Reduction In Time Research Articles

Efficient Visual Computing With Camera RAW Snapshots.

Conventional cameras capture image irradiance (RAW) on a sensor and convert it to RGB images using an image signal processor (ISP). The images can then be used for photography or visual computing tasks in a variety of applications, such as public safety surveillance and autonomous driving. One can argue that since RAW images contain all the captured information, the conversion of RAW to RGB using an ISP is not necessary for visual computing. In this paper, we propose a novel ρ-Vision framework to perform high-level semantic understanding and low-level compression using RAW images without the ISP subsystem used for decades. Considering the scarcity of available RAW image datasets, we first develop an unpaired CycleR2R network based on unsupervised CycleGAN to train modular unrolled ISP and inverse ISP (invISP) models using unpaired RAW and RGB images. We can then flexibly generate simulated RAW images (simRAW) using any existing RGB image dataset and finetune different models originally trained in the RGB domain to process real-world camera RAW images. We demonstrate object detection and image compression capabilities in RAW-domain using RAW-domain YOLOv3 and RAW image compressor (RIC) on camera snapshots. Quantitative results reveal that RAW-domain task inference provides better detection accuracy and compression efficiency compared to that in the RGB domain. Furthermore, the proposed ρ-Vision generalizes across various camera sensors and different task-specific models. An added benefit of employing the ρ-Vision is the elimination of the need for ISP, leading to potential reductions in computations and processing times.

Sustained inflation and chest compression versus 3:1 chest compression to ventilation ratio during cardiopulmonary resuscitation of asphyxiated newborns (SURV1VE): A cluster randomised controlled trial

ObjectiveIn newborn infants requiring chest compression (CC) in the delivery room (DR) does continuous CC superimposed by a sustained inflation (CC+SI) compared with a 3:1 compression:ventilation (3:1 C:V) ratio decreases...

Accelerating Blockchain Applications on IoT Architecture Models—Solutions and Drawbacks

More and more IoT use cases require trustworthy computing from cloud/back-end services, which cannot necessarily provide a fully trusted execution environment, data immutability, and traceability. The integration of IoT with the blockchain technology is one of the most promising solutions to achieve the previously mentioned features in the IoT networks. Researchers are also interested in integration solutions, and several solutions are already present in the scientific literature. However, there are still some uncertainties in establishing a direct and effective interaction between an IoT device and the given blockchain. In this work, we propose the first IoT hardware architecture model designed to accelerate time-consuming operations of IoT-Blockchain. The proposed IoT hardware architecture model is programmed in SystemC-TLM and can provide a significant reduction in execution time, 53% and 18% when running Hyperledger Sawtooth and Ethereum applications, respectively.

Significant reduction of the culturing time required for bacterial identification and antibiotic susceptibility determination by infrared spectroscopy.

Rapid testing of bacteria for antibiotic susceptibility is essential for effective treatment and curbing the emergence of multidrug-resistant bacteria. The misuse of antibiotics, coupled with the time-consuming classical testing methods, intensifies the threat of antibiotic resistance, a major global health concern. In this study, employing infrared spectroscopy-based machine learning techniques, we significantly shortened the time required for susceptibility testing to 10 hours, a significant improvement from the 24 hours in our previous studies as well as the conventional methods that typically take at least 48 hours. This remarkable reduction in turnaround time (from 48 hours to 10 hours), achieved by minimizing the culturing period, offers a game-changing advantage for clinical applications. Our study involves a dataset comprising 400 bacterial samples (200 E. coli, 100 Klebsiella pneumoniae, and 100 Pseudomonas aeruginosa) with an impressive 96% accuracy in the taxonomic classification at the species level and up to 82% accuracy in bacterial susceptibility to various antibiotics.

A Computational Strategy for the Rapid Identification and Ranking of Patient-Specific T Cell Receptors Bound to Neoantigens.

T cell receptor (TCR) recognition of a peptide-major histocompatibility complex (pMHC) is crucial for adaptive immune response. The identification of therapeutically relevant TCR-pMHC protein pairs is a bottleneck in the implementation of TCR-based immunotherapies. The ability to computationally design TCRs to target a specific pMHC requires automated integration of next-generation sequencing, protein-protein structure prediction, molecular dynamics, and TCR ranking. A pipeline to evaluate patient-specific, sequence-based TCRs to a target pMHC is presented. Using the three most frequently expressed TCRs from 16 colorectal cancer patients, the protein-protein structure of the TCRs to the target CEA peptide-MHC is predicted using Modeller and ColabFold. TCR-pMHC structures are compared using automated equilibration and successive analysis. ColabFold generated configurations require an ≈2.5× reduction in equilibration time of TCR-pMHC structures compared to Modeller. The structural differences between Modeller and ColabFold are demonstrated by root mean square deviation (≈0.20nm) between clusters of equilibrated configurations, which impact the number of hydrogen bonds and Lennard-Jones contacts between the TCR and pMHC. TCR ranking criteria that may prioritize TCRs for evaluation of in vitro immunogenicity are identified, and this ranking is validated by comparing to state-of-the-art machine learning-based methods trained to predict the probability of TCR-pMHC binding.

ChatGPT prompts for generating multiple-choice questions in medical education and evidence on their validity: a literature review.

ChatGPT's role in creating multiple-choice questions (MCQs) is growing but the validity of these artificial-intelligence-generated questions is unclear. This literature review was conducted to address the urgent need for understanding the application of ChatGPT in generating MCQs for medical education. Following the database search and screening of 1920 studies, we found 23 relevant studies. We extracted the prompts for MCQ generation and assessed the validity evidence of MCQs. The findings showed that prompts varied, including referencing specific exam styles and adopting specific personas, which align with recommended prompt engineering tactics. The validity evidence covered various domains, showing mixed accuracy rates, with some studies indicating comparable quality to human-written questions, and others highlighting differences in difficulty and discrimination levels, alongside a significant reduction in question creation time. Despite its efficiency, we highlight the necessity of careful review and suggest a need for further research to optimize the use of ChatGPT in question generation. Main messages Ensure high-quality outputs by utilizing well-designed prompts; medical educators should prioritize the use of detailed, clear ChatGPT prompts when generating MCQs. Avoid using ChatGPT-generated MCQs directly in examinations without thorough review to prevent inaccuracies and ensure relevance. Leverage ChatGPT's potential to streamline the test development process, enhancing efficiency without compromising quality.

Facile Synthesis of Multifunctional Mesoporous Starch-Based Microparticle for Effective Hemostasis and Wound Healing.

Uncontrolled hemorrhage and infection are the principal causes of mortality associated with trauma in both military and civilian medical settings. Modified starch granules have emerged as a safe hemostatic agent for irregular and noncompressible wounds, but their performance is constrained by limited hemostasis efficiency and modest antibacterial activity. This study reported a directed self-assembly approach for a multifunctional mesoporous starch-based microparticle loaded with chitosan and calcium ions (Ca@MSMP) used for rapid hemostasis and wound healing. Directed self-assembly of uniform Ca@MSMP with a hierarchical hollow structure in the presence of chitosan was confirmed by scanning electron microscopy (SEM) analysis and pore structure analysis. The resulting Ca@MSMP exhibited a well-defined spherical shape and uniform size of 1 μm and demonstrated excellent antibacterial activity (>95%) without hemolytic activity. Importantly, Ca@MSMP enhanced blood coagulation and platelet aggregation via the synergistic effect of rapid calcium release and chitosan-mediated electrostatic interactions, leading to a significant decrease in blood loss and reduction in hemostasis time in rat tail amputation and liver injury models. In comparative analyses, Ca@MSMP significantly outperformed the commercial hemostatic agent Quickclean, notably enhancing the healing of full-thickness skin wounds in vivo by effectively preventing infection. These results underscore the potential of this innovative hemostatic material in diverse clinical scenarios, offering effective solutions for the management of bleeding in wounds that are irregularly shaped and noncompressible.

Bilateral insular cortical lesions reduce sensitivity to the adverse consequences of acute ethanol intoxication in Pavlovian conditioning procedures.

Sensitivity to the adverse post-ingestive effects of ethanol likely serves as a deterrent to initiate alcohol consumption early in drinking and later may contribute to efforts to remain abstinent. Administering ethanol to naïve rats prior to Pavlovian conditioning procedures elicits robust ethanol-conditioned taste and place avoidance (CTA; CPA) mediated by its subjective interoceptive properties. The insular cortex (IC) has been implicated as a region involved in mediating sensitivity to the interoceptive properties of ethanol. Here, we examined whether bilateral lesions of the IC affect the acquisition and expression of taste and place avoidance in ethanol-induced CTA and CPA paradigms. Adult male and female Wistar rats received bilateral excitotoxic lesions (ibotenic acid; 20 mg/mL; 0.3 μL) of the IC prior to conditioning procedures. Subsequently, rats were conditioned to associate a novel taste stimulus (0.1% saccharin) and context with the effects of ethanol (1.0 g/kg) in a combined CTA/CPP procedure. Conditioning occurred over 8 alternating CS+/CS- days, followed by tests for expression of taste and place preferences. Data from IC-lesioned rats were compared with neurologically intact rats. Our findings revealed that neurologically intact rats showed a significantly stronger ethanol-induced CTA than IC-lesioned rats. There were no significant differences in total fluid intake when rats consumed water (CS-). As with CTA effects, intact rats showed a strong CPA, marked by a greater reduction in time spent on the drug-paired context, while IC-lesioned rats failed to display CPA to ethanol. These results indicate that proper IC functioning is necessary for responding to the adverse interoceptive properties of ethanol regardless of which Pavlovian paradigm is used to assess interoceptive responsivity to ethanol. Blunted IC functioning from chronic ethanol use may reduce interoceptive signaling specifically of ethanol's adverse effects thus contributing to increased alcohol use.

High-Performance Krawtchouk Polynomials of High Order Based on Multithreading

Orthogonal polynomials and their moments serve as pivotal elements across various fields. Discrete Krawtchouk polynomials (DKraPs) are considered a versatile family of orthogonal polynomials and are widely used in different fields such as probability theory, signal processing, digital communications, and image processing. Various recurrence algorithms have been proposed so far to address the challenge of numerical instability for large values of orders and signal sizes. The computation of DKraP coefficients was typically computed using sequential algorithms, which are computationally extensive for large order values and polynomial sizes. To this end, this paper introduces a computationally efficient solution that utilizes the parallel processing capabilities of modern central processing units (CPUs), namely the availability of multiple cores and multithreading. The proposed multi-threaded implementations for computing DKraP coefficients divide the computations into multiple independent tasks, which are executed concurrently by different threads distributed among the independent cores. This multi-threaded approach has been evaluated across a range of DKraP sizes and various values of polynomial parameters. The results show that the proposed method achieves a significant reduction in computation time. In addition, the proposed method has the added benefit of applying to larger polynomial sizes and a wider range of Krawtchouk polynomial parameters. Furthermore, an accurate and appropriate selection scheme of the recurrence algorithm is introduced. The proposed approach introduced in this paper makes the DKraP coefficient computation an attractive solution for a variety of applications.

The mechanochemistry of lanthanum dihydride (LaH\(_{2}\)) with hydrogen (H\(_{2}\)) using the ball-mill process and the effect of oxidation on the resulting products

The complex behavior of LaH2 during ball milling was investigated in this study, with its mechanical, chemical, and morphological changes explored. The relationship between milling time and hydrogen pressure reduction was uncovered through detailed experiments, reflecting the dynamic nature of the process. A transient yet significant event was observed upon unsealing the milling jar post-milling: the emergence of a minor fire ember, indicative of the interplay between mechanical forces and chemical reactivity within the LaH2 powder. Profound changes in the structure, composition, and shape were unraveled using advanced techniques such as X-ray diffraction (XRD), scanning electron microscopy coupled with energy-dispersive X-ray spectroscopy (SEM/EDX), and particle size distribution analysis. The resulting powder exhibited a dual-phase composition of lanthanum dihydride (LaH2, 68.1% to 71.5%) and lanthanum oxide (La2O3, 28.5% to 31.9%), reflecting a dynamic chemical equilibrium during milling. Particle size distribution analysis revealed a notable increase in average diameter to 6420 nm, accompanied by a polydispersity index (PDI) of 0.831, signifying a broadening compared to the initial LaH2 powder. The morphological evolution of the powder was elucidated through SEM imaging, showing predominantly spherical and rounded forms, indicating extensive particle agglomeration and plastic deformation during milling. Additionally, the formation of oxide layers on the powder surface, intertwined with pronounced particle agglomeration, was highlighted through EDX mapping, shedding light on the mechanical aspects of morphological evolution during milling. These findings contribute to our understanding of LaH2 behavior under extreme mechanical and chemical conditions and have implications for materials processing, hydrogen storage technologies, and broader applications in materials science and engineering.

The pivotal role of the robotic nurse in the management of the robotic urologic surgical environment

AbstractIntroduction and ObjectivesRobotic surgery has revolutionized urology. However, upfront costs, time investments and knowledge required to proficiently operate and maintain robotic equipment and supplies often make it advantageous to implement the role of dedicated robotic nurse (DN) teams. Herein, we aimed to investigate the impact of DN on operative times during robotic urological procedures.Materials and MethodsWe retrospectively identified consecutive robotic uro‐oncological procedures performed at our institution from Jan to Oct 2023. The cohort was stratified based on the presence or absence of a DN team (DN vs no‐DN). The DN team was defined as having independently managed ≥100 robotic urological procedures. Endpoints were preoperative nursing time (pre‐NT), postoperative nursing time (post‐NT), surgeon operative time (surg‐OT), and total operative time (t‐OT). Continuous variables were compared using Student's t‐test. Subgroup analyses were conducted for specific procedures: robot‐assisted radical cystectomy (RARC), radical prostatectomy (RARP), partial nephrectomy (RAPN), and radical nephrectomy (RARN).ResultsOverall, 421 procedures were included. The DN group exhibited shorter pre‐NT (13.5 vs 23.7, p < 0.001), post‐NT (12.2 vs. 15, p < 0.001), surg‐OT (117 vs. 136, p < 0.001), and t‐OT (143.2 vs. 174.9, p < 0.001) compared to the no‐DN group. Subgroup analysis revealed shorter pre‐NT in the DN group for all procedures: RARP (13.2 vs. 23.5, p < 0.001), RARC (16.4 vs. 29.2, p < 0.001), RARN (12.6 vs. 18.9, p < 0.001) and RAPN (12.7 vs. 20.8, p < 0.001). Conversely, DN group exhibited shorter post‐NT in RARP (12 vs. 15.8, p < 0.001) and RARN (12.4 vs. 15.3, p: 0.01), but not in RARC (13 vs. 15, p: 0.06) and RAPN (12.2 vs. 12.7, p: 0.4) subgroup. Similarly, DN group demonstrated shorter t‐OT in RARP (162.7 vs. 185.4, p < 0.001), but not in RARC (232.8 vs. 245, p: 0.44), RAPN (91.2 vs. 107.4, p: 0.35) and RARN (118.5 vs. 127.2, p: 0.4) subgroups. Finally, no differences in surg‐OT were recorded between the DN and non‐DN groups for any of the individual procedures (Table ; Figure ). Comparison of time per procedures between dedicated and non‐dedicated teams. Variables mean (SD) RAPN group Variables mean (SD) RARP group Variables mean (SD) Overall procedures Dedicated nurse team (86) No dedicated nurse team (35) p‐value Dedicated nurse team (86) No dedicated nurse team (35) p‐value Dedicated nurse team (86) No dedicated nurse team (35) p‐value Preoperative nursing time 12.7 (±2.7) 20.8 (±5.9) <0.001 Preoperative nursing time 13.2 (±3.3) 23.5 (±9.8) <0.001 Preoperative nursing time 13.5 (±3.6) 23.7 (±9.7) <0.001 Postoperative nursing time 12.2 (±3.7) 12.7 (±3.4) 0.4 Postoperative nursing time 12 (±4.1) 15.8 (±6.3) <0.001 Postoperative nursing time 12.2 (±3.9) 15 (±5.6) <0.001 Operative time 66.2 (±29.1) 73.8 (±44.4) 0.3 Operative time 137.5 (±37.2) 146.1 (±41.1) 0.18 Operative time 117 (±68) 136 (±68.8) <0.001 Total operation time 91.2 (±30.2) 107.4 (±46.1) 0.35 Total operation time 162.7 (±37.8) 185.4 (±39.6) <0.001 Total operation time 143.2 (±69) 174.9 (±72,1) <0.001 Variables mean (SD) RARC group Variables mean (SD) RARN group Dedicated nurse team (86) No dedicated nurse team (35) p‐value Dedicated nurse team (86) No dedicated nurse team (35) p‐value Preoperative nursing time 16.4 (±4.8) 29.2 (±8.9) <0.001 Preoperative nursing time 12.6 (±2.8) 18.9 (±4.7) <0.001 Postoperative nursing time 13 (±3.5) 15 (±4.5) 0.06 Postoperative nursing time 12.4 (±3.9) 15.3 (±5.7) 0.01 Operative time 203.4 (±68.5) 200.8 (±69.7) 0.86 Operative time 93.5 (±38.7) 93 (±42.2) 0.9 Total operation time 232.8 (±66.4) 245 (±72.6) 0.44 Total operation time 118.5 (±38.9) 127.2 (±43.1) 0.4 Note: Bold indicates significant value. Comparison of preoperative nursing time DN Team versus NoDN Team.imageConclusionsOur study suggests that the implementation of a dedicated robotic nursing team can lead to substantial reductions in overall operative time. This benefit is primarily attributed to shorter preoperative nursing times, highlighting the crucial role of the robotic nurse in optimizing the robotic surgical environment.

3D analytical magnetic model calculation for Halbach PM induction heater planar topology

In this work, a 3D analytical magnetic model based on hybridation of the sub-domain’s method and the image’s theory to compute magnetic field and translational motion eddy current in the conducting plate of the Halbach permanent magnet induction heater planar topology is developed. The main objective is to remedy the problem of the transverse edge effect, and hence improving the efficiency of analytical model and the accuracy of results. The developed model also allows fast and precise simulations of 3D magnetic phenomena, and presents an important reduction in computation time compared to 3D finite element simulations.

Localized Path Planning for Mobile Robots Based on a Subarea-Artificial Potential Field Model.

The artificial potential field method has efficient obstacle avoidance ability, but this traditional method suffers from local minima, unreasonable paths, and sudden changes in heading angles during obstacle avoidance, leading to rough paths and increased energy consumption. To enable autonomous mobile robots (AMR) to escape from local minimum traps and move along reasonable, smooth paths while reducing travel time and energy consumption, in this paper, an artificial potential field method based on subareas is proposed. First, the optimal virtual subgoal was obtained around the obstacles based on the relationship between the AMR, obstacles, and goal points in the local environment. This was done according to the virtual subgoal benefit function to solve the local minima problem and select a reasonable path. Secondly, when AMR encountered an obstacle, the subarea-potential field model was utilized to solve problems such as path zigzagging and increased energy consumption due to excessive changes in the turning angle; this helped to smooth its planning path. Through simulations and actual testing, the algorithm in this paper demonstrated smoother heading angle changes, reduced energy consumption, and a 10.95% average reduction in movement time when facing a complex environment. This proves the feasibility of the algorithm.

OEM: An operation-aware event matching algorithm for content-based Pub/Sub systems

Due to the continuous expansion of the user base and the heightened demands for quality of service (QoS), real-time data distribution has encountered significant challenges in event matching within large-scale content-based publish/subscribe (CPS) systems. Many efficient algorithms have been proposed to enhance the matching performance. However, most existing algorithms scarcely consider operation characteristics, which often leads to performance degradation due to a multitude of repetitive operations, such as comparisons, additions, and assignments. In this paper, we propose an Operation-aware Event Matching algorithm called OEM, which takes into account the efficiency of CPU operations, SIMD instructions, and multi-thread architecture. First, we introduce a bitset-based subscription cache mechanism (SCM), which enables the efficient execution of bitwise OR operations during the matching process. Furthermore, we propose two optimizations, namely an optimal skewness-aware cell grouping (OSCG) strategy and an attribute-based clustering mechanism (ACM), to enhance the efficiency of OEM. These optimizations effectively address the challenges posed by data skewness, low cache efficiency, and high dimensionality. In addition, we establish a performance analysis model that characterizes the trade-off between memory consumption and performance improvement. We conducted extensive experiments to evaluate the performance of OEM. Compared to six state-of-the-art algorithms, namely REIN, Ada-REIN, TAMA, OpIndex, PEM and fgPEM, OEM achieves an average reduction in matching time by 40.4×, 38.5×, 12.0×, 33.1×, 27.8×, and 25.5×, respectively.

AN EFFICIENT ITERATIVE METHOD TO SIMULATE THE VARYING STATIC RECEPTANCE OF A THIN-WALLED WORKPIECE

This paper introduces an accelerated iterative Conjugate Gradient method (CG-method) for simulating the varying static receptance of a thin-walled workpiece. The proposed approach enhances computational efficiency by computing an optimal initial displacement guess for a given cutter location. This guess is based on the previously calculated displacement vector from the preceding cutter location, serving as the start iteration vector for the CG-method. The validation of efficiency gains through this method is demonstrated by a significant reduction in computation time for a numerical example. Moreover, to ensure the accuracy of the accelerated iterative CG-method, comparisons are made with the Cholesky method. The results confirm the precision of the proposed approach.

REVIEW OF DATA ANALYTICS AND INFORMATION SYSTEMS IN ENHANCING EFFICIENCY IN FINANCIAL SERVICES: CASE STUDIES FROM THE INDUSTRY

This study explores the transformative impact of integrating data analytics and information systems on enhancing efficiency in the financial services industry. The research highlights significant improvements in operational efficiency, risk management, and customer satisfaction through detailed case studies of JPMorgan Chase, Allstate Insurance, BlackRock, and Bank of America. The findings reveal that AI-driven analytics tools at JPMorgan Chase led to a 30% reduction in fraud-related losses and a 20% increase in customer satisfaction. Through predictive analytics, Allstate Insurance achieved a 40% reduction in claims processing time and a 25% improvement in underwriting accuracy. BlackRock reported a 35% increase in portfolio returns due to machine learning and predictive analytics. In comparison, Bank of America experienced a 22% increase in customer retention and a 15% rise in satisfaction through data-driven CRM systems. These outcomes underscore the critical role of advanced data analytics and information systems in driving innovation and operational excellence in financial services. The study emphasises the importance of continuous technological advancements and strategic implementation to maximise the benefits of these tools in the industry.

Ablation Resistance of ZrB2-SiC-Si Composite Coatings Applied on Graphite Substrate by Spark Plasma Sintering

AbstractIn this research, a protective composite coating made of ultra-high-temperature ceramics (UHTCs) was applied on the graphite substrate to boost the ablation resistance of the graphite substrate. Spark plasma sintering (SPS) was used to create such coatings on the graphite substrates. Efforts were made to identify the appropriate chemical composition for the composite coating and the conditions for the SPS (temperature, pressure and holding time). The coatings with a base composition of ZrB2-15 vol.% Si-15 vol.% SiC as well as MoSi2 and WC additives in the same amounts of 0 vol.%, 2.5 vol.% and 5 vol.% of each were applied on the graphite substrates under sintering conditions of final temperature of 1875 ± 25°C, initial pressure of 10 MPa, final load of 25 MPa and holding time of 5 min. The results obtained from the ablation test by oxyacetylene flame verified that the presence of the protective composite coating significantly increases the ablation performance of the graphite substrate. The sample without WC and MoSi2 additives had the lowest mass reduction in longer ablation times, which indicates the high ablation resistance of the base sample compared to the samples with those additives.

Interval uncertainty-oriented impedance force control for space manipulator with time-dependent reliability

For specific missions of spacecraft including on-orbit assembly, docking, grasp, etc., the presence of uncertainties leads to difficulties in the analysis of impedance force control systems and evaluations of safety for space manipulators. Therefore, in this study, a novel interval uncertainty-oriented impedance force control method for space manipulators with time-dependent reliability was established. The proposed method can accurately and rapidly characterize uncertainties. Additionally, it can reflect the reliability of a control system based on an overall time history. Once the uncertainties of the dynamic models and state responses are known, their uncertainty bounds can be obtained using an established non-probabilistic propagation method with high accuracy. An interval process model and the first passage theory are employed in the non-probabilistic time-dependent model. A joint reliability index is established based on the single reliability indexes, which can more efficiently evaluate the control system reliability than the individual indexes. A numerical example related to space manipulator is used to study the developed impedance force control method and verify the effectiveness of the method. The results from the response curves reveal validity of the proposed method, while the reduction in time proves efficiency of the method.

Influence of Base Layer Thickness and Property on Flexible Pavement Behavior

When designing the pavement layers, a suitable thickness must be chosen to protect the pavement from environmental conditions and traffic loads and ensure the structure's durability up to the design life. To investigate the behavior of flexible pavement, the characteristics and thickness of each layer are programmed into the finite element method (FEM). The Abaqus program is one of the infinite-element analysis programs. The use of the Abaqus program leads to a reduction in cost and time compared to laboratory tests. In this study, the Abaqus program analyzed a three-dimensional model of a multi-layered road section, and all materials have elastic behavior. The model comprises five layers (wearing, binder, base, subbase, and subgrade). The model was looked at with different base layer thicknesses (15, 25, and 30 cm) and elasticity moduli (1655, 2070, and 3000 MPa). Critical parameters were looked at in the present research: vertical displacement at the wearing layer's top, horizontal tensile strain in the asphalt layer's lowest point, and vertical compressive strain at the subgrade's surface. The outcomes indicated that the pavement is more susceptible to rutting than fatigue as a result of static load. An increase in thickness and modulus of elasticity for the base layer leads to a reduction in rutting risks.

Simultaneous optimization of activation conditions of alperujo (two-phase olive mill waste) using a desirability function: Production of porous carbons for adsorption and energy storage

Valorization of agro-industrial waste is considered an essential step towards a circular economy. The potential reuse of these byproducts in practical applications requires feasibility assessments and optimization of variables in order to be profitable. In the present study, an abundant and problematic residue from the olive oil industry, “alperujo” (two-phase olive mill waste), was used as precursor material for activated carbons. A simultaneous optimization methodology was applied to maximize two response variables: energy storage capacity and lead (Pb) removal efficiency. The simultaneous optimum conditions were 3.66 g of KOH per g of carbonized residue and a temperature of 650 °C for 120 min. The result was a microporous activated carbon with an energy storage capacity of 252 F/g and a lead removal from aqueous solutions of 68.8 %. These values are comparable with those offered by commercial activated carbons, evidencing that porous materials obtained from alperujo using unique activating conditions for both applications present a remarkable performance. The novelty of the present study lies in the application of a useful statistical methodology for simultaneous optimization through the “desirability function”, resulting in a reduction in cost and time. Our results have demonstrated that it is possible to generate activated carbons with the highest performance for two different applications using the same combination of activation variables.