed analysis of four roundabouts reconstructed from the at-grade intersection according to the Croatian Guidelines for Roundabout Design. The analysis included a comparison of speeds at the location - a standard intersection- before and a roundabout – after reconstruction, time distribution of operational speed and applied design elements as well as their correlations. Experimentally gathered data on operational speed showed that the expected effect of speed reduction was not achieved in most of the analyzed cases. The conclusion is that given that the Croatian Guidelines set a very wide range for roundabout elements, the designer has a great influence on the selection and, consequently, on the effects of the roundabout implementation. The revision of the Guidelines is suggested.

In recent years, the proliferation of digital communication systems has been fueled by the increasing use of connected devices in both consumer and industrial sectors. The predominant reliance on radio frequency communication for data transfer faces challenges due to physical limitations and regulatory restrictions on frequency ranges. Responding to these challenges, Visible Light Communication (VLC) has emerged as an innovative solution, utilising the visible light spectrum to modulate the intensity of light emitted by sources like white LEDs. This paper explores the feasibility of employing Red/Green/Blue (RGB) LEDs for three-dimensional visible light communications using off-the-shelves equipment. This type of communication is known as Wavelength Division Multiplexing (WDM) as data is encoded in the three colour wavelength. On-Off Keying (OOK) is used as modulation on each wavelength. Afterwards, the more efficient Orthogonal Frequency Division Multiplexing (OFDM) modulation scheme is explored. The implemented solution involves three emission circuits, each modulating the one colour component of the RGB LED current. The reception's side involves dichroic R/G/B colour filters and a Transimpedance Amplifier (TIA) circuit, with a Raspberry Pi 4 facilitating offline data decoding sampled by an Arduino UNO. The prototype demonstrates successful VLC communication with OOK modulation at distances up to 1.5m for red and blue LEDs and 0.75m for green LEDs, achieving data rates up to 800 bit/s.

The startup ecosystem in Peru has experienced remarkable growth in recent years, driven by technol- ogy and innovation across various industries. This article presents a web application developed in PHP, designed to centralize and facilitate access to information about startups, incubators, and investors in Peru. The methodology employed encompasses several stages, from establishing prerequisites to data preprocessing, model selection and training, rigorous evaluation, and the technical development of the platform. The application offers key functionalities such as exploring a comprehensive database of star- tups, detailed access to information about incubators and accelerators, identification of investors, and advanced analysis tools for market proposal prediction. Additionally, it integrates educational resources and allows users to suggest new startups through detailed surveys. The results demonstrate that the ap- plication improves access to relevant data, fosters collaboration among ecosystem actors, and supports informed decision-making. The platform also stands out for its security and privacy measures, as well as its ability to drive innovation and competitiveness in the market. In conclusion, the application has a sig- nificant impact on the Peruvian entrepreneurial ecosystem, and its continuous improvement is essential for the sustainable growth of startups in Peru and globally

The life cycle of a microgrid covers all the stages from idea to implementation, exploitation until its end of life, but with a lifespan of around 25 years. Covering them usually requires several software tools, which can make integration of results from different stages very difficult and may imply costs hard to estimate from the beginning of a project. This paper proposes a single platform of four modules developed in Matlab to assist all the processes a microgrid passes during its lifetime. The authors detail the architecture, functions and the development of the platform, either by using existing tools available in Matlab, or by developing new ones and designing new user interfaces linked with scripts based on its complex mathematical libraries. A proof-of-concept for this platform was presented by applying the life-cycle assessment process on a real-case study, a microgrid consisting in a photovoltaic plant, an office building as consumer and energy storage units.

Developments in artificial intelligence techniques allow for an improvement in sustainable mobility strategies with particular reference to energy consumption estimates of electric vehicles (EVs). This research proposes a vehicle energy model developed on the basis of Deep Neural Network (DNN) technology. The study also explores the potential application of the model developed for the movement data of new vehicles in the province of Enna, Sicily, Italy. which is characterized by numerous attractors and the increasing number of hybrid and electric cars circulating. The energy model for electric vehicles shows high accuracy and versatility, requiring vehicle velocity and acceleration as input data to predict energy consumption. The research article also provides recommendations for the energy modeling of electric vehicles and outlines additional steps for model development. The implemented methodological approach and its results can be used by transport decision makers to plan new transport policies in Italian cities aimed at optimizing vehicle charging infrastructure. They can also help vehicle users accurately estimate energy consumption, generate maps, and identify locations with the highest energy consumption.

This research aims to propose a novel approach to evaluate and minimize the scrap rate in the industrial production of premium food cans with distortion printing. Beyond cost considerations, a critical aspect of modern food can manufacturing is the aesthetic quality of the graphical display. In addition to traditional formability requirements, a waving requirement is defined. Detailed real production conditions are provided and discussed. The material of interest is a double cold-reduced (DR) low-carbon steel sheet and chromium-coated tin-free steel with a thickness of 0.16 mm. These sheets are laminated on both sides with PET film before distortion printing on the exterior. A material parameter identification method is proposed and illustrated to address the challenges in characterizing such a thin sheet. The thickness profile and flange length are key criteria for this identification. Digital image correlation (DIC) and a microscope are used to measure the thickness distribution and flange length. Within the manufacturing system, uncertainties arising from material properties and forming processes can lead to scraps or defects. Finite element analysis (FEA) is adopted for process analysis and validated with experiment. Uncertainty propagation via metamodeling, employing radial basis function (RBF) neural networks, is adopted for the scrap rate evaluation. The study concludes with process optimization recommendations to reduce scrap.

Hail causes damage to property, including roofs, automobiles, and crops, with an average annual loss of $850 million. In residential structures, tile roofing systems are common in the southern U.S. due to their resistance to hail impact and long service life. Additionally, the commercial buildings and some portions of the residential structures have low-sloped or flat roof systems. Commercial low-slope roof systems are equally prone to hail-strike damages as steep residential roof systems. The objectives of this paper are to present a literature review, inspection protocol, and case studies on a comparative assessment of hail threshold for built-up roof (BUR) and tile roof (TR) systems. The published papers determining the hail impact assessment of different roofing systems from 1969 through 2023 were studied and analyzed. This study develops a comparative hail damage assessment table between BUR and TR systems, and the hail threshold for various built-up roof composition systems. In addition, the different failure modes and their causes, the characteristics of hail impacts, and the variables influencing the impact resistance of these roofing systems were examined using field studies. To better understand the effects, it is recommended that an intelligent model be developed to predict the hail resistance threshold of various configurations of BUR and TR systems with critical variables.

Human-robot collaboration is a new trend in modern manufacture. The safety or human protection is of great significance due to the share of the same workshop between humans and robots. To achieve effective protection, in this paper, a contact force sensor based on the 8-shaped wound polymer optical fiber is proposed. The 8-shaped wound structure can convert the normal contact force to the shrinkage of the 8-shaped optical fiber ring. The macro-bending loss of the optical fiber is used to detect the contact force. Compared with conventional sensors, the proposed scheme has the advantage of high flexibility, low cost, fast response, high repeatability, which shows the great promise on actively alarming collision and passively reducing the hurt.

Groundwater is becoming increasingly important in the Pacific Northwest of the USA due to declining snowpack volumes and shifts in precipitation type and timing, all connected with climate change. The Upper Williamson Basin of the Klamath Watershed is a groundwater dominated watershed with massive fluctuations in year-to-year streamflow volumes over the past four decades, including the complete absence of any live flow for several years. The precise relationship between groundwater and streamflow in the basin has been difficult to assess due to a limited number of monitoring wells and significant gaps in the water level time history. To address this challenge, we use a novel imputation technique that leverages Earth observations and machine learning to impute gaps in water level records. We use these more complete datasets to compute a groundwater storage change time series for the basin. We show that groundwater storage is highly correlated to streamflow and that groundwater storage is correlated to rainfall with a three- to four-year delay that appears variable depending on groundwater storage volumes. The tools and relationships we present make it possible for water managers to estimate when streamflows will return to the basin and be more informed to support better management.

This paper introduces a novel decision-making approach grounded in insights into human visual perception of change. Modern technologies such as internet of things (IoT) provide us with large amounts of sensor data that need to be processed in real time and decisions made with a high degree of accuracy and reliability. Artificial intelligence (AI) methods are welcome in this context and need to be upgraded to meet actual challenges. While modern computing capabilities facilitate rapid data processing, the real-time demands of vast sensor data necessitate swift responses across the cyber chain, often leading to compromises in solution quality to circumvent combinatorial search complexities. Determining the adequacy of a solution entails varied approaches, often relying on heuristic methodologies. We illustrate our original approach with an example of a selected detail of a differential evolution algorithm, where we have to make a decision to adopt the best solution so far. We propose an approach inspired by human perceptual features that exploits the Weber-Fechner law to emulate human judgements, and offers a promising way to improve decision making in AI applications and real-time requirements fulfillment. Our proposed methodology demonstrates applicability across diverse AI scenarios involving numerical data, effectively mirroring human perception abilities.

In this paper, we present a full coverage path planning (CPP) algorithm for the marine surveys conducted in the convex polygon shaped search area. The survey is supposed to carry out by torpedo-type AUVs (autonomous underwater vehicles). Due to their nonholonomic mechanical characteristics, these vehicles have nonzero minimum turning radius. For any given polygon shaped search area, it can always be partitioned into one or more convex polygons. With this in mind, this paper proposes a novel search algorithm called CbSPSA (Calculation based Shortest Path Search Algorithm) for full coverage of any given convex polygon shaped search area. By aligning the search inter-tracks alongside the edge with the minimum height, we can guarantee the minimum number of the vehicle’s turns. In addition, the proposed method can guarantee the planned path is strictly located inside the polygon area without overlapped or crossed path lines, and also has the total path length as short as possible. Considering the vehicle’s nonzero minimum turning radius, we also propose a sort of smoothing algorithm to smooth the waypoint path searched by CbSPSA so as for the vehicle to exactly follow it. The smoothed path is also guaranteed to be strictly located inside the polygon. Numerical simulation analyses are also carried out to verify the effectiveness of the proposed schemes.

Reliable models of pavement performance play a crucial role in effective decision-making for maintaining and rehabilitating this class of infrastructure assets. Probabilistic modeling approaches have gained popularity in pavement performance modeling because they account not only for the stochastic nature of pavement behavior and deterioration factor variations but also for the imperfections and inadequacy of pavement condition data in certain situations. One of these approaches, Markov chains, has been used extensively to model the probabilistic performance of pavements through an interesting variety of methodological tweaks in the Markov model structure. Unfortunately, the current literature lacks a synthesis of Markov chain models and their associated methodologies, as used in this manner. It is anticipated that a comprehensive synthesis of these models and their various forms can provide some insight into the variations of Markov model forms and methodologies, and the appropriate Markov model type to use for pavement deterioration and performance modeling under given conditions of data types and availability. To address this issue, this paper reviews Markov chain models used in the literature to model pavement deterioration and the methodologies used to estimate the transition probabilities matrix which is the key feature of Markov chain models. The paper presents a critical analysis of various aspects of Markov chain models as they were applied in the literature, reveals gaps in knowledge, and offers suggestions to address these gaps. The paper also develops a decision tree to select the appropriate Markov model type and TPM estimation methodology to model pavement deterioration under given conditions of data availability. This paper therefore provide guidance and decision support for researchers and highway agencies in selecting an appropriate probabilistic technique for modeling their pavement infrastructure performance in a robust manner.

The effect of spanwise wing non-planarity, implemented in conjunction with a Gurney flap is presented. Testing was undertaken in a low speed wind tunnel using a rectangular wing with an aspect ratio of 3. The outer 1/3 of the wing was non-planar which took the form of dihedral or a circular arc. A 2% high Gurney flap was implemented such that it could extend over the entire span, or the planar inboard section. Loads were measured using a sting balance. The data shows that non-planarity increases the maximum lift coefficient and the wing’s lift curve slope. Gurney flap lift modulation was enhanced in the presence of non-planarity. The addition of Gurney flaps caused a greater minimum drag coefficient increment for the non-planar wings. The Gurney flaps reduced the lift dependent drag of the wings; however, the minimum drag coefficient was observed to increase. The Gurney flaps reduced the maximum lift to drag ratio (L/D) for the non-planar wings; however, the flat wing showed a small L/D increment with flap addition.

This study developed a three-dimensional kinematic and dynamic model of a center-driven linkage system (CDLS). Force analysis was conducted to determine the required driving torque to ensure that the input link of the CDLS rotates at a constant speed with a certain angular velocity, even in the presence of frictional resistance torque. The primary objective was to optimize the maximum angular acceleration of the output links to reduce wiper noise. This optimization can enhance the stability of the linkage system while maintaining its operational functionality. In the adopted methodology, the frames of the fixed links are assumed to remain unchanged, and the input link is considered to operate at a constant angular speed and have a constant length. To meet standard vehicle windshield wiper requirements, 10 constraints were imposed on the optimization problem, which was solved using the differential evolution method. The lengths of both output links were reduced by over 10% after optimization.

The integration of advanced image analysis using artificial intelligence (AI) is pivotal for the evolution of autonomous vehicles (AVs). This article provides a thorough review of the most significant datasets and the latest state-of-the-art AI solutions employed in image analysis for AVs. Datasets such as Cityscapes, NuScenes, and CARLA form the benchmarks for training and evaluating different AI models, with unique characteristics catering to various aspects of autonomous driving. Key AI methodologies, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Transformer models, and Generative Adversarial Networks (GANs), are discussed. The article also presents a comparative analysis of various AI techniques in real-world scenarios, focusing on semantic image segmentation, 3D object detection, and vehicle control in virtual environments. Simultaneously, the role of multisensor datasets and simulation platforms like AirSim, TORCS, and SUMMIT in enriching the training data and testing environments for AVs is highlighted. By synthesizing information on datasets, AI solutions, and comparative performance evaluations, the article serves as a crucial resource for researchers, developers, and industry stakeholders. Offering a clear view of the current landscape and future directions in autonomous vehicle image analysis technologies.

Heat pumps are recognized as a key tool in the energy transition toward a carbon-neutral society, enabling the electrification of the heating sector at least for low- and medium-temperature heat demands. In recent years, natural refrigerants have been reconsidered due to their low environmental impact: among them, CO2 is a safe option without impact on the ozone layer and low global warming potential compared to synthetic fluids. However, as a consequence of its thermophysical properties, its thermodynamic cycle is transcritical and is particularly suitable for specific end-user temperature profiles. This paper analyzes in a systematic and thorough way the most significant modifications to the reference cycle that have been proposed in the literature to improve the performance, finding how the optimum configurations change with a change in the rated operating conditions (inlet temperature and temperature glide of the heat demand, and ambient temperature). Exergy analysis explains why there is an optimal gas cooler pressure and why its trend with the average temperature is split into two distinct regions, clearly recognizable in all cycle layouts. The maximum coefficient of performance (COP) of the reference cycle varies in the 1.52–3.74 range, with a second-law efficiency of 6.436.1, for an optimal gas cooler pressure of up to 15.45MPa, depending on ambient temperature and end-user temperature profile. The most effective modification is the cycle with ejector and internal heat exchanger, which raises the COP to 1.84–4.40 (second-law efficiency 8.745.56). The presented results provide an extensive guide to understanding the behavior of a transcritical CO2 cycle and predict its performance in heat pump applications.

As the demand of heating and air conditioning is increasing day by day due to getting a comfortable life, for that reason, the market for heat pump and air conditioning systems or both, is seeing a steady increase in low global warming potential. All researchers on heat pump and air conditioning systems are searching for environmentally friendly refrigerants. However, a significant number of contemporary refrigerants (HCFCs) have the potential to cause global warming and ozone depletion, such as those found in heat pump air conditioning systems. Using more environmentally friendly refrigerants, including hydrocarbon and blend refrigerant mixtures, is one way to lower GWP and Ozone depletion potential. This study examines the performance of several alternative refrigerants, including R32, R134A, R410A, R22, and R290. In order to examine the performance of R290, theoretical and simulation results are compared on various refrigerants while taking into account a number of performance parameters, including the refrigerant energy efficiency ratio, mass flow rate, coefficient of performance, cooling capacity, and compressor work for one ton of refrigeration (1TR) air conditioners. One player with a very low GWP is R290. However, the only drawback is that it can catch fire and is hazardous to use in split-type commercial heat pump air conditioning applications. The environmental and thermo physical properties of HC-290 are significantly better than those of other materials, according to theoretical and simulation studies utilizing Cool Pack software, hence it is a viable replacement.

The research presented in the article is devoted to the study of the influence of phase compounds on the friability of the Fe-Si-Mn-Al complex alloy. The urgency of the problem lies in the development of technology for producing a non-scatterable alloy from manganese-containing ores and high-ash coals. The main goal of the work is to determine the range of alloy compositions and the resulting phases that affect the dispersibility of the alloy, which is critically important for its industrial implementation. Research methods include thermodynamic diagram analysis (TDA) using data on the standard enthalpy of formation of intermetallic compounds, as well as experimental tests in an ore-thermal electric furnace with a capacity of 200 kV*A. The results show that Fe-Si-Mn-Al complex alloys form a variety of silicide and aluminide phases, including intermetallic compounds and ternary systems, which is critical for understanding and controlling their physicochemical properties. When smelting a complex alloy, the content of leboite (Fe3Si7) in the Fe-Si-Mn-Al system plays a significant role. The development of smelting technology will be aimed at avoiding the FeSi2-Fe3Si7-F2(FeAl3Si2)-Mn11Si19 tetrahedron area. This approach to controlling the composition of a complex alloy is critical to ensuring its consistent friability properties in industrial applications. Thus, this work represents an important step in understanding the physical properties and stability of Fe-Si-Mn-Al complex alloys, which have potential for widespread use in metallurgical and other industrial applications.

The shift towards autonomous excavators in construction and mining is a significant leap towards enhancing operational efficiency and ensuring worker safety. However, it presents challenges such as the need for sophisticated decision-making and environmental perception due to complex terrains and diverse conditions. Our study introduces the E-GTN framework, a novel approach tailored for autonomous excavation that leverages advanced multisensor fusion and a custom-designed convolutional neural network to address these challenges. Results demonstrate that GridNet effectively processes grid data, enabling the reinforcement learning algorithm to make informed decisions, thereby ensuring efficient and intelligent autonomous excavator performance. The study concludes that the E-GTN framework offers a robust solution for the challenges in unmanned excavator operations, providing a valuable platform for future advancements in the field.

High temperature Ni-superalloys are limited by reliance on critical elements and complex processing steps. High entropy alloys (HEAs) have demonstrated stable single/dual phase microstructures with strong ordering tendencies. A dual phase (FCC+BCC) Al0.5Co0.5CrFeNi1.5Ti0.25 alloy (developed using CALPHAD) was tested for room and high temperature compression behavior. The microstructure mainly comprises of coarse ordered FCC(L12) surrounding decomposed lamellae of ordered FCC(L12) and BCC(L21) phases, which coarsened upon annealing at high temperature (1100 °C) into a bi-continuous structure of disordered FCC+BCC(B2). Additionally, cold rollability of the cast alloy increased from <5% to ~40% after annealing, with a corresponding softening observed in room temperature compression mainly due to disordering of FCC and BCC phases. Peak flow stresses of 793.45MPa, 535.12MPa, and 324.40MPa were achieved at 800oC, 900oC, and 1000oC respectively for the high temperature annealed alloy.

A comprehensive evaluation system of rural building energy consumption from the innovative composite perspective was established, which was suitable for southwest of China. The index system was established by Brainstorming method and Delphi method, the weights of the comprehensive evaluation model were calculated by Network Process (ANP) method, the scoring criteria of all evaluation indexes were leveld based on fuzzy evaluation theory. The system model was verified by case analysis, at the countryside around Chengdu Second Circle. With the highest weight, lowest comprehensive score, and broadest range of comprehensive scores taken into consideration, three key factors affecting the target layer, namely "Percentage of Clean Energy Use", "Thermal Performance of Exterior Walls", and "Implementation Rate of Energy saving Measures". The distribution of comprehensive indicators and evaluation factors has certain spatial distribution characteristics, and the overall spatial distribution shows a characteristic of "high in the southeast and low in the northwest". Finally, Based on key factors and regional distribution characteristics, energy-saving measures have been proposed from three aspects: increasing sunrooms, adding wall insulation layers, and standardizing air conditioning temperature settings.

This paper proposes an innovative approach to address the challenge of softening deformation of thermal-curable thermosets during additive manufacturing. This approach involves the development of a composite powder composed of thermosetting materials with distinct curing temperatures, the utilization of Cold Spray Additive Manufacturing (CSAM) technology for 3D printing, and the application of a stepwise isothermal curing process. The corresponding effectiveness is validated by case studies. The results demonstrate that samples printed via CSAM maintain a solid state without deformation. During the post-curing process, each component of the composite material cures independently while the other components remain in a solid state, providing structural support for the whole material, thereby effectively reducing the softening deformation during the post-curing process. This approach offers a novel perspective for the additive manufacturing of thermal-curable thermosets.

To increase the calculation speed of the computational fluid dynamics (CFD) based simulation for the gas-solid flow in fluidized beds, a Markov chain model (MCM) is developed to simulate the particle movement in a two-dimensional (2D) circulating fluidized bed (CFB) riser. As a statistic model, the MCM takes the CFD-discrete element method (DEM) results as samples to calculate the transition probability matrix of particles, which can describe the macroscopic regularities of the particle movement and guide the numerical simulation of the particle motion based on the Monte Carlo method. The particle distribution snapshots, residence time distribution (RTD) and mixing obtained from both the CFD-DEM and the MCM are compared. Results show that the computational speed of the MCM is faster than that of the CFD-DEM by about 2 orders of magnitude, and the difference of the mean particle residence time calculated by the two models is less than 2%. Besides, the MCM can well describe the time-averaged particle mixing compared to the CFD-DEM.

This study presents a unique model for assessing the dependability of continuous parts of combined systems in open-pit mining through the application of fuzzy logic. Continuous sub-systems as part of the combined system of coal exploitation in surface mines have the basic function of ensuring safe operation, high capacity with high reliability and low costs. These subsystems are usually part of the thermal power plant's coal supply system and ensure stable fuel supply. The model integrates various independent partial indicators of dependability into an expert system specifically designed for evaluating these systems. It deconstructs the complex parameter of system dependability into distinct partial indicators: reliability, maintainability, and logistical support. These indicators are then integrated using fuzzy composition (max-min composition). Historical data from 2018 to 2023 is utilized alongside the fuzzy model to provide a retrospective analysis of system dependability, serving to validate the model's effectiveness. What sets this model apart from conventional approaches is its consideration of practical dependability indicators, thereby obviating the need for extensive long-term monitoring and data collection to portray the system's status accurately over time. This model serves as a valuable tool for assisting decision-makers in open-pit mining operations, facilitating planning, exploitation control, and the selection of maintenance strategies to ensure consistent production and cost reduction. Designed for quick assessment, the model relies on expert judgments and assessments to determine system dependability efficiently.

This study examines the overall needs of the green construction scheme with ‘carbon neutrality’ as the centre in the Zhejiang provincial green development target area. By aggregating and organising the construction and development data of Zhejiang Province, the entropy weight TOPSIS model is formed according to the statistical modelling for quantitative examination of the data, and the scientific assessment scheme of ‘carbon neutrality’ in the regional construction industry of Zhejiang Province is developed. This study aids in completely exhibiting and dynamically understanding the advancement of the ‘carbon neutral’ capacity of the urban construction industry. The objective is to discover the weak link in the advancement of carbon neutrality in several regional construction industries, which is of great relevance for further examining and forecasting the strategic outlook of carbon neutrality and modifying the planning of carbon neutrality strategy in special regional construction industries.

In the future, the power of commercial ocean current generators can reach MW level, and the corresponding mooring rope tension is very great. But the power of the ocean current generator in research stage is KW level and it can bear less rope tension. Its main mooring rope adopts a single cable and a single foundation. This paper studies the dynamic response and rope tension of the MW-level ocean current generator mooring system. It is assumed that the commercial MW-level ocean current generator is similar to the research-type KW level, and the similarity law and several dimensionless similar parameters are proposed, including for the turbine and platform the power number, tip speed ratio, hydrodynamic damping and stiffness coefficient and others. Based on these dimensionless similar formula and the known parameters of the researched KW-level convertor, all parameters of the MW-level ocean current generator are derived. In order to overcome the extreme tension of a MW-level mooring system and provide good stability, this paper proposes the pulley-traction rope design to replace the traditional single traction rope design. The static and dynamic mathematical models of this mooring system subjected to typhoon wave impact and current are proposed and analytical solutions are obtained. The study found that the dynamic rope tension of the MW-level system with the traditional single rope system is significantly greater than its fracture strength and the dynamic tension of the pulley- traction rope system. It means that this design can effectively reduce the dynamic rope tensions of the mooring system. Moreover, if the length ratio of rope A to the seabed depth is within a safe range, the maximum rope dynamic tension will be less than the fracture strength. In addition to the MW level, the dynamic response of the 700kW level power generation system under the action of typhoon waves is also studied. It is discovered that the dynamic tension of rope D is the largest. In addition, the dynamic tension of rope D for 700kW system will exceed the original strength of design. However, only the specification of rope D is increased, the new dynamic tension is still close to the original such that the dynamic tension of rope D is significantly less than the adjusted fracture strength and the mooring system becomes safety.

Carbon fiber reinforced plastic (CFRP) is one of the representative lightweight material. The automotive industry has focused on producing the steel/CFRP hybrid part to reduce the weight. After manufacturing, delamination can occur at the interface between the CFRP and steel owing to the hybrid part constituting dissimilar materials. However, most studies have focused only on designing the manufacturing processes for hybrid part or evaluating the adhesive used at the interface. Therefore, it is necessary to predict the behavior of the interface after demolding the hybrid part. This study aimed to predict the interface behavior of a steel/CFRP hybrid part by considering its forming and cohesive properties. First, double cantilever beam (DCB) and end-notched flexure (ENF) tests were performed to obtain cohesive parameters, such as energy release rate of modes Ⅰ and Ⅱ (GⅠ, GⅡ). The experimentally obtained properties were applied to the bonding area of the hybrid part. Subsequently, a forming simulation was performed to obtain the stress of the steel blank in the hybrid part. The stress distribution after forming was utilized as the initial condition for spring-back simulation. Finally, the interface behavior of the hybrid part was predicted by a spring-back simulation, and the results were compared with the experimental results for verification.

Deploying Cellular Internet of Things (CIoT) devices in urban and remote areas faces significant energy efficiency challenges. This is especially true for Narrowband IoT (NB-IoT) devices, which are expected to function on a single charge for up to 10 years while transmitting small amounts of data daily. The 3rd Generation Partnership Project (3GPP) has introduced energy-saving mechanisms in Releases 13 to 16, including Early Data Transmission (EDT) and Preconfigured Uplink Resources (PUR). These mechanisms extend battery life and reduce latency by enabling data transmission without an active Radio Resource Control (RRC) connection or Random Access Procedure (RAP). This paper examines these mechanisms using the LENA-NB simulator in the ns-3 environment, which is a comprehensive framework for studying various aspects of NB-IoT. The LENA-NB has been extended with PUR, and our analysis shows that PUR significantly enhances battery life and latency efficiency, particularly in high-density environments. Compared to the default RAP method, PUR reduces energy consumption by more than 2.5 times and increases battery life by 1.6 times. Additionally, PUR achieves latency reductions of 2.5-3.5 times. The improvements with PUR are most notable for packets up to 125 bytes. Our findings highlight PUR’s potential to enable more efficient and effective CIoT deployments across various scenarios. This study represents a detailed analysis of latency and energy consumption in a simulated environment, advancing the understanding of PUR’s benefits.

The paper investigates the accuracy of the angular velocity estimation based on measurements of the components of the induction vector of the Earth's magnetic field. A comparative analysis of angular velocity estimates using the Boer formula, the derivative of the induction vector of the Earth's magnetic field and direct measurements of the components of the angular velocity vector using a gyroscopic sensor for the ISOI small spacecraft (SXC3-219) is carried out. The results obtained make it possible to investigate the accuracy of estimates of the angular velocity of a small spacecraft in various ways to improve the quality of its target tasks.

This study presents a comprehensive review of the development and progression of autonomous underwater vehicles (AUVs) in polar regions, aiming to synthesize past experiences and provide guidance for future advancements and applications. We extensively explore the history of notable polar AUV deployments worldwide, identifying and addressing the key technological challenges these vehicles face. These include advanced navigation techniques, strategic path planning, efficient obstacle avoidance, robust communication, stable energy supply, reliable launch and recovery, and thorough risk analysis. Furthermore, the study categorizes the typical capabilities and applications of AUVs in polar contexts, such as under-ice mapping and measurement, water sampling, ecological investigation, seafloor mapping, and surveillance networking. We also briefly highlight existing research gaps and potential future challenges in this evolving field.

Diffusion magnetic resonance imaging (dMRI) is an imaging technique that provides information on the microstructural organization of biological tissue. dMRI allows for non-invasive visualization and quantitative assessment of white matter architecture in the brain by characterizing restrictions on the random motion of water molecules. Ultra-high field MRI scanners, such as those operating at 7 Tesla (7T) or higher, can boost signal-to-noise ratio (SNR) to improve dMRI compared with what is attainable at conventional field strengths such as 3T or 1.5T. However, at 7T wavelength effects cause lower transmit magnetic field efficiency in the human brain, mainly in the posterior fossa region, manifesting as signal dropouts in this region. Recently, we reported a simple approach of using a wireless radiofrequency (RF) surface array to improve transmit efficiency and signal sensitivity at 7T. Here we demonstrate the feasibility and effectiveness of using this RF enhancer for in-vivo dMRI at 7T. The use of the RF enhancer can effectively recover signal dropouts in the regions with intrinsically lower SNR such as cerebellum, leading to a better depiction of principal fiber orientations and enhanced visualization of extended tracts to the brainstem and pons enabling more comprehensive delineation of the full white matter architecture.

Low dropout (LDO) regulators are widely used in portable electronic devices because they 1 occupy small chip and printed circuit board (PCB) areas. This work presents the design of a low 2 noise low dropout (LDO) linear voltage regulator using folded cascode operational amplifier and 3 source follower buffer for frequency compensation. The proposed design achieves low output noise 4 with feedback loops, which makes it possible to improve load and line regulations as well as the 5 transient response for voltage applications in comparison with the literature. The designed LDO 6 linear regulator works under the input voltage of 2.8-4.8 V and provides up to 100 mA load current 7 for an output voltage of 2.6 V. 8 9 Moreover, the operational amplifier design contributes to the major reduction in the frequency 10 noise, and make sure the stability of LDO under different conditions. Furthermore, the novel LDO 11 design topology has very less dropout voltage, fast transient response and minimized power dissi- 12 pation under maximum load current, worst process corner, and maximum temperature to achieve 13 high power efficiency, maintain output voltage in the presence of fast load changes, and to lower the 14 power loss across the pass element especially at high load. Design criteria are addressed in detail. 15 16 This LDO topology is able to achieve ultra-low noise i.e. 2 /muv/√Hz for frequency >1kHz. The 17 proposed LDO is implemented in the cadence virtuoso H9A with CMOS 130 μm technology.

Approximate entropy (ApEn) and sample entropy (SampEn) are statistical indices designed to quantify the regularity or predictability of time series data. Although ApEn has been a prominent choice for use in the analysis of non-linear data, it is currently unclear as to which method and parameter selection combination is optimal for its application in biomechanics. The goal of this research was to examine the differences between ApEn and SampEn related to center of pressure (COP) data during tandem standing balance tasks, while also changing the tolerance window, r. Six participants completed five, 30-second trials, feet-together and tandem standing with eyes-open and eyes-closed. COP data (fs = 60 Hz, downsampled from 1200 Hz) from ground reaction force platforms were collected. ApEn and SampEn were calculated using a constant vector length, i.e., m = 2, but differing values of r (tolerance window). For each of the participants, four separate one-way analysis of variance analyses (ANOVA) were conducted for ApEn and SampEn in the anterior-posterior (AP) and medial-lateral (ML) directions. Dunnett's intervals were applied to the one-way ANOVA analyses to determine which tandem conditions differed significantly from the baseline condition. ApEn and SampEn provided comparable results in the predictability of patterns for different stability conditions, with increasing instability, i.e., tandem eyes closed postures, being associated with greater unpredictability. The selection of r had a relatively consistent effect on mean ApEn and SampEn values across r = 0.15 * SD to r = 0.25 * SD, where both entropy methods tended to decrease as r increased. Mean SampEn values were generally lower than ApEn values. The results suggest that both ApEn and SampEn indices demonstrated relative consistency and were equally effective in quantifying the level of the center of pressure signal regularity during quiet tandem standing postural balance tests.

Expandable graphite (EG) was modified with a charring agent and organic-inorganic hybridised intumescent flame retardants (MEG) were synthesized. This study uses a cone calorimeter (CCT) and a DaqPRO 5300 radiation heat flow meter to evaluate the fire-resistant properties influenced by MEG on intumescent fire-retardant coatings. The impact of MEG on the thermal degradation of these coatings was investigated through the use of thermogravimetric analysis (TGA). The results obtained by CCT demonstrated that the incorporation of MEG markedly diminished the heat release rate and total heat release rate of the coating, in addition to enhancing the char residue compared to coatings with only expandable graphite (EG). Furthermore, TGA results demonstrate that adding MEG increases the weight of the char residue at elevated temperatures, suggesting improved thermal stability. Based on these findings, MEG exhibits a synergistic flame retardant effect when combined with intumescent fire retardant (IFR) systems. This synergy not only improves the flame-retardant properties of the coatings but also enhances their overall thermal stability, making MEG a promising additive for developing more efficient fire-retardant materials. Thus, MEG-modified coatings offer superior protection against fire hazards, highlighting their potential for practical applications in fire safety.

This article raise a topic of critical examination of polypropylene, a key polymeric material, and its extensive application within the automotive industry, particularly focusing on the manufacturing of brake fluid reservoirs. The study aims to enhance the understanding of polypropylene's behavior under mechanical stresses through a series of laboratory destruction tests and numerical simulations, emphasizing the Finite Element Method (FEM). A novel aspect of this research is the introduction of the PEAK parameter, a groundbreaking approach designed to assess the material's resilience against varying states of strain, known as triaxiality. This parameter facilitates the identification of critical areas prone to crack initiation, thereby enabling the optimization of component design with a minimized safety margin, which is crucial for cost-effective production. The methodology involves conducting burst tests to locate crack initiation sites, followed by FEM simulations to determine the PEAK threshold value for the Sabic 83MF10 polypropylene material. The study successfully validates the predictive capability of the PEAK parameter, demonstrating a high correlation between simulated results and actual laboratory tests. This validation underscores the potential of the PEAK parameter as a predictive tool for enhancing the reliability and safety of polypropylene automotive components. The research presented in this article contributes significantly to the field of material science and engineering by providing a deeper insight into the mechanical behavior of polypropylene and introducing an effective tool for predicting crack initiation in automotive components. The findings hold promise for advancing the design and manufacturing processes in the automotive industry, with potential applications extending to other sectors.

The study was conducted on a cohort of 16 subjects, who were monitored with infrared images captured from their hands, following a specific protocol established by a preliminary analysis. Experimental results revealed that thermography can detect focal points and regions exhibiting either hyper- or hypo-thermia across the skin surface and muscular regions. This capability allows for extracting and categorizing precise medical data, difficult to determine during the early stages of the disease, thus facilitating ongoing monitoring efforts and early diagnosis in Parkinson research.

In the digital age, 3D as-built monitoring of structures and buildings is becoming increasingly important. It provides the basis for automated quality control and progress monitoring through as-built and as-planned model- comparisons, as well as 3D modelling for automated repair, refurbishment and reuse in existing construction. Especially for 3D model-based manufacturing processes such as additive manufacturing, efficient as-built monitoring is essen- tial to ensure the quality and accuracy of buildings and products. Therefore, three different 3D scanning devices have been evaluated for 3D as- built monitoring of concrete structures with different characteristics: An indus- trial handheld laser scanner commonly used in automotive and mechanical en- gineering, a mobile device with lidar for commercial public use, and a terrestri- al laser scanner commonly used for surveying. The devices were tested on 3D printed, cast, and assembled structures of different sizes and geometries. The scan results are compared to the as-planned models based on their dimen- sional distances using Cloud-to-Cloud Comparison (C2C). The results show that the applicability is highly dependent on the size, geometric complexity and surface texture of the concrete structures. While small and flat structures can be captured completely and with micrometer accuracy using the handheld industri- al laser scanner, terrestrial laser scanners are suitable for capturing large struc- tures and environments. The handheld, photogrammetry-based mobile device has demonstrated the broadest applicability for concrete structures. The accura- cy and coverage of the point cloud results were respectively high. Therefore, photogrammetry-based scanning with advanced mobile devices is a cost- effective, fast and portable alternative for 3D as-built monitoring and data ac- quisition of a wide range of concrete structures.

Modern automotive systems should must satisfy strict reliability requirements. Most of the real vehicle systems and safety-critical networks have complex interconnections. Sensitivities and probabilistic uncertainties of reliability of System with Complex Interconnections (SwCI) can be investigated by the Monte-Carlo Simulation (MCS). The paper focuses on sensitivities and parametrical uncertainties of SwCIs reliability. The proposed method can be used forimplemented in the investigation of uncertainties of SwCIs reliability – in other wordsi.e., for in the determination of critical system elements and to estimateion of the Required Number of Spare Parts (RNSP) of the system, which depends on the probability of allowable spare equipment shortage.

Evaluating flood risk though numerical simulations in areas where hydrometric and bathymetric data are scarcely available is a challenge. This is however of paramount importance, particularly in urban areas, where huge losses of human life and extensive damage can occur. This paper focuses on the June 2-3, 2023 event at Léogâne in Haiti, where the Rouyonne River partly flooded the city. Water depths in the river were recorded since April 2022 and a few discharges were measured manually but it was not sufficient to produce a reliable rating curve. Using a uniform flow assumption combined with the BaRatin method (Bayesian rating curve), it was possible to extrapolate the existing data to higher discharges. From there, a rainfall-runoff relation was developed for the site using the AtHyS distributed hydrological model, which allowed to determine the discharge of the June 2023 event, which was estimated as twice the maximum conveying capacity of the river in the measurement section. Bathymetric data were obtained using drone-based photogrammetry and two-dimensional simulations were carried out using the WATLAB computational environment to represent the flooded area and the associated water depths. By comparing the water depths of 21 measured high-water marks with the simulation results, we obtain KGE and NSE criteria of, respectively, 0.890 and 0.882. This allows us to conclude that the model is sufficiently accurate and could be used by flood managers and decision-makers to assess flood risk and vulnerability in Haiti.

Deep cavity configurations are common in various industrial applications, including automotive windows, sunroofs, and many other applications in aerospace engineering. Flows over such a geometry can result in an Aero-Acoustic coupling between the cavity shear layer and the acoustic modes of the surrounding. This phenomenon can lead to significant noise and may cause damage to structures due to resonance caused by high-pressure fluctuations produced nearby the cavity. The aims when employing a passive control device is to disturb the cavity flow in order to reduce or eliminate the involved resonance. An experimental set up was developed to study the effectiveness of using either a cylinder or a profiled cylinder positioned upstream from the cavity. A decrease of as high as 30 dB was obtained in the Sound Pressure Levels (SPL). Additionally, Particle Image Velocimetry (PIV) technique was employed to get the kinematic fields of the flow past the cavity for both controlled and non-controlled configurations. A Snapshot Proper Orthogonal Decomposition (POD) was applied to better understand the cavity flow dynamics in both controlled and non-controlled cases. Furthermore, the interaction of the wake of the control mechanisms with the shear layer of the cavity and its consequence on the Aero-Acoustic coupling was investigated and new flow physics is revealed.

Based on the composite perspective of environment-building-resource(EBR), influencing factors of the greenness of rural buildings were sorted out by brainstorming and expert consultation, an innovative comprehensive evaluation system was constructed and the weight ofthe impact factors were calculated by fuzzy analytic network process(FANP). The EBR greenness comprehensive scoring standards of each influence factor were established by means of questionnaire, field measurement and numerical simulation. As a typical representative of human populated areas in southwest China, 13 villages in the second circle of Chengdu were selected as case study areas by means of the regional average distribution theory and minimum sampling criteria. Innovative combination of obstacle degree model and spatial interpolation analysis, the key factors affecting the greening of rural buildings were diagnosed. The results showed that, indoor thermal environment(E21), indoor light environment(E22), popularity of biogas facilities (R12), green building material usage(R13) have a significant impact on the EBR greenness score. The EBR score of study area shows a spatial distribution pattern of high in the west and low in the east, with extremely uneven scores in various dimensions. According to the key factors obtained from the systematic evaluation, the refined exploration of promotion strategies and measures was carried out, the greenness improvement suggestion was put forward.

The cellular behavior of Voronoi tessellation has generated interest due to its applicability in various fields and its notable structural properties. Controlling factors such as the gradient of the cells, the position of seed points and the thickness of the arms allows for adjusting rigidity and flexibility according to specific needs. This article analyzes the state of the art of this technique, exploring its modification for applications in engineering and design, complemented with information on natural structural properties. This comprehensive analysis provides a complete overview of Voronoi tessellation and its potential in engineering and design, categorizing methodologies according to selected processing methods and highlighting techniques for altering structural behavior. Additionally, it emphasizes the integration of biomimetic approaches, connecting nature with technology to foster continuous innovation. Finally, the article addresses encountered limitations, offering future perspectives for the cellular technique and highlights the complexity of reproducibility due to reserved or generalized steps, despite the significant diversity in implemented techniques.

This study explores the impact of carbon fiber length and content on the rheological properties and performance of coatings used in lost foam casting. The investigation encompassed fiber lengths of 1 mm, 3 mm, and 6 mm, and fiber contents of 0.2%, 0.5%, and 0.8%. The effects on coating viscosity, shear stress, coating weight, and surface morphology were meticulously evaluated. The results demonstrate that incorporating carbon fibers significantly enhances coating viscosity and shear stress compared to fiber-free coatings, with more pronounced effects observed at higher fiber contents and longer fiber lengths. Nevertheless, excessive fiber content and length can lead to agglomeration, negatively impacting coating uniformity. Optimal fiber length and content were identified, striking a balance between improved rheological properties and coating performance. These findings provide critical insights for the development and industrial application of high-performance coatings in lost foam casting.

Background: The last-mile delivery phase, the final stage where goods move from a distribution center to customers, is pivotal but faces significant inefficiencies and high costs due to its complexity. Recent advancements in Edge AI or Edge Intelligence (EI) offer promising solutions to these challenges. Methods: This study explores how AI-driven technologies and real-time data processing, combined with EI, can enhance last-mile delivery operations. A thorough literature review was conducted to assess technological advancements, and a Delphi method was used to systematically and empirically assess the impact of EI solutions on both operational efficiency and customer satisfaction. Results: Although EI technologies offer substantial benefits, EU companies are hesitant to adopt these innovations due to high implementation costs. However, firms that have embraced these technologies report significant improvements, including better route optimization, reduced delivery times, and enhanced service reliability. These findings highlight the need for a culture of innovation and the recruitment of experts with advanced qualifications to drive technological advancement in last-mile logistics. Conclusions: The integration of EI represents a significant step towards more efficient, cost-effective, and customer-focused last-mile delivery solutions. Future research should aim to refine these technologies and explore their long-term impacts on the logistics industry.

Digitalization plays a crucial role in improving the performance of chemical companies. In this context, different modeling, simulation, and optimization techniques like MILP, discrete-event simulation (DES), and data-driven (DD) models are being used. Due to their heterogeneity, these techniques must be executed individually, and holistic optimization is a manual and time-consuming process. We propose Benders decomposition to combine these techniques into one rigorous optimization procedure. The main idea is that heterogeneous models can simultaneously be optimized as Benders subproblems. We illustrate this concept with the distributed permutation flow shop scheduling problem (DPFSP) and assume that a MILP, DES, and DD model exist for three flow shops. Our approach can compute bounds and report gap information on the optimal makespan for five medium-sized literature instances. The approach is promising because it enables the optimization of heterogeneous models and makes it possible to build optimization capabilities on an existing model and tool landscape in chemical companies.

This paper explores the development of a sophisticated system for automatic test generation tailored specifically for Java web applications utilizing REST interfaces. Writing tests ensures the quality of the software solution event after the introduction of subsequent changes, because automated regression testing can detect potential bugs in the code event before the application is resealed into production. By utilizing and comparing various automatic test generators, the system described provides users with a visual representation of code structure and coverage, enhancing insights through call graph visualization. The paper describes the concepts of theoretical automatic test generators, gives an overview of generators, and details the design and implementation of the implemented solution, which integrates the system test generator and the unit test generator based on open source to achieve the maximum code coverage level.

This paper presents a particular tunneling method or new Austrian tunneling method (NATM) which plays an important role in reducing the subsidence of the Earth surface and the damage to the structures in urban areas. It has a wide range of applications in shallow tunneling projects all over the world. In this study, numerical modeling of third-line Metro tunnel in Tehran is under discussion which is designed and stabilized by NATM. The foregoing tunnel is excavated manually with a one-meter advancing step. In this project, the constructors used a lattice girder and sprayed concrete with 31 cm thickness as the initial lining. A suitable numerical software for this modeling is Plaxis 3D tunnel, which allows performing high-resolution finite element modeling (FEM) of the studied object. The performance of this method is investigated and compared with other NATM methods. As a result of numerical modeling, we give a value of 30.01 mm earth subsidence in the most damaged area of the settlement, in comparing with that of earth surface monitoring, which confirmed it with a dramatically low difference. Moreover, this tunnel was drilled and excavated with various methods, among which the less settlement has obtained by the proposed method. The results are promising to continue the tunneling with this method to expand the subway line in the city.

In this study, Fluent software 2020R2 is used to simulate vertical axis turbine in two dimensions, aiming to analyze the influence of turbulence intensity on wake characteristics. The detailed flow field data are obtained by simulating the operation of turbine under different turbulence intensities. On this basis, the influence factor of turbulence intensity are creatively introduced into the wake mathematical formula of vertical axis turbine established by Lam, and some parameter calculations of the formula are supplemented and perfected. The results show that the theoretical model can well reflect the influence of turbulence intensity on the wake distribution of vertical axis turbines. The formula can provide a theoretical basis for the design and optimization of vertical axis turbines and the array arrangement, and can be used to predict the wake characteristics under different turbulence intensity operating conditions.

In order to serve the online monitoring of the aging state of high-voltage (HV) cables, a novel method based on impedance spectroscopy has been proposed. Firstly, the method uses the Debye model to restore the dielectric response of the insulating material from the mechanism. Secondly, based on the transmission line equation, a calculation model for the impedance spectroscopy of HV cables was established. Then, the characteristics of the impedance spectroscopy under different overall aging and local deterioration states were analyzed. Further, the simulation analysis of the influence of factors such as load factor, length, metal resistivity, and geometric parameters on the impedance spectroscopy was carried out. Finally, diagnostic criteria were proposed, which are divided into two major categories: known cable parameters and unknown cable parameters. If the geometric parameters are known, the aging state can be determined using the resonance peak values and the resonance peak frequencies. If the geometric parameters are unknown, it is necessary to test the impedance spectroscopy in the healthy condition of the cable as the baseline curve. By comparing the impedance spectroscopy of the cable under test with the baseline curve, the aging development trend of the cable can also be determined.

In this work, we analyzed non-Darcy two-component single-phase isothermal flow in naturally fractured tight gas reservoirs for enhanced gas recovery (EGR) and carbon dioxide storage as a possible application. We used the Peng-Robinson equation of state to evaluate the thermodynamic properties of the components, and we performed the discretization of the governing partial differential equations using the Finite Volume Method. This process leads to two subsystems of algebraic equations, which, after linearization and use of an operator splitting method, are solved by the Conjugate Gradient (CG) and Biconjugate Gradient Stabilized (BiCGSTAB) methods for determining the pressure and fraction molar, respectively. We include inertial effects using the Barree and Conway model, gas slippage via a more recent model than Klinkenberg’s and we use a simplified model for the effects of effective stress. We also utilize a mesh refinement technique to represent the discrete fractures. Finally, we implemented simulations to show the influence of inertial, slippage and stress effects on production in fractured tight gas reservoirs.

Mechanical response of composite structures concerning also natural frequencies is a function of material and geometrical describing plates and shells. In the present paper the optimization of those parameters is demonstrated. It is based on the formulation that uses the key-points (spline functions) and evolutionary algorithms. The numerical solutions dealing with the optimal design of axisymmetric cylindrical shells having variable radius are presented and discussed in details.

(1) Background: We aim to construct a machine learning (ML) algorithm to predict the risk of distant metastasis (DM) of T1 and T2gallbladder cancer (GBC); (2) Demographic and clinical pathological data of T1 and T2 GBC patients were extracted from the National Institutes of Health (NIH)’s Surveillance, Epidemiology, and End Results (SEER) database between 2004 and 2015 to develop seven ML algorithm models. Models were evaluated based on accuracy, precision, recall rate, F1- score, and area under the receiver operating characteristic curve (AUC); (3) Results:A total of 4371 patients were included in the study, of whom 764 (17.4%) developed DM. Multivariate logistic regression showed that age, histology, tumor size, T and N stages were independent factors in GBC with DM. A novel nomogram was established to predict distant metastasis in early T stage GBC patients. Evaluation indicators of the best model Random Forest (RF) were as follows: accuracy (0.828), recall rate (0.862), precision (0.811), F1- score (0.836), and AUC value (0.913); (4) Con-clusions: The RF model constructed in this study could accurately predict distant metastasis in GBC patients, which may provide clinicians with more personalized clinical decision-making recom-mendations.

A fundamental aspect in the evolution of Time-to-Digital Converters (TDCs) implemented within Field-Programmable Gate Arrays (FPGAs), given the increasing demand for detection channels, is the optimization of resource utilization. This study reviews the principal methodologies employed for implementing low-resource TDCs in FPGAs. It outlines the foundational architectures and interpolation techniques utilized to bolster TDC performances without unduly burdening resource consumption. Low-resource Tapped Delay Line, Vernier Ring Oscillator, and Multi-Phase Shift Counter TDCs, including the use of SerDes, are reviewed. Additionally, novel low-resource architectures are scrutinized, including Counter Gray Oscillator TDCs and interpolation expansions using Voltage-Temperature-Consumption stable IODELAYS. Furthermore, the advantages and limitations of each approach are critically assessed, with particular emphasis on resolution, precision, non-linearities, and, especially, resource utilization. A comprehensive summary table encapsulating existing works on low-resource TDCs is provided, offering a comprehensive overview of the advancements in the field.

Fully sustainable hydrogen production demands renewable energy sources. This study uses an approach that combines solar photovoltaic (PV) systems with minimal batteries to tailor the energy supply to the unique demands of anion exchange membrane (AEM) electrolyzers. An open source DC-DC adjustable converter is designed, prototyped, and tested to enable an AEM to operate at its optimum efficiency without disrupting the continuous operation of existing loads. A structured operating schedule is simulated to align PV performance with AEM electrolyzer characteristics. The results show the >90% efficiency open-source converter was able to directly power the electrolyzer while taking advantage of solar energy surplus for hydrogen production. By strategically scheduling the electrolyzer to maximize output and minimize waste the system only utilizes excess solar energy. By employing this sustainable method, the study highlights a scalable solution that not only enhances the efficiency of hydrogen production, but also promotes the deployment of PV.

With increasing research focus on industrial anomaly detection, numerous methods have emerged in this domain. Notably, memory bank-based approaches, coupled with kNN distance metrics, have demonstrated remarkable performance in anomaly detection (AD) and anomaly segmentation (AS). However, upon examination of the Back to the Feature (BTF) method applied to the Mvtec-3D AD dataset, it was observed that while it exhibited exceptional segmentation performance, its detection performance was lacking. To address this discrepancy, this study base improves the implementation of BTF, especially the improvement of the anomaly score metric. It posits that different "clusters" necessitate distinct k values in kNN distance metrics. For simplify, this assumption is distilled into the proposition that AD and AS tasks impose differing requirements on the k value in kNN distance metrics. Consequently, the paper introduces the BTM method, which utilizes distinct distance metrics for AD and AS tasks. This innovative approach yields superior AD and AS performance (I-AUROC 93.0%, AURPO 96.9%, P-AUROC 99.5%), representing a substantial enhancement over the BTF method (I-AUROC 5.7% ↑, AURPO 0.5% ↑, P-AUROC 0.2% ↑).

This study investigates the energy profile of a regional hospital in North America using an ASHRAE Level 2 Energy Audit. The audit aimed to assess the efficiency of existing systems, identify areas for improvement, and recommend cost-effective measures to reduce energy consumption and environmental impact. The analysis revealed a well-maintained heating system with efficient boilers. However, a critical concern emerged regarding operational risks due to the lack of redundancy in the cooling system. To address this vulnerability, the study recommends installing an air-cooled chiller for increased operational resilience. The economic feasibility of proposed improvements was evaluated, highlighting the balance between initial investment and long-term savings in energy costs and reduced greenhouse gas emissions. The findings contribute to the discourse on sustainable healthcare practices, emphasizing the importance of energy audits, strategic planning, and redundancy measures for optimized performance and environmental responsibility in hospitals.

We propose a theoretical Photocurrent - Voltage characteristic (PC-V) model to assess the interfacial phenom-ena for a photo-electrochemical cell (PEC). The origin of voltage deficits and the distribution of the photocur-rent across the semiconductor-electrolyte interface (SEI) are reported for the cell. The model predicts the hole exchange current parameter to extract the cell polarization data at the SEI. The potential drop across the SEI across the cell is mapped for the n-type cells. The simulation results of the Pc-V model are used to differentiate the effect of the bulk and space charge region (SCR) recombination in the semiconductor cells. A deep neural network model is developed to assess the electron-hole transfer mechanism using the Pc-V characteristic curve. The applicability of the model is tested and validated with the real time data. The results show good agreement with the experimental data.

The use of Physical Unclonable Functions (PUFs) linked to the manufacturing process of the electronic devices supporting applications that exchange critical data over the Internet has made these elements essential to guarantee the authenticity of said devices, as well as the confidentiality and integrity of the information they process or transmit. This paper describes the development of a configurable PUF/TRNG module based on Ring Oscillators (ROs) that takes full advantage of the structure of modern programmable devices offered by Xilinx 7 Series families. In this work, the increase in hardware efficiency of the proposed architecture is used with a double objective. On the one hand, perform an exhaustive statistical characterization of the results derived from the exploitation of RO configurability. On the other hand, undertake the development of a new version of the module that requires a smaller amount of resources while considerably increasing the number of output bits compared to other proposals previously reported in the literature. The design as a highly parameterized Intellectual Property (IP) module connectable through a standard interface to a soft- or hard-core general-purpose processor greatly facilitates its integration into embedded solutions, while accelerating the validation and characterization of this element on the same electronic device that implements it. The studies carried out reveal adequate values of reliability, uniqueness and unpredictability when the module acts as a PUF, as well as acceptable levels of randomness and entropy when it acts as a True Random Number Generator (TRNG). They also illustrate the ability to obfuscate and recover identifiers or cryptographic keys of up to 4 096 bits using an implementation of the PUF/TRNG module that requires only a 4×4 CLB array to accommodate the RO bank.

This paper presents a novel normative framework for assessing granular base materials in rigid pavements, incorporating evaluations of California Bearing Ratio (CBR) alongside hydraulic conductivity tests. A critical component of this research adheres to Federal Highway Administration (FHWA) standards, which specify a permeability coefficient range of 0.05 to 0.20 cm/sec. Our investigations indicate that increased compaction energy inversely affects permeability, highlighting a sophisticated interplay between compaction dynamics and hydraulic characteristics. The study further elucidates that CBR values are profoundly impacted by the content of coarse-grained soil, advocating for a tailored approach to base layer assessment. Multivariable CBR analysis facilitated the identification of an optimal water content, underscoring that maintaining a minimum 80% CBR—while minimizing water content—can mitigate fatigue effects and enhance structural performance under heightened energy conditions. This research proposes bespoke, scientifically validated standards that integrate local geotechnical nuances, aimed at refining material selection processes and extending the durability of pavement infrastructures through foundational yet meticulous geotechnical evaluations.

In a new construction of a seventeen-storey building, it is necessary to remove a prop on the second floor, which has made it possible to complete the structure of the building. In the drafting of the executive project, no security measures were described, because it was understood that they were not necessary. In the construction phase, the need to monitor the process was raised, and to be able to stop it, or reverse it if necessary. It is a reinforced concrete structure, with metal elements and composite elements, with a torsional behavior of the cores making the slabs serve as horizontal diaphragms, so the final behavior could be different from what was expected. To make this process possible, two pairs of four metallic cantilevers were introduced into which four Hydraulic pistons were inserted. Before starting the process, they were loaded to be sure that they would not fail. Strain gauges were also placed to control the stresses and how the situation changed during the unshoring process. The process of cutting the provisional pillar was a success, obtaining less deformations than expected, having a complementary safety.

We present an autonomous system that remotely monitors the state of reinforced concrete structures. This system performs the real-time follow-up of the corrosion rate of rebars (iCORR), along with other relevant parameters, such as temperature, corrosion potential (ECORR) and electrical resistance of concrete (RE), at many of structures’ control points by using embedded sensors. Its corrosion sensor is stressed, to which a novel electrochemical polarization technique is applied. The custom electronic system manages the sensor network, consisting of a measurement board per control point connected to a central single-board computer in charge of processing measurement data and uploading results to a server via 4G connection. In this work, we report the results obtained after implementing the sensor system into a reinforced concrete wall, where two well-differentiated representative areas were monitored. The obtained corrosion parameters showed consistent values. Similar conclusions are obtained with ECORR recorded in rebars. With the iCORR follow-up the corrosion penetration damage diagram is built. This diagram is particularly useful for identifying critical events during the corrosion propagation period and to be able to estimate structures’ service life. Hence the system is presented as a useful tool for the structural maintenance and service life predictions of new structures.

This review paper provides an overview of recent advances in computational fluid dynamics (CFD) simulations of ejector pumps for vacuum generation. It examines various turbulence models, multiphase flow approaches, and numerical techniques employed to capture complex flow phenomena like shock waves, mixing, phase transitions, and heat/mass transfer. Emphasis is placed on comprehensive assessment of flow characteristics within ejectors, including condensation effects such as nucleation, droplet growth, and non-equilibrium conditions. The review highlights efforts in optimizing ejector geometries and operating parameters to enhance the entrainment ratio, a crucial performance metric for ejectors. The studies reviewed encompass diverse working fluids, flow regimes, and geometric configurations, underscoring the significance of ejector technology across various industries. While substantial progress has been made in developing advanced simulation techniques, several challenges persist, including accurate modeling of real gas behavior, phase change kinetics, and coupled heat/mass transfer phenomena. Future research efforts should focus on developing robust multiphase models, implementing advanced turbulence modeling techniques, integrating machine learning based optimization methods, and exploring novel ejector configurations for emerging applications.

This paper investigates, experimentally and numerically, the buckling behavior of innovative sustainable Low-Cost Bamboo Composite (LCBC) structural columns under compressive loading. The LCBC columns are manufactured from bamboo culms in combination with bio-based resins to form composite structural columns. Different LCBC cross-sectional configurations are investigated in this study including the Russian doll (RD), Big Russian doll (BRD), Hawser (HAW), and Scrimber (SCR). Extra-large, large, medium, and small sizes of bamboo are employed to form the proposed configurations. Two bio-based resins including one bio-epoxy and one furan-based resin, in addition to a soft bio-based filler and a synthetic epoxy resin are applied. The bamboo species used as the cast-in-place giant bamboo for all configurations include Moso, Guadua, and Tali. In addition to experimental testing of the slender LCBC short columns, finite element analysis (FEA) software ABAQUS is applied to model the composite columns and predict their behavior. A buckling analysis using static Riks method is carried out to determine the LBCB’s response to axial compressive loading. A model is calibrated using test results with various imperfections, accounting for the variable behavior of each bamboo specimen, to accurately predict the behavior of LCBC columns with different resin contents. Slender LCBC columns showed maximum stress at buckling up to 60 MPa, highlighting the potential of bio-based resins for structural applications. The study found that the samples with bio-epoxy resin (BE2) exhibited enhanced material stiffness when compared to with synthetic epoxy (EPX) and furan-based resin (PF1), while PF1 specimens demonstrated increased ductility. The BE2 samples showed strain hardening in their stress-strain behavior, and the PF1 samples exhibited a long plateau phase before reaching the maximum load. Among the specimens with Moso bamboo and BE2 resin, those with SCR and HAW configurations achieved the highest compressive strengths.

Acousto-Optical Tunable Filters (AOTFs) are a promising technology, exceptionally suited for advanced applications in spectroscopy and imaging across various scientific fields, including space exploration. This is due to their flexibility and high-speed functionality for precise wavelength selection via periodic modulation of the crystal’s refractive index using sound waves. These AOTFs are in turn driven by Radio Frequency (RF) signals through a transducer creating sound waves inside the crystal. This work presents a detailed comparison of three distinct topologies of Radio Frequency Power Amplifiers (RFPAs) for driving these AOTFs within the 30 to 300 MHz frequency range. The analysis focuses on three critical parameters: efficiency, bandwidth, and linearity, which are essential for optimizing the performance of AOTFs. By evaluating the trade-offs among these parameters, this research aims to identify the most effective design approach for RFPAs in this application context.

In this study, we demonstrate a high luminous efficacy of 118 lm/W and high reliability through 450°C thermal aging of white LEDs (WLEDs) utilizing four-inch YAG: Ce3+ phosphor-in-glass (PiG) plates designed for vehicle headlights. The sintering process of mixing glass and phosphor typically generates pores, which can scatter light and reduce the luminous efficacy of the fabricated PiG. In this study, we produced four-inch PiG plates under four different fabrication conditions to evaluate their luminous efficacy. Our results revealed that the PiG plate with a thin thickness of 0.08 mm exhibited a 16.83% increase in luminous efficacy compared to the 0.15 mm plate, attributed to reduced light interaction with the pores. Unlike silicone-based phosphor WLEDs, which offer high performance but lower reliability due to the silicone resin's low transition temperature (150°C), our novel thin PiG plate achieves high performance and reliability. This advancement suggests that the proposed thin PiG plate could replace traditional silicone-based phosphors, enabling the development of high-quality WLEDs for vehicle headlights in automotive applications.

Graphene-based materials (GBMs) are a prospective material of choice for rechargeable battery electrodes because of their unique set of qualities, which include tunable interlayer channels, high specific surface area, and strong electrical conductivity characteristics. The market for commercial rechargeable batteries is now dominated by lithium-ion batteries (LIBs), however issues with organic electrolyte safety, high lithium pricing, and limited lithium resources have hindered LIB development. Zinc-based rechargeable batteries have emerged as a viable substitute for rechargeable batteries due to their affordability, safety, and improved performance. This review article explores recent developments in the synthesis and advancement of GBMs for rechargeable zinc‐air batteries (ZABs), and common graphene-based electrocatalyst types. An outlook on the difficulties and probable future paths of this extremely promising field of study is provided at the end.

This study explores the integration of computer vision with topology optimization for additive manufacturing, with a focus on maximizing eigenfrequency in a design domain. Utilizing cus-tom-developed photogrammetry software, high-resolution images are processed to generate de-tailed 3D models, subsequently converted to STL files with precision. Adaptive meshing in COMSOL Multiphysics, controlled through a MATLAB API, ensures optimal mesh resolution. Prioritizing resource conservation in extraterrestrial environments, the original volume is reduced by 50% while preserving structural integrity. The design domain undergoes rigorous topology optimization in MATLAB, supported by COMSOL's advanced FEM simulation. The optimized design exhibits a 57% performance improvement and a 50% weight reduction, maintaining desired vibration characteristics, validating the efficacy of the modifications. Moreover, the case with an eccentric mass shows a significant 64% increase in eigenfrequency.

In order to study the effect of explosion load on the flexural performance of RC beams, two RC beams of the same size were carried out explosion test and bearing capacity test successively. Piezoelectric intelligent aggregate was embedded in the beams in advance to monitor the damage state of the beams, which realized the whole process of damage monitoring of the concrete inside the structure from explosion damage to compression damage.Based on wavelet packet analysis and residual bearing capacity damage evaluation criteria, the beam damage was quantitatively evaluated, and the bending bearing capacity of RC beam after explosion damage was compared and analyzed.The results show that the change of concrete strain gauge signal, the phenomenon of crack development and the increase of internal crack development index indicate that the RC beam is damaged by explosion load.After explosion damage, the damage index of the beam increases and the residual bending capacity decreases, which proves that the piezoelectric intelligent aggregate sensor can effectively monitor the bending capacity change of the component after explosion damage.

A spike-timing-dependent-plasticity (STDP) device with a Ga-Sn-O (GTO) conductance change layer deposited by a mist-CVD method has been developed. First, the memristive characteristic is analyzed. Next, based on it, spike waveforms are determined. Finally, the STDP characteristic is successfully confirmed. This is the first report on the realization of a STDP characteristic using a thin film deposited by the mist-CVD method, which is achieved by the GTO properties and a well-designed clear methodology to realize a STDP characteristic from a memristive characteristic.

The integration of Industry 4.0 technologies in photovoltaic (PV) installations offers significant potential to enhance efficiency, reliability, and economic viability. This study evaluates the technical and economic impacts of implementing the Internet of Things (IoT), artificial intelligence (AI), and advanced data analytics in PV systems within the residential and industrial sectors in the Region of Murcia. Real-time monitoring technologies based on IoT sensors were analyzed to collect data on the operational performance of pilot PV installations. Advanced data analysis techniques, including machine learning, were analyzed to process the collected data, enabling predictive maintenance, performance optimization, and future production estimation. The study also explored the use of drones and computer vision for automated inspection of solar panels, identifying issues such as hotspots and dirt accumulation. The results demonstrate significant improvements in energy efficiency, system availability, and reduction of failures and downtime. Economic analyses indicate cost savings in operation and maintenance and increased overall system reliability. This research identifies key barriers and opportunities for adopting 4.0 technologies in the PV sector and proposes strategies to overcome challenges and leverage benefits. The findings contribute to the advancement of knowledge and the promotion of sustainable and efficient energy systems in the solar industry.