The rising global prevalence of diabetes mellitus, a chronic metabolic disorder, poses significant challenges to healthcare systems worldwide. This study examined in-hospital mortality among patients diagnosed with non-insulin-dependent diabetes mellitus (NIDDM) of ICD-10, or Type 2 Diabetes Mellitus (T2DM), in Indonesia, utilizing hospital claims data spanning from 2017 to 2022 obtained from the Indonesia Health Social Security Agency or Badan Penyelenggara Jaminan Sosial (BPJS) Kesehatan. The analysis, which included 610,809 hospitalized T2DM patients, revealed an in-hospital mortality rate of 6.6%. Factors contributing to an elevated risk of mortality included advanced age, the presence of comorbidities, and severe complications. Additionally, patients receiving health subsidies and those treated in government hospitals were found to have higher mortality risks. Geographic disparities were observed, highlighting variations in healthcare outcomes across different regions. Notably, the complication of ketoacidosis emerged as the most significant risk factor for in-hospital mortality, with an odds ratio (OR) of 10.86, underscoring the critical need for prompt intervention and thorough management of complications to improve patient outcomes. Full article

Indoor wireless positioning has long been a dynamic field of research due to its broad application range. While many commercial products have been developed, they often are not open source or require substantial and costly infrastructure. Academically, research has extensively explored Bluetooth Low Energy (BLE) for positioning, yet there are a noticeable lack of studies that comprehensively compare traditional algorithms under these conditions. This research aims to fill this gap by evaluating classical positioning algorithms such as K-Nearest Neighbor (KNN), Weighted K-Nearest Neighbor (WKNN), Naïve Bayes (NB), and a Received Signal Strength-based Neural Network (RSS-NN) using BLE technology. We also introduce a novel method using Convolutional Neural Networks (CNN), specifically tailored to process RSS data structured in an image-like format. This approach helps overcome the limitations of traditional RSS fingerprinting by effectively managing the environmental dynamics within indoor settings. In our tests, all algorithms performed well, consistently achieving an average accuracy of less than two meters. Remarkably, the CNN method outperformed others, achieving an accuracy of 1.22 m. These results establish a solid basis for future research, particularly towards enhancing the precision of indoor positioning systems using deep learning for cost-effective, easy to set up applications. Full article

Background: Adopting advanced digital technologies as diagnostic support tools in healthcare is an unquestionable trend accelerated by the COVID-19 pandemic. However, their accuracy in suggesting diagnoses remains controversial and needs to be explored. We aimed to evaluate and compare the diagnostic accuracy of two free accessible internet search tools: Google and ChatGPT 3.5. Methods: To assess the effectiveness of both medical platforms, we conducted evaluations using a sample of 60 clinical cases related to urological pathologies. We organized the urological cases into two distinct categories for our analysis: (i) prevalent conditions, which were compiled using the most common symptoms, as outlined by EAU and UpToDate guidelines, and (ii) unusual disorders, identified through case reports published in the ‘Urology Case Reports’ journal from 2022 to 2023. The outcomes were meticulously classified into three categories to determine the accuracy of each platform: “correct diagnosis”, “likely differential diagnosis”, and “incorrect diagnosis”. A group of experts evaluated the responses blindly and randomly. Results: For commonly encountered urological conditions, Google’s accuracy was 53.3%, with an additional 23.3% of its results falling within a plausible range of differential diagnoses, and the remaining outcomes were incorrect. ChatGPT 3.5 outperformed Google with an accuracy of 86.6%, provided a likely differential diagnosis in 13.3% of cases, and made no unsuitable diagnosis. In evaluating unusual disorders, Google failed to deliver any correct diagnoses but proposed a likely differential diagnosis in 20% of cases. ChatGPT 3.5 identified the proper diagnosis in 16.6% of rare cases and offered a reasonable differential diagnosis in half of the cases. Conclusion: ChatGPT 3.5 demonstrated higher diagnostic accuracy than Google in both contexts. The platform showed satisfactory accuracy when diagnosing common cases, yet its performance in identifying rare conditions remains limited. Full article

The Least Absolute Shrinkage and Selection Operator (LASSO) regression technique has proven to be a valuable tool for fitting and reducing linear models. The trend of applying LASSO to compositional data is growing, thereby expanding its applicability to diverse scientific domains. This paper aims to contribute to this evolving landscape by undertaking a comprehensive exploration of the $L^1$-norm for the penalty term of a LASSO regression in a compositional context. This implies first introducing a rigorous definition of the compositional $L^p$-norm, as the particular geometric structure of the compositional sample space needs to be taken into account. The focus is subsequently extended to a meticulous data-driven analysis of the dimension reduction effects on linear models, providing valuable insights into the interplay between penalty term norms and model performance. An analysis of a microbial dataset illustrates the proposed approach. Full article

Aim: The purpose of the present study is the three-dimensional (3D) analysis of molar and incisor movements that occur during the correction of the upper midline deviation by using the Mesial-Distalslider appliance. Materials and Methods: A total of 20 consecutive patients (12 women and 8 men; mean age 19.6 ± 11.1 years) were selected from the Orthodontic Department of Heinrich-Heine University of Düsseldorf. To correct the upper midline deviation (>2 mm), the patients were treated with asymmetric mechanics (mesialization on one side and distalization on the contralateral side) with the aid of Mesial-Distalslider. Dental casts were taken for each patient before (T0) and after the treatment (T1). The casts were 3D digitized and the models were superimposed on the palatal anterior region. Three-dimensional molar movements and sagittal incisor movements (proclination and retroclination) were assessed for T0 and T1. Results: At the end of the treatment, the total movements of the molars resulted in 4.5 ± 2.2 mm (antero-posterior direction), −0.4 ± 2.4 mm (transverse direction) and 0.3 ± 0.9 mm (vertical direction) on the mesialization side, and −2.4 ± 1.7 mm (antero-posterior direction), −0.5 ± 1.5 mm (transverse direction) and 0.2 ± 1.4 mm (vertical direction) on the distalization side. Incisor displacement was 0.9 mm ± 1.7 (mesialization side) and 0.6 mm ± 0.7 (distalization side). Conclusion: The Mesial-Distalslider appliance could be considered a valuable tool in orthodontic treatment for upper midline correction. Within the limits of a retrospective study, asymmetric molar movements appeared possible without clinically relevant anchorage loss. Full article

Traditional treatments of cancer have faced various challenges, including toxicity, medication resistance, and financial burdens. On the other hand, bioactive phytochemicals employed in complementary alternative medicine have recently gained interest due to their ability to control a wide range of molecular pathways while being less harmful. As a result, we used a network pharmacology approach to study the possible regulatory mechanisms of active constituents of Cordia myxa for the treatment of liver cancer (LC). Active constituents were retrieved from the IMPPAT database and the literature review, and their targets were retrieved from the STITCH and Swiss Target Prediction databases. LC-related targets were retrieved from expression datasets (GSE39791, GSE76427, GSE22058, GSE87630, and GSE112790) through gene expression omnibus (GEO). The DAVID Gene Ontology (GO) database was used to annotate target proteins, while the Kyoto Encyclopedia and Genome Database (KEGG) was used to analyze signaling pathway enrichment. STRING and Cytoscape were used to create protein–protein interaction networks (PPI), while the degree scoring algorithm of CytoHubba was used to identify hub genes. The GEPIA2 server was used for survival analysis, and PyRx was used for molecular docking analysis. Survival and network analysis revealed that five genes named heat shot protein 90 AA1 (HSP90AA1), estrogen receptor 1 (ESR1), cytochrome P450 3A4 (CYP3A4), cyclin-dependent kinase 1 (CDK1), and matrix metalloproteinase-9 (MMP9) are linked with the survival of LC patients. Finally, we conclude that four extremely active ingredients, namely cosmosiin, rosmarinic acid, quercetin, and rubinin influence the expression of HSP90AA1, which may serve as a potential therapeutic target for LC. These results were further validated by molecular dynamics simulation analysis, which predicted the complexes with highly stable dynamics. The residues of the targeted protein showed a highly stable nature except for the N-terminal domain without affecting the drug binding. An integrated network pharmacology and docking study demonstrated that C. myxa had a promising preventative effect on LC by working on cancer-related signaling pathways. Full article

Clinicians often have to face infections caused by microorganisms that are difficult to eradicate due to their resistance and/or tolerance to antimicrobials. Among these pathogens, Pseudomonas aeruginosa causes chronic infections due to its ability to form biofilms on medical devices, skin wounds, ulcers and the lungs of patients with Cystic Fibrosis. In this scenario, the plant world represents an important reservoir of natural compounds with antimicrobial and/or antibiofilm properties. In this study, an extract from the leaves of Combretum micranthum G. Don, named Cm4-p, which was previously investigated for its antimicrobial activities, was assayed for its capacity to inhibit biofilm formation and/or to eradicate formed biofilms. The model strain P. aeruginosa PAO1 and its isogenic biofilm hyperproducer derivative B13 were treated with Cm4-p. Preliminary IR, UV-vis, NMR, and mass spectrometry analyses showed that the extract was mainly composed of catechins bearing different sugar moieties. The phytocomplex (3 g/L) inhibited the biofilm formation of both the PAO1 and B13 strains in a significant manner. In light of the obtained results, Cm4-p deserves deeper investigations of its potential in the antimicrobial field. Full article

Background: LC-MS is an ever-increasingly used methodology for clinical applications. Due to the superior selectivity and sensitivity, in certain situations, it can offer an advantage or be the only option for diagnostics and biomonitoring applications. Methods: A high selectivity sensitive LC-MS/MS method was developed for direct quantification of free plasma polyunsaturated fatty acids as well as conjugated membrane polyunsaturated fatty acids, using isocratic reverse phase elution. A quick and simple sample purification method was used in order to ensure high-throughput analysis of biological samples. The method was validated with regard to selectivity, sensitivity, linearity, accuracy, precision, carryover, and recovery, as well as other relevant parameters. Results and Conclusions: The method was developed and validated with respect to all relevant parameters and was successfully used in a number of clinical diagnostics and biomonitoring applications. The simple sample purification process allowed for an easy learning curve for analysts and other users, while ensuring a low chance of systematic or random errors and thus reliable results usable in a clinical setting. Full article

This study introduces a novel approach for analyzing thin film interference spectra by employing a micro-spectrometer equipped with a spectral chip. Focusing on zinc oxide (ZnO) thin films prepared via the sol–gel method, this research aims to explore the films’ physical properties through spectral analysis. After obtaining the interference spectrum of the ZnO thin films, the peak positions within the spectrum were cataloged. Mathematical simulation was used to adjust the refractive index and thickness of the films to match the simulated interference peak positions with the observed peak positions. The thickness of the prepared ZnO film was estimated to be 4.9 μm and its refractive index at 80 °C was estimated to be 1.96. In addition, the measurement system was used to detect environmental changes, including temperature changes and gas exposure. It was observed that the optical characteristics of ZnO films exhibit marked variations with temperature shifts, enabling the establishment of a temperature calibration curve based on spectral feature displacement. In addition, experiments using a variety of gases showed that NO₂ and gaseous isopropanol significantly affect the interference spectrum of ZnO, with the peak of the interference spectrum shifted by 2.3 nm and 5.2 nm, respectively, after injection of the two gases. This indicates that interferometric spectroscopy can serve as an effective tool for ZnO monitoring, capable of selectively detecting specific gases. Full article

This paper focuses on the digital closed-loop design for a step-up converter with hybrid switching. For this purpose, for the first time, the control-to-output small-signal transfer function of a hybrid switching converter is determined in the rational form. Based on it, a type 3 analog controller is designed, and then, its digitized counterpart is found, and the digital controller is designed using a digital signal processor. The closed-loop operation is then validated both through simulation and practical implementation. Full article

Lung adenocarcinoma (LUAD) is the most prevalent and aggressive subtype of lung cancer, exhibiting a dismal prognosis with a five-year survival rate below 5%. DEAD-box RNA helicase 18 (DDX18, gene symbol DDX18), a crucial regulator of RNA metabolism, has been implicated in various cellular processes, including cell cycle control and tumorigenesis. However, its role in LUAD pathogenesis remains elusive. This study demonstrates the significant upregulation of DDX18 in LUAD tissues and its association with poor patient survival (from public databases). Functional in vivo and in vitro assays revealed that DDX18 knockdown potently suppresses LUAD progression. RNA sequencing and chromatin immunoprecipitation experiments identified cyclin-dependent kinase 4 (CDK4), a cell cycle regulator, as a direct transcriptional target of DDX18. Notably, DDX18 depletion induced G1 cell cycle arrest, while its overexpression promoted cell cycle progression even in normal lung cells. Interestingly, while the oncogenic protein c-Myc bound to the DDX18 promoter, it did not influence its expression. Collectively, these findings establish DDX18 as a potential oncogene in LUAD, functioning through the CDK4-mediated cell cycle pathway. DDX18 may represent a promising therapeutic target for LUAD intervention. Full article

In order to obtain more data concerning the influence of fertilizers (organic and mineral) on different forage plants in the northeastern Romanian grassland, the mass fractions of 14 essential, enzymatic, or toxic elements were determined by instrumental neutron activation analysis together with the amount of crude proteins, ash, fibers, as well as fat ether extract. The final results showed a significant variance in the content of analyzed elements on organic as well as on mineral fertilized experimental plots. At the same time, increased content of crude protein and fat ether extract was evident in fertilized grasses for all applied fertilizers, while other global indicators such as neutral and acid fibers of sulfuric lignin content decreased, suggesting significantly higher nutritional values for fertilized forage plants. Full article

Shadows in hyperspectral images lead to reduced spectral intensity and changes in spectral characteristics, significantly hindering analysis and applications. However, current shadow compensation methods face the issue of nonlinear attenuation at different wavelengths and unnatural transitions at the shadow boundary. To address these challenges, we propose a two-stage shadow compensation method based on multi-exposure fusion and edge fusion. Initially, shadow regions are identified through color space conversion and an adaptive threshold. The first stage utilizes multi-exposure, generating a series of exposure images through adaptive exposure coefficients that reflect spatial shadow intensity variations. Fusion weights for exposure images are determined based on exposure, contrast, and spectral variance. Then, the exposure sequence and fusion weights are constructed as Laplacian pyramids and Gaussian pyramids, respectively, to obtain a weighted fused exposure sequence. In the second stage, the previously identified shadow regions are smoothly reintegrated into the original image using edge fusion based on the p-Laplacian operator. To further validate the effectiveness and spectral fidelity of our method, we introduce a new hyperspectral image dataset. Experimental results on the public dataset and proposed dataset demonstrate that our method surpasses other mainstream shadow compensation methods. Full article

Background: Undifferentiated pleomorphic sarcomas (UPSs) are amongst the most common subtypes of soft-tissue sarcomas. Few real-world data on the use of immune checkpoint blockade (ICB) in UPS patients and other high-grade pleomorphic STS patients are available. Purpose: The purpose of our study is to describe the efficacy and toxicity of ICB in patients with advanced UPSs and other high-grade pleomorphic sarcomas treated at our institution. Methods: This is a retrospective, observational study of all patients with metastatic high-grade pleomorphic sarcomas treated with FDA-approved ICB at MD Anderson Cancer Center between 1 January 2015 and 1 January 2023. Patients included in trials for which results are not yet published were excluded. Results: Thirty-six patients with advanced/metastatic pleomorphic sarcomas were included. The median age was 52 years. A total of 26 patients (72%) had UPSs and 10 patients (28%) had other high-grade pleomorphic sarcomas. The median follow-up time was 8.8 months. The median PFS was 2.9 months. The 3-month PFS and 6-month PFS were 46% and 32%, respectively. The median OS was 12.9 months. The 12-month OS and 24-month OS were 53% and 29%, respectively. The best response, previous RT, and type of ICB treatment were significantly and independently associated with shorter PFS (p = 0.0012, p = 0.0019 and p = 0.036, respectively). No new safety signal was identified, and the toxicity was overall manageable with no toxic deaths and only four patients (11%) stopping treatment due to toxicity. Conclusions: Real-world retrospective data are consistent with the published literature, with a promising 6-month PFS of 32%. Partial or stable responders to ICB treatment have significantly improved PFS compared to progressors. Full article

Background: A measurable feature of the efficiency of vehicle use in transportation companies is the revenue from transport orders, which has a significant impact on their profitability. Therefore, it is important to skillfully analyze the parameters related to the operation of vehicles and their impact on the bottom line. Transportation companies, when managing their operations, take steps to reduce operating costs. The above makes a large number of studies available in the literature on the analysis of vehicle damage or wear of system components, as well as ways to predict them. However, there is a lack of studies treating the impact of the parameters of specific orders on economic efficiency, which is a research niche undertaken in the following study. Methods: The purpose of this article was to analyze the economic efficiency of vehicle operation in terms of the financial security of enterprises. The main research problem was formulated in the form of the question of how the various parameters of a transport order affect its profitability. During our study, critical analysis of the literature, mathematical modeling and inference were used. A detailed analysis of transport orders executed by SMEs (small and medium-sized enterprises), which are characterized by a fleet of light commercial vehicles with a capacity of up to 3.5 t, was carried out in the FMCG (Fast-Moving Consumer Good) industry in Poland in 2021–2022. Due to the binary variable form, a logistic regression model was elaborated. The estimated parameters of the model and the calculated odds ratios made it possible to assess the influence of the selected factors on the profitability of orders. Results: Among other things, it was shown that in the case of daily vehicle mileage, the odds quotient indicates that with each additional kilometer driven, the probability of profitability of an order increases by 1%. Taking into account the speed of travel, it is estimated that with an increase in its value by 1 km/h, the probability of profitability of an order decreases by 3%. On the other hand, an increase in cargo weight by 1 kg makes the probability of a profitable order increase by 9%. Conclusion: Through this study, the limited availability of low-cost analytical tools that can be applied during transportation fleet management in SME companies was confirmed, as was the use of simple and non-expansive mathematical models. At the same time, they are not “black boxes” and therefore enable drawing and implementing model conclusions into operations. The results obtained can help shape the overall strategy of companies in the area of vehicle operation and can support the decision-making process related to the management of subsequent orders, indicating those that will bring the highest profit. The above is very important for SME companies, which often operate on the verge of profitability. Full article

This paper delves into the realm of human activity recognition (HAR) by leveraging the capabilities of Siamese neural networks (SNNs), focusing on the comparative effectiveness of contrastive and triplet learning approaches. Against the backdrop of HAR’s growing importance in healthcare, sports, and smart environments, the need for advanced models capable of accurately recognizing and classifying complex human activities has become paramount. Addressing this, we have introduced a Siamese network architecture integrated with convolutional neural networks (CNNs) for spatial feature extraction, bidirectional LSTM (Bi-LSTM) for temporal dependency capture, and attention mechanisms to prioritize salient features. Employing both contrastive and triplet loss functions, we meticulously analyze the impact of these learning approaches on the network’s ability to generate discriminative embeddings for HAR tasks. Through extensive experimentation, the study reveals that Siamese networks, particularly those utilizing triplet loss functions, demonstrate superior performance in activity recognition accuracy and F1 scores compared with baseline deep learning models. The inclusion of a stacking meta-classifier further amplifies classification efficacy, showcasing the robustness and adaptability of our proposed model. Conclusively, our findings underscore the potential of Siamese networks with advanced learning paradigms in enhancing HAR systems, paving the way for future research in model optimization and application expansion. Full article

Sport is considered an exceptional activity for promoting healthy lifestyles, but the relationship between sport and alcohol consumption is inconclusive. Research on personal values may shed light on this issue and thus make it possible to find effective ways to prevent alcohol misuse in adolescents. The main objectives of this study were to explore the relationships between personal values and alcohol consumption amongst adolescent athletes and to validate the Portrait Values Questionnaire-21 (PVQ-21) in this population. A total of 914 athletes (aged 11–19; 55.4% female) participated in this study. Confirmatory multidimensional scaling analysis and confirmatory factor analysis were performed. Logistic regression models were fitted to assess the predictive power of personal values on alcohol use. Openness to change values positively predicted high-frequency alcohol use and high-frequency heavy episodic drinking, whereas the opposite occured with athletes who held conservation values. Furthermore, the probability of presenting heavy episodic drinking was negatively associated with conservation values. Finally, the PVQ-21 presented adequate psychometric properties to assess personal values among adolescent athletes. It is crucial to consider the personal values of adolescent athletes when promoting healthy lifestyles through sport. Full article

In the plastics industry, CFD simulation has been used for many years to support mold design. However, using simulation as a substitute for experimentation remains a major challenge to this day. This is due to the unknown congruence between simulation and experiment. The present work focuses on a comparison between simulation (generated with the software Moldflow Insight Ultimate from Autodesk Inc., San Francisco, CA, USA) and experiment by using molds of different complexity, where, in contrast to a large number of previous investigations, both the characteristics of the parts and the time series of the process parameters were compared with each other. For this purpose, the high-resolution time series of the process parameters injection pressure, flow rate, and cavity pressure as well as the mass and the dimensions of the manufactured parts were acquired during the experiments and the results were compared with the computations obtained from the simulation. In addition, potential causes like the material data, mesh and solver parameter, and the machine-specific behavior were analyzed to assess which of these causes may be decisive for a deviation between simulation and experiment. Full article

Over the last three years, the rates of all the main nuclear reactions involving the destruction and production of ²⁶Al in stars (²⁶Al(n, p)²⁶Mg, ²⁶Al(n, α)²³Na, ²⁶Al(p, γ)²⁷Si and ²⁵Mg(p, γ)²⁶Al) have been re-evaluated thanks to new high-precision experimental measurements of their crosssections at energies of astrophysical interest, considerably reducing the uncertainties in the nuclear physics affecting their nucleosynthesis. We computed the nucleosynthetic yields ejected by the explosion of a high-mass star (20 M_⊙, Z = 0.0134) using the FRANEC stellar code, considering two explosion energies, 1.2 × 10⁵¹ erg and 3 × 10⁵¹ erg. We quantify the change in the ejected amount of ²⁶Al and other key species that is predicted when the new rate selection is adopted instead of the reaction rates from the STARLIB nuclear library. Additionally, the ratio of our ejected yields of ²⁶Al to those of 14 other short-lived radionuclides (³⁶Cl, ⁴¹Ca, ⁵³Mn, ⁶⁰Fe, ⁹²Nb, ⁹⁷Tc, ⁹⁸Tc, ¹⁰⁷Pd, ¹²⁶Sn, ¹²⁹I, ³⁶Cs, ¹⁴⁶Sm, ¹⁸²Hf, ²⁰⁵Pb) are compared to early solar system isotopic ratios, inferred from meteorite measurements. The total ejected ²⁶Al yields vary by a factor of ~3 when adopting the new rates or the STARLIB rates. Additionally, the new nuclear reaction rates also impact the predicted abundances of short-lived radionuclides in the early solar system relative to ²⁶Al. However, it is not possible to reproduce all the short-lived radionuclide isotopic ratios with our massive star model alone, unless a second stellar source could be invoked, which must have been active in polluting the pristine solar nebula at a similar time of a core-collapse supernova. Full article

Recreational waterbodies with high levels of faecal indicator bacteria (FIB) pose health risks and are an ongoing challenge for urban-lake managers. Lake Burley Griffin (LBG) in the Australian Capital city of Canberra is a popular site for water-based recreation, but analyses of seasonal and long-term patterns in enterococci that exceed alert levels (>200 CFU per 100 mL, leading to site closures) are lacking. This study analysed enterococci concentrations from seven recreational sites from 2001–2021 to examine spatial and temporal patterns in exceedances during the swimming season (October–April), when exposure is highest. The enterococci concentrations varied significantly across sites and in the summer months. The frequency of the exceedances was higher in the 2009–2015 period than in the 2001–2005 and 2015–2021 periods. The odds of alert-level concentrations were greater in November, December, and February compared to October. The odds of exceedance were higher at the Weston Park East site (swimming beach) and lower at the Ferry Terminal and Weston Park West site compared to the East Basin site. This preliminary examination highlights the need for site-specific assessments of environmental and management-related factors that may impact the public health risks of using the lake, such as inflows, turbidity, and climatic conditions. The insights from this study confirm the need for targeted monitoring efforts during high-risk months and at specific sites. The study also advocates for implementing measures to minimise faecal pollution at its sources. Full article

We present a two-country model (North and South) that describes the phenomenon of offshoring and reshoring. The model is a continuous time-controlled Markov chain with binary states. The main trade-off involves production costs and transaction costs between one country and another. In the first part of this paper, we identify the key parameters of the model: the difference in unit production costs between the two countries considered, the marginal cost of transitioning between countries, and the incentive paid by the North country to all companies that have not relocated at the end of the planning interval. The final goal of our paper is to understand how national tax incentives can influence this process. Full article

The selection of growth substrate geometries for the mechanical dry transfer of carbon nanotubes to device substrates depends on the precision of the assembly equipment. Since these geometries play a decisive role in the overall efficiency of the process, an investigation of the most important geometry parameters is carried out. The substrate geometry affects the number of carbon nanotubes suspended during the growth process and the speed of mechanical assembly at the same time. Since those two criteria are interlinked and affect productivity, a meta-model for the growth and selection of the nanotubes is simulated and a time study of the resulting assembly motions is subsequently performed. The geometry parameters are then evaluated based on the total number of suspended carbon nanotubes and the throughput rate, measured in transfers per hour. The accuracy specifications are then taken into account. Depending on the overall accuracy that can be achieved, different offset angles and overlaps between the growth and receiving substrate can be reached, which affect productivity differently for different substrate geometries. To increase the overall productivity, growth substrate designs are adapted to allow fully automated operation. This measure also reduces the frequency of substrate exchanges once all carbon nanotubes have been harvested. The introduction of substrates with multiple, polygonally arranged edges increases the total number of nanotubes that can be harvested. The inclusion of polygonally arranged edges in the initial analysis shows a significant increase in overall productivity. Full article