Shane Oberloier, Nicholas G. Whisman, Joshua M. Pearce

3D Printing and Additive Manufacturing • 2023

As additive manufacturing rapidly expands the number of materials including waste plastics and composites, there is an urgent need to reduce the experimental time needed to identify optimized printing parameters for novel materials. Computational intelligence (CI) in general and particle swarm optimization (PSO) algorithms in particular have been shown to accelerate finding optimal printing parameters. Unfortunately, the implementation of CI has been prohibitively complex for noncomputer scientists. To overcome these limitations, this article develops, tests, and validates PSO Experimenter, an easy-to-use open-source platform based around the PSO algorithm and applies it to optimizing recycled materials. Specifically, PSO Experimenter is used to find optimal printing parameters for a relatively unexplored potential distributed recycling and additive manufacturing (DRAM) material that is widely available: low-density polyethylene (LDPE). LDPE has been used to make filament, but in this study for the first time it was used in the open source fused particle fabrication/fused granular fabrication system. PSO Experimenter successfully identified functional printing parameters for this challenging-to-print waste plastic. The results indicate that PSO Experimenter can provide 97% reduction in research time for 3D printing parameter optimization. It is concluded that the PSO Experimenter is a user-friendly and effective free software for finding ideal parameters for the burgeoning challenge of DRAM as well as a wide range of other fields and processes.

Deborah L. Donohoe, Katherine Dennert, Rajeev Kumar et al.

3D Printing in Medicine • 2021

Abstract Background The ability of 3D printing using plastics and resins that are magnetic resonance imaging (MRI) compatible provides opportunities to tailor design features to specific imaging needs. In this study an MRI compatible cradle was designed to fit the need for repeatable serial images of mice within a mouse specific low field MRI. Methods Several designs were reviewed which resulted in an open style stereotaxic cradle to fit within specific bore tolerances and allow maximum flexibility with interchangeable radiofrequency (RF) coils. CAD drawings were generated, cradle was printed and tested with phantom material and animals. Images were analyzed for quality and optimized using the new cradle. Testing with multiple phantoms was done to affirm that material choice did not create unwanted image artifact and to optimize imaging parameters. Once phantom testing was satisfied, mouse imaging began. Results The 3D printed cradle fit instrument tolerances, accommodated multiple coil configurations and physiological monitoring equipment, and allowed for improved image quality and reproducibility while also reducing overall imaging time and animal safety. Conclusions The generation of a 3D printed stereotaxic cradle was a low-cost option which functioned well for our laboratory.

Magdalene Fogarasi, James C. Coburn, Beth Ripley

3D Printing in Medicine • 2022

Abstract Background 3D printing (3DP) has enabled medical professionals to create patient-specific medical devices to assist in surgical planning. Anatomical models can be generated from patient scans using a wide array of software, but there are limited studies on the geometric variance that is introduced during the digital conversion of images to models. The final accuracy of the 3D printed model is a function of manufacturing hardware quality control and the variability introduced during the multiple digital steps that convert patient scans to a printable format. This study provides a brief summary of common algorithms used for segmentation and refinement. Parameters for each that can introduce geometric variability are also identified. Several metrics for measuring variability between models and validating processes are explored and assessed. Methods Using a clinical maxillofacial CT scan of a patient with a tumor of the mandible, four segmentation and refinement workflows were processed using four software packages. Differences in segmentation were calculated using several techniques including volumetric, surface, linear, global, and local measurements. Results Visual inspection of print-ready models showed distinct differences in the thickness of the medial wall of the mandible adjacent to the tumor. Volumetric intersections and heatmaps provided useful local metrics of mismatch or variance between models made by different workflows. They also allowed calculations of aggregate percentage agreement and disagreement which provided a global benchmark metric. For the relevant regions of interest (ROIs), statistically significant differences were found in the volume and surface area comparisons for the final mandible and tumor models, as well as between measurements of the nerve central path. As with all clinical use cases, statistically significant results must be weighed against the clinical significance of any deviations found. Conclusions Statistically significant geometric variations from differences in segmentation and refinement algorithms can be introduced into patient-specific models. No single metric was able to capture the true accuracy of the final models. However, a combination of global and local measurements provided an understanding of important geometric variations. The clinical implications of each geometric variation is different for each anatomical location and should be evaluated on a case-by-case basis by clinicians familiar with the process. Understanding the basic segmentation and refinement functions of software is essential for sites to create a baseline from which to evaluate their standard workflows, user training, and inter-user variability when using patient-specific models for clinical interventions or decisions.

Orly Talyosef

Architext • 2020

Three-dimensional (3D) printing, also called additive manufacture (AM), is a novel, automated method of printing a structure layer-by-layer directly from a 3D digital design model. Its potential ability to build complex shapes in a less costly and more sustainable manner may revolutionize the construction industry. There are three main 3D printing techniques: (a) contour crafting; (b) concrete printing, and (c) D-shape. As a disruptive technology, 3D printing creates a new market and value network, thus disturbing the established market. Building information modeling (BIM) is a comprehensive management approach encompassing the entire life cycle of the architecture and construction (A&C) process, including architectural planning, geometrical data, scheduling, material, equipment, resource and manufacturing data, and post-construction facility management. By maintaining safety and productivity in large-scale digital processes, BIM is critical to 3D printing’s success in construction. Integrating BIM and 3D printing techniques into A&C can potentially lead to an ecological architectural process that reduces waste and energy inefficiency, and prevents injuries and fatalities on construction sites, while increasing productivity and quality. This paper examines BIM-based 3D printing of sustainable buildings, which may revolutionize the construction industry and contribute to a sustainable environment

Jana Macháčková, Alena Komersová, Marie Nevyhoštěná et al.

3D Printing and Additive Manufacturing • 2024

Naomi C. Paxton

3D Printing in Medicine • 2023

Ayşegül ASLAN, Yaren ÇELİK

International Journal of 3D Printing Technologies and Digital Industry • 2022

This study will offer domestic and foreign studies on the application of 3D printing technologies in the fields of education. The aim of this study was to review the studies that had been done in the literature on the application of 3D printing technology in the field of education. Purposive sampling method was used in the study. In this context, it was decided that in the current study, variables such as the distribution of studies on the use of 3D printer technology in the field of education by years, publication types, sample types and sizes, data collection tools and analysis methods were planned to be examined, and at the same time, in-depth analysis of the results was the most appropriate method. 101 studies were accessed in accordance with this research. One of the qualitative research methods used in the study was document analysis, and the data was analysed by examining at the documents that contained details regarding the facts that were the focus of the study. According to the data obtained, it is seen that the studies carried out on 3D printing technology between 2009-2022 are within the scope of educational activities at the K-12 level (physics, chemistry, biology, mathematics) and their numbers have increased especially after 2017. It was determined that undergraduate students were preferred the most as the sample group. It was found that, on general, qualitative research methodologies were preferred in the studies under consideration. It was seen that in-class assessments, observations and questionnaires were mostly used as data collection tools. It has been determined that content analysis is generally used in the analysis of the collected data. When the relationship between education and 3D printing technology was examined, it was determined that it can be adapted to all ages and fields and provides great convenience in interdisciplinary studies. Based on these findings, it is thought that it will be more effective to focus on the instructional aspect of 3D printing technology.

Pieter De Backer, Charlotte Allaeys, Charlotte Debbaut et al.

3D Printing in Medicine • 2021

Abstract Background Carotid Artery Stenting (CAS) is increasingly being used in selected patients as a minimal invasive approach to carotid endarterectomy. Despite the long standing tradition of endovascular treatments, visual feedback during stent-deployment is impossible to obtain as deployment is performed under fluoroscopic imaging. Furthermore, the concept of stent-placement is often still unclear to patients. 3D Printing allows to replicate patient-specific anatomies and deploy stents inside them to simulate procedures. As such these models are being used for endovascular training as well as patient education. Purpose To our knowledge, this study reports the first use of a low-cost patient-specific 3D printed model for teaching CAS deployment under direct visualization, without fluoroscopy. Methodology A CT-angiogram was segmented and converted to STL format using Mimics inPrint™ software. The carotid arteries were bilaterally truncated to fit the whole model on a Formlabs 2 printer without omitting the internal vessel diameter. Next, this model was offset using a 1 mm margin. A ridge was modelled on the original vessel anatomy which was subsequently subtracted from the offset model in order to obtain a deroofed 3D model. All vessels were truncated to facilitate post-processing, flow and guide wire placement. Results Carotid artery stents were successfully deployed inside the vessel. The deroofing allows for clear visualization of the bottlenecks and characteristics of CAS deployment and positioning, including stent foreshortening, tapering and recoil. This low-cost 3D model provides visual insights in stent deployment and positioning, and can allow for patient-specific procedure planning. Conclusions The presented approach demonstrates the use of low-cost 3D Printed CAS models in teaching complex stent behavior as observed during deployment. Two main findings are illustrated. On one hand, the feasibility of low-cost in-hospital model production is shown. On the other hand, the teaching of CAS deployment bottlenecks at the carotid level without the need for fluoroscopic guidance, is illustrated. The observed stent characteristics as shown during deployment are difficult to assess in radiologic models. Furthermore, printing patient-specific 3D models preoperatively could possibly assist in accurate patient selection, preoperative planning, case-specific training and patient education.

Carly M. Cooke, Teresa E. Flaxman, Lindsey Sikora et al.

3D Printing in Medicine • 2023

Abstract Objective Developments in 3-dimensional (3D) printing technology has made it possible to produce high quality, affordable 3D printed models for use in medicine. As a result, there is a growing assessment of this approach being published in the medical literature. The objective of this study was to outline the clinical applications of individualized 3D printing in gynecology through a scoping review. Data sources Four medical databases (Medline, Embase, Cochrane CENTRAL, Scopus) and grey literature were searched for publications meeting eligibility criteria up to 31 May 2021. Study eligibility criteria Publications were included if they were published in English, had a gynecologic context, and involved production of patient specific 3D printed product(s). Study appraisal and synthesis methods Studies were manually screened and assessed for eligibility by two independent reviewers and data were extracted using pre-established criteria using Covidence software. Results Overall, 32 studies (15 abstracts,17 full text articles) were included in the scoping review. Most studies were either case reports (12/32,38%) or case series (15/32,47%). Gynecologic sub-specialties in which the 3D printed models were intended for use included: gynecologic oncology (21/32,66%), benign gynecology (6/32,19%), pediatrics (2/32,6%), urogynecology (2/32,6%) and reproductive endocrinology and infertility (1/32,3%). Twenty studies (63%) printed 5 or less models, 6/32 studies (19%) printed greater than 5 (up to 50 models). Types of 3D models printed included: anatomical models (11/32,34%), medical devices, (2/32,6%) and template/guide/cylindrical applicators for brachytherapy (19/32,59%). Conclusions Our scoping review has outlined novel clinical applications for individualized 3D printed models in gynecology. To date, they have mainly been used for production of patient specific 3D printed brachytherapy guides/applicators in patients with gynecologic cancer. However, individualized 3D printing shows great promise for utility in surgical planning, surgical education, and production of patient specific devices, across gynecologic subspecialties. Evidence supporting the clinical value of individualized 3D printing in gynecology is limited by studies with small sample size and non-standardized reporting, which should be the focus of future studies.

Yu-Hui Huang, Bonnie Lee, Jeffrey A. Chuy et al.

3D Printing in Medicine • 2022

Abstract Background Advanced diagnostic imaging is an essential part of preoperative planning for oral and maxillofacial surgery in veterinary patients. 3-dimensional (3D) printed models and surgical guides generated from diagnostic imaging can provide a deeper understanding of the complex maxillofacial anatomy, including relevant spatial relationships. Additionally, patient-specific 3D printed models allow surgeons and trainees to better examine anatomical features through tactile and visuospatial feedback allowing for improved preoperative planning, intraoperative guidance, and enhanced trainee education. Furthermore, these models facilitate discussions with pet owners, allowing for improved owner understanding of pathology, and educated decision-making regarding treatment. Case presentation Our case series consists of three 3D printed models segmented from computed tomography (CT) and cone beam CT (CBCT) and fabricated via desktop vat polymerization for preoperative planning and intraoperative guidance for resection of maxillary osteosarcoma, mandibular reconstruction after mandibulectomy, and gap arthroplasty for temporomandibular joint ankylosis in dogs. Conclusions We illustrate multiple benefits and indications for 3D printing in veterinary oral and maxillofacial surgery. 3D printed models facilitate the understanding of complex surgical anatomy, creating an opportunity to assess the spatial relationship of the relevant structures. It facilitates individualized surgical planning by allowing surgeons to tailor and augment the surgical plan by examining patient-specific anatomy and pathology. Surgical steps may also be simulated in advance, including planning of osteotomy lines, and pre-contouring of titanium plates for reconstruction. Additionally, a 3D printed model and surgical guide also serve as invaluable intraoperative reference and guidance. Furthermore, 3D printed models have the potential to improve veterinary resident and student training as well as pet owner understanding and communication regarding the condition of their pets, treatment plan and intended outcomes.

Naomi C. Paxton

3D Printing in Medicine • 2023

Abstract 3D printing technology has become increasingly popular in healthcare settings, with applications of 3D printed anatomical models ranging from diagnostics and surgical planning to patient education. However, as the use of 3D printed anatomical models becomes more widespread, there is a growing need for regulation and quality control to ensure their accuracy and safety. This literature review examines the current state of 3D printing in hospitals and FDA regulation process for software intended for use in producing 3D printed models and provides for the first time a comprehensive list of approved software platforms alongside the 3D printers that have been validated with each for producing 3D printed anatomical models. The process for verification and validation of these 3D printed products, as well as the potential for inaccuracy in these models, is discussed, including methods for testing accuracy, limits, and standards for accuracy testing. This article emphasizes the importance of regulation and quality control in the use of 3D printing technology in healthcare, the need for clear guidelines and standards for both the software and the printed products to ensure the safety and accuracy of 3D printed anatomical models, and the opportunity to expand the library of regulated 3D printers.

Nof Nathansohn, Elisheva Gillis, Gitit Linker et al.

3D Printing and Additive Manufacturing • 2025

Imagine a world in which architecture will be 3D printed from living materials. That buildings will germinate, bloom, wither, produce new kinds of materials, and return back to the soil. This article introduces an innovative approach to sustainable architecture, through the utilization of 3D-printed structures crafted from locally sourced soil and plant seeds. After printing, the seeds germinate over time, forming load-bearing designs with interwoven root systems, which exhibit remarkable strength and resilience, reducing reliance on conventional construction materials. The research evaluates the mechanical properties of 3D-printed living structures through a set of material experiments to find a material combination that will allow maximum growth within 3D-printed architectural scale objects. The successful pilot project demonstrated their strength and capacity to support plant growth. The study also addresses the esthetic, cultural, and social dimensions of this novel fabrication technique, offering personalized, native plant-based patterns, and fostering community engagement. In conclusion, this research underscores the transformative potential of 3D-printed root-built structures as a sustainable architectural solution. By harnessing local soil and plant roots, these living constructions offer an eco-friendly alternative to conventional materials, with diverse environmental and social benefits. This study contributes to the evolving knowledge base of eco-conscious building practices, encouraging further exploration and adoption of nature-based solutions in architecture. With ongoing development, root-built buildings hold the promise of revolutionizing design, construction, and habitation, promoting a harmonious coexistence between humans and the natural environment.

Andong Wang, Junhao Guo, Chenkang Shao et al.

3D Printing and Additive Manufacturing • 2024

Currently, there is great demand for flexible three-dimensional (3D) printable thermoplastic polyurethane (TPU) wires with excellent ultraviolet (UV) resistance, which have broad application prospects in wearable products. In this study, UV-resistant TPU composites were obtained using a blending modification method. The relationship between the optimized parameters of fused deposition modeling 3D printing and mechanical properties of the TPU composite is discussed using an orthogonal test. This study observed that the UV absorption properties of TPU composites were enhanced, and the TiO 2 and TiO 2 /ZnO fillers improved the tensile strength of TPU composites. After UV aging, the tensile strength and elongation of the TPU composite slightly decreased, but were still much higher than those of pure TPU. Among the printing parameters, printing speed had the greatest influence on the mechanical properties of TPU composites. When the printing speed was 80 mm/s, printing layer thickness was 0.25 mm, nozzle temperature was 220°C, and hot bed temperature was 50°C, the TPU composites exhibited the best elongation at break and tensile strength. After regression analysis, two regression models for the elongation at break and tensile strength of TPU composites were obtained and verified, which provide a reference for predicting the relationship between the printing parameters and mechanical properties of flexible TPU composites.

Bora Uzun

3D Printing and Additive Manufacturing • 2024

Scaffolds' designs and physical properties have an important place in tissue engineering. Using different biomaterials, scaffolds with other structures can be developed. The thermal and mechanical properties of biomaterials used in producing scaffolds with the fused deposition modeling method are significant for the application's success. The material must be suitable for both the production method and to be used as a scaffold. Therefore, this study designed three different scaffolds made of the same polylactic acid (PLA) material, but with different lattice structures. To determine the mechanical properties of PLA scaffolds formed, 800 N axial compression load at a 20 mm/min velocity was applied to the samples, with n = 3 in each group. To determine the stiffness of scaffolds, the stress-strain values were calculated by measuring the maximum displacement data under load in each group. Also, finite element analysis was performed on PLA scaffold models. At the same time, scanning electron microscope, differential thermal analysis-thermogravimetric analysis, differential scanning calorimetry, and X-ray powder diffraction pattern analyses were carried out. As a result, it has been concluded that the design significantly affects mechanical properties. Besides the material, the scaffold design is the most important parameter in tissue engineering studies.

Hussain S

Bioequivalence & Bioavailability International Journal • 2020

The pharmaceutical industry is advancing at an incredible rate. Novel drug formulations for targeted therapy have been developed all thanks to advances in modern sciences. Even so, the manufacturing sector of novel dosage forms is minimal, and the industry continues to rely on traditional drug delivery systems, particularly modified tablets. The use of 3D printing technologies in pharma companies has opened up new possibilities for printed products and device research and production. 3D Printing has slowly progressed from its original use as pre-surgical imaging templates and tooling molds to produce one-of-a-kind instruments, implants, tissue engineering scaffolds, testing platforms, and drug delivery systems. The most significant advantages of 3D printing technologies include the ability to produce small batches of drugs with custom dosages, forms, weights, and drug release profiles. The production of medicines in this manner could eventually contribute to the realization of the principle of personalized medicine. The biomedical industry and academia have also embraced 3D printing in recent years. It offers commercially available medical devices as well as a forum for cutting-edge studies in fields such as tissue and organ printing. This mini-review provides an overview of 3D printed technology in medicines.

Maxwell Lohss, Elliott Hammersley, Anish Ghodadra

3D Printing in Medicine • 2023

Abstract Background The rapid expansion and anticipated U.S Food and Drug Administration regulation of 3D printing at the point-of-care necessitates the creation of robust quality management systems. A critical component of any quality management system is a document control system for the organization, tracking, signature collection, and distribution of manufacturing documentation. While off-the-shelf solutions for document control exist, external programs are costly and come with network security concerns. Here, we present our internally developed, cost-effective solution for an electronic document control system for 3D printing at the point-of-care. Methods We created a hybrid document control system by linking two commercially available platforms, Microsoft SharePoint and Adobe Sign, using a customized document approval workflow. Results Our platform meets all Code of Federal Regulations Title 21, Part 11 guidances. Conclusion Our hybrid solution for document control provides an affordable system for users to sort, manage, store, edit, and sign documents. The system can serve as a framework for other 3D printing programs to prepare for future U.S Food and Drug Administration regulation, improve the efficiency of 3D printing at the point-of-care, and enhance the quality of work produced by their respective program.

Xiaomei Zheng, Yongqing Wang, Guohong Du et al.

3D Printing and Additive Manufacturing • 2024

3D printing is an indispensable technology in modern life and is widely used in aerospace, exoskeleton, and architecture. The increasing accuracy requirements of 3D printed objects in these fields require high-precision measurement methods to obtain accurate data. Based on the precision measurement requirements, in this study, a fast multifrequency phase unwrapping method based on 3D printing object appearance acquisition is proposed. By performing standard image acquisition of 3D printed objects that are not limited to materials and sampling locations, the surface shape and texture details of the objects can be accurately reconstructed using this method, independent of ambient light, with high robustness. Compared with the conventional multifrequency method, the required projection pattern is reduced from 12 to 9 and the overall measurement efficiency is improved by 25%, while maintaining the advantages of the independent pixel calculation method of the multifrequency method. In addition, the effectiveness of the method is experimentally verified by complex surface reconstruction experiments and plaster model experiments, which provide accurate measurement accuracy with high efficiency and precision. Therefore, the method can provide accurate measurements for 3D printed objects.

Giovanni Biglino, Carina Hopfner, Joakim Lindhardt et al.

3D Printing in Medicine • 2023

Abstract This editorial presents the vision for the newly formed (2022) European 3D Special Interest Group (EU3DSIG) in the landscape of medical 3D printing. There are four areas of work identified by the EU3DSIG in the current landscape, namely: 1) creating and fostering communication channels among researches, clinicians and industry, 2) generating awareness of hospitals point-of-care 3D technologies; 3) knowledge sharing and education; 4) regulation, registry and reimbursement models.

Maria Mavri, Vangelis Mennis

3D Printing and Additive Manufacturing • 2022

The contribution of the fight against COVID-19 to the incorporation of 3D printing technology into the manufacturing industry is the research question of this study. By observing the structure of initiatives of hobbyists and enterprises in the 3D printing industry that are printing health care equipment for nursing staff, we conclude that 3D printing technology could be used for mass production under a different production model. We propose two different typologies of a factory's structure, calling them “Adjust-Semi Cloud Factory 1” and “Semi-Cloud Factory 2.” To measure the effectiveness of these new types of factories, we propose a framework based on characteristics and aspects of knowledge management.

Grace M. Thiong’o, Mark Bernstein, James M. Drake

3D Printing in Medicine • 2021

Abstract Objectives The objectives of this manuscript were to review the literature concerning 3D printing of brain and cranial vault pathology and use these data to define the gaps in global utilization of 3D printing technology for neurosurgical education. Methods Using specified criteria, literature searching was conducted to identify publications describing engineered neurosurgical simulators. Included in the study were manuscripts highlighting designs validated for neurosurgical skill transfer. Purely anatomical designs, lacking aspects of surgical simulation, were excluded. Eligible manuscripts were analyzed. Data on the types of simulators, representing the various modelled neurosurgical pathologies, were recorded. Authors’ countries of affiliation were also recorded. Results A total of thirty-six articles, representing ten countries in five continents were identified. Geographically, Africa as a continent was not represented in any of the publications. The simulation-modelling encompassed a variety of neurosurgical subspecialties including: vascular, skull base, ventriculoscopy / ventriculostomy, craniosynostosis, skull lesions / skull defects, intrinsic brain tumor and other. Finally, the vascular and skull base categories together accounted for over half (52.8 %) of the 3D printed simulated neurosurgical pathology. Conclusions Despite the growing body of literature supporting 3D printing in neurosurgical education, its full potential has not been maximized. Unexplored areas of 3D printing for neurosurgical simulation include models simulating the resection of intrinsic brain tumors or of epilepsy surgery lesions, as these require complex models to accurately simulate fine dissection techniques. 3D printed surgical phantoms offer an avenue for the advancement of global-surgery education initiatives.

Maxwell W. Walker, Christodoulos Kaoutzanis, Nicholas M. Jacobson

3D Printing in Medicine • 2023

Abstract Background Phalloplasty procedures are performed to create a phallus, typically as a gender-affirming surgery for treating gender dysphoria. Due to the controversial nature of this specific procedure, more innovation is needed to directly assist surgical teams in this field. As a result, surgeons are left to improvise and adapt tools created for other procedures to improve surgical outcomes. This study developed a patient-specific 3D printed model from segmented computed tomography (CT) scans to accurately represent the relevant vasculature necessary for anterolateral thigh (ALT) flap phalloplasty. The surgical procedure seeks to maintain intact vessels that derive from the descending branch of the lateral circumflex femoral artery, typically found traveling within the intermuscular septum between the rectus femoris and vastus lateralis. Methods In this study, we created and printed 3D models of the leg and vasculature using two techniques: (1) a standard segmentation technique with the addition of a reference grid and (2) a bitmap method in which the total CT volume is colorized and printed. Results The results gathered included the physician’s view on the model’s accuracy and visualization of relevant anatomy. Bitmap-printed models resulted in a high amount of detail, eliciting surgeons’ undesirable reactions due to the excess of information. The hybrid method produced favorable results, indicating positive feasibility. Conclusions This study tested the ability to accurately print a patient-specific 3D model that could represent the vasculature necessary for ALT flap procedures and potentially be used in surgical reference and planning in the future. A surgeon performing phalloplasty procedures discussed their approval of both models and their preference for grid creation and application.

Yanlu Wang

3D Printing in Medicine • 2024

Abstract Background 3D printers have gained prominence in rapid prototyping and viable in creating dimensionally accurate objects that are both safe within a Magnetic Resonance Imaging (MRI) environment and visible in MRI scans. A challenge when making MRI-visible objects using 3D printing is that hard plastics are invisible in standard MRI scans, while fluids are not. So typically, a hollow object will be printed and filled with a liquid that will be visible in MRI scans. This poses an engineering challenge however since objects created using traditional Fused Deposition Modeling (FDM) 3D-printing techniques are prone to leakage. Digital Light Processing (DLP) is a relatively modern and affordable 3D-printing technique using UV-hardened resin, capable of creating objects that are inherently liquid-tight. When printing hollow parts using DLP printers, one typically requires adding drainage holes for uncured liquid resin to escape during the printing process. If this is not done liquid resin will remain inside the object, which in our application is the desired outcome. Purpose We devised a method to produce an inherently MRI-visible accessory using DLP technology with low dimensional tolerance to facilitate MRI-guided breast biopsies. Methods By hollowing out the object without adding drainage holes and tuning printing parameters such as z-lift distance to retain as much uncured liquid resin inside as possible through surface tension, objects that are inherently visible in MRI scans can be created without further post-processing treatment. Results Objects created through our method are simple and inexpensive to recreate, have minimal manufacturing steps, and are shown to be dimensionally exact and inherently MRI visible to be directly used in various applications without further treatment. Conclusion Our proposed method of manufacturing objects that are inherently both MRI safe, and MRI visible. The proposed process is simple and does not require additional materials and tools beyond a DLP 3D-printer. With only an inexpensive DLP 3D-printer kit and basic cleaning and sanitation materials found in the hospital, we have demonstrated the viability of our process by successfully creating an object containing fine structures with low spatial tolerances used for MRI-guided breast biopsies.

Unknown Author

Fuel Cells Bulletin • 2021

Danish companies Blue World Technologies and Clayton Power are collaborating to develop a small-scale mobile methanol fuel cell solution for stationary and auxiliary power. The aim is to develop a solution in the 5–15 kW power range, that can be applied to heavy-duty trucks for powering air-conditioning and onboard appliances.

Jingwei Qi, Ming Hu, Pengcheng Xu et al.

Fuel • 2023

Arshia Fathima, Yong Zheng Liam, IMSK Ilankoon et al.

Bioresource Technology • 2022

Mathematical modelling of microbial fuel cells (MFC) facilitates their scale-up by maintaining dimensionless parameters across reactor volumes for consistent performance. This study developed data-driven correlations to predict areal power density for a batch-fed dual-chamber MFC using hybridised first-principle mechanistic model and Buckingham's Pi theorem. The established correlations were validated using experimentally-derived data for pre-enriched electroactive biofilm from mixed cultures. The biochemical model parameters are infilled with stoichiometric and thermodynamics estimations. Results showed that the correlations using logistic kinetics (Nash-Sutcliffe Efficiency, NSE = 0.59) outperformed Monod kinetics (NSE = 0.52) as the latter was not suitable for representing the first-order biochemical kinetics under limited substrate conditions. Sensitivity analysis on varying pH and bicarbonate concentration improved model predictions by ± 50%, though relative absolute error was ± 20% due to inherent error of estimated biochemical parameters. The application of hybridised approach for modelling MFC provides renewed perspectives for their rational design and scale-up applications.

Jiao Meng, Shufan Liu, Le Gao et al.

Microbial Cell Factories • 2023

Abstract Background Methanol, synthesized from CO 2 , is a potentially sustainable one-carbon (C1) resource for biomanufacturing. The use of methanol as a feedstock to produce single cell protein (SCP) has been investigated for decades as an alternative to alleviate the high global demand for animal-derived proteins. The methylotrophic yeast Pichia pastoris is an ideal host for methanol-based SCP synthesis due to its natural methanol assimilation ability. However, improving methanol utilization, tolerance to higher temperature, and the protein content of P. pastoris are also current challenges, which are of great significance to the economical industrial application using methanol as a feedstock for SCP production. Results In the present work, adaptive laboratory evolution (ALE) has been employed to overcome the low methanol utilization efficiency and intolerance to a higher temperature of 33 °C in P. pastoris , associated with reduced carbon loss due to the lessened detoxification of intracellular formaldehyde through the dissimilation pathway and cell wall rearrangement to temperature stress resistance following long-term evolution as revealed by transcriptomic and phenotypic analysis. By strengthening nitrogen metabolism and impairing cell wall synthesis, metabolic engineering further increased protein content. Finally, the engineered strain via multi-strategy produced high levels of SCP from methanol in a pilot-scale fed-batch culture at 33 °C with a biomass of 63.37 g DCW/L, methanol conversion rate of 0.43 g DCW/g, and protein content of 0.506 g/g DCW. SCP obtained from P. pastoris contains a higher percentage of protein compared to conventional foods like soy, fish, meat, whole milk, and is a source of essential amino acids, including methionine, lysine, and branched-chain amino acids (BCAAs: valine, isoleucine, leucine). Conclusions This study clarified the unique mechanism of P. pastoris for efficient methanol utilization, higher temperature resistance, and high protein synthesis, providing a P. pastoris cell factory for SCP production with environmental, economic, and nutritional benefits.

Prince Atta Opoku, Huang Jingyu, Li Yi et al.

Renewable Energy • 2023

Unknown Author

Fuel Cells Bulletin • 2021

Performance verification is nearing completion on new fuel cell electric terminal tractors, as the Zero Emissions for California Ports (ZECAP) project prepares to launch. GTI [see also page 6] and its partners have spent 16 months designing and assembling the tractors, which will now be assessed in a demanding, real-world cargo handling application. The hydrogen refueling equipment is in final assembly and scheduled for installation this spring, alongside delivery of the trucks.

Ruggero Rossi, Andy Y. Hur, Martin A. Page et al.

Water Research • 2022

Luis Caballero-Sanchez, Pedro E. Lázaro-Mixteco, Alejandra Vargas-Tah et al.

Microbial Cell Factories • 2023

Abstract Background A processing methodology of raw starch extraction from avocado seeds (ASs) and a sequential hydrolysis and fermentation bioprocess in just a few steps was successfully obtained for the bioethanol production by a single yeast Saccharomyces cerevisiae strain and this research was also to investigate the optimum conditions for the pretreatment of biomass and technical procedures for the production of bioethanol. It successfully resulted in high yields and productivity of all the experiments from the laboratory scale and the pilot plant. Ethanol yields from pretreated starch are comparable with those in commercial industries that use molasses and hydrolyzed starch as raw materials. Results Before the pilot-scale bioethanol production, studies of starch extraction and dilute sulfuric acid-based pretreatment was carefully conducted. The amount of starch extracted from dry and fresh avocado seed was 16.85 g ± 0.34 g and 29.79 ± 3.18 g of dry starch, representing a yield of ∼17% and 30%, respectively. After a dilute sulfuric acid pretreatment of starch, the released reducing sugars (RRS) were obtained and the hydrolysate slurries containing glucose (109.79 ± 1.14 g/L), xylose (0.99 ± 0.06 g/L), and arabinose (0.38 ± 0.01 g/L). The efficiency of total sugar conversion was 73.40%, with a productivity of 9.26 g/L/h. The ethanol fermentation in a 125 mL flask fermenter showed that Saccharomyces cerevisiae (Fali, active dry yeast) produced the maximum ethanol concentration, p max at 49.05 g/L (6.22% v/v) with a yield coefficient, Y p/s of 0.44 g Ethanol/ g Glucose , a productivity or production rate, r p at 2.01 g/L/h and an efficiency, Ef of 85.37%. The pilot scale experiments of the ethanol fermentation using the 40-L fermenter were also successfully achieved with essentially good results. The values of p max, Y p/s , r p , and Ef of the 40-L scale were at 50.94 g/L (6.46% v/v), 0.45 g Ethanol/ g Glucose , 2.11 g/L/h, and 88.74%, respectively. Because of using raw starch, major by-products, i.e., acetic acid in the two scales were very low, in ranges of 0.88–2.45 g/L, and lactic acid was not produced, which are less than those values in the industries. Conclusions The sequential hydrolysis and fermentation process of two scales for ethanol production using the combination of hydrolysis by utilizing dilute sulfuric acid-based pretreatment and fermentation by a single yeast Saccharomyces cerevisiae strain is practicable and feasible for realistic and effective scale-up strategies of bioethanol production from the starch of avocado seeds.

Isabel Thiele, Lara Santolin, Svea Detels et al.

Microbial Biotechnology • 2024

Abstract The transition towards a sustainable bioeconomy requires the development of highly efficient bioprocesses that enable the production of bulk materials at a competitive price. This is particularly crucial for driving the commercialization of polyhydroxyalkanoates (PHAs) as biobased and biodegradable plastic substitutes. Among these, the copolymer poly(hydroxybutyrate‐ co ‐hydroxyhexanoate) (P(HB‐ co ‐HHx)) shows excellent material properties that can be tuned by regulating its monomer composition. In this study, we developed a high‐cell‐density fed‐batch strategy using mixtures of fructose and canola oil to modulate the molar composition of P(HB‐ co ‐HHx) produced by Ralstonia eutropha Re2058/pCB113 at 1‐L laboratory scale up to 150‐L pilot scale. With cell densities >100 g L −1 containing 70–80 wt% of PHA with tunable HHx contents in the range of 9.0–14.6 mol% and productivities of up to 1.5 g L −1 h −1 , we demonstrate the tailor‐made production of P(HB‐ co ‐HHx) at an industrially relevant scale. Ultimately, this strategy enables the production of PHA bioplastics with defined material properties on the kilogram scale, which is often required for testing and adapting manufacturing processes to target diverse applications.

Sima Malekmohammadi, Seyed Ahmad Mirbagheri

Environmental Technology • 2023

Despite the high efficiency of microbial fuel cells (MFCs), MFCs cannot be a suitable alternative for treatment plants because of insufficient power generation and tiny reactors. Additionally, the increased reactor size and MFC stack result in a reduction in production power and reverse voltage. In this study, a larger MFC with a volume of 1.5 L has been designed called LMFC. A conventional MFC, called SMFC, with a volume of 0.157 L, was constructed and compared with LMFC. Moreover, the designed LMFC can be integrated with other treatment systems and generate significant electricity. In order to evaluate MFC's ability to integrate with other treatment systems, the LMFC reactor was converted into MFC-MBBR by adding sponge biocarriers. A 9.5 percent increase in reactor volume resulted in a 60 percent increase in power density from 290 (SMFC) to 530 (LMFC). An agitator effect was also investigated for better mixing and circulating substrate, which positively affected the power density by about 18%. Compared with LMFCs, the reactor with biocarriers generated a 28% higher power density. The COD removal efficiency of SMFC, LMFC, and MFC-MBBR reactors after 24 h was 85, 66, and 83%, respectively. After 80 h of operation, the Coulombic efficiency of the SMFC, LMFC, and MFC-MBBR reactors was 20.9, 45.43, and 47.28%, respectively. The doubling of coulombic efficiency from SMFC to LMFC reactor shows the design's success. The reduction of COD removal efficiency in LMFC is the reason for integrating this reactor with other systems, which was compensated by adding biocarriers.

Dishant Patel, Sweta L. Bapodra, Datta Madamwar et al.

Bioresource Technology • 2021

Pim de Jager, Daniel Groen, David P.B.T.B. Strik

Renewable Energy • 2023

Mouna Mothey

Kuwait Journal of Machine Learning • 2023

Software testing is one of the most critical processes toward achieving software quality and reliability. However, this is a time-consuming and resource-intensive process. Integration of Machine Learning into such a process in software testing could be seen as promising for automating or optimising such processes. This report discusses how ML techniques can assist in streamlining some of these testing activities, such as test case generation, fault detection, and test prioritization. Predictive analytics and ML algorithms make testing better in terms of effectiveness, accuracy, and adaptability. Although much has been accomplished, there are many issues related to fully implementing ML in traditional testing frameworks that still need research.

Karthika Balasubramani, Uma Maheswari Natarajan

Babylonian Journal of Machine Learning • 2024

Traffic go with the flow forecasting is essential in urban planning and management, optimizing transportation structures and resource allocation. However, accurately predicting visitors glide is tough because of its inherent complexity, nonlinearity, and diverse uncertain factors. The trouble declaration underscores the issue in as it should be forecasting site visitors flow, mainly in urban environments characterized through dynamic and complex site visitor’s styles. In the existing paintings there are numerous traditional devices getting to know models used for visitors flow prediction, however those conventional strategies show off barriers in reaching excessive prediction accuracy. Therefore, the proposed work targets to put into effect hybrid optimization techniques for correct prediction in shipping machine. Here fuzzy wavelet neural community (FWNN) is used to address complicated nonlinear structures with uncertain conditions and hybrid optimization method called hybrid firefly and particle swarm optimization (HFO-PSO) which combines the exploration and exploitation talents of firefly and this fusion allows the version to capture intricate visitor’s styles efficiently and optimize the prediction technique, improving accuracy and efficiency. Moreover, the prediction performance of the proposed model is established and compared by means of the usage of distinct measures.

Kavita Rajora, Nazar salih Abdulhussein

Babylonian Journal of Machine Learning • 2023

High false positive rates impede the adoption of anomaly detection methods, which have promise for detecting novel cyber threats. Techniques reviewed include Extreme Learning Machine (ELM), Hidden Markov Models (HMM), situation awareness frameworks, ensemble methods, and feature selection algorithms when applied to contemporary benchmark datasets. Findings show combinations of ELM, HMMs, and ensemble classifiers can achieve reduced false positive rates. However, gaps still exist in research using current representative data.

Mahdi Salah Mahdi AL-Inizi

Babylonian Journal of Machine Learning • 2025

Government bodies around the world are going digital and slowly starting to make use of data driven technologies to make better, faster and more transparent decisions. From these technologies, machine learning (ML) has become one of the most significantly employed tools, especially via its ability to predict. Predictive analytics allows governments to identify obscure trends that previously were hidden, predict potential future scenarios with an acceptable level of certainty and better inform decision-making in important areas, such as public finance, healthcare planning, emergency management, and resource allocation. In this work we explore the use of predictive modeling (implemented as our own Linear Regression, Decision Trees, Random Forests and Artificial Neural Networks) in the context of governmental decision models. The models were tested on real-world cases such as quarterly budget planning or estimation of healthcare service demand or emergency resource allocation using publicly available data from open government data platforms. Performance was evaluated based on the well-known RMSE, MAE and R² score. Results show that Artificial Neural Network always leads the highest in predictive accuracy, especially in dense or complex data setting, and there is no significant difference between Random Forest and Neural Network (the Random Forest has more generalization between interpretability and predictive power. On the other hand, Linear Regression and Decision Trees are more interpretable but have restrictions in using non-linear or high-dimensional datasets. In addition, the paper covers practical challenges including algorithmic bias, data quality considerations, and infrastructure capabilities, and ethical implications of automated decision making. This study has implications for the growing smart governance by proposing an integrated machine learning framework suitable for evidence-based policymaking. Future work involves improving the accuracy of prediction by incorporating explainable AI methodologies and customizing the model locally to enhance transparency, accountability, and generalization across different regional offices.

Sumana Sharma Poudel, Suresh Pokharel, Mohan Timilsina

Machine Learning with Applications • 2024

Akeel Shaker Mahmoud, Olfa Lamouchi, Safya Belghith

Babylonian Journal of Machine Learning • 2024

Chronic kidney disease (CKD) is a prevalent and debilitating condition worldwide, characterized by progressive loss of kidney function over time. Early detection plays a crucial role in mitigating its impact on patient health and healthcare systems. In recent years, there has been a burgeoning interest in leveraging machine learning (ML) and deep learning (DL) techniques to enhance the early diagnosis of CKD. This comprehensive review explores the advancements in ML and DL models applied to CKD diagnosis, focusing on their ability to integrate diverse data sources including clinical biomarkers, imaging modalities, and patient demographics. Key ML algorithms such as Support Vector Machines (SVM), Random Forests (RF), and neural network architectures like Convolutional Neural Networks (CNNs) and Long Short-Term Memory networks (LSTMs) are examined in the context of their performance in predicting CKD progression, classifying disease stages, and identifying at-risk populations. Furthermore, the review discusses challenges such as data quality, model interpretability, and integration into clinical practice, alongside emerging trends in explainable AI, transfer learning, federated learning, and integration with electronic health records (EHRs). By synthesizing findings from recent literature, this paper aims to provide insights into current methodologies, identify gaps for future research, and underscore the transformative potential of ML and DL in revolutionizing early CKD diagnosis and management..