Optimization Process Research Articles

Optimal energy generation of hybrid energy systems considering economic and environmental multi-objective functions

This research is dedicated to exploring and identifying the most effective design for an energy source tailored specifically to meet the electricity demands of a residential community. In an era where energy efficiency and sustainability are paramount, this study emphasizes the importance of both technical and economic considerations in energy sourcing. It posits that any viable solution must not only be efficient in its energy production and consumption but also reliable in its delivery and financially feasible for the residents who will depend on it. To address this multifaceted challenge, the study proposes the innovative use of a rotation-invariant coordinate convolutional neural network in conjunction with binary battle royale optimization techniques. These advanced methodologies are selected for their potential to enhance the modelling and optimization processes involved in energy source design. The primary goal of employing these methods is to minimize two critical factors: the net present cost of the energy system and the overall energy cost incurred by the residents. By focusing on these objectives, the research aims to ensure that the proposed energy solutions are not only cost-effective but also sustainable over the long term. To rigorously test the proposed model and evaluate its performance, the research is conducted using the MATLAB platform. The study employs established methodologies and performance metrics to assess the outcomes of the model, ensuring that the findings are both credible and applicable to real-world scenarios. Through comprehensive testing and detailed analysis, this research aims to provide significant insights and actionable recommendations for the optimal design of energy sources in residential areas. By contributing to the ongoing discourse on sustainable energy solutions, the study seeks to inform policymakers, energy planners, and community stakeholders about effective strategies for meeting residential energy demands while promoting environmental sustainability. Ultimately, the findings of this research could play a crucial role in shaping the future of energy sourcing in residential communities, paving the way for more resilient and sustainable energy systems

A Lagrange barrier approach for the minimum concave cost supply problem via a logarithmic descent direction algorithm

The minimisation of concave costs in the supply chain presents a challenging non-deterministic polynomial (NP) optimisation problem, widely applicable in industrial and management engineering. To approximate solutions to this problem, we propose a logarithmic descent direction algorithm (LDDA) that utilises the Lagrange logarithmic barrier function. As the barrier variable decreases from a high positive value to zero, the algorithm is capable of tracking the minimal track of the logarithmic barrier function, thereby obtaining top-quality solutions. The Lagrange function is utilised to handle linear equality constraints, whilst the logarithmic barrier function compels the solution towards the global or near-global optimum. Within this concave cost supply model, a logarithmic descent direction is constructed, and an iterative optimisation process for the algorithm is proposed. A corresponding Lyapunov function naturally emerges from this descent direction, thus ensuring convergence of the proposed algorithm. Numerical results demonstrate the effectiveness of the algorithm.

Lie-theory-based dynamic model identification of serial robots considering nonlinear friction and optimal excitation trajectory

Abstract Accurate dynamic model is essential for the model-based control of robotic systems. However, on the one hand, the nonlinearity of the friction is seldom treated in robot dynamics. On the other hand, few of the previous studies reasonably balance the calculation time-consuming and the quality for the excitation trajectory optimization. To address these challenges, this article gives a Lie-theory-based dynamic modeling scheme of multi-degree-of-freedom (DoF) serial robots involving nonlinear friction and excitation trajectory optimization. First, we introduce two coefficients to describe the Stribeck characteristics of Coulomb and static friction and consider the dependency of friction on load torque, so as to propose an improved Stribeck friction model. Whereafter, the improved friction model is simplified in a no-load scenario, a novel nonlinear dynamic model is linearized to capture the features of viscous friction across the entire velocity range. Additionally, a new optimization algorithm of excitation trajectories is presented considering the benefits of three different optimization criteria to design the optimal excitation trajectory. On the basis of the above, we retrieve a feasible dynamic parameter set of serial robots through the hybrid least square algorithm. Finally, our research is supported by simulation and experimental analyses of different combinations on the seven-DoF Franka Emika robot. The results show that the proposed friction has better accuracy performance, and the modified optimization algorithm can reduce the overall time required for the optimization process while maintaining the quality of the identification results.

Optimized design of a fault-tolerant 12-slot/10-pole six-phase surface permanent magnet motor with asymmetrical winding configuration for electric vehicles

This paper presents a comprehensive methodology for optimizing the design of a 12-slot/10-pole permanent magnet (PM) motor with a six-phase winding configuration tailored for electric vehicles (EVs). The design aims to enhance motor performance under both healthy and fault conditions. While the single neutral configuration offers superior torque during faults, it also introduces zero sequence currents and additional space harmonics, which can lead to increased torque ripple that is difficult to control. This study addresses these challenges through innovative machine design optimization. The optimization process begins with sizing equations to establish an initial design. K-means clustering techniques are then employed to identify distinct loading points that accurately represent the full EV driving cycle, effectively minimizing computational power requirements. Following this, the Full Range Minimum Loss (FRML) strategy is applied to determine optimal current profiles across these loading points, significantly reducing copper losses. Finally, a multi-objective optimization approach is utilized to minimize torque ripple, enhance average torque, and optimize machine losses. The results demonstrate substantial improvements in torque and reduced ripple, validated through experiments conducted with a 2 kW lab-scale motor. This integrated approach not only ensures a robust and efficient motor design but also enhances fault tolerance, making it well-suited for advanced EV applications.

Grid-search method for short-term over long-term average parameter tuning: an application to Stromboli explosion quakes

The collection of a significant catalogue of seismo-volcanic data involves the selection of relevant parts of raw signals, which can be automatised by using the short-term over long-term average (STA/LTA) method. The STA/LTA method employs the “Characteristic Function” to describe a section of a seismic record in terms of trace amplitude and first-time difference. This function is calculated in a short-term and long-term window; the ratio between the two windows defines a quantity that is controlled through threshold values, i.e., trigger on and trigger off. These threshold values indicate whether there is an increase in the energy in the seismic signal compared to the background noise. The common approach to the selection of the STA/LTA values is the adoption of literature-suggested ones. This could be a limitation as there may be cases in which a choice adapted to a specific raw signal may significantly help in the extraction of the relevant parts. To overcome the possible drawbacks of a non-adaptive choice imposed by such standard literature values, in this study, we propose a methodology for the automatic selection of STA/LTA values that can optimise the extraction of explosion quakes (EQs) from a seismo-volcanic raw signal. The values are obtained through a grid search over an index named quality–numerosity index (QNI) that measures the accordance in the automatic cuts and the consequent number of triggered seismo-volcanic events with the ones suggested by a human expert. The method was applied in the volcano domain for the specific application of the explosion quake signal extraction at Stromboli volcano. The experiments were conducted by selecting a subset of the dataset as training where to search for the best values, which were subsequently adopted in a test set. The results prove that the values suggested by our approach significantly improve the quality of the relevant part compared to the one extracted by adopting the values indicated in the literature. The methodology presented in this study can be applied to a wider typology of signals of volcanic, seismic, and other origin, potentially becoming a widely used approach in parameter optimisation processes.

Replacing manual planning with automatic iterative planning for locally advanced rectal cancer VMAT treatment.

To develop and implement a fully automatic iterative planning (AIP) system in the clinical practice, generating volumetric-modulated arc therapy plans combined with simultaneous integrated boost technique VMAT (SIB-VMAT) for locally advanced rectal cancer (LARC) patients. The designed AIP system aimed to automate the entire planning process through a web-based service, includingauxiliary structuregeneration,plan creation,field configuration,plan optimization,dose calculation, andplan assessment. The system was implemented based on theEclipse scriptingapplication programming interface and an efficientiterative optimization algorithm was proposed to reduce the required iterations in the optimization process. To verify the performance of the implemented AIP system, we retrospectively selected a total of 106 patients and performed dosimetric comparisons between the automatic plans (APs) and the manual plans (MPs), in terms of dose-volume histogram (DVH) metrics, homogeneity index (HI), and conformity index (CI) for different volumes of interest. The AIP system has successfully created 106 APs within clinically acceptable timeframes. The average planning time per case was 36.8±6.5 min, with an average iteration number of 6.8(±1.1) in plan optimization. Compared to MPs, APs exhibited better performance in the planning target volume conformity and hotspot control ( ). The organs at risk (OARs) sparing was significantly improved in APs, with mean dose reductions in the femoral heads, the bone marrow, and the SmallBowel-Avoid of 0.53Gy, 1.18Gy, and 1.00Gy, respectively ( ). Slight improvement was also observed in the urinary bladder and the small bowel . Additionally, quality variation between plans from different planners was observed in DVH metrics while the APs represented better plan quality consistency. An AIP system has been implemented and integrated into the clinical treatment planning workflow. The AIP-generated SIB-VMAT plans for LARC have demonstrated superior plan quality and consistency compared with the manual counterparts. In the meantime, the planning time has been reduced by the AIP approach. Based on the reported results, the implemented AIP framework has been proven to improve plan quality and planning efficiency, liberating planners from the laborious parameter-tuning in the optimization phase.

Compact Ultra-Wideband MIMO Antenna for 5G Communication Systems

Ultra-wideband (UWB) technology is highly respected in modern communication systems because it can enable high-speed, high-data-rate communication over short distances. It is still difficult to achieve optimal multiple-input multiple-output (MIMO) performance in real-world multipath settings, even with breakthroughs in traditional antenna designs. UWB-MIMO antenna solutions have been created and simulated in order to address this problem and fulfill present demands. Nevertheless, maintaining high antenna efficiency and the requirement for antenna designs tailored to a particular bandwidth are two of the difficulties UWB systems must overcome. Recently, a variety of optimization strategies have been employed to obtain high gain and UWB performance by fine-tuning antenna characteristics. The major obstacles are resolved by the optimization process, including the time and computational resource requirements. The difficulty is further compounded by the intricacy of accurately modifying antenna settings to maximize gain. A multi-objective advancement for the design and improvement of the reduced UWB-MIMO test radio wire for 5G was offered as a solution to these problems. In order to attain the UWB feature, we first aim to use a single elliptical-square form antenna that operates from 3.4 to 70 GHz with isolation larger than 29.54 dB. Furthermore, we optimize the impedance bandwidth by tuning the parameters of the structural dimensions using the improved proportional topology optimization (IPTO) technique. Additionally, we present the orthogonal correction using the binarized convolutional neural network (BCNN).

Bayesian Optimization for Controlled Chemical Vapor Deposition Growth of WS2.

We applied Bayesian optimization (BO), a machine learning (ML) technique, to optimize the growth conditions of monolayer WS2 using photoluminescence (PL) intensity as the objective function. Through iterative experiments guided by BO, an improvement of 86.6% in PL intensity is achieved within 13 optimization rounds. Statistical analysis revealed the relationships between growth conditions and PL intensity, highlighting the importance of critical conditions, including the tungsten source concentration and Ar flow rate. Furthermore, the effectiveness of BO is demonstrated by comparison with random search, showing its ability to converge to optimal conditions with fewer iterations. This research highlights the potential of ML-driven approaches in accelerating material synthesis and optimization processes, paving the way for advances in two-dimensional (2D) material-based technologies.

A Composite Control Method Based on Model Predictive Control and a Disturbance Observer for the Acquisition, Tracking, and Pointing System

Nonlinear disturbances, such as friction, backlash, and base vibration, are the main factors restricting the further improvement of the dynamic performance of the Acquisition, Tracking and Pointing (ATP) system. However, the modeling and compensation of the time-varying nonlinearities are still challenging. In this paper, a compound control algorithm based on model predictive control (MPC) and a Proportional Multiple-Integral State-Augmented Kalman Filter (PMISAKF) disturbance observer is proposed to improve the disturbance rejection performance. Specifically, a PMISAKF is used to estimate the system’s state and external disturbances in real-time. These observed disturbances are then treated as known external inputs at the current or future time points and incorporated into the predictive model of MPC. This allows the optimization process to take these external factors into consideration, enabling the calculation of an optimal control strategy and achieving high pointing and tracking accuracy for nonlinear time-varying disturbances. Experimental results show that the proposed method can effectively improve the system’s anti-interference ability and tracking accuracy.

Modelling and optimizing the impact resistance of engineered cementitious composites with Multiwalled carbon nanotubes using response surface methodology

Engineered Cementitious Composites (ECC) are highly regarded in construction owing to their tensile ductility and crack control capabilities, making them suitable for various structural applications. The accumulation of multi-walled carbon nanotubes (MWCNTs) further enhances their mechanical properties. However, there’s a significant knowledge gap concerning MWCNTs-ECC impact resistance. The objective of this study is to tackle the challenges associated with evaluating, optimizing, and predicting MWCNTs-ECC impact resistance to ensure its safe and widespread use in critical infrastructure by applying response surface methodology (RSM). Moreover, the 13 mixtures of ECC combined with several quantities of PVA fiber and MWCNTs as input elements were utilized to calculate the first (E1) and final (E2) impact energies. The findings demonstrated that the MWCNTs-ECC combinations’ impact resistance improved as the input ingredient concentrations increased. Besides, the optimum E1 and E2 of ECC combined with 1% of PVA fiber were noted by 1398 Joules and 12,956 Joules at 0.065% of MWCNTs on 28 days respectively. Furthermore, Response prediction models for E1 and E2 were created, and after being validated with an analysis of variance (ANOVA), it was determined that they had high R2 readings of 99.30% and 99.07%, correspondingly. The optimization process produced an ideal number of input variables for MWCNTs and PVA fiber, respectively, of 0.066% and 1%, with a desirability value of 100%. Moreover, it is recommended that the usage of 0.066% of MWCNTs in ECC combined with 1.0–1.50% PVA fiber provides optimum results for the construction industry.

Optimizing Exopolysaccharide Production by Bacillus subtilis Using Spoiled Fig and Grape.

The study aimed to enhance exopolysaccharides (EPSs) production by the bacterial strain Bacillus subtilis ES (OR501464) isolated from sugar cane juice. Spoiled grape and fig extract were utilized as cost-effective substrates for EPS synthesis by B. subtilis ES (OR501464), and the impact of nutritional factors on EPS synthesis was assessed. Among nineteen bacterial isolates evaluated for EPS production, the isolate with the highest EPS yield was identified through a combination of phenotypic and genotypic analyses. The optimization process revealed that the highest EPS yield of 4.7g/L was achieved in a production medium containing 4% sucrose, 0.1% NaNO3, 0.002% Na2SO4, and 2% NaCl at 30°C and pH 9. Additionally, the study explored EPS generation by B. subtilis ES (OR501464) using spoiled grape and fig extract as substrates. The addition of 2% NaCl to spoiled grape extract increased EPS production to 4.357mg/mL compared to 3.977mg/mL with grape alone. However, 2% NaCl did not enhance EPSs production in fig waste. Supplementing spoiled fig or grape extract with 0.2g/L Na2SO4 and 1g/L NaNO3 increased EPS production by B. subtilis ES (OR501464). The EPS was analyzed using GC-MS and FTIR spectroscopy for partial characterization. The study found that spoiled figs and grapes can be used as effective substrates for EPS production. The highest yield was achieved by adding 0.2g/L Na2SO4 and 1g/L NaNO3. This study highlights the use of spoiled figs and grapes to produce valuable biopolymers, promoting sustainable and eco-friendly bioprocessing technologies.

Optimization of Connection Parameters of Self-Adaptive Modular Floating Wind Farms

Connection parameters are the key factors influencing the responses of modular floating structures; due to the complexity of structural response properties, the assessment and optimization of connection parameters are still vital issues in designing modular floating structures. In the present work, for a novel structural configuration of self-adaptive modular floating wind farms based on existing works from the case of a mobile offshore base, a quantified approach for the assessment of connection parameters is established based on frequency domain numerical analysis taking into account both the economic effects and generalized performance. Based on the quantified assessment, an optimization process is carried out, to obtain a connection parameter combination. From the optimized connection parameters, it can be found that the most appropriate tri-axial stiffness according to present studies is in the magnitude from 1 × 106 to 7 × 107 N/m, and the damping ratio is close to 1.0 for most connection structures. By contrast with feasible uniform connection parameters, the optimization methodology is confirmed to be able to reduce both connection parameters and responses. Then, a time domain approach for the calculation of motions of interconnected modular floating structures taking into account geometry nonlinearity is proposed and obtained in good accordance with the frequency domain results, and the effectiveness of both the frequency domain and time domain is validated.

Automatic Design of Robot Swarms under Concurrent Design Criteria: A Study Based on Iterated F‐Race

Automatic design is an appealing approach to realizing robot swarms. In this approach, a designer specifies a mission that the swarm must perform, and an optimization algorithm searches for the control software that enables the robots to perform the given mission. Traditionally, research in automatic design has focused on missions specified by a single design criterion, adopting methods based on single‐objective optimization algorithms. In this study, we investigate whether existing methods can be adapted to address missions specified by concurrent design criteria. We focus on the bi‐criteria case. We conduct experiments with a swarm of e‐puck robots that must perform sequences of two missions: each mission in the sequence is an independent design criterion that the automatic method must handle during the optimization process. We consider modular and neuroevolutionary methods that aggregate concurrent criteria via the weighted sum, hypervolume, or ‐norm. We compare their performance with that of Mandarina, an original automatic modular design method. Mandarina integrates Iterated F‐race as an optimization algorithm to conduct the design process without aggregating the design criteria. Results from realistic simulations and demonstrations with physical robots show that the best results are obtained with modular methods and when the design criteria are not aggregated.

Effect of graphene on the key electrical, optical, and magnetic properties of polymethylmethacrylate: a study based on molecular modeling.

In this work, a new polymeric structure was designed consisting of a nanometric sheet of graphene (G) and a polymethylmethacrylate (PMMA) repeat unit, which was designated as PMMA-G. Three degrees of polymerization of PMMA-G were considered: monomer (PMMA-G1), dimer (PMMA-G2), and trimer (PMMA-G3). The effect of incorporating a nanometric sheet of graphene into the molecular structure of PMMA on the modification of some of its main optical, magnetic, and electrical properties was investigated. Currently, the study presented here is of great relevance since various areas of technology require new materials with specific properties for the development of new devices. The results of our study reveal that the dielectric constant of PMMA is reduced when graphene is incorporated. However, a percentage increase of 14.48% in the refractive index of PMMA when graphene is inserted to form the nanocomposite is observed. It is found that the absolute value of molar magnetic susceptibility of PMMA increases considerably when reinforced with graphene. Finally, when reinforcing PMMA with graphene to obtain the PMMA-G nanocomposite, the electrical resistivity increases by almost an order of magnitude. We used computational tools under Materials Studio (MS) software. We built a PMMA molecule with three degrees of polymerization, graphene sheet, and polymethylmethacrylate-graphene composite (PMMA-G) was built also with three degrees of polymerization using a concentration of 50% graphene over the PMMA polymer. For each structure, we used computational code DMol3 of MS, which is based on the Density Functional Theory, and the geometry optimization process was carried out to obtain the most stable structures. Finally, using the connectivity indices method together with topological properties of the molecular structures, implemented in Synthia computational code of MS software, we calculated the dielectric constant, magnetic susceptibility, refractive index, and electrical resistivity, for pure PMMA and PMMA-G structures for their three degrees of polymerization. The results were analyzed, and the changes in these properties were discussed in terms of the effect of an electric and magnetic field on the molecular structures of PMMA-G with respect to PMMA.

Optimization of ultrasonic-assisted debittering of Ganoderma lucidum using response surface methodology, characterization, and evaluation of antioxidant activity.

Ganoderma lucidum (G. lucidum) has gained increasing attention as a potential health care product and food source. However, the bitter taste of G. lucidum has limited its development and utilization for the food industry. The response surface methodology was employed to optimize the inclusion conditions for the debittering of G. lucidum. The effects of 2-hydroxypropyl-β-cyclodextrin concentration (12-14 g/mL), ultrasound temperature (20-40°C and host-guest ratio (1:1-2:1) on response variables were studied. The physical characteristics of inclusion complexes prepared through spray drying and freeze drying were analyzed. The antioxidant activity of the different treated samples was subsequently investigated. Study results showed that, in comparison to the control group, the inclusion solution displayed a significantly enhanced taste profile under optimal processing conditions, exhibiting an 80.74% reduction in bitterness value. Fourier transform infrared spectroscopy (FTIR) and proton nuclear magnetic resonance (NMR) studies indicated the successful formation of inclusion compounds. The moisture content and bulk density of spray-dried powder were found to be significantly superior to those of freeze-dried powder (p<0.05). In comparison to the diluted solution, the inclusion liquid demonstrated a 20.27%, 30.01% and 36.55% increase in ferric ion reducing antioxidant power (FRAP), hydroxyl radical scavenging and 2,2-diphenyl-1-picrylhydrazyl radical (DPPH) scavenging respectively. Further, the DPPH clearance of microencapsulated powder was not significantly different from that of tocopherol at a concentration of 25 mg/mL. In summary, the study provides theoretical basis and methodological guidance to eliminate the bitterness of G. lucidum, and therefore provide potential options to the use of G. lucidum as a food source.

Simultaneous Energy Optimization of Heating Systems by Multi-Zone Predictive Control—Application to a Residential Building

Climate change has made energy management a global priority. In France, the Grenelle Environment has set very ambitious progress targets for positive-energy buildings, particularly in terms of reducing and managing energy consumption. However, effective energy management in multi-zone buildings presents significant challenges, particularly when considering the inter-zone dynamics and heat transfer. This study examines multi-zone heating control, using a data-driven model for predictive indoor temperature modeling in intelligent buildings taking into account the influence of interconnected adjacent zones. The research methodology uses dynamic thermal simulation, parallel predictive models based on multiple linear regressions, and a multi-objective non-dominated sorting genetic algorithm II (NSGA-II) for the optimization process, which evaluates various generated heating strategies. This research introduces an approach to improve building energy efficiency by considering inter-zone dynamics and reducing heating-related energy consumption compared to a conventional heating strategy. By applying this model predictive control on a simulated case, a reduction in energy consumption due to heating is observed while respecting thermal comfort. This work contributes by implementing a method that independently controls temperatures in different building zones simultaneously while applying distinct constraints to each zone. This approach empowers occupants to manage heating consumption based on their preferences, ensuring personalized comfort. In addition, a comparison was made using a model that did not account for inter-zone interactions. This comparison demonstrates that incorporating these interactions into the predictive model enhances the effectiveness of the model predictive control approach. The multi-zone approach was also validated experimentally by using real experimental data, demonstrating significant reductions in energy consumption.

Map Construction and Positioning Method for LiDAR SLAM-Based Navigation of an Agricultural Field Inspection Robot

In agricultural field inspection robots, constructing accurate environmental maps and achieving precise localization are essential for effective Light Detection And Ranging (LiDAR) Simultaneous Localization And Mapping (SLAM) navigation. However, navigating in occluded environments, such as mapping distortion and substantial cumulative errors, presents challenges. Although current filter-based algorithms and graph optimization-based algorithms are exceptionally outstanding, they exhibit a high degree of complexity. This paper aims to investigate precise mapping and localization methods for robots, facilitating accurate LiDAR SLAM navigation in agricultural environments characterized by occlusions. Initially, a LiDAR SLAM point cloud mapping scheme is proposed based on the LiDAR Odometry And Mapping (LOAM) framework, tailored to the operational requirements of the robot. Then, the GNU Image Manipulation Program (GIMP) is employed for map optimization. This approach simplifies the map optimization process for autonomous navigation systems and aids in converting the Costmap. Finally, the Adaptive Monte Carlo Localization (AMCL) method is implemented for the robot’s positioning, using sensor data from the robot. Experimental results highlight that during outdoor navigation tests, when the robot operates at a speed of 1.6 m/s, the average error between the mapped values and actual measurements is 0.205 m. The results demonstrate that our method effectively prevents navigation mapping distortion and facilitates reliable robot positioning in experimental settings.

Optimizing Urban Technological Innovation through GGFs: A Systemic and Spatial Analysis

The implementation of the Government Guides Funds (GGFs) policy by China’s government plays an important role in guiding domestic private capital to participate in innovation and entrepreneurship activities. This study empirically analyzed the impact of GGFs in facilitating the domestic optimization process of urban innovation in China. Based on panel data from 285 cities from 2008 to 2021, this study uses the Spatial Durbin Model (SDM) to verify the impact of GGFs on urban innovation and its mechanism. The findings reveal that GGFs exert a positive influence on the urban innovation of both the immediate regions and the neighboring areas of the policy-origin city. The effectiveness of GGFs varies geographically, with more pronounced spatial spillover impacts observed in the eastern regions of China. Further research shows that industrial upgrading amplifies the direct impact of GGFs on urban innovation, while its indirect effectiveness is not significant. Finally, based on empirical findings, this study also underscores the necessity of local governments in tailoring the management of GGFs to local conditions and promoting sustainable urban innovation and regional development. Our findings provide noteworthy implications for governmental administrators and regulators in promoting the systemic optimization of GGFs operations within the context of sustainable urban development.

Metal–Polymer Joining by Additive Manufacturing: Effect of Printing Parameters and Interlocking Design

Additive manufacturing has a strong potential to produce sound metal–polymer joints using controlled polymer deposition on a metallic substrate. In this way, this study aimed to explore the morphological and mechanical properties of metal–polymer joints produced through material-extrusion-based AM using a pin-based macro-mechanical interlocking mechanism. Joints were fabricated with polylactic acid deposited onto a heated aluminium alloy substrate to form the connection. The optimisation process was focused on improving the printing parameters and pin geometries to reduce voids and enhance joint integrity. The results indicate that optimised samples exhibit superior mechanical resistance, achieving a maximum load improvement with an overall strength increase of 368.97% compared to non-optimised joints. A combined pin geometry (50% cylindrical, 50% conical) was found to be the most effective. Morphological analysis confirmed uniform polymer deposition, ensuring reliable joint performance. These findings underscore the critical role of geometric optimisation in enhancing the strength and durability of metal–polymer joints in AM applications.

Lactic acid bacteria sequential fermentation improves viable counts and quality of fermented apple juice via generating two logarithmic phases

This study investigated the impact of lactic acid bacteria (LAB) sequential fermentation on viable counts and apple juice quality. The optimal fermentation conditions were obtained by a step-by-step optimization process, including pH 4.5, temperature 37 °C, the second inoculation time 16 h, total fermentation time 40 h and fermentation sequence (first 21,805 + 21,828, second 20,241). Under the optimal conditions, sequential fermentation allowed LAB to experience two logarithmic phases, increasing viable counts to 1.38 × 108 CFU/mL, exceeding simultaneous fermentation for 24 h and 40 h by 4.10 × 107 CFU/mL and 5.40 × 107 CFU/mL, respectively. This process enhanced sugar utilization, yielding more lactic acid and polyphenols. Furthermore, sequential fermentation improved DPPH (71.71 %) and ABTS (84.79 %) scavenging rates, and enriched volatile compounds, particularly beta-Damascenone, potentially contributing to floral and richer apple flavor. Sequential fermentation also achieved optimal sensory acceptability. This study proposes a novel strategy for high-density LAB fermentation to produce high-quality apple juice.