Sedimentation is an undesirable phenomenon that complicates the design of microsystems that exploit dense microparticles as delivery tools, especially in biotechnological applications. It often informs the integration of continuous mixing modules, consequently impacting system footprint, cost, and complexity. The impact of sedimentation is significantly worse in systems designed with the intent of particle metering or binary encapsulation in droplets. Circumventing this problem involves the unsatisfactory adoption of gel microparticles as an alternative. This paper presents two solutions – a hydrodynamic solution that changes the particle sedimentation trajectory relative to a flow-rate dependent resultant force; and induced hindered settling (i-HS), which exploits Richardson-Zaki (RZ) corrections of Stokes’ law. The hydrodynamic solution was validated using a multi-well fluidic multiplexing and particle metering manifold. Computational image analysis of multiplex metering efficiency using this method showed an average reduction in well-to-well variation in particle concentration from 45% (Q = 1 mL/min, n = 32 total wells) to 17% (Q = 10 mL/ min, n = 48 total wells). By exploiting a physical property (cloud point) of surfactants in the bead suspension in vials, the i-HS achieved 58% reduction in the sedimentation rate. This effect results from surfactant phase change, which increases the turbidity (transient increase in particle concentration), thereby exploitingthe RZ theories. Both methods can be used independently or synergistically to eliminate bead settling in microsystems or minimize particle sedimentation.

The existing collaborative channel perception suffered from unreasonable data fusion weight allocation, which mismatched the channel perception capability of node devices. This often led to significant deviations between the channel perception results and the actual channel state. To solve this issue, this paper integrated the data fusion algorithm from evidence fusion theory with data link channel state perception. It applied the data fusion advantages of evidence fusion theory to evaluate the traffic pulse statistical capability of network node devices. Specifically, the typical characteristic parameters describing the channel perception capability of node devices were regarded as evidence parameter sets under the recognition framework. By calculating the credibility and falsity of the characteristic parameters, the differences and conflicts between nodes were measured to achieve a comprehensive evaluation of the traffic pulse statistical capabilities of node devices. Based on this evaluation, the geometric mean method was adopted to calculate channel state perception weights for each node within a single-hop range, and a weight allocation strategy was formulated to improve the accuracy of channel state perception.

The use of natural chloride in the salt fuel has been sometimes promoted for the chlorine based molten salt fast reactor using for example NaCl-UCl4- UCl3. Most of the chemistry based R&D has been carried out on natural chloride, while in reactor physics most of the calculations are based on enriched chloride-37. In any case, the use of natural chloride (75.77% 35Cl and 24.23% 37Cl) induces specific ecological and neutron economical issues. An ecological issue is the production of 36Cl by neutron activation of 35Cl. Chlorine-36 is a soluble long live nuclide that makes it a ‘first’ nuclide in the safety assessment of the repository. We explore the build-up of 36Cl with burnup for various 37Cl (activation product) enrichment. The neutron economical aspect is gained by achieving reactor criticality with a lower amount of fissile material achieved through the use of enriched chloride-37. The criticality (k) of a core is much larger for enriched chloride than for the natural one. The reactor with 99% 37Cl would have a k99/knat of 1.04346 which would allow to reduce the required uranium enrichment and thus the fuel cost. This reactivity gain is evaluated for various 37Cl enrichment factors to estimate uranium enrichment gain achievable through 37Cl enrichment. However, from economical point of view it could be of interest to use as some kind of compromise solution natural NaCl, which will be analysed, too. Production/enrichment of 37Cl can be performed by physical separation e.g. gas centrifugation of 1H35/37Cl or by chemical separation using anion partitioning and elution of 23Na35/37Cl on solid adsorbent columns or in liquid – liquid extraction systems. These techniques require however extensive investigations including chemical thermodynamics and hydrodynamics. Comparisons are suggested on the basis of the separation factor α. Recommendations are finally given for utilising 37Cl in the fast molten salt reactor. The enrichment work for 37Cl is smaller than the work needed for 235U enrichment.

For semiconducting two-dimensional transition metal dichalcogenides, carrier transport proper-ties of the material are affected by strain engineering. In this work, we investigate the carrier mo-bility of monolayer hafnium disulphide (HfS2) under different biaxial strains by first-principles calculations combined with Kubo-Greenwood mobility approach and compact band model. The decrease/increase in effective mass of conduction band of monolayer HfS2 caused by biaxial ten-sile/compressive strain is the major reason for the enhancement/degradation of its electron mobil-ity. Lower hole effective mass of valence bands in the monolayer HfS2 under biaxial compressive strain results in the better hole transport performance of monolayer HfS2 than the one with biaxial tensile strain. In summary, biaxial compressive strain decreases both effective mass and phonon scattering rate of monolayer HfS2, resulting in an increase in carrier mobility. When the biaxial compressive strain reaches 4%, for the conduction band of monolayer HfS2, electron mobility en-hancement ratio is ~90%. For the valence band of monolayer HfS2, the maximum hole mobility enhancement ratio appears ~13% at biaxial compressive strain of 4%. Our results indicate that the carrier transport performance of monolayer HfS2 can be greatly improved by the strain engineer-ing.

This study investigates the performance and environmental impact of n-butanol blended with Jet-A fuel in turbo-engines, aiming to assess its viability as a sustainable aviation fuel (SAF). The research involves experimental testing of various blends, ranging from low to high concentrations of methanol, to determine their effects on engine performance and emissions. The experimental setup includes comprehensive measurements of engine parameters such as thrust, fuel consumption rates, and exhaust gas temperatures. Emissions of nitrogen oxides (NOx), particulate matter (PM), carbon monoxide (CO), and unburned hydrocarbons (HC) are also analysed to evaluate environmental impacts. Key findings indicate that n-butanol/Jet-A blends can significantly enhance combustion efficiency and reduce emissions compared to conventional Jet-A fuel. Higher n-butanol concentrations lead to improved thermal efficiency and lower NOx and CO emissions. However, PM emissions slightly increase with higher methanol content. The study underscores the potential of n-butanol as a SAF for turbo-engines, highlighting its ability to mitigate environmental impacts while maintaining or improving engine performance. This research supports the feasibility of integrating n-butanol into Jet-A blends for turbo-engine applications, emphasizing its role in achieving more environmentally friendly aviation operations.

This study presents a numerical analysis of rock deformation and damage behaviors induced by dynamic loadings, specifically focusing on the fracturing of tight oil reservoirs. An elastoplastic model based on finite element methods was utilized to simulate the effects of varying loading and perforation parameters. Three distinct scenarios were modeled: a single perforation, boundary loading on three sides, and multiple perforations, each providing unique insights into the mechanical responses of the rock material. The analysis revealed that dynamic effects are highly localized around perforations and loaded boundaries across all scenarios. Acceleration magnitudes are captured with rapid attenuation observed as the distance from the perforation increased. This indicated that the highest stress concentrations and deformation occurred close to the points of loading or perforation. Strain rate analysis in the x direction showed significant fluctuations near the perforations, reflecting intense stress redistributions and compressive deformation, especially prominent in the scenario with multiple perforations. Plastic strain, representing irreversible damage, was found to concentrate near perforation tips and loaded boundaries. However, the pattern of plastic strain accumulation varied: in the small domain scenario with boundary loading, plastic strain did not immediately accumulate next to the loading boundary, whereas in scenarios with single and multiple perforations, damage developed from the loading boundaries. These findings highlight the need for precise control in perforation and dynamic loading processes to manage stress and deformation effectively. It is concluded that optimizing perforation patterns and loading strategies can enhance reservoir stimulation efficiency while minimizing unintended damage to the surrounding formation.

This study reviews methods for detecting fracture closure pressure in both unconventional and conventional reservoirs using mathematical models and fluid flow equations. It evaluates tech-niques such as the Nolte method, tangent method, and compliance method. The investigation re-lies on observing changes in fluid flow regimes from pre-closure to post-closure, using fluid flow equations to examine the post-closure flow regime effect on the G function. Reverse calculations model pressure decline across synthesized flow regimes, facilitating a detailed investigation of the closure process. The analysis reveals that the tangent method is sensitive to post-closure fluid flow, while the compliance method is less effective in reservoirs with significant tortuosity or natural fractures. The paper recommends assessing natural fractures' characteristics and perme-ability to identify the source of leak-off before selecting a technique. It proposes integrating vari-ous methods for a comprehensive understanding of subsurface formations, combining their strengths for accurate fracture closure identification and better understanding of subsurface formations. The new proposed workflow employs the Continuous Wavelet Transform (CWT) technique for fracture closure detection, avoiding physical model pre-assumptions or simplifica-tions to confirm the results. This approach offers guidance on selecting appropriate methods by integrating different techniques.

This study explores the development and characterization of ceramic rod reinforced TWIP (Twinning Induced Plasticity) steel matrix composites, fabricated using a lost foam casting process. The incorporation of ceramic rods aims to enhance the mechanical properties of TWIP steel, which is known for its excellent plasticity. Mechanical testing demonstrated a significant improvement in flexural strength and ductility, with the composite exhibiting over double the strength of the unreinforced TWIP steel. Heat treatment at 500°C for 60 minutes further enhanced these properties, increasing the composite's flexural strength to 1023 MPa and improving its ductility. The observed improvements are attributed to the synergistic effects of the hard ceramic reinforcement and the ductile matrix, as well as the microstructural modification conferred by heat treatment. These findings suggest that ceramic rod reinforced TWIP steel composites are promising materials for applications requiring a combination of high strength and ductility.

This paper investigates the energy dissipation level of a nonlinear vehicle suspension system. The suspension system is modelled as a mass-spring-damper system, which is designed using the output frequency response function (OFRF) method. This method enables the derivation of an explicit relationship, between the system output spectrum (or Performance metric) and the system nonlinear parameters of interest. The OFRF polynomial, of the energy dissipation level, was derived in terms of the nonlinear parameters of interest. The results obtained show that the OFRF model, of the energy dissipation level, could be used to estimate the amount of heat dissipated, for a set of design parameters. This has potentials of providing some insights, in the design of vibration-based energy harvesters, as some of the wasted heat energy could be converted to electrical energy.

Global growth and sustainability highly depend upon the output of industries, corporations, governments, and non-governmental organisations requiring comprehensive energy transition in the face of a worldwide crisis demanding carbon neutralisation. The carbon neutralisation challenges required for energy transition could be overcome by detailed surveying, recording and the analysis of the carbon dioxide emissions. It is crucial to study innovative materials like metal-organic frameworks for their transdisciplinary applications towards carbon capture to have a sustainable strategy to capture harmful emissions. In addition, the CO₂ solubility, adsorption and absorption capacity, and several reinforcing characteristics and factors for carbon dioxide capture using MOFs are discussed in detail. Further, for various multidisciplinary applications towards commercialisation, MOFs solubility, adsorption and absorption parameters were considered. Moreover, the essential organic qualities of MOFs and their organo-derivatives are considerably elaborated. Overall, to align with the current sustainable development goals, it is significant that carbon dioxide emissions must be reduced along with its capture in a scientific manner. Relevant projections for carbon capture through MOFs have been reviewed industrially and universally in alignment with the United Nations Sustainable Development Goals (UNSDGs).

In Brain-Computer Interface Motor Imagery (BCI-MI) systems, Convolutional Neural Networks (CNNs) have traditionally dominated as the deep learning method of choice, demonstrating significant advancements in state-of-the-art studies. Recently, Transformer models with attention mechanisms have emerged as a sophisticated technique, enhancing the capture of long-term dependencies and intricate feature relationships in BCI-MI. This research investigates the performance of EEG-TCNet and EEG-Conformer models, which are trained and validated using various hyperparameters and bandpass filters during preprocessing to assess improvements in model accuracy. Additionally, this study introduces the EEG-TCNTransformer, a novel model that integrates the convolutional architecture of EEG-TCNet with a series of self-attention blocks employing a multi-head structure. The EEG-TCNTransformer achieves an accuracy of 82.97% without the application of bandpass filtering. The source code of EEG-TCNTransformer is available on GitHub.

The existing PDC(Polycrystalline Diamond Compacts) -cone hybrid bit bearing adopts a unilateral support structure, which is prone to stress concentration in the journal area, resulting in fracture and wear failure of the bearing, thus reducing the service life of the hybrid bit. In this paper, a new type of double supported bearing hybrid bit is proposed. The static strength analysis of unilateral and bilateral support bearing structures is carried out by finite element simulation, and the stress and strain distribution of the two structures under the load of 20KN-100KN is obtained. Experimental devices for unilateral and bilateral support bearing structures are designed and manufactured to complete 50KN-100KN static pressure loading experiments. The results showed that the stress and strain of unilateral and bilateral support bearing increased linearly with the increase of load. Compared with unilateral bearing, when the load was 100KN, the maximum Mises stress of bilateral bearing decreased from 358.80MPa to 211.10MPa, with a decrease of 41.16%. The maximum contact stress decreased from 415.20MPa to 378.10MPa, decreased by 8.94%, and the maximum principal strain decreased from 11.01e-4 to 9.71e-4, decreased by 11.81%. The axial strain in the danger zone was reduced by 14.68% and 17.35%, respectively. It is found that the contact stress of the simulation data is highly correlated with the bearing life, and the service life of the bilateral bearing bit is increased by 8.94%. The simulation data and experimental results provide data support for the production of hybrid bits with bilateral bearing support.

The study addresses the challenge of efficiently peeling pineapples, which have a distinct elliptical form, thick skin, and small eyes that are difficult to detect with conventional automated methods. This results in significant flesh waste. To improve the process, the researchers developed an integrated system combining an enhanced BlendMask method, termed SAAF-BlendMask, and a PCP method. SAAF-BlendMask improves the detection of small pineapple eyes, while PCP ensures accurate posture adjustment for precise path planning. The system uses 3D vision and deep learning technologies, achieving an average precision (AP) of 73.04% and a small object precision (APs) of 62.54% in eye detection, with a path planning success rate reaching 99%. The fully automated electromechanical system was tested on 110 real pineapples, demonstrating a reduction in flesh waste by 11.7% compared to traditional methods. This study highlights the potential of advanced machine vision and robotics in enhancing the efficiency and precision of food processing.

Segregating one space from another is a rudimentary act of architecture and the basic principle of this act is creating differentiation between outside and inside. Thresholds, in their expanded or summed form, also produce spaces. They establish the staging for threshold areas in conjunction with space-defining elements. Threshold spaces are typically complicated spatial formations that serve as transition points. They pose a challenge to the interacting human senses in terms of perception. In this research threshold areas have been studied under a specific typology of architecture i.e the traditional housing in historic cities of India. The traditional dwelling settlements have evolved and grown over multiple layers of history, influenced by the culture, society, economy, climate and topography. The purpose of this research is to examine the characteristics of these transition spaces of the traditional dwelling units in relation to the symbolic reference and social meanings assigned to them that influence the architectural and urban spaces attached to them. It seeks to describe transition space in terms of its nature, character, morphology, and social significance. Transparency and material articulation, as well as hierarchy and enclosure, will be investigated. The importance and quality of the nature of "transition" will be examined from the inside to the outside. The research reveals that the space has great functional and socio-cultural importance. It allows multiple activities, provides protection from sun and rain, defines boundaries, connects people within the neighborhood, enhances security and gives an identity to the owner. The contributions made by this research have the potential to improve design considerations for transition areas between the dwelling units and the street.

This study investigates the impact of vertical greenery systems (VGS) applied to heavyweight walls on indoor thermal conditions in a building module situated in the Mediterranean climate of Catania, Italy. Using dynamic simulations in TRNSYS, the research compares the thermal behavior of walls constructed from hollow clay blocks (Poroton) and lava stone blocks with a lightweight wall setup already in place at the University of Catania. The primary focus is on evaluating the VGS’s effect on reducing peak inner surface temperatures and moderating heat flux fluctuations entering the building. The findings indicate that adding a vertical greenery layer to heavyweight walls can decrease the peak inner surface temperature by up to 1.0°C compared to the same wall without vegetation. However, the greenery’s impact on mitigating heat flux fluctuations is less pronounced than in the case of the lightweight wall. This research underscores the potential of green facades in enhancing indoor thermal comfort in regions with climates similar to the Mediterranean, providing valuable insights for sustainable building design and urban planning.

In this paper, based on the cumulated damage generated by the cyclical application of the testing vibration profile, a methodology to determine the reliability index of the analyzed component is given. In the analysis, the testing time, component geometry, applied weight, and material resonant frequency are all of them considered by using a dynamic, and amplitude factor. Then, from the generated vibration response data of the analysis, the corresponding bending stress is determined for each one of the rows of the testing profile. And by the cyclical application of the whole testing profile, the corresponding damage is cumulated to each profile cycle until the cumulated damage is D=1. Finally, the reliability that each one of the cumulated blocks (cyclical profile application) is determined by considering, the random vibration is a stress variable. Thus, we use the cumulative damage model with the inverse power law stress/life relationship to determine the two parameter Weibull distribution used to estimate the reliability index of each cumulated block. As a numerical application, we analyze a cantilever aluminum beam Al 6061-T6.

As waterflooding multi-layer reservoirs get into the high-water-cut stage, inter-layer conflicts become increasingly serious, leading to a worsening development effect over time. Production layers regroup is an effective approach for resolving inter-layer conflicts and improving waterflooding efficiency. At the current stage, there are limitations for most of the methods of production layers regroup. This article proposes a smart method for optimizing the layer regroup plan based on genetic algorithm. Comprehensively considering various factors that affect the regroup of layers, based on the combination principle of "smaller intra-group variance and larger inter-group variance of each influencing factor are expected ", genetic algorithm is used to calculate the fitness value of the initial combination schemes, the advantageous schemes with higher fitness values are selected as the basis of the next generation. Then crossover and mutation operations are performed to those advantageous schemes to generate new schemes. Through continuous selection and evolution, until the global optimal solution with the highest fitness value is found, the optimal combination scheme is determined. Comparative analysis with numerical simulation results demonstrates the reliability of this intelligent method, with an increased oil recovery of 4.34% for the sample reservoir. Unlike selecting a preferable plan from a limited number of predefined combination schemes, this method is an automatic optimization to solve the optimal solution of the problem. It improves both efficiency and accuracy as compared to conventional reservoir engineering methods, numerical simulation methods and most of the mathematical methods, thus could provide effective guidance for EOR strategies of waterflooding reservoirs in high-water-cut stage.

To address the issue of intelligent optimization algorithms being prone to local optima, resulting in insufficient feature extraction and low fault type recognition rates when optimizing Variational Mode Decomposition (VMD) and Support Vector Machine (SVM) parameters, this paper proposes a fault diagnosis method based on an improved Artificial Gorilla Troops Optimization (GTO) algorithm. The GTO algorithm is enhanced using Logistic chaotic mapping, a linear decreasing weight factor, the global exploration strategy of the Osprey Optimization Algorithm, and the Levy flight strategy, improving its ability to escape local optima, adaptability, and convergence accuracy. This algorithm is used to optimize the parameters of VMD and SVM for fault diagnosis. Experiments on fault diagnosis with two datasets of different sample sizes show that the proposed method achieves a diagnostic accuracy of no less than 98% for samples of varying sizes, with stable and reliable results.

In addressing the multifaceted problem of multiple-input multiple-output (MIMO) detection in wireless communication systems, this work highlights the pressing need for enhanced detection reliability under variable channel conditions and MIMO antenna configurations. We propose a novel method that sets a new standard for deep unfolding approaches to MIMO detection by integrating the iterative conjugate gradient method with Tikhonov regularization, combining the adaptability of modern deep learning techniques with the robustness of classical regularization. Unlike conventional techniques, our strategy treats the regularization parameter of Tikhonov regularization as well as step size values and search direction coefficients of conjugate gradient (CG) method as trainable parameters within the deep learning framework, allowing for dynamic modification according to channel conditions and MIMO antenna configurations. Detection performance is significantly improved by the proposed approach in variety of conditions. In different MIMO settings, the suggested method consistently shows better bit error rate (BER) and normalized minimum mean square error (NMSE) performance. Across a range of MIMO configurations and channel conditions, the proposed method exhibits significantly lower BER and NMSE values than well-known techniques such as DetNet and CG. The proposed method has superior performance over CG and other model-oriented methods, especially in small number of iterations. Consequently, the simulation results demonstrate the flexibility of the proposed approach, making it a viable choice for MIMO systems with a range of antenna configurations and different channnel conditions.

In order to achieve the dual carbon goal and simplify the decarbonization process, a CO2 recovery process of the fire-flooding exhaust was designed. The process adopted the two-stage membrane separation method to recover CO2 and obtain high purity CO2 rich flow, and then through the mixed refrigerant liquefaction and rectification column, food grade liquid CO2 was obtained. HYSYS process simulation shows that for the fire-flooding exhaust with a flow rate of 1000 kg/h and a given composition, the process can achieve a CO2 recovery of 69.02%, total product sales S=2086.8 yuan/h, and specific power consumption of 2.818 kW∙h/kg. On this basis, a hybrid simulation platform of HYSYS & Matlab was established. Taking the specific power consumption of the product as the objective function, sequential quadratic programming (SQP) algorithm was adopted to optimize the process, and the optimized specific power consumption was reduced by 18.8%. Finally, by adjusting N2/CO2 components, the adaptability of the optimization condition was analyzed.

Microgrid (MG) control is essential for efficient, reliable, and sustainable energy management in distributed energy systems. Genetic Algorithm-based energy management systems (GA-EMS) can optimally control a MG by finding global optima and handling complex, non-linear, and non-convex problems, but they can be computationally intensive, making real-time control challenging. Model Predictive Control (MPC)-based EMS predicts future behaviour over a defined horizon, ensuring optimal performance and constraint satisfaction but typically relies on linear models. This paper proposes a novel Genetic Predictive Control (GPC) method, combining GA and MPC to enhance resource allocation, balance multiple objectives, and adapt dynamically to changing conditions. Integrating GA with MPC significantly improves the handling of non-linearities and non-convexity in the system model, leading to more accurate and effective control. Comparative analysis shows that GPC significantly reduces excess power production—surplus power generated beyond load demand—improving resource allocation efficiency. For instance, in the Mutation–Random Selection scenario, GPC reduced excess power to 76.0W compared to 87.0W with GA. Additionally, GPC balances cost, emissions, and power efficiency. In the Crossover-Elitism scenario, GPC achieved a daily cost of $113.94 compared to GA's $127.80, and carbon emissions of 52.83 kg CO2e compared to GA's 69.71 kg CO2e, demonstrating GPC's cost efficiency and environmental benefits. While MPC optimizes a weighted sum of objectives, designing appropriate weights can be challenging and may result in non-convex problems. GA handles multiple, conflicting objectives through multi-objective optimization, providing Pareto-optimal solutions. GPC's dynamic adaptability maintains optimal performance by forecasting future load demands and adjusting control actions. GPC consistently achieves lower emissions than GA across various scenarios and, although it sometimes incurs higher costs, such as $113.94 compared to $131.90 in the Crossover–Random Selection scenario, its holistic approach balances these costs with other metrics, making it cost-effective in the long run. By reducing excess power production and emissions, GPC contributes to economic savings and promotes sustainable practices. These findings underscore GPC's potential as a versatile, efficient, and environmentally beneficial optimization tool for power generation systems.

In recent years, the global energy sector has seen significant transformation, particularly in Europe, with a notable increase in intermittent renewable energy integration. Italy and the European Union (EU) have been among the leaders in this transition, with renewables playing a substantial role in electricity generation as of mid-2020s. The adoption of Battery Energy Storage Systems (BESS) has become crucial for enhancing grid efficiency, sustainability, and reliability by addressing the intermittent renewable sources. This paper investigates the feasibility and economic viability of batteries into wholesale electricity markets as per EU regulation, focusing on the dynamics of very different markets, namely the Day-Ahead Market (DAM) based on system marginal price and the Cross-Border Intra-day Market (XBID) based on continuous trading. A novel model is proposed to enhance BESS operations, leveraging price arbitrage strategies based on zonal price predictions, levelized cost of storage (LCOS), and uncertain bid acceptance in continuous trading. Machine learning and deep learning techniques are applied for price forecasting and bid acceptance prediction, respectively. The study finds that data-driven techniques outperform reference models in price forecasting and bid acceptance prediction (+7-14% accuracy). Regarding market dynamics, the study reveals a higher competitiveness of the continuous market compared to the DAM, particularly with increased risk factor in bids leading to higher profits. This research provides insights into compatibility between continuous markets and BESS, showing substantial improvements in economic profitability and the correlation between risk and profits in the bidding strategy (+9 M€ yearly revenues are obtained with a strategic behavior that reduces by 60% awarded energy).

One of the most common solutions to access underground is through the construction of circular shafts. At preliminary design stage analytical expressions are often used to estimate the horizontal earth pressure acting on the lining. However, most of these solutions are based on active state conditions and do not consider the restrictions imposed on the shaft lateral displacements by the rigidity of its lining or by the construction method employed. To overcome this limitation a new methodology to account with the shaft lateral displacements in the determination of the horizontal earth pressure acting on circular shafts is developed and validated against the results of relevant physical scale models. The obtained results show good agreement with the measured data, reproducing adequately the reduction of the horizontal earth pressure from its initial value to an active state condition with the increase of the shaft lateral displacement. Following this approach more realistic predictions of the earth pressure acting on the lining of circular shafts can be performed.

Hydrogen is in the spotlight in the energy world, and it will remain so for the next years. In Europe, the necessity to integrate ever growing amounts of Renewable Energy Sources (RES) in order to implement the ambitious European decarbonization policy (package Fit-for-55) and to preserve the security of energy supply (package Repower-EU) are feeding the interest in hydrogen. The paper will provide a thorough analysis of prospects and criticalities for the development of hydrogen both as a carrier and as a feedstock and, in definitive, as a key element for the implementation of the European decarbonization policies. First, the present regulatory framework will be highlighted, taking European Union as a main reference since it has presently one of the most advanced hy-drogen legislations in the world. Then, both hydrogen offer (technologies and) and demand (both as a feedstock and as an energy carrier) will be dealt with in detail. Two additional sections will take care to illustrate, respectively, the interactions between hydrogen and the electricity grid and the issues related to the creation of a liquid hydrogen market. Finally, a conclusion section will wrap up and summarize the most urgent issues to be tackled to create a well-functioning hydrogen economy.

Plug and abandonment (P&A) operations demand valuable time and resources with operational procedures and materials to establish the well barrier element. This study proposes the use of a water-based fluid as a liquid well barrier element for temporary abandonment, based on esti-mates of its lifespan and survival probability on downhole conditions acquired through acceler-ated life tests. For that, the water-based formulation was tested and exposed to 95, 110, 140 and 150°C, for time intervals ranging from 1 to 10 days. After temperature exposure, the fluid prop-erties were verified and failure was detected by accounting for any deterioration in rheological parameters and/or a substantial increase in filtrate volume. Statistical analysis of failure data was performed in R software using Weibull Model, and fluid average lifespan and survival probability were estimated for P&A temperatures. The results obtained demonstrates that the degradation of the fluid only was observed for 140 and 150°C and is mostly related to the CMC deterioration. The reliability metrics shows that until 80°C the fluid is a promising alternative for temporary abandonment, with no need of monitoring. For higher downhole temperatures, theses metrics should be considered to define the maximum time of operation and requirements for monitoring management in the operation design.

A major problem of the technological production process of the metallurgical-grade silicon is formation of a large amount of microsilica. However, this material can be a significant source of raw materials for its own production. Hence, this study is aimed to solve the problem of micro-silica utilization, i.e. the technogenic dust-like waste generated during the melting of the technical-grade silicon. Within this study, the technology of the joint briquetting of microsilica was investigated with the carbonaceous reducing agents, i.e. by obtaining of mono-charge and by melting of the technical-grade silicon from the obtained mono-charge in the 200kVA ore-thermal furnace. Results of the study show that the briquettes in the melting process of the technical-grade silicon can be used in the range from 0 to 50%. The using of briquettes may significantly improve the technological parameters. When briquetted charge is applied only, the technological parameters will deteriorate significantly. The material balance of silicon melting showed the recovery rates of the main elements (Si, Al, Ca and Fe). It recorded a decrease in content of aluminum from 1.9-2.1% to 0.5-0.7%, calcium from 0.2-0.3% to 0.07-0.1% and titanium from 0.2-0.23% to 0.11-0.13% during the transition to briquetted monocharge. Phosphorus content increased from 0.004-0.005 to 0.011-0.013%.

This study aimed to evaluate the impact of the built environment (i.e., walk, transit, and bike scores) on the location and transportation or “alternative transportation” credit (LTc1) in Leadership in Energy and Environmental Design for Existing Buildings version 4 (LEED-EB-v4) Platinum (n1 = 24), Gold (n2 = 112), Silver (n3 = 46), and Certified (n4 = 34) office buildings in the USA. Spearman’s test was used to estimate the correlation between built environment scores and LTc1 scores for each certification level. The Wilcoxon–Mann–Whitney test was used to estimate pairwise comparison between unpaired Platinum, Gold, Silver, and Certified groups. The Platinum, Gold, and Silver certification levels showed positive and significant correlations between the built environment and LTc1 (0.44 ≤ r ≤ 0.74, p ≤ 0.0312). By contrast, Certified certification levels showed a non-significant correlation (0.18 ≤ r ≤ 0.33, p ≥ 0.0579). Pairwise comparison showed that Platinum and Gold groups outperformed Certified groups in terms of built environment and LTc1 scores (Cliff’s δ ≥ 0.37, p ≤ 0.0099). Thus, for LEED-EBv4-certified projects located in downtown areas, LTc1 can be seen as a significant lever to achieve high levels of LEED certification.

The semi-active cab suspension system for trucks is gaining increasing importance due to its economic advantages, low energy consumption, and significant enhancement of ride comfort. This paper investigates the effects of three control methods on improving ride comfort of semi-active cab suspension systems under random and bump road conditions: Proportional-Integral-Derivative (PID) control, fuzzy PID control, and Model Predictive Control (MPC). Initially, an accurate multi-degree-of-freedom truck cab suspension model was developed and validated through actual road tests. Based on this model, three control strategies were designed and implemented. Finally, the effectiveness of each control strategy was evaluated under various road conditions, including random and bump road scenarios. The results demonstrate that these control strategies can effectively reduce vibrations and impacts, significantly enhancing ride comfort, which is crucial for reducing driver fatigue and improving driving safety.

KThe promising possibility of organic photodetector (OPD) is emerging in the field of sensing applications for its tunable absorption range, flexibility, and large-scale fabrication abilities. In this work, we fabricated bulk heterojunction OPD with a device structure of glass/ITO/PEDOT:PSS/ P3HT:PC61BM/Al by spin coating process and characterized the dark and photocurrent at different applied bias conditions for red, green, and blue incident LED light. OPD photocurrent density exhibited up to 2.5–3 orders higher magnitude compared to the dark current density at -1 V bias while it increased up to 3–4 orders at zero bias conditions for red, green, and blue lights showing an increasing trend higher voltage applied in the negative direction. Different OPD inner periphery shapes, OPD to LED distance, and OPD area were also considered to bring the variation in OPD dark and photocurrent, which can affect the on/off ratio of the OPD–LED hybrid system and is a critical phenomenon for any sensing application.

This study investigates the optical properties of Yttrium Orthoferrite thin films fabricated via plasma laser deposition. Yttrium Orthoferrite, a ferrimagnetic material known for its potential applications in spintronics and photonics, was deposited on single-crystal substrates under controlled conditions to analyze its optical characteristics. The influence of deposition time, on the film quality and optical properties was examined.

Objectives. The aim of the study is to identify relevant research topics based on pooled bibliometric data of International Journal of Hydrogen Energy publications from 2022-2024, collected from The Lens and ScienceDirect platforms. Data source. Bibliometric records of International Journal of Hydrogen Energy articles published in 2022-2024 from The Lens and ScienceDirect platforms were used. Programs and utilities used. VOSviewer, Scimago Graphica, Inkscape, FP-growth utility. Results. The study showed the feasibility of merging bibliometric records from the open platforms ScienceDirect and The Lens, which complement each other. The 'Fields of Study' data of The Lens can be rationally interpreted as system keywords similar to Scopus Index Keywords. The possibility of using 'Fields of Study' data in a method similar to bibliographic coupling is shown. The practicality of using an Alluvial diagram to show the co-occurrence of the four terms was demonstrated. The study also emphasizes the advisability of joint use of VOSviewer and Scimago Graphica programs for complex visualization of bibliometric analysis results. The study identified two dominant publication topics in International Journal of Hydrogen Energy for 2022-2024, which can be described in terms of: 'Catalysis, Hydrogen, Physical_chemistry, Chemical_engineering, Nanotechnology, Electrochemistry, Organic_chemistry, Materials_science' and 'Electrical_engineering, Hydrogen_production, Renewable_energy, Environmental_science, Hydrogen_economy, Hydrogen, Engineering'.

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

Based on an in-depth analysis of the factors influencing the compressive strength of ultra-high performance concrete (UHPC), this study examined the impact of both single factors and combined factors on UHPC performance using experimental data. The correlation analysis indicates that cement content, water content, steel fiber, and fly ash significantly affect the strength of UHPC, whereas silica fume, superplasticizers, and slag powder have a relatively smaller influence. This analysis provides a scientific basis for model development. Furthermore, the support vector regression (SVR) model was optimized using the arithmetic optimization algorithm (AOA). The superior performance and computational efficiency of the AOA-SVR model in predicting UHPC compressive strength were validated. Compared to SVR, support vector machine（SVM）, and other single models, the AOA-SVR model achieves the highest R2 value and the lowest error rates. The results demonstrate that the optimized AOA-SVR model possesses excellent generalization ability and can more accurately predict the compressive strength of UHPC.

This study investigates the fabrication and characterization of large-sized ceramic-reinforced TWIP (Twinning-Induced Plasticity) steel matrix composites using the lost foam casting technique. Various ceramic shapes and sizes, including blocky, flaky, rod-like, and granular forms, were evaluated for their suitability as reinforcement materials. The study found that rod-like and granular ceramics exhibited superior structural integrity and formed strong interfacial bonds with the TWIP steel matrix, compared to blocky and flaky ceramics, which suffered from cracking and fragmentation. Detailed microstructural analysis using scanning electron microscopy (SEM) and industrial computerized tomography (CT) revealed the mechanisms influencing the composite formation. The results demonstrated that rod-like and granular ceramics are optimal for reinforcing TWIP steel composites, providing excellent mechanical stability and enhanced performance. This work contributes to the development of advanced composite materials with potential applications in industries requiring high-strength and durable materials.

The human eye is a vital source of collecting information worldwide via daily observation. The computer science area recognizes it in research as a human-computer interaction. Human blink is an integral part of this observation as a research topic. Several studies have been conducted on eye blinking and left-right eye movements. Previous studies conducted on the eye blink consider the availability of hardware devices high in the budget, high Precision, and low light expo-sure, unlike our study, which uses low-budget devices as simple as a webcam and more versatile usable techniques, incredibly both dim and high light to name a few. This study offers the observational approach via ResNet101v2, VGG-19, and Convolution Neural Network (CNN) architectures. Outperforming both VGG-19 and CNN in this study, ResNet101v2 achieves an impressive accuracy of 98.2%. In contemporary times, deep learning in AI is taking an advanced form. This study attempts to provide a new insight into the real-world implementation of the eye blink.

Stormwater runoff from large roads is a major source of pollutants to receiving waters. Porous pavements have been shown to substantially reduce many of these pollutants, but studies are lacking on arterial roads. We sampled typical stormwater pollutants in runoff from sections of an arterial road 9-16 years after installation of three pavement types: control with conventional asphalt, porous asphalt overly, and full-depth porous asphalt. Both types of porous pavements substantially reduced most of the stormwater pollutants measured. Total suspended solids, turbidity, total lead, total copper, and 6PPD-quinone were all reduced by >75%. Total nitrogen, ammonia, total phosphorus, biochemical oxygen demand, total and dissolved copper, total mercury, total zinc, total polycyclic aromatic hydrocarbons, and di-2-ethylhexyl phthalate were all reduced by >50%. Reductions were lower or absent for nitrate, orthophosphate, E. coli, dissolved lead, and dissolved zinc. Most reductions were statistically significant. Many pollutants exceeded applicable water quality standards in the control samples but met them with both types of porous pavement. This study demonstrates that porous overlays and full-depth porous asphalt can provide substantial reductions of several priority stormwater pollutants on arterial roads for many years after installation.

Biochar has a carbon-based porous structure that is advantageous for adsorbing air pollutants. The pore structure of biochar ranges from several tens to hundreds of m²/g depending on the production temperature. We compared the pore characteristics of biochar with its NOx and SOx adsorption performances to evaluate its adsorbing potential. Biochar was produced from cypress biomass via slow pyrolysis in a fixed-bed reactor and CO2 partial gasification. Slow pyrolysis experiments at 500–700°C were conducted to produce biochar, followed by yield, proximate, elemental, and surface area analyses. CO2 partial gasification activated the biochar produced by slow pyrolysis at the same temperature, and similar analyses were performed. NO and SO2 adsorption experiments were conducted separately and together for biochar produced under each condition. Biochar produced at 600°C showed the highest specific surface area. CO2 partial gasification increased the surface area at each temperature, reaching 680.3 m²/g at 700°C. In single adsorption experiments, N2 carbonized biochar showed NO and SO2 adsorption efficiencies of 84–90% and 88.2–91.4%, respectively, with similar trends in simultaneous adsorption experiments. CO2 activated biochar exhibited higher adsorption efficiencies due to its increased surface area, with NO and SO2 adsorption at 89.8–93.9% and 92–98.9%, respectively. Biochar activated at 700°C simultaneously adsorbed approximately 99% NO and SO2. The NO adsorption efficiency increased with increasing surface area of the small pores, whereas the SO2 adsorption efficiency improved with the development of both small and large pores.

Tunnel fires are generally detected using various sensors, including measuring temperature, CO concentration, and smoke concentration. To address the ambiguity and inconsistency in multi-sensor data, this paper proposes a tunnel fire detection method based on an improved Dempster-Shafer (DS) evidence theory for multi-sensor data fusion. To solve the problem of evidence conflict in DS theory, a two-level multi-sensor data fusion framework is adopted. The first level of fusion involves feature fusion of the same type of sensor data, removing ambiguous data to obtain characteristic data, and calculating the basic probability assignment (BPA) function through the feature interval. The second level fusion derives basic probability numbers from the BPA, calculates the degree of evidence conflict, normalizes to obtain the relative conflict degree, and optimizes the BPA using the trust coefficient. The classical DS evidence theory is then used to integrate and obtain the probability of tunnel fire occurrence. Different heat release rates, tunnel wind speeds, and fire locations are set, forming four fire scenarios. Sensor monitoring data under each simulation condition are extracted and fused using the improved DS evidence theory. The results show that the proposed improved DS evidence theory method detects the probability of fire occurrence in the four scenarios as 67.5%, 71.0%, 82.8%, and 83.5%, respectively, and identifies fire occurrence in approximately 2.4 seconds, an improvement of 64.7% to 70% over traditional methods. This demonstrates the feasibility and superiority of the proposed method, highlighting its significant importance in ensuring personnel safety.

This paper presents and demonstrates the development of a new lightweight coating for aluminium alloy using a novel multicomponent alloy based on the AlSiMgCu system. This coating was applied using a newly designed approach that combined High-Velocity Oxy-Fuel (HVOF) and Plasma Spraying processes. This hybrid technique enables the deposition of coatings with enhanced performance characteristics. The microstructure was studied using optical microscopy (OM) and scanning electron with energy dispersive X-ray spectroscopy (SEM+EDS) microscopy, revealing good adhesion and compaction between the multicomponent coating and the A6061 substrate with 50 to 150 µm thickness. Vickers indentations were performed on the substrate and the new coating material, revealing a 50% improvement in the material's hardness compared to the casted alloy. Additionally, electrical conductivity measurements showed approximative x3.3 times values compared to the alloy in as-cast condition due to high solidification rates altering the morphology of the phases. Corrosion tests demonstrated a lower corrosion rate compared to other multicomponent aluminium alloys and similar to thermal treated A6061 alloy. Finally, tribological results obtained using ball on disc (BOD) test and laser scanning confocal microscopy, confirmed that the new multicomponent coating exhibited reductions in both friction coefficient and wear rate by more than 20% and 50%, respectively. This study demonstrates that using multi-component aluminium coatings could lead to developing of new automotive parts with enhanced properties for corrosion resistance, electrical conductivity, and wear conditions. Optimizing the thermal spraying parameters could further enhance the coating, achieving greater and more homogeneous thicknesses and yielding even higher performance properties.

Tsallis entropy has been widely used in image thresholding because of its non-extensive properties. The non-extensive parameter q contained in this entropy plays an important role in various adaptive algorithms and has been successfully applied in bi-level image thresholding. In this paper, the relationships between parameter q and pixels’ long-range correlations have been further studied within multi-threshold image segmentation. It is found that the pixels’ correlations are remarkable and stable for images generated by a known physical principle, such as infrared images and medical CT images. And the corresponding non-extensive parameter q can be evaluated by using self-adaptive Tsallis entropy algorithm. The results of this algorithm are compared with those of Shannon entropy algorithm and the original Tsallis entropy algorithm, in terms of quantitative image quality evaluation metrics PSNR (Peak Signal-to-Noise Ratio) and SSIM (Structural Similarity). Furthermore, we observed that for image series with the same background, the q values determined by the adaptive algorithm are consistently kept in a narrow range. Therefore, similar or identical scenes during imaging would produce similar strength of long-range correlations, which provides potential applications for unsupervised image processing.

Computational fluid dynamics (CFD) modeling has emerged as a valuable tool for investigating complex processes like microencapsulation. This paper aims to validate the ability of CFD simulations to predict particle size distribution in a polymer microencapsulation process. The CFD modeling approach employed an Eulerian multiphase framework, incorporating a discrete population balance model to track the evolution of the droplet population. A realizable k-ε turbulence model and a multiple reference frame strategy were utilized to capture the system’s flow dynamics. The results reveal that while the CFD simulations align well with experimental data at higher agitation speeds (>10000 rpm), discrepancies arise at lower speeds (<7500 rpm), indicating a challenge in accurately capturing turbulent viscous regimes. Despite these challenges, the CFD model demonstrates robust predictive capabilities for droplet formation and distribution in microencapsulation processes, validated by error margins within acceptable limits. The validated model can be used as a reliable tool to guide experimental efforts and optimize process parameters, contributing to an enhanced understanding and control of microencapsulation processes.

This paper introduces LUNARMINERS framework and applies it for lunar water-ice extraction in Shackleton Crater at the lunar South Pole. The LUNARMINERS combines artificial intelligence swarm robotics with biomimetic swarm behavior. In the proposed framework, the foraging behavior of ants, the light attraction behavior of fireflies, and the migratory flight strategy of wild geese have been comprehensively analyzed and applied to robotic swarms for lunar mining. This research takes into consideration important aspects of the lunar environment, such as permanently shadowed regions (PSRs) and communication limitations in lunar craters, by simulating them in Robot Operating System (ROS). The LUNARMINERS framework has advantages over other methods in that it allows for the survey of an area of 0.025 km2 per day while achieving 100% water-ice deposition effectiveness. Studies show that by applying the LUNARMINER framework, the exploration of the entire Shackleton Crater can be completed in 3.75 Earth years via 15 robots. Simulations of the implementation of the proposed LUNARMINERS framework have demonstrated reliable high-levels of extraction efficiency, with the framework producing 12.9 metric tons of water-ice per day, which potentially can support up to 1,990 people per day based on astronaut’s daily water requirements from NASA's Human Integrated Design Handbook (HIDH) and thus, establishing a sustainable benchmark for lunar habitation. The results from this study show that the advances in the proposed LUNARMINERS framework can contribute to future lunar exploration and ISRU, setting new standards for sustainable off-Earth mining.

This research aims to explore the use of sodium benzoate C6H5-COOH as corrosion inhibitor added to the mixing water of concrete, and test the mechanical performances of five types of concretes over several time periods, 7, 28, 56 and 90 days, in the absence and in the presence of limestone fillers. Hence the results confirm the increase in this resistance over time. Through this, tests were carried out cement paste after addition of sodium benzoate and limestone fillers, we see clearly that the start and the and setting time of cement shift out considerably which indicates that sodium benzoate aims to delay start and end setting time of cement. In addition, Abrams cone slump tests on concrete in the fresh state were carried out in order to see the influence of the addition of sodium benzoate and tests on the cement consistencies in the absence and presence of superplasticizing adjuvant “ORAFLOW THM 17200” which has the main role of improving the workability and quality of the concrete while comparing with ordinary concrete.

A novel approach is developed to improve the convergence of Physics-Informed Neural Networks (PINNs), aiming to employ them as real-time computational models within the framework of the digital twin for manufacturing processes. This method entails the weighting of physical equations, boundary conditions, and initial conditions to ensure their comparable magnitudes, with power being the chosen quantity in this study. The approach is applied to thermal problems, which are crucial for predicting manufacturing part defects. Different configurations, including complex boundary conditions and complex physics, were tested to assess the model’s robustness. The W-PINN demonstrates good predictions and strong stability compared to the classical PINN.

A recently published article posited the use of polymer matrix composites (PMCs) as a replacement material for low-carbon steel (LCS) in large power transformer (LPT) tanks [1]. PMCs offer potential advantages such as weight reduction, corrosion prevention, and self-healing of the tank material which may not be achievable with traditional LCS. LCS can arrest ballistic projectiles at relatively low velocities, but at higher velocities, penetration can occur leading to a potential major transformer failure. Numerical simulations of steel projectile impacts upon LCS plates and glass and/or carbon fiber polymer composite laminates showed that with sufficient thickness both could prevent projectile penetration. This prevention was especially efficient when the plates were coated with a high strain rate sensitive material such as polyurea (PU). Our simulations show that the addition of a 3 mm coating of PU to a standard 10 mm thick LCS plate used in LPT tanks could prevent penetration of a 400 m/s steel projectile. For PMC plates, the simulations with the same projectile velocity showed three times faster deceleration with a 3 mm PU coating than without the coating. Thus, two realistic approaches to improving the ballistic protection of LPTs are presented. In addition to their other advantages, this research clearly shows that PMCs with the coating could provide significant ballistic protection for the next generation of LPT tanks.

In previous studies, drones have been used to carry out visual inspections of heritage elements. In order to complement the information obtained with the common drone, a new underwater drone model is proposed for the specific case of old bridges. The aim is to verify the applicability of underwater drones for the inspection of submerged elements of old heritage bridges, given the large number of bridge disasters and collapses linked to failures in their foundations. To this end, we carried out an underwater inspection of the visible submerged elements (submerged but not buried) of three old bridges crossing different rivers. The different locations and characteristics of these bridges allow us to draw some initial conclusions. The results of the operations carried out show that underwater drones are not only useful for this type of inspection, but that the work is much safer than with the means currently used (underwater divers). In addition, the use of drones makes it possible to study certain parameters that can anticipate subsequent foundation problems affecting the entire bridge, such as the settlement or rotation of piers or abutments.

Digital solutions in the field of restorative neurology offer significant assistance, enabling patients to engage in rehabilitation activities remotely. This research introduces ReMoVES, an Internet of Medical Things (IoMT) system delivering telemedicine services specifically tailored for Multiple Sclerosis rehabilitation, within the overarching framework of the STORMS project. The ReMoVES platform facilitates the provision of a rehabilitative exercise protocol, seamlessly integrated into the Individual Rehabilitation Project, curated by a multidimensional medical team operating remotely. This manuscript delves into the second phase of the study, elucidating the technology employed, the outcomes achieved, and the practical, professional, and academic implications. The STORMS initiative, as the genesis of digital telerehabilitation solutions, aims to enhance the quality of life for Multiple Sclerosis patients.

Ultrasonic transducers are widely used for non-destructive testing (NDT) and evaluation in various industries. While capacitive micromachined ultrasonic transducers (CMUTs) have emerged as significant novel transducers, their application in NDT has been limited. This study designed and fabricated a novel two-element CMUT device for NDT applications. The CMUT’s structural parameters were introduced, and the device was fabricated using silicon fusion wafer-bonding technology. The transducer’s sensitivity, bandwidth, and pulse-echo response performance were tested and analyzed. Testing with aluminum blocks demonstrated that the two-element transducer can transmit and receive ultrasonic waves, with central operating frequencies of 3.35 MHz. In addition, at 3.4 MHz, the receive sensitivities of both elements were –197.434 dB and –197.413 dB, respectively, meeting the requirements for NDT systems. In pulse-echo response testing, the CMUT successfully detected signals from the aluminum block interface and located the interface’s position. This study highlights the potential of CMUTs for NDT testing and evaluation applications.

The nonlinear dynamics exhibited by aircraft flying at high angle of attack (AOA) pose great challenges to the design of flight control. The control design based on nonlinear dynamic inversion (NDI) is to reversely find the required control input based on the current nonlinear dynamics of the aircraft and the control objectives to be achieved. Since it is derived from the nonlinear dynamics of the aircraft, a single NDI controller can cover the entire flight envelope without the need for controller switching or gain scheduling. This paper employs the layered structure of the flight control loop to implement the NDI controller through the combination of aerodynamic control and thrust vectoring control (TVC). The resulting NDI controller is applied to an experimental F-16 installed with TVC to verify its high AOA maneuverability in the post-stall region. The verification simulation shows that the experimental F-16 under the NDI control can effectively track the high AOA command and successfully demonstrate the Herbst maneuver by performing the heading reversal with 70° AOA.

The autonomous displacement and displacement measurement functions of the maglev ruler are performed by the mover core. The stability of the four magnetic field in air gaps and the levitation position of the maglev ruler is one of the key factors for the stability of the thrust force on the power core, and it is also one of the key factors for ensuring the precision of the maglev ruler. There is cross-coupling between the two ends of the mover core of the maglev ruler, resulting in a strongly coupled, nonlinear, multi-input and multi-output system for the levitating system of the maglev ruler. This paper establishes a mathematical model for the levitating system of the maglev ruler, and designs a levitating control system for the maglev ruler based on ADRC control algorithm to achieve decoupling and disturbance suppression. Through simulation analysis and experimental testing of the levitating system with starting and disturbance, it is proved that the levitating control system of the maglev ruler has good dynamic characteristics, static characteristics, and robustness.

The article presents the issues related to the operation and modelling of the ship main propulsion steam boiler. Such objects are used widely in seagoing ships especially the very large crude oil carriers as a main propulsion turbine feed. The advantages of such along with simple construction thus great reliability make oil feed boilers an excellent source of power for chosen type of the large ships. It must be pointed out that a lot of research work was done in the previous years regarding automatic control of boiler automation but since then this technology was not in the focus of the researchers. In the future the fossil fuels will become less important than they are now, but the control techniques of boilers would eventually change due to different fuel parameters therefore, development of such is necessary. The article presents the results of research collected in real ship boilers during their operation and then, based on the results, a digital model of such an object was created. This model was implemented into Hardware-In-Loop which is the initial step for preparing the full model (including sensors etc.) of a steam heated boiler. Theoretical background along with chosen simulation and experimental results of control implemented into HIL were presented to validate assumptions...

To improve the current density distribution and electromagnetic performance of air-core inductors, a structural optimization method combining BP neural network and genetic algorithm was proposed for the study of axial and radial spiral multi-winding inductors. The Monte Carlo method was used to extract the structural size samples of the inductors, and the training dataset was obtained through finite element calculation of electromagnetic fields. Based on BP neural networks, nonlinear mapping models between the inductance value, volumetric inductance density, current distribution non-uniformity coefficient, and inductor structural parameters were constructed. The sensitivity analysis of the inductor inductance value affected by structural parameters was conducted through the Sobol index calculation. Using the current distribution non-uniformity coefficient as the fitness function and the volumetric inductance density as the constraint condition, the genetic algorithm was applied to optimize the structural parameters of the inductor globally. The optimization results were verified through finite element comparison. The results show that, under the requirement of satisfying the volumetric inductance density, the current distribution non-uniformity coefficient of AHI type inductor was reduced by 4.57% compared with the best sample in the sampling, while that of RHI type inductor was reduced by 5.33%, demonstrating the practicality of the BP-GA joint algorithm in the structural optimization design of inductors.

With the development of agriculture, the complexity and dynamism of orchard environments pose challenges to the perception and positioning of inter row environments for agricultural machinery. The paper proposes a method for extracting navigation lines and measuring pedestrian obstacles. The improved YOLOv5 algorithm is used to detect tree trunks between left and right rows in orchards. The experimental results show that the average angle deviation of the extracted navigation lines is less than 5 degrees, verifying its accuracy. Due to the variable posture of pedestrians and ineffective camera depth, a distance measurement algorithm based on four zone depth comparison is proposed for pedestrian obstacle distance measurement. Experimental results show that within a range of 6m, the average relative error of distance measurement does not exceed 1%, and within a range of 9m, the maximum relative error is 2.03%. The average distance measurement time is 30 milliseconds, which can accurately and quickly achieve pedestrian distance measurement in orchard environments. On the publicly available TUM RGB-D dynamic dataset, YOLOD-SLAM2 significantly reduces the RMSE index of absolute trajectory error compared to ORB-SLAM2 algorithm, which is less than 0.05 m/s. In actual orchard environments, YOLOD-SLAM2 has a higher degree of agreement between the estimated trajectory and the true trajectory when the car is traveling in straight and circular directions. The RMSE index of the absolute trajectory error is less than 0.03m/s, and the average tracking time is 47ms, indicating that the YOLOD-SLAM2 algorithm proposed in this paper can meet the accuracy and real-time requirements of agricultural machinery positioning in orchard environments.

Directed Energy Deposition (DED) processes necessitate a consistent material flow to the melt pool, typically achieved through pneumatic conveying of metal powder via thin pipes. This study aims to record and analyze the multiphase fluid-solid flow. An experimental setup utilizing a high-speed camera and specialized optics was constructed, the flow through thin transparent pipes was recorded. The resulting information was analyzed and compared with coupled Computational Fluid Dynamics - Discrete Element Modeling (CFD-DEM) simulations, with special attention to the solids flow fluctuations. The paper details the experimental design, video analysis using Particle Tracking, and a novel method for deriving volumetric concentrations and flowrate from flat images. The findings confirm the accuracy of the CFD-DEM simulations and provide insights into the dynamics of pneumatic conveying, which could be used to enhance the efficiency of DED processes.

Electric mobility is a global trend and necessity, with electric and solar boats offering a promising alternative for transportation electrification and carbon emission reduction, especially in the Amazon region. This study analyzes the system of a solar boat from the SIMA project, to be implemented at UFPA, using MATLAB software for modeling. The Simulink tool was utilized to model the system, focusing on operational parameters such as module voltage, converter voltage, and speed. Results indicate that the solar boat's operational cost is significantly lower compared to a similar internal combustion model, considering diesel's high consumption and cost. The environmental impact is also reduced, with nearly 72 tons of CO2 emissions avoided annually, thanks to Brazil's renewable energy matrix. Simulations confirmed the project's parameters, demonstrating the efficiency of digital twin technology in monitoring and predicting system performance. The study underscores the importance of digital twins and renewable energy in promoting sustainable transportation solutions, advocating for the replication of such projects globally. Future research should focus on further advancing digital twin applications in electric mobility to enhance predictive maintenance and operational efficiency.

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

Shape memory alloys (SMA) are becoming a more important factor in actuation technology. Due to their unique features, they bring the potential to safe weight, installation space and reduce energy consumption. The system integration of the generally small diameter NiTi wires is an important cornerstone for the emerging technology. Crimping, a common method for the mechanical and electrical connection of SMA wires, has several drawbacks when it comes to miniaturization and high force outputs. For high force applications for example, multiple SMA wires in parallel are needed to keep actuation frequencies high, while scaling up the actuation force. To meet these challenges, the proposed study deals with the development of a resistance welding process for manufacturing NiTi wire bundles. The wires are welded to sheet metal substrate, resulting in promising functional properties and high joint strengths. The welding process benefits from low cost, easy to control parameters and good automation potential. A method for evaluating the resistance welding process parameters is presented. With these parameters in place, a manufacturing process for bundled wire actuators is discussed and implemented. The welded joints are examined by peel tests, microscopy, and fatigue experiments. The performance of the manufactured bundle actuators is demonstrated by comparison to a single wire with the same accumulated cross-sectional area.

Traditional lime-based materials have considerable potential for use as components of green materials for plastering and finishing building surfaces in both new construction and historic heritage conservation. The paper presents the preliminary results of a research activity aimed at developing pre-packaged products based on Traditional Lime Putty (TLP) by partially replacing natural aggregates with Stone Waste Sludge (SWS), with a low rate of recovery from Apricena limestone production district in Apulia. The mineralogical and chemical analysis carried out by means XRD (X-Ray Diffraction), TG-DTA (Thermo Gravimetry-Differential Thermal Analysis) and hydrochloric acid attack test showed that the SWS consisted of 98.4 % CaCO3 by mass. The particle sizes measured by laser diffraction technique are below 22.5 μm for the 92% by mass of the sample. The high fineness of the stone waste was confirmed by the Blaine specific surface method equal to 9273.79 cm2/gr. The behavior of three fresh mixtures for prepacked coarse plaster, fine plaster, and finishing plaster with 12.90%, 17.94%, and 18.90 by mass of SWS, respectively, was evaluated by spreading test and applicability tests on a perforated ceramic slab. The finishing plaster has the highest consistency value of 235 mm, while the fine plaster and the coarse plaster have values of 205 mm and 155 mm, respectively. The coarse plaster is suitable for the use as base plaster (arriccio) or second layer rendering (tonachino) up to a thickness of approximately 1 cm. Both the fine plaster and finishing plasters can be used for the surfaces finishing with the application of layers of a few millimeters thick.

Mining industry is undergoing a revolution with the introduction of automation, especially through autonomous haul truck systems and is expected to have further essential developments due to advancements in swarm robotics and its applications. This study evaluates the performance of four different swarm robot models, i.e. baseline, ant, firefly, and honeybee, focusing on mining efficiency, scalability, reliability, and selectivity. Enhancements such as role specialization in the ant model, advanced communication in the firefly model, improved localization and the hybrid control system combining centralized and decentralized controls in the honeybee model are integrated to optimize their functionality. Mining simulations presented in this study include swarm robots of various designs, each resulting in certain advantages for autonomous operations. Their capabilities have been evaluated against performance criteria on a spider chart, identifying where each model excelled or failed in an actual extraction operation in mining. This study aims to identify the most optimal swarm models in relation to unique mining site conditions and goals. The applications of the suggested swarm models could contribute to achieving further improvement in productivity, scalability, selectivity and reliability, resulting in a more sustainable, automated and effective operations in mining.

Electric vehicles (EVs) are pivotal in addressing the escalating environmental crisis. While EV drivetrains excel compared to those of vehicles with internal combustion engines (ICE), their energy storage systems are hampered by limited range, lifespan, and lengthy charging times. Hybrid energy storage systems (HESS) present a viable current solution to these issues. This review thoroughly explores state-of-the-art in the emerging field of multisource EVs that utilize HESS, incorporating any combination of batteries (BTs), supercapacitors (SCs), flywheels (FWs), fuel cells (FCs), and/or transmotors. In addition, the paper systematically categorizes and evaluates different hybrid configurations, detailing potential topologies and their respective advantages and limitations. Moreover, the paper examines diverse control algorithms used to manage these complex systems, focusing on their effectiveness and operational efficiency. By identifying current research gaps and technological challenges, this study aims to delineate future research directions that could enhance the deployment and optimization of multisource EVs, thereby addressing critical challenges such as energy density, system reliability, and cost-effectiveness.

In recent years, the introduction of electric vehicles as a means of transportation has been gradually growing in most developing and developed countries, especially in terms of energy efficiency, energy diversification, and environmental sustainability. While developed countries have been trying to keep up with electric vehicle usage and buying trends, most developing countries, especially African countries, are still riddled with severe power outages and electricity load shedding. The growing adoption of electric vehicles (EVs) has led to a corresponding increase in the availability of charging infrastructure, thereby exerting notable impacts on the power grids of most African countries. Recent evidence suggests that various charging strategies and grid integration approaches are being developed to mitigate the negative impacts of electric vehicle (EV) charging and enhance the advantages of integrating EVs with the grid. However, transportation researchers have struggled to develop applicable and sensible mitigation strategies to tackle this issue, which has seriously rendered the sustainability of electric vehicle usage in Africa in serious doubt. The primary aim of this research is to provide a conceptual theoretical framework based on previous and current literature reviews on electric vehicles to examine the challenges facing the adoption of electric vehicles in Africa. In addition, we explored the environmental and economic benefits of electric vehicles in Africa regarding urban sustainability and charging infrastructure on the existing transportation infrastructure in the African transportation network. This study contributes to the expanding body of literature on electric vehicles by emphasising the significance of incorporating sustainability principles in the adoption and usage of electric vehicles in Africa. It also examines the effects of electric vehicle usage on the existing transportation infrastructure.

In the realm of physical activity and sports science, motion tracking emerges as an important tool for specialists aiming to analyze movement data, whether for daily life activities or enhancing athletic performance. This study introduces an innovative approach to motion tracking through the development of a fully embedded smart shoe sensor system, which employs an inertial measurement unit (IMU) affixed to the shoe sole, coupled with six pressure sensors, to provide real-time calculation of individual stride lengths along with other vital movement statistics. Traditional motion tracking methods, which typically rely on various body-attached sensors, often necessitate substantial post-processing, delaying the feedback loop. In contrast, our system is designed to overcome these limitations by offering high-accuracy, real-time feedback with a minimal error rate of 1.88% from our experiments. Such prompt data acquisition facilitates immediate interventions by sports performance coaches or healthcare professionals, significantly contributing to injury prevention and performance enhancement. The experimental outcomes of this study underscore the system’s capability to deliver precise stride length measurements instantaneously, marking a significant advancement in motion tracking technology and its application in sports science and healthcare.

Model-based fault diagnosis serves as a powerful technique for addressing fault detection and isolation issues in control systems. However, diagnosing faults in closed-loop control systems is more challenging due to their inherent robustness. This paper aims to detect and isolate actuator and sensor faults in the cascade electro-hydraulic control system of a turbofan engine. Based on the fault characteristics, we design a robust unknown perturbation decoupling residual generator and an optimal fault observer specifically for the inner and outer control loops to detect potential faults. To locate the faults, we analyze the steady-state propagation laws of actuator and sensor faults within the loops using the final value theorem. Based on this, we establish the minimal-dimensional fault influence distribution matrix specific to the cascade turbofan engine control system. Subsequently, we construct the normalized residual vectors and monitor its vector angles against each row of the fault influence distribution matrix to isolate faults. Experiments conducted on an electro-hydraulic test bench demonstrate that our proposed method can accurately locate four typical faults of actuators and sensors within the cascade electro-hydraulic control system. This study enriches the existing fault isolation methods for complex dynamic systems, and lays the foundation for guiding component repair and maintenance.

The last decade has seen a significant growth in low-cost air quality monitoring systems (LCAQMS) adoption, mostly driven by the spatial density limitations of traditional regulatory grade networks which restrict their effectiveness in several settings. The EU's regulatory framework advise for deploying one air quality monitoring unit per roughly 170,000 people or 15 square kilometers which is insufficient for capturing high spatio-temporal variance in complex urban scenarios. Often these settings are characterized by distributed emissions in small roads subjected to intense cars traffic, house heating driven emissions and frequently subjected to canyon effects. The Air Heritage project in Portici, funded by the EU's Urban Innovative Actions (UIA) program, addressed these challenges by integrating low-cost sensors with traditional monitoring systems and engaging the community in pervasive measurement strategies. The project highlights the importance of individualized sensor calibration and frequent recalibrations to maintain accuracy. It also showcases the benefits of citizen science approaches, IoT infrastructures, AI components, and geostatistical sensor fusion algorithms for mobile and opportunistic air quality measurements. This paper reports on the lessons learned and experiences from the Air Heritage project, aiming to guide future implementations in similar contexts. By sharing these insights along with the gathered datalake, we aim to inform stakeholders, including researchers, public authorities, and citizens, about effective strategies for deploying and utilizing LCAQMS to enhance air quality monitoring and public awareness.

This study investigates the microstructural evolution and mechanical properties of steel subjected to high-temperature heat treatment, with a focus on the formation and effects of dark phase precipitates. High Temperature Confocal Laser Scanning Microscopy (HTCLSM) was employed to observe the in-situ changes in microstructure as the steel was heated from room temperature to 900°C. The observations revealed that dark phase precipitates, identified as chromium carbides (Cr7C3), began forming at approximately 820°C and increased in quantity and size up to 900°C. Scanning Electron Microscopy (SEM) and Energy-Dispersive X-ray Spectroscopy (EDX) confirmed that these precipitates significantly enhance the material's hardness, with microhardness values of 320 HV for the precipitates compared to 270 HV for the matrix. Tensile tests showed that the tensile strength of the steel increases with soaking time at 900°C, reaching a maximum after 3 minutes due to the strengthening effect of the precipitates. Beyond this point, strength decreases due to grain coarsening.

The review considers physics and some applications of a new approach that promises to be a breakthrough in efficiency and instrumentation of various methods for nondestructive testing (NDT) of composites. The approach is based on frequency-selective properties of defects which fall into resonance (Local Defect Resonance (LDR)) at certain frequencies. An activation of LDR enables to route the input acoustic energy directly to the defect that dramatically increases its vibration amplitude and thus facilitates defect-selective imaging, rises efficiencies of various derivative acoustic NDT methods (nonlinear, acousto-thermal) and enables to develop some new methodologies, like Resonant Air-Coupled Emission (RACE), machine and remote imaging.

The two combined technologies WPT and AI still have huge prospects for several industries such as consumer electronics and industry. This paper looks at the recent developments in WPT, particularly concerning AI-improved efficiency, range, and reliability. AI-driven also works to control power, manage device alignment, and test when maintenance is necessary, this makes powering smarter and more efficient. Accuracy is given by deploying machine learning and computer vision, and on the other hand, the maintenance procedure is done through predictions. Furthermore, the ability of the management system to include the use of AI in power regulation enables the control of consumption based on the load. In this study, the positive future effect of AI is discussed concerning improvements in WPT technologies, which is paramount in subsequent applications. Based on the results, it is essential to continue the investigation of the topic within this interdisciplinary science to advance existing problems and discover novel possibilities for evolution. Data based on experiments shows a significant improvement in the efficiency of information transfer where AI is used with rates up to 25% higher than those using the traditional ways. Additionally, advanced maintenance forecasting algorithms improve system availability by cutting down on 30% of the downtimes when applied in real-life applications of AI technology. The study also looks at the possibilities of the outlook to the future including emergence of the distributed artificial intelligence for autonomous WPT network, and additional potential of quantum computing for the use to improve the algorithm. This indicates that there is a new era for the convergence of WPT and AI to enhance the progress of power transfer system in conditions of elegant, efficient, and robust.

The real-time input of ground motions can effectively assess earthquake disasters in building structures. In this study, we proposed a nearby real-time simulation method that can be applied to assess regional earthquake disasters using structural monitoring recordings based on an urban earthquake simulation system. Real-time accelerations recorded during the 5.1 magnitude Tangshan earthquake were used to update and modify the computing models. El Centro waves were calibrated to 400 cm/s2 for structural seismic resilience analyses. The results indicated that approximately 70% of the structural functions were lost during rare earthquakes. Regional numerical models of 216 buildings were constructed and timeously updated using the Geographic Information System and measured data. The inputting ground motions recorded in the 5.1 magnitude Tangshan earthquake and typical El Centro waves were selected to analyze the structural seismic response, in which the damage indexes were computed, and the damage predictions of regional 216 buildings were also simulated in different levels of earthquakes by being combined with the simplified principles of structural damage estimation. Finally, the evaluation results were visualized in three dimensions using ParaView software. The simulated results of earthquake disasters at an urban scale will promote the prediction abilities of local earthquake administration agencies and have considerable potential to provide essential information for emergency responses and regional disaster mitigation.

Rock materials failures are accompanied by the co-existence of various failure mechanisms, in-cluding rock fracturing, shearing, and compaction yield. These mechanisms manifest macro-scopically as multiple failure modes and nonlinear strength characteristics related to stress levels. Considering the limitations of current rock mechanics strength theories, which are primarily derived from single failure mechanisms, this study evaluates the applicability of alternative strength theories. Based on the extensional-strain criterion and the PMC (Paul-Mohr-Coulomb) model, a piecewise linear strength model was proposed that is suitable for analyzing multiple failure mechanisms in rocks and revealing the intrinsic mechanisms of multi-mechanism rock material failure. A multiple failure mechanism strength model in the form of inequalities was proposed, using the generalized shear stress, mean stress, and stress Lode angle as parameters. Strength tests conducted on sandstone and granite rock material samples under different stress conditions revealed distinct piecewise linear strength characteristics for both rock types, vali-dating the rationality and applicability of the multiple failure mechanism model. The findings construct a multi-mechanism failure model for rocks, providing enhanced predictive capabilities and aiding in the prevention of rock structural failures.

Sour waters are one of the main aqueous byproducts generated during petroleum refining and require processing in Sour Water Treatment Units (SWTUs) to remove contaminants such as H₂S and NH₃ in compliance with environmental legislations. Therefore, monitoring the composition of SWTU effluents, including acid gas, ammoniacal gas, and treated water, is essential. This study aims to present an AI (artificial intelligence) hybrid-based methodology to develop soft sensors capable of real-time prediction of H₂S and NH₃ mass fractions in the effluents of SWTUs and validate them using real data from industrial units. Initially, a new database based on the dynamic simulation of a two stripping columns SWTU phenomenological model, developed in Aspen Plus Dynamics® V10, was generated, aiming at non-faulty runs, unlike our previous work [1]. Ensemble methods (Decision Trees), such as Gradient Boosting and Random Forest, and Support Vector Machines were compared for soft sensor creation using these simulated data. The best outcome was the development of six soft sensors based on Random Forest with R² greater than 0.87, MAE less than 0.12, MSE less than 0.17, and RMSE less than 0.41. Variable importance analysis revealed that the temperature of the second stage of Column 1 significantly influences the thermodynamic equilibrium of H₂S and NH₃ separation from sour waters, being critical for five of the six soft sensors. After this initial stage using data from the phenomenological model, data from an industrial-scale SWTU were used to develop real soft sensors. The results proved the effectiveness of the conjugated use of physical model and industrial data approach in the development of soft sensor for two-column SWTUs.

This paper deals with the design and kinematic analysis of a novel mechanism for the elbow joint of an upper-limb exoskeleton, with the aim of helping operators, in terms of effort and physical resistance, in carrying out heavy operations. In particular, the proposed eight-bar el-bow joint exoskeleton mechanism consists of a motorized Watt I six-bar linkage and a suitable RP dyad, which connects mechanically the external parts of the human arm with the corre-sponding forearm by hook and loop velcro and thus, helping their closing relative motion for lifting objects during repetitive and heavy operations. This relative motion is not a pure rotation and thus, the upper part of the exoskeleton is fastened to the arm, while the lower part is not rigidly connected to the forearm, but through a prismatic pair which allows both rotation and sliding along the forearm axis. Instead, the human arm is sketched by means of a crossed four-bar linkage, which coupler link is considered as attached to the glyph of the prismatic pair, that is fastened to the forearm. Therefore, the kinematic analysis of the whole ten-bar mecha-nism, which is obtained by joining the Watt I six-bar linkage and the RP dyad to the crossed four-bar linkage, is formulated to investigate the main kinematic performance and for design purposes. The proposed algorithm has given several numerical and graphical results. Finally, a double-parallelogram linkage, as particular case of the Watt I six-bar linkage, was considered in combination with the RP dyad and the crossed four-bar linkage, by giving a first mechanical de-sign and a 3D printed prototype.

Efficient beam steerable high-gain antennas enable high-speed data rates over long-distance networks, including wireless backhaul, satellite communications (SATCOM), and SATCOM On-the-Move. These characteristics are essential for advancing contemporary wireless communication networks, particularly within 5G and beyond. Various beam-steering solutions have been proposed in the literature, with passive beam-steering mechanisms employing planar metasurfaces emerging as cost-effective, power-efficient, and compact options. These attributes make them well-suited for use in confined spaces, large-scale production and widespread distribution to meet the demands of the mass market. Utilizing a dual-band antenna terminal setup is often advantageous for full duplex communication in wireless systems. Therefore, this article presents a comprehensive review of the dual-band beam steering techniques for enabling full-duplex communication in modern wireless systems, highlighting their design methodologies, scanning mechanisms, physical characteristics, and constraints. Despite the advantages of planar metasurface-based beam steering solutions, literature on dual-band beam steering antennas supporting full duplex communication is limited. This review article identifies research gaps and outlines future directions for developing economically feasible passive dual-band beam-steering solutions for mass deployment.

Froth flotation, a widely used mineral beneficiation technique, generates substantial volumes of data, offering the opportunity to extract valuable insights from these data for production line analysis. The quality of flotation data is critical to designing accurate prediction models and process optimisation. Unfortunately, industrial flotation data are often compromised by quality issues such as outliers that can produce misleading or erroneous analytical results. A general approach is to preprocess the data by replacing or imputing outliers with data values that have no connection with the real state of the process. However, this does not resolve the effect of outliers, especially those that deviate from normal trends. Outliers often occur across multiple variables and their values may occur in normal observation ranges, making their detection challenging. An unresolved challenge in outlier detection is how far is far enough for an observation to be considered an outlier. Existing methods rely on domain experts’ knowledge which is difficult to apply when experts encounter large volumes of data with complex relationships. In this paper, we propose an approach to conduct outlier analysis on a flotation dataset. The approach uses a 2σ rule as the threshold to find quasi-outliers and multiple machine learning (ML) algorithms including k-Nearest Neighbour (kNN), Local Outlier Factor (LOF), and Isolation Forest (ISF) to identify true outliers. The approach then analyses the mutual coverage between quasi-outliers and outliers from the ML algorithms to identify the most effective outlier detection algorithm. We found that the outliers by kNN cover outliers of other methods. We use the experimental results to show that outliers affect model prediction accuracy and excluding outliers from training data can reduce the average prediction errors.

Information theoretic quantities such as entropy, entropy rate, information gain, and relative entropy are often used to understand the performance of intelligent agents in learning applications. Mean squared error has not played a role in these analyses, primarily because it is not felt to be a viable performance indicator in these scenarios. We build on a new quantity, the log ratio of entropy powers, to establish that minimum mean squared error (MMSE) estimation, prediction, and smoothing are directly connected to Mutual Information Gain or Loss in an agent learning system modeled by a Markov chain for many probability distributions of interest. Expressions for mutual information gain or loss are developed for MMSE estimation, prediction, and smoothing and an example for fixed lag smoothing is presented.

The main purpose of this study was to create a prototype of an unmanned aerial system equipped with intelligent hardware and software technologies necessary for monitoring the health and growth of crops in orchards. Another important objective was to use low-cost sensors that accurately measure ultraviolet solar radiation. The device, which needs to be attached to the commercial DJI Mini 4 Pro drone, should be small in size, portable, and have very low energy consumption. For this purpose, the widely used Vishay VEML6075 digital optical sensor was selected and implemented in a prototype, alongside a Raspberry Pi Zero 2W minicomputer. To collect data from these sensors, a program written in Python was used, containing specific blocks for data acquisition from each sensor, to facilitate the monitoring of ultraviolet (UV) radiation, or battery current. By analyzing the data obtained from the sensors, several important conclusions are drawn that may provide valuable pathways for the further development of mobile or modular equipment. Furthermore, the results of the plant condition analysis with proposed models in the Geographic Information System (GIS) environment were also presented. The visualization of maps indicating variations in vegetation condition led to the identification of problem areas like hydric stress.

This study introduces a cascade nonlinear proportional integral retarded (CNPIR) controller for the first time in the literature, aiming to achieve real-time precise control of a double-tank liquid level system. It begins by outlining the characteristics of de-lay-based systems and detailing the dynamic behavior of the liquid-level system. The study evaluates the system's performance by developing and analyzing the mathe-matical models of both the feedforward proportional-integral (FF-PI) controller and the CNPIR controller. Two different control techniques were employed, with real-time tests conducted to assess the system's axis movement performance. The FF-PI controller was used as the initial control technique, while the CNPIR controller was introduced to enhance system performance during trajectory tracking. Experimental results demon-strated that the CNPIR method significantly improved trajectory tracking response, reduced overshoot, and achieved better steady-state error under external disturbances compared to the FF-PI controller. Thus, the experimental study, highlights the effec-tiveness of the proposed control design and its superiority over some existing classical methods.

This article titled "AI and IoT in Automation and Security: A Vision for Next Generation Applications," explored the implications of marrying IoT with AI in the realms of automation and security; this piece goes further into what that entails. In this paper, we present innovative application descriptions arising from this technology convergence and its future-generation applications. We found that AI brings strong cyber security, predictive maintenance, and decision-making capabilities to the table due to its advanced algorithms and machine learning models. IoT has the potential to allow businesses to extract and generate big data, automate processes, and monitor in real-time thousands and even millions of businesses at once. For now, we believe some of the possible main conclusions will be that the smart city, stand-alone systems, and user experience will be quite consumer-friendly. Importantly, the study also assessed facets such as data protection, system interoperability, and a few others. Our vision underscores the essential of a strong structure to connect the full latent of AI and IoT, finally aiming to create more capable, protected, and approachable environments. Significant results include achieving 95.8% accuracy in anomaly detection and 93.4% accuracy in predictive maintenance. The showcasing results efficacy of AI models to enhance system reliability as well as operational efficiency. Vigorous encryption protocols performance ensures data security hence addressing issues of privacy. These culminations underscore the transformative potential of AI-IoT integration in next-generation automation and security applications.

The alternating stress caused by periodic high-pressure injection and extraction in gas storage can potentially induce fault slippage, compromising the sealing integrity of faults within these facil-ities. Understanding the mechanical behavior of faults under alternating stress is crucial for en-suring the long-term stability and safety of gas storage operations. To explore the impact of fault dip angle and fault gouge strength on fault slip characteristics, fault samples were prepared with uniaxial compressive strengths of 20.1, 30.2, 42.4, and 51.4 MPa at two distinct dip angles. Triaxial compression experiments were conducted under alternating stress conditions corresponding to operational pressures at a specific gas storage in China. Results indicate that faults with dip angles of 30° and 45° tend to fail at their weakest points. Increasing strength of fault gouge shifts failure mechanisms from interfacial failure between gouge and surrounding rock towards internal gouge failure, often accompanied by shear failure across sections, resulting in characteristic "X"-shaped conjugate shear failures. The decrease in the ratio of bedrock strength to fault gouge strength elucidates the observed phenomena of initial reduction followed by increased fault deformation. Transition points for faults with 30° and 45° dips occur around the strength ratios of 1.7/1 and 1.2/1, respectively. Fault damage exhibits a negative correlation with fault gouge strength and a positive correlation with fault dip angle. Samples with higher strength fault gouge at a 30° dip angle generally incur less damage compared to those with lower strength fault gouge at a 45° dip angle. Moreover, higher maximum static friction coefficients denote greater fault resistance to slipping, with 30° faults consistently demonstrating superior resistance compared to 45° faults. Additionally, higher strength fault gouge consistently enhances slip resistance under identical dip angles.

In this work, a method for measuring tow angles in carbon fiber components, based on the use of a polarized camera, is analyzed from a metrological point of view. Carbon fibers alter the direction of the reflected light electrical field, so that in each point of the surface of a composite piece the angle of polarization of reflected light matches the fiber orientation. A statistical analysis of the angle of linear polarization (AoLP) in each pixel of each examined area allows to evaluate the average winding angle. An evaluation of the measurement uncertainty of the method on a cylinder obtained by a filament winding process is carried out, and the result appears adequate for the study of the distribution of angles along the surface of the piece, in order to optimize the process.

The implementation of morphing wing mechanisms holds promise in enhancing aircraft perfor-mance, as indicated in current literature. The Clean Sky 2 AirGreen 2 European project is currently conducting ground and wind tunnel tests to validate innovative studies on morphing wing structures. The project aims to showcase the effectiveness of these morphing architectures on a potentially flying demonstrator at true scale. This article describes design methodology and struc-tural testing of a scaled morphing flap structure capable of 3 different morphing modes required for low-speed (take-off/landing) and high-speed (cruise) conditions. A significant scale factor of 1:3 was selected to enhance the importance and relevance of the wind tunnel test campaign. Geometrical scaling to the real flap prototype was deemed impractical due to limitations associated with scaling the embedded mechanisms and actuators responsible for shape changing. Static analyses were performed using the finite element method to address the most critical loads determined through three-dimensional CFD analysis. The Finite Element (FE) analysis results were briefly outlined, and comparisons were drawn between the obtained results and the empirical data collected during the structural test. Good correlations were observed between the results and numerical predictions, encompassing static deflections under applied loads and elastic deformations. Consequently, the modeling approaches utilized during the design and testing phases were considered highly suc-cessful. Therefore, based on the results extrapolated from simulations under ultimate load condi-tions relevant to the wind tunnel test campaign, the scaled flap prototype was deemed suitable for testing.

The ever-growing concerns about global warming and the rapid depletion of fossil fuels have triggered a rising interest in research for cleaner, more efficient and cost-effective energy generation. Organic Rankine cycle systems have immense potential to become a strong alternative to conventional methods of energy generation. However, such systems’ efficiency is limited by the performance of the incorporated gas expanders or prime movers. Conventional gas expanders often utilize ports or cam-operated valves. Ported expanders offer limited efficiency and controllability, and produce high emissions causing a large amount of heated and compressed gas to be wasted. Cam-operated valves, in contrast, increase expansion performance compared to ported expanders, however, they have no variability and adaptability to changes in system parameters such as gas pressure, temperature, dryness fraction, and load variation to name a few. On a positive note, this issue can be addressed by adopting a variable timing and fast-operating, control valve with an accurate and adaptive control mechanism at the expander inlet. A properly designed and controlled valve can greatly improve the system performance of gas expanders and pave the way for an efficient low-cost alternative energy generation. This manuscript provides a comparative review of recent progress on the design, modeling, optimization, and control aspects of valves for gas expanders. A clear pathway on the scope of further development is also drawn based on the present state of the art.

There is a growing need to implement modern technologies, such as digital twinning, to improve the efficiency of transport fleet maintenance processes and maintain the company's operational capacity at the required level. Therefore, the paper reviews the existing literature to present an up-to-date and content-relevant analysis in this field. The proposed methodology is a systematic literature review using the Primo multi-search tool following Preferred Reporting Items for Systematic Reviews and Meta-Analyzes (PRISMA) guidelines. The main inclusion criteria included the publication dates (studies published from 2012–2024) and studies published in English. This resulted in the selection of 201 of the most relevant papers in the area of investigation. Finally, selected articles in this review were categorized into seven groups: a) air transportation, b) railway transportation, c) land transportation (road), d) in-house logistics, e) water and intermodal transportation, f) supply chain operation, and g) other applications. One of the main advantages of the study is that the results are obtained from different scientific sources/databases thanks to using the multi-search tool. Moreover, a bibliometric analysis of the selected papers was performed. The results have led the authors to specify the main research problems and trends related to the analyzed area and to identify the main research gaps for future investigation from academic and engineering perspectives. In addition, based on the review results, a framework for DT in transportation system maintenance was developed. The study ends with conclusions and future research directions.

This study investigates the microstructural evolution and mechanical properties of high-nickel shipbuilding steel during thermal processing using High Temperature Confocal Laser Scanning Microscopy (HTCLSM). The in-situ observation of the heating and holding processes reveals critical insights into phase transformations, grain growth behavior, and the formation of precipitates. The experimental results demonstrate that austenitization begins at approximately 700°C, with significant grain boundary nucleation. At 900°C, the formation of black precipitates was observed, and their persistence up to temperatures exceeding 1000°C was confirmed. Scanning Electron Microscopy (SEM) and Energy Dispersive Spectroscopy (EDS) analyses identified these precipitates as chromium carbides (Cr7C3), which significantly contribute to the material's strength. The comprehensive analysis using Transmission Electron Microscopy (TEM) confirmed the presence and distribution of Cr7C3 within the grains and along grain boundaries. These findings provide a deeper understanding of the microstructural dynamics in high-nickel steels, guiding the optimization of heat treatment processes to enhance mechanical properties for maritime applications.

The task of automatically and intelligently diagnosing faults in marine equipment is of great significance due to the numerous duties that shipboard professionals must handle. Incorporating automated and intelligent systems on ships allows for more efficient equipment monitoring and better decision-making. This approach has attracted considerable interest in both academia and industry because of its potential for economic savings and improved safety. Several fault diagnosis methods are documented in the literature, often involving mathematical and control theory models. However, due to the inherent complexity of some processes, not all characteristics are precisely known, making mathematical modeling highly challenging. As a result, fault diagnosis often depends on data or heuristic information. Fuzzy logic theory is particularly well-suited for processing this type of information. Therefore, this paper employs fuzzy models to diagnose faults in a marine pneumatic servo-actuated valve. The proposed fault diagnosis framework analyzes the discrepancy signals between the outputs of the fuzzy models and the actual process outputs. These discrepancies, known as residuals, help in detecting and isolating equipment faults. The fault isolation process uses an intelligent decision-making approach to determine the specific fault in the system. This method is applied to diagnose abrupt faults in a marine pneumatic servo-actuated valve.

Optical Wireless Communication (OWC) technology has gained significant attention in recent years due to its potential to provide high data rate wireless connections through the large license-free bandwidth available. A key challenge in OWC systems, similar to high-frequency Radio Frequency (RF) systems, is the presence of dead zones caused by obstacles like buildings, trees, and moving individuals, which can degrade signal quality or disrupt data transmission. Traditionally, relays have been used to mitigate these issues. Intelligent Reflecting Surfaces (IRS) have recently emerged as a promising solution, enhancing system performance and flexibility by providing reconfigurable communication channels. This paper presents an overview of the application of IRS in OWC systems. Specifically, we categorize IRS into two main types: mirror array-based IRS and metasurfaces-based IRS. Furthermore, we delve into modeling approaches of mirror array-based IRS in OWC and analyze recent advances in IRS control, which are classified into system power or gain optimization oriented, system link reliability optimization oriented, system data rate optimization oriented, and system security optimization oriented approaches. Finally, we discuss the key challenges and potential future directions for integrating IRS with OWC systems, providing insights for further research in this promising field.

This research evaluates the effect of varying hexagonal opening angles on the flexural and shear capacity of castellated beams. This study is conducted through experimental and numerical approaches using WF 150.75.5.7 steel profiles. The hexagonal opening angles are varied to 20°, 30°, 45°, 55°, and 60°. Experimental testing results show that beams with a 20° opening angle have higher flexural and shear capacities compared to larger opening angles. Numerical testing results are consistent with experimental results, showing more uniform deformation patterns and stress distribution at smaller opening angles. These findings contribute significantly to the design of more efficient and effective castellated beams in modern structural applications, particularly in constructions requiring high flexural and shear capacities.

Matters relating to preservation of environmental resources and climate change, social inclusion and diversity, and open and sound governance have rapidly gained global attention. However, little is known about the construction industry in meeting the environmental, social and governance (ESG) criteria towards sustainable development. In this study, the purpose is to identify the key ESG indicators for construction development. A systematic literature review was undertaken using data from Scopus, PubMed, Google Scholar, JSTOR, ProQuest, and Web of Science. The results show some of the key ESG indicators as climate resilience and green construction activities (at the project level), and active stakeholder engagement and effective project governance (at the firm level) and meeting environmental and social regulation requirements (industry/national level). The limitation of the article is in the sample size of twenty-six journal articles with future expectation of increment as ESG gain more acceptance in the construction industry. This study will be significant for the design of practice framework and policies on ESG in the construction industry. Comprehensively, this study presents relevant ESG indicators for construction management and presents essential information for future research.

Image processing is a computationally intensive task that can benefit significantly from parallel techniques. In this study, we evaluate the efficiency of image processing using parallel techniques compared to sequential techniques. We use Dask to parallelize the loading and processing of images and compare the execution times for various image processing techniques, including smoothing, edge detection, rotation, and thresholding. The results show that parallel processing significantly reduces execution time compared to sequential processing.

This study investigated the effects of icon familiarity on visual search performance. An equal number of familiar and unfamiliar icons were selected as background icons. These background icons were randomly arranged, and a visual search experiment was conducted to locate the target icon. The results showed no significant difference in the time required to find the target icon, regardless of whether the target icon was familiar or unfamiliar. This finding contrasts with previous studies that have suggested a positive effect of target icon familiarity on visual search performance, indicating that the influence of target icon familiarity may not always be consistent and could depend on the characteristics of the background icons. However, when background icons were grouped based on their familiarity, the time taken to locate the target icon was significantly reduced compared to when the icons were randomly arranged. To understand the underlying mechanism of this effect, participants' gaze fixation times were observed. The results revealed that participants spent more time fixating on areas where the target icon was located, rather than on areas without the target icon, suggesting a more efficient visual search process. Moreover, the shorter the visual search time, the higher the proportion of time spent fixating on the area containing the target icon. Additionally, this study examined the contrast ratio between the color of the background space and the dominant color of the target icon, comparing it with the visual search time. The findings indicated that as the contrast ratio increased, the visual search time decreased.

This article presents the theoretical study, numerical simulation and the fabrication of a phase shifter and stub resonator in microstrip ridge gap waveguide (MGCW) technology with the use of liquid crystal (LC) in its substrate as a reconfigurable material. The phase shifter and the stub resonator are filled with LC and thanks to LC's dielectric anisotropy properties, the phase shift and resonance response can be easily controlled using an external electric or magnetic bias field. The phase shifter was designed to operate in the range of 10 to 20 GHz and the resonator was designed to operate in the range of 7.8 to 8.8 GHz. The phase shifter's insertion losses, associated with both parallel and perpendicular LC's permittivity, as well as the phase response were computed and measured. Finally, the figure of merit (FoM) was extracted. The resonator's frequency response, associated with both parallel and perpendicular LC's permittivity, was computed. The resonator's frequency responses, provided different polarization voltages, were measured and compared to the simulation results. The technological issues were discussed. The good agreement between simulation and measurement results establishes this technology as a viable approach to the development of RF reconfigurable devices.

The fuel prices are increasing nowadays, causing a burden on the power system. From this, diesel generators that are still in use, must be hybridized with renewable energy (RE) to levelized the overall projects costs. This study aims to schedule an operation of a hybrid system photovoltaic (PV)/ diesel in a remote area which located in Wadi Rum district in Jordan to serve factory load in addition to obtain the best economical system combination that can serve the needs of a continuous electrical load without any interruption. Based on the results of the analysis using HOMER software, the configuration in the system will be a combination between PV modules, and diesel generator. The hybrid system has met the target for electricity production which is documented in upcoming sections.

The transition to renewable energy sources is critical for sustainable development, particularly in rural regions with limited access to electricity. Solar-powered microgrids offer a promising solution for rural electrification by providing reliable, clean energy that can enhance economic opportunities and improve quality of life. This paper presents a techno-economic analysis of solar-powered microgrids for rural areas, evaluating their feasibility, costs, and benefits. The analysis encompasses technological aspects, such as solar panel efficiency, battery storage, and grid management, alongside economic factors including initial investment, maintenance costs, and potential returns. Additionally, the study assesses the social impact of solar-powered microgrids, exploring their effects on local communities, including improvements in education, health, and economic activities. The findings indicate that solar microgrids can be a viable and impactful solution for rural electrification, with significant long-term benefits for both economic development and social well-being.

The dominant part of energy performance of a building consists of consumption of heating and lighting. Both heating and lighting systems of buildings work at their designed efficiency for most of the buildings’ lifetimes. Interference with existing systems is costly considering replacements and construction adjustments. Therefore, considerable effort must be put into the design of these systems during the building design phase. The article is focused on luminaire layout design strategy, which affects the number of luminaires in a building and therefore their power consumption and the sustainability of the building. A genetic algorithm with radiosity implemented has been used to find suitable placements of luminaires of a single type in a model room to decrease the number of luminaires as much as possible. Three outcomes of optimal luminaire layout design are presented in this paper, including design strategy conclusions. Results of calculation outcomes are verified by software DIALux, that is commonly used for designing lighting systems.

One of the main products of the forest, which has the greatest economic impact, is wood. Due to its characteristics, the wood requires certain types of vehicles for transport it. For this reason, the accessibility of the stands with forest roads plays significant role in the efficiency of the wood logging process, which aims to bring the wood from the forest to the harvesting area located, frequently, next to a permanent transport route. The process of wood transport from the forest initially takes place on forest roads and after that on public roads. The technological process of wood transport represents an important stage in this process, and its good organization can have implications on the final price of the wood material. Based on those mentioned above, the paper aims to highlight the factors that can influence productivity in the case of loading wood in forestry trucks and forestry platforms, in different conditions. Thus, the research was carried out in four counties in Romania and addressed the loading of wood in different forestry trucks and platforms, with or without trailers. Following the evaluation of the work phases specific to the process of round wood loading in vehicles of transport, it was found that sometimes a significant importance is given to the working phases that can be carried out before the loading process, as phases of preparation of the wooden material in order to load it. Also, the facilities of the transport vehicles, the assortments of wood material, as well as the experience of the operators are the main factors that can lead to an increase in productivity.

This paper introduces an innovative approach that enables automated and precise prediction of the steel's chemical composition based on the desired Jominy curve. The microstructure, and in fact the presence of martensite, is decisive for the hardness of the steel, so the study considered the occurrence of this phase at particular distances from the quenched-end of the Jominy sample. Steels for quenching and tempering and case hardening were investigated. With representative collected dataset of hardness values from quenched-end of Jominy specimen, microstructure and chemical composition of steels, the complex regression model was made using supervised artificial neural networks. The balance between cost and required hardenability can be achieved through optimizing the chemical composition of steel. This model of designing steel with required hardenability can be of great benefit in the mechanical engineering and manufacturing industry. The model is verified experimentally.

Object detection systems are widely used in various fields such as autonomous vehicles and facial recognition. In particular, object detection using deep learning networks enables real-time processing in low-performance edge devices, and can maintain high detection rates. However, edge devices that operate far from administrators are vulnerable to various physical attacks by malicious adversaries. In this paper, we implement a function for detecting traffic signs by using three versions of You Only Look Once (YOLO) and Faster-RCNN, which can be adopted by edge devices of autonomous vehicles. Then, assuming the role of a malicious attacker, we executed adversarial patch attacks with Adv-Patch and Dpatch. As a result of experimenting with misdetection of traffic stop signs using Adv-Patch and Dpatch, we confirmed the attacks can succeed with a high probability. To defeat these attacks, we propose an image reconstruction method using an autoencoder and the Structural Similarity Index Measure (SSIM). We confirm that the proposed method can sufficiently defend against an attack, attaining an AP above 91.46% even when two adversarial attacks are launched.

This study constructs a comprehensive line loss management evaluation knowledge graph, considering the impact of various factors such as power grid structure characteristics, equipment's physical parameters, power grid operation characteristics, electricity consumption features, and natural conditions. It establishes the association between line loss characteristics and line loss retrieval and configures the data source for line loss management evaluation indicators. Based on Principal Component Analysis (PCA), this study selects comprehensive evaluation indicators for line loss management, verifies the rationality of the indicators, and proposes an integrated evaluation method for municipal power grid line loss management. A full-process composite evaluation method based on DEMATEL-Entropy Weight-TOPSIS is constructed, and the accuracy of the evaluation method is verified through ablation experiments. The TOPSIS analysis method is applied to empirical research on cross-sectional data from a city in Guangxi from 2012 to 2022 to validate the evaluation system's effectiveness, scientific nature, and objectivity. The case analysis shows that the line loss evaluation index system established in this paper effectively reduces the redundancy of line loss data and can objectively and rationally reflect the actual status of line loss management work, serving as a basis for decision-making in future line loss management evaluations.

Some wrong decisions and faulty practices applied during the repair and restoration of traditional buildings cause more damage to the structures due to the materials used in the repair. In this study, brick, mortar, plaster, and wood samples were taken from one registered building in the Konuralp neighborhood of Düzce Province, which has survived to the present day by preserving its original structural features and reflecting the characteristics of traditional housing. Texture, chemical and petrographic analyses were carried out on the samples. In line with these analyses, a scientific basis was created for selecting material properties in the repair and reuse processes of traditional buildings, and suggestions are made for the analysis of materials specific to traditional buildings in Konuralp.

Perennial ryegrass (Lolium perenne) is an underutilized lignocellulosic biomass that has several benefits such as high availability, renewability, and biomass yield. The grass press-juice obtained from the mechanical pretreatment can be used for the bio-based production of chemicals. Lactic acid is a platform chemical that has attracted consideration due to the broad area of applications. For this reason, a more sustainable production of lactic acid is expected to increase. In this work, lactic acid was produced using complex medium at bench- and reactor scale, and the results were compared to those obtained using an optimized press-juice medium. Bench-scale fermentations were carried out in a pH-control system and lactic acid production reached about 22 g/L in com-plex medium, and 26.61 ± 1.2 g/L in press-juice medium. In bioreactor, the production yield was 0.91 ± 0.07 g/g, corresponding to 1.4-fold increase respect to the complex medium with fructose. As comparison to the traditional ensiling process, whole grass fractions of different varieties har-vested in summer and autumn was performed. Ensiling showed variations in lactic acid yields, with a yield up to 15.2% dry mass for the late-harvested samples, surpassing typical silage yields of 6-10% dry mass.

An energy production system consisting solar collector, biogas dry reforming reactor and solid oxide fuel cell (SOFC) was assumed to be installed in Kolkata, India. This study aims to understand the impact of climate condition on the performance of solar collectors with different lengths of parabolic trough solar collector (dx) and mass flow rate of heat transfer fluid (m). In addition, this study has evaluated the amount of H2 produced by biogas dry reforming (GH2), the amount of power generated by SOFC (PSOFC) and the maximum possible households (N) whose electricity demand could be met by the energy system proposed, considering the performance of solar collector with the different dx and m. As a result, the optimum dx was found to be 4 m. This study revealed that temperature of heat transfer fluid (Tfb) decreased with the increase in m. Tfb in March, April and May was higher than that in other months, while Tfb from June to December was the lowest. GH2, PSOFC and N in March, April and May were higher than that in other months irrespective of m. The optimum m was 0.030 kg/s.