Efficient Process Research Articles

Through-Space Charge-Transfer Organogold(III) Complexes Enable High-Performance X-ray Scintillation and Imaging.

Organic scintillators have recently attracted growing attention for X-ray detection in industrial and medical applications. However, these materials still face critical obstacles of low attenuation efficiency and/or inefficient triplet exciton utilization. Here we developed a new category of organogold(III) complexes, Tp-Au-1 and Tp-Au-2, through adopting a through-space interaction motif to realize high X-ray attenuation efficiency and efficient harvesting of triplet excitons for emission. Thanks to the efficient through-space charge transfer process, this panel of complexes achieved higher photoluminescence quantum yield and shorter radiative lifetimes compared with the through-bond reference complexes. Inspiringly, these organogold(III) complexes exhibited polarity-dependent emission origins: thermally activated delayed fluorescence and/or phosphorescence. Under X-ray irradiation, Tp-Au-2 manifested intense radioluminescence together with a record-high scintillation light yield of 77,600 photons MeV-1 for organic scintillators. The resulting scintillator screens demonstrated high-quality X-ray imaging with >16.0 line pairs mm-1 spatial resolution, outstripping most organic and inorganic scintillators. This finding provides a feasible strategy for the design of superior organic X-ray scintillators.

Data Communication Algorithm of HPDB Parallel Database System Based on Computer Network

High-Performance Database (HPDB) is designed to meet the demands of today’s modern data-driven world, as massive volumes of information need to be accessed and analyzed with minimal latency. A pivotal aspect of their operation lies in efficient data communication among nodes in a computer network, which is essential for parallel database systems. HPDBs may involve distributed architectures and parallel database systems to store and process data across multiple nodes or servers in a network. Hence, an algorithm called Hybridized Partitioning Strategy (HPS)-based Communication (C) for achieving HPDB has been proposed to facilitate data transmission and coordination across a computer network using the Message Passing Interface (MPI) protocol. The proposed HPS-C-HPDB technique includes partitioning and distributing data, query routing, and load balancing strategies to achieve high-performance levels. The (HPS) combines hash and range partitioning methods for effective processing and retrieval to balance data distribution, reduce communication overhead in parallel databases, and improve the system’s performance. In parallel database systems, query routing effectively routes requests into the optimal nodes or partitions based on the query’s conditions and the data’s placement and guarantees efficient data processing and retrieval. The proposed scheme is evaluated using various performance metrics like throughput, response time, speedup, and communication overhead analysis.

Purification of Anti-Mycobacterium tuberculosis MPT64 Immunoglobulin-Y from Egg-Yolk Supernatant Using Thiophilic Adsorption Chromatography

The significance of immunoglobulin Y (IgY) as a particular antibody equal to mammalian IgG is well understood. However, due to a lack of reliable purification procedures, producing highly pure IgY remains problematic. In this study, we aimed to optimize the recovery of pure IgY anti-MPT64 using thiophilic adsorption chromatography. The purification of IgY anti-MPT64 was achieved by initial PEG lipid precipitation, then an optimized purification by varying the gradient concentration of elution buffer into five steps gradient (0-20, 20-40, 40-60, 60-80, and 80-100%v/v) for three injection column volume (CV) each and two steps gradient (0-50 and 50-100%v/v) in eight CV for each concentration. The obtained IgY was characterized by SDS-PAGE and dot-blot then determined its content levels using the Lowry method. The results showed that the five steps gradient purification was found to provide a better purity level of IgY than the two steps gradient. However, the IgY content obtained in the two steps gradient purification (2.2632± 0.011 mg/mL) was higher than the five steps gradient purification (1.35482 ± 0.023 mg/mL). Nevertheless, both purified IgY results can recognize MPT64 protein through a dot blot test. Therefore, it can be summarized that thiophilic adsorption chromatography with five steps gradient of purification was an efficient process to obtain a higher purity of IgY anti-MPT64, especially to be targeted as a diagnostic kit component for MPT64 detection.

Metal‐Oxo Electronic Tuning via In Situ CO Decoration for Promoting Methane Conversion to Oxygenates over Single‐Atom Catalysts

AbstractDirect methane conversion (DMC) to oxygenates at low temperature is of great value but remains challenging due to the high energy barrier for C−H bond activation. Here, we report that in situ decoration of Pd1‐ZSM‐5 single atom catalyst (SAC) by CO molecules significantly promoted the DMC reaction, giving the highest turnover frequency of 207 h−1 ever reported at room temperature and ~100 % oxygenates selectivity with H2O2 as oxidant. Combined characterizations and DFT calculations illustrate that the C‐atom of CO prefers to coordinate with Pd1, which donates electrons to the Pd1−O active center (L−Pd1−O, L=CO) generated by H2O2 oxidation. The correspondingly improved electron density over Pd−O pair renders a favorable heterolytic dissociation of C−H bond with low energy barrier of 0.48 eV. Applying CO decoration strategy to M1‐ZSM‐5 (M=Pd, Rh, Ru, Fe) enables improvement of oxygenates productivity by 3.2–11.3 times, highlighting the generalizability of this method in tuning metal‐oxo electronic structure of SACs for efficient DMC process.

Augmenting roadway safety with machine learning and deep learning: Pothole detection and dimension estimation using in-vehicle technologies

Detection and estimation of pothole dimensions is an essential step in road maintenance. Aging, heavy rainfall, traffic, and weak underlying layers may cause pavement potholes. Potholes can cause accidents when drivers lose control after hitting or swerving to avoid them, which may lead to injuries or fatal crashes. Also, potholes may result in property damages, such as flat tires, scrapes, dents, and leaks. Additionally, potholes are costly; for example, in the United States, potholes cost drivers about $3 Billion annually. Traditional ways of attending to potholes involve field surveys carried out by skilled personnel to determine their sizes for quantity and cost estimates. This process is expensive, prone to errors, subjectivity, unsafe, and time-consuming. Some authorities use sensor vehicles to carry out the surveys, a method that is accurate, safer, and faster than the traditional approach but much more expensive; therefore, not all authorities can afford them. To avoid these challenges, a modern, real-time, cost-effective approach is proposed to ensure the efficient and fast process of pothole maintenance. This paper presents a Deep Learning model trained using the You Only Look Once (YOLO) algorithm to capture potholes and estimate their dimensions and locations using only built-in vehicle technologies. The model attained 93.0 % precision, 91.6 % recall, 87.0 % F1-score, and 96.3 % mAP. A statistical analysis of the on-site test results indicates that the results are significant at a 5 % level, with a p-value of 0.037. This approach provides an economical and faster way of monitoring road surface conditions.

Nanowire Array Breath Acetone Sensor for Diabetes Monitoring.

Diabetic ketoacidosis (DKA) is a life-threatening acute complication of diabetes characterized by the accumulation of ketone bodies in the blood. Breath acetone, a ketone, directly correlates with blood ketones. Therefore, monitoring breath acetone can significantly enhance the safety and efficacy of diabetes care. In this work, the design and fabrication of an InP/Pt/chitosan nanowire array-based chemiresistive acetone sensor is reported. By incorporation of chitosan as a surface-functional layer and a Pt Schottky contact for efficient charge transfer processes and photovoltaic effect, self-powered, highly selective acetone sensing is achieved. The sensor has exhibited an ultra-wide acetone detection range from sub-ppb to >100 000ppm level at room temperature, covering those in the exhaled breath from healthy individuals (300-800ppb) to people at high risk of DKA (>75ppm). The nanowire sensor has also been successfully integrated into a handheld breath testing prototype, the Ketowhistle, which can successfully detect different ranges of acetone concentrations insimulated breath samples. The Ketowhistle demonstrates the immediate potential for non-invasive ketone monitoring for people living with diabetes, in particular for DKA prevention.

Combined Strategies Enable Highly Selective Light Olefins and para-Xylene Production on Single Catalyst Bed.

Achieving multiple high-value-added chemical production through novel reaction processes and shape-selective catalytic strategy is the key to realizing efficient low-carbon catalytic processes. In this work, a methanol-toluene coreaction system was developed, and combined control strategies of reaction pathway guidance and shape-selective catalysis were applied for the successful production of light olefins and para-xylene on single HZSM-5 catalyst bed. Cofeeding toluene additionally provides reactive and flowing aromatic hydrocarbon pool species that change the dominant reaction pathway in the complex network of the methanol reaction on HZSM-5 and promote the formation of ethylene. For the first time, the key reaction intermediates methylmethylenecyclodiene are directly captured and identified by experimental and theoretical techniques. This helps to propose the catalytic cycle for the dominant generation of ethylene and, more importantly, enriches the methanol-to-hydrocarbons (MTH) chemistry and hydrocarbon pool mechanism. Furthermore, 0.4HZSM-5@S-1-CLD, an optimized HZSM-5 catalyst modified by the silicalite-1 epitaxial growth followed by silanization approach, realizes highly selective production of light olefins (especially ethylene) and para-xylene, while excellent reactant activity is maintained. This highly efficient coreaction route gives an important leading significance in synthesizing the raw materials for the polyolefin and polyester industries. The establishment of the combined control strategies provides a model for the joint production of multiple target chemicals in complex catalytic processes.

Simple one-step treatment for saccharification of mango peels using an optimized enzyme cocktail of Aspergillus niger ATCC 9642.

Developing efficient microbiological methods to convert polysaccharide-rich materials into fermentable sugars, particularly monosaccharides, is vital for advancing the bioeconomy and producing renewable chemicals and energysources. This study focused on optimizing the production conditions of an enzyme cocktail from Aspergillus niger ATCC 9642 using solid-state fermentation (SSF) and assessing its effectiveness in saccharifying mango peels through a simple, rapid, and efficient one-step process. A rotatable central composite design was employed to determine optimal conditions of moisture, time, and pH for enzyme production in SSF medium. The optimized enzyme cocktail exhibited cellulase activity (CMCase) at 6.28 U/g, filter paper activity (FPase) at 3.29 U/g, and pectinase activity at 117.02 U/g. These optimal activities were achieved with an SSF duration of 81 h, pH of 4.66, and a moisture content of 59%. The optimized enzyme cocktail effectively saccharified the mango peels without the need for chemical agents. The maximum saccharification yield reached approximately 81%, indicating efficient conversion of mango peels into sugars. The enzyme cocktail displayed consistent thermal stability within the tested temperature range of 30-60°C. Notably, the highest sugar release occurred within 36 h, with glucose, arabinose, galactose, and xylose being the primary monosaccharides released during saccharification. This study highlights the potential application of Aspergillus niger ATCC 9642 and SSF for enzymatic production, offering a simple and high-performance process for monosaccharide production. The optimized enzyme cocktail obtained through solid-state fermentation demonstrated efficient saccharification of mango peels, suggesting its suitability for industrial-scale applications.

Mission-oriented scenarios: a new method for urban foresight

PurposeThe purpose of this study is to propose an innovative and efficient process in urban policy-making that combines a divergent and creative method with a convergent and strategic one. At the same time, the purpose is also to propose a useful innovation to enforce the usability of both methods. On the one hand, mission-oriented policies run the risk of being overly focused on the present and of not being able to develop preparedness in organization. On the other hand, scenario development has the reverse problem it often does not point out how to use scenario narratives to inform and devise short-term strategic actions.Design/methodology/approachThe paper proposes an innovative methodological approach, the mission-oriented scenarios, which hybridizes Mazzucato's mission-oriented public policy framework with Jim Dator's Manoa school four futures method. The proposed methodological innovation emerges from a urban foresight academic-led project carried out in the context of the Metropolitan City of Turin, Italy, where a first application of the mission-oriented scenarios was tested on six different focal issues (from reindustrialization to cultural policies) and the scenario narratives were used as sources for the grounding of 12 missions and 48 strategic actions towards 2030.FindingsMission-oriented scenarios can contribute to the generation of more sustainable and inclusive urban public policies. This methodological proposal is based on an original mix of knowledge exchange procedures borrowed from methodological approaches with different backgrounds: the mission-oriented and the archetypal scenarios. Their conjunction could support the formulation of ambitious yet pragmatic policies, giving a plurality of actors the opportunity to act and establish fruitful and lasting partnerships.Originality/valueThe paper reconstructs one of the first urban foresight projects carried out in a major Italian city by two prestigious universities and exposes a methodological innovation resulting from reflection on the strengths and weaknesses of the project, which opens the door to the development of a new scenario technique.

Leveraging Generative AI for Data-Driven Insights and Visualization in Python

In this work, we introduce a novel solution that integrates Python, Streamlit, OpenAI, and embeddings to extract insights and generate visualizations from various data sources. Our approach empowers users to interact with data in plain text, enabling a more intuitive and efficient data analysis process. By leveraging Generative AI, our system not only simplifies data exploration but also addresses the challenges of identifying and generating charts, making data visualization more accessible. The solution accommodates diverse data sources, including PostgreSQL and CSV files, while ensuring a secure and user-friendly experience. This paper aims to demonstrate the effectiveness of combining unsupervised and supervised learning algorithms in real-time text analysis, providing a smart, quick, and scalable method for data-driven decision-making.

Neuromorphic Nanoionics for Human-Machine Interaction: From Materials to Applications.

Human-machine interaction (HMI) technology has undergone significant advancements in recent years, enabling seamless communication between humans and machines. Its expansion has extended into various emerging domains, including human healthcare, machine perception, and biointerfaces, thereby magnifying the demand for advanced intelligent technologies. Neuromorphic computing, a paradigm rooted in nanoionic devices that emulate the operations and architecture of the human brain, has emerged as a powerful tool for highly efficient information processing. This paper delivers a comprehensive review of recent developments in nanoionic device-based neuromorphic computing technologies and their pivotal role in shaping the next-generation of HMI. Through a detailed examination of fundamental mechanisms and behaviors, the paper explores the ability of nanoionic memristors and ion-gated transistors to emulate the intricate functions of neurons and synapses. Crucial performance metrics, such as reliability, energy efficiency, flexibility, and biocompatibility, are rigorously evaluated. Potential applications, challenges, and opportunities of using the neuromorphic computing technologies in emerging HMI technologies, are discussed and outlooked, shedding light on the fusion of humans with machines.

Examining information systems use to facilitate the workplace accommodation process.

The workplace accommodation process is often affected by ineffective and inefficient communications and information exchanges among disabled employees and other stakeholders. Information systems (IS) can play a key role in facilitating a more effective and efficient accommodation process since IS has been shown to facilitate business processes and effect positive organizational changes. Since there is little to no research that exists on IS use to facilitate the workplace accommodation process, this paper, as a critical first step, examines how IS have been used in the accommodation process. Thirty-six interviews were conducted with disabled employees from various organizations. Open, axial, and selective coding were part of the analysis. Fuzzy set qualitative comparative analysis was used to identify different levels of IS use based on participants' descriptions. An IS used in the workplace accommodation process consists of electronic request form, accommodation checklist, special budget, specific role, ancillary service, formal policy and procedure. There are different levels of IS use in the current accommodation process. The high-level IS use often results in a better accommodation performance than the low-level IS use, including high efficiency, high effectiveness, and low emotional tolls. Nevertheless, the high-level IS use often uses a specific, inflexible template as well as disregards human elements in the accommodation process. This work provides implications that future IS design should raise awareness of disability and accommodation, account for individual differences, involve multiple stakeholder inputs, as well as address the fundamental social issues in the accommodation process.

Insurance Based on Waqf and Blockchain Technology: A Strong Social Impact & Efficiency

We surveyed to measure the satisfaction of policyholders in Morocco, and the results clearly show that the majority of customers do not appreciate the current services. They suffer from the ambiguity of contracts, and delays in reimbursement and do not feel the real impact of insurance in society. To solve this problem, we propose an innovative insurance based on blockchain and waqf. We suggest in this paper, to use smart contracts to create an efficient and automatic process in the collection of premiums and reimbursement of policyholders. The goal of this paper is to build insurance that reflects the true meaning of solidarity through Waqf while integrating transparency and speed through Fintech. This insurance model is supposed to be resilient in times of crisis, have a strong social impact, and be attractive to customers. Many advantages of the proposed model are discussed in the paper. In addition, the suggested insurance model will be represented through simulations on the NetLogo platform. We carry out the analysis in normal times and evaluate the behavior of policyholders in choosing a specific type of insurance, depending on some decision-making tools. We also analyze the impact of insurance during a time of crisis, as a particular example, the crisis experienced during the coronavirus pandemic. The simulations aim to evaluate the model in different situations and prove its efficiency.

Exosome Isolation Using Chitosan Oligosaccharide Lactate-1-Pyrenecarboxylic Acid-Based Self-Assembled Magnetic Nanoclusters.

Exosomes are small extracellular vesicles that play a crucial role in intercellular communication and offer significant potential for a wide range of biomedical applications. However, conventional methods for exosome isolation have limitations in terms of purity, scalability, and preservation of exosome structural integrity. To address these challenges, an exosome isolation platform using chitosan oligosaccharide lactate conjugated 1-pyrenecarboxylic acid (COL-Py) based self-assembled magnetic nanoclusters (CMNCs), is presented. CMNCs are characterized to optimize their size, stability, and interaction dynamics with exosomes. The efficiency of CMNCs in isolating exosomes is systematically evaluated using various analytical methods to demonstrate their ability to capture exosomes based on amphiphilic lipid bilayers. CMNC-based exosome isolation consistently yields exosomes with structural integrity and purity similar to those obtained using traditional methods. The reusability of CMNCs over multiple exosome isolation cycles underscores their scalability and offers an efficient solution for biomedical applications. These results are supported by western blot analysis, which demonstrated the superiority of CMNC-based isolation in terms of purity compared to conventional methods. By providing a scalable and efficient exosome isolation process that preserves both structural integrity and purity, CMNCs can constitute a new platform that can contribute to the field of exosome studies.

Dauer juvenile recovery transcriptome of two contrasting EMS mutants of the entomopathogenic nematode Heterorhabditis bacteriophora.

The entomopathogenic nematode Heterorhabditis bacteriophora, symbiotically associated with enterobacteria of the genus Photorhabdus, is a biological control agent against many insect pests. Dauer Juveniles (DJ) of this nematode are produced in industrial-scale bioreactors up to 100 m3 in liquid culture processes lasting approximately 11days. A high DJ yield (> 200,000 DJ·mL-1) determines the success of the process. To start the mass production, a DJ inoculum proceeding from a previous monoxenic culture is added to pre-cultured (24h) Photorhabdus bacteria. Within minutes after contact with the bacteria, DJ are expected to perceive signals that trigger their further development (DJ recovery) to reproductive hermaphrodites. A rapid, synchronized, and high DJ recovery is a key factor for an efficient culture process. In case of low percentage of DJ recovery, the final DJ yield is drastically reduced, and the amount of non-desired stages (males and non-fertilized females) hinders the DJ harvest. In a preliminary work, a huge DJ recovery phenotypic variability in H. bacteriophora ethyl methanesulphonate (EMS) mutants was determined. In the present study, two EMS-mutant lines (M31 and M88) with high and low recovery phenotypes were analyzed concerning their differences in gene expression during the first hours of contact with Photorhabdus supernatant containing food signals triggering recovery. A snapshot (RNA-seq analysis) of their transcriptome was captured at 0.5, 1, 3 and 6h after exposure. Transcripts (3060) with significant regulation changes were identified in the two lines. To analyze the RNA-seq data over time, we (1) divided the expression profiles into clusters of similar regulation, (2) identified over and under-represented gene ontology categories for each cluster, (3) identified Caenorhabditis elegans homologous genes with recovery-related function, and (4) combined the information with available single nucleotide polymorphism (SNP) data. We observed that the expression dynamics of the contrasting mutants (M31 and M88) differ the most within the first 3h after Photorhabdus supernatant exposure, and during this time, genes related to changes in the DJ cuticle and molting are more active in the high-recovery line (M31). Comparing the gene expression of DJ exposed to the insect food signal in the haemolymph, genes related to host immunosuppressive factors were not found in DJ upon bacterial supernatant exposure. No link between the position of SNPs associated with high recovery and changes in gene expression was determined for genes with high differential expression. Concerning specific transcripts, nine H. bacteriophora gene models with differential expression are provided as candidate genes for further studies.

Comparative Study of Friction Stir, Stud and Seam Welding of Aluminium Alloys Using Different Grades

Friction stud welding is an innovative and efficient welding process used to create strong and reliable bonds between studs or fasteners and workpieces, particularly in applications where rapid and secure fastening is critical. This welding technique involves generating friction and heat between the stud and workpiece to forge a strong joint. The process consists of several key steps, including preparation, rotation, weld formation, and cooling. Friction stud welding offers numerous advantages, such as speed, consistency, minimal distortion, and the elimination of the need for additional filler materials. It is widely employed in various industries, including construction, automotive, and aerospace, for applications ranging from structural steel connections to automotive component assembly. However, successful friction stud welding necessitates careful selection of materials, welding parameters, and skilled operators to ensure durable and dependable welded joints. This abstract provides a concise overview of the friction stud welding process and its essential features.

Interrogating site dependent kinetics over SiO2-supported Pt nanoparticles

A detailed knowledge of reaction kinetics is key to the development of new more efficient heterogeneous catalytic processes. However, the ability to resolve site dependent kinetics has been largely limited to surface science experiments on model systems. Herein, we can bypass the pressure, materials, and temperature gaps, resolving and quantifying two distinct pathways for CO oxidation over SiO2-supported 2 nm Pt nanoparticles using transient pressure pulse experiments. We find that the pathway distribution directly correlates with the distribution of well-coordinated (e.g., terrace) and under-coordinated (e.g., edge, vertex) CO adsorption sites on the 2 nm Pt nanoparticles as measured by in situ DRIFTS. We conclude that well-coordinated sites follow classic Langmuir-Hinshelwood kinetics, but under-coordinated sites follow non-standard kinetics with CO oxidation being barrierless but conversely also slow. This fundamental method of kinetic site deconvolution is broadly applicable to other catalytic systems, affording bridging of the complexity gap in heterogeneous catalysis.

Assessing the Impact of Preprocessing and Conversion Technologies on the Sustainable Aviation Fuel Supply from Forest Residues in the Southeast U.S.A.

Forest residues have been suggested as potential feedstock for sustainable aviation fuel (SAF) production through different pathways. However, the bulky density and scatter distribution require efficient size-reduction processes to enhance the feedstock supply. Thus, this study analyzes the impact of alternative preprocessing and conversion technologies on the SAF supply chains from logging residues (the predominant share of forest residues) using a two-stage mixed-integer linear programming model. The model determines the optimal location of biomass preprocessing depots, conversion facilities, and airports receiving SAF by minimizing the net present value of the total supply chain cost over 10 years. Using high-resolution spatial data in the Southeast U.S.A. as a case study, the results show that the technology and scale of the facilities heavily influenced the SAF’s breakeven cost and maximum supply quantity. The most economically efficient system that adopts a rotary shear milling system and pyrolysis conversion process could generate up to 650 million gallons of SAF from logging residues in the region, with nearly 62% of the SAF being produced from hardwood residues. Also, the most efficient system has an estimated breakeven cost of US$5.51 per gallon of SAF.

PREPARATION AND JUSTIFICATION OF NANOFIBRES-LOADED MAFENIDE USING ELECTROSPINNING TECHNIQUE TO CONTROL RELEASE

Objective: The primary objective was to fabricate a novel drug delivery system capable of providing a controlled and prolonged release of antibiotics. Methods: The experimental design was formulated using Design-Expert® software (version 13), enabling systematic and efficient fabrication process optimization. The study involved the preparation of various nanofiber formulations with different ratios of the three polymers to assess their impact on drug release behavior. Mafenide, a widely used antibiotic, was chosen as the model drug for this investigation. The electrospinning process allowed for producing uniform and fine nanofibers with a high surface area, ensuring a large drug-loading capacity. The synthesized nanofibers were characterized using scanning electron microscopy (SEM) and Fourier transform infrared spectroscopy (FTIR) to evaluate their morphology, chemical interactions, and thermal properties. The drug release kinetics of the antibiotic-loaded nanofibers were studied under different physiological conditions to assess their sustained release behavior. Results: The final nanofiber formula was successfully prepared using the electrospinning technique. The Fourier Transform Infrared Spectroscopy (FTIR) analysis was achieved to confirm the possibility of chemical interaction and bond formation between mafenide and the polymers. Present. The SEM picture of the optimized nanofiber formula showed the homogeneity and excellent entanglement of the electrospun nanofibers at a resolution of 5 µm. PVA/chitosan/HPMC and mafenide pure drug have been successfully fabricated with sufficient strength to resist swelling after absorbing wound exudate. The polymer network becomes more compact when chitosan and Hydroxypropyl Methyl Cellulose (HPMC) are combined with polyvinyl alcohol (PVA), enabling regulated swelling during solvent ingress. The polymer composite's three-dimensional network influenced how quickly the medication was released from the matrix. Sample 2's polymer network traps the medication, gradually releasing after controlled swelling, resulting in a sustained release profile compared to blank sample according to the cumulative release (%) study of mafenide loaded nanofiber and mafenide drug blank sample. Conclusion: This research successfully demonstrated the fabrication of sustained-release antibiotic nanofibers using electrospinning and three biocompatible polymers. The systematic optimization approach using Design-Expert® software proved effective in tailoring the drug release behavior of nanofibers. The developed drug delivery system holds great promise for pharmaceutical applications, particularly in improving antibiotic therapies and patient care.

Multi-criteria structural optimization of a hood inner structure using machine learning technique

This article presents an efficient multi-criteria structural optimization process based on machine learning for shape and size optimization of the hood inner. For this purpose, a low dimensional parameterization based on two-dimensional mapping and dimensionality reduction is used to decrease the number of design variables. There are many design parameters to be considered for designing this structure. In this study, the effect of three important criteria consisting of head injury, eigenfrequency, and static load were analyzed. A full vehicle finite element model is prepared for a newly developed product because head injury analysis requires a full vehicle model due to contact with the inner components of the engine compartment. Then, an experimental study is performed based on the real test scenario for model verification. A new rating method for analyzing the head injury criteria evaluated in different points is proposed and different machine learning techniques are compared in the approximation of the objective function. Finally, the problem is solved as both single and multi-objective optimization problems. Results present considerable improvement in accuracy and efficiency in the identified structure while the method has a low computational cost and the implementation is not difficult.