Experimental Validation Research Articles

TMOD-16. ZUCLOPENTHIXOL REDUCES ACTIVITY-DEPENDENT GLIOBLASTOMA PROLIFERATION

Abstract A major challenge in modeling glioblastoma in the laboratory is the complex microenvironment in which tumors evolve in humans. The varied architecture and immune privilege of the brain limits the external validity of many experiments performed in modern cell biology laboratories. Organotypic human cortical slices present an opportunity to model glioblastoma and test the effect of drugs against glioma in ex-vivo human tissue. Cortical slice cultures maintain the cellular organization of the human brain, more closely approximating glioblastoma proliferation in patients. In this study, we test the effect zuclopenthixol, a repurposed antipsychotic inhibitor of glioblastoma-induced neuronal hyperexcitability, on the proliferation of glioblastoma cells in an organotypic human cortical slice culture. Primary patient-derived glioblastoma cultures were established, and human cortical tissues were emersed in oxygenated ACSF agarose gel before preparing 250 µm thick slices. Slices were either exogenously seeded with glioblastoma cells or stained for endogenous tumor infiltration. Slices with either endogenous or exogenous seeding were then treated with zuclopenthixol or regular media change and cultured for 10 days. Proliferation was measured with Ki67 staining and quantified via automated pixel summation using the Pillow library in Python. 0.1 µM zuclopenthixol treated slices had significantly lower proliferation index in both the endogenous tumor condition (0.04 vs 0.30, P < 0.05) and the exogenous seeding condition (0.0013 vs 0.028, P = 0.033). Reduced tissue integrity was observed beginning day 8 of culture, as tissue surrounding tumor seeding sites eroded. Here, we model glioblastoma proliferation in live human tissue and quantify the anti-proliferative effect of zuclopenthixol. We show that zuclopenthixol treatment significantly reduced the proliferation index. Our experimental model allowed the optimization of organotypic human cortical slice cultures for immunofluorescence studies and live-cell imaging.

Modeling of Heat Transfer and Airflow Inside Evacuated Tube Collector With Heat Storage Media: Experimental Validation Powered by Artificial Neural Network

ABSTRACTSolar air heater (SAH) is a widely employed technology for harnessing solar thermal energy in numerous applications. Contemporary research optimizes designs within identical spatial constraints to maximize energy output. However, there is a recognized need to conduct analytical validation to stimulate the experimental setup and formulate an artificial neural network (ANN) model to govern and predict the operation system. This investigation involved developing and assessing an evacuated tube solar air heater (ETSAH) integrated with annulus‐filled heat storage media. Furthermore, this study introduced an ANN model and analytical solution to predict performance parameters, representing a noteworthy contribution. The proposed ANN model achieved its optimal validation performance with a mean square error of 5.4018 × 10−6 after 11 epochs within 31 of training. Also, correlation findings show that the optimal architecture of a feed‐forward backpropagation is achieved when the 5‐40‐40‐40‐2 model architecture is used. In this case, there are five neural nodes in the input layer that represent timing, temperature, radiation, and airflow rate. The power output of the ETSAH device shows a strong correlation with the flow rate, reaching its peak at 0.05 kg/s with a value of 2261 W and dropping to 368 W at 0.006 kg/s. Correspondingly, the greatest energy efficiency was measured at airflow rates of 0.05, 0.01, and 0.006 kg/s, accounting for 48.38%, 27.32%, and 19.65%, respectively.

Machine learning-guided design of direct methanol fuel cells with a platinum group metal-free cathode

Direct methanol fuel cells (DMFCs) offer a promising solution for clean electricity generation, particularly in small electronics and remote auxiliary power units. However, optimizing their efficiency and performance is challenging due to the complex interactions between various factors. Here, we present a novel approach that integrates experiments with machine learning to model and predict the performance of these fuel cells using atomically dispersed platinum group metal (PGM)-free catalysts at the cathode. Our machine learning models, trained on diverse input parameters, allow for the comprehensive optimization of DMFC performance prior to fabrication and testing. Through extensive experimental validation, we demonstrate that this data-driven approach accurately predicts key performance metrics, such as maximum power output and polarization curves. By combining our models with interpretable game-theory methods, we provide deep insights into the factors governing fuel cell performance, ultimately paving the way for the design of scalable and efficient DMFC technologies.

Exploring Biological Targets of Magnolol and Honokiol and their Nature-Inspired Synthetic Derivatives: In Silico Identification and Experimental Validation of Estrogen Receptors.

In this work, we describe the results of a computational investigation aimed at identifying potential biological targets of honokiol, magnolol and a series of synthetic prodrug derivatives obtained through esterification of the free hydroxyl groups. The ligand-based and structure-based analyses revealed that these compounds potentially interact with several biological targets, some of which are known while others are new. Honokiol, magnolol, and three of the newly synthesized derivatives may bind to estrogen receptors ERα and ERβ. Biological testing confirmed that these compounds modulate estrogen-regulated transcriptional activity mediated by ERα or ERβ with potencies in the nanomolar range. In particular, magnolol and one of its derivatives (10) behaved as partial antagonists of ERα and ERβ, while compounds 8 and 11 behaved as partial agonists. These findings validate the computational predictions and shed light on the mechanism of action of these natural compounds, paving the way for further investigation in the context of targeted therapies.

CSIG-28. DISSECTING THE FUNCTIONAL IMPAIRMENT OF WILD-TYPE P53 IN HUMAN GLIOBLASTOMA

Abstract Glioblastoma (GBM) is the most common malignant brain tumor in adults and is characterized by heterogeneous nature, invasive potential, and poor treatment response. Work from The Cancer Genome Atlas (TCGA) identified the tumor suppressor gene TP53 as a GBM driver. In contrast to several cancers, only ~35% of GBM cases carry mutations in TP53. Work from our lab showed that TP53 mutations emerge as ‘late’ events during GBM growth. Therefore, we hypothesized that wild-type (wt) p53 function is impaired in GBM. We used the TCGA data of 591 GBM patients to compare the clinical and molecular characteristics of wt and mutant TP53 patients. Gene expression data were analyzed to compare the correlation of TP53 with the expression of key p53 regulators and identify differentially expressed regulators between our groups. Furthermore, we evaluated the expression of p53 and selected regulators in patient-derived cells (PDCs) and control lines with known TP53 status. Our results show that wt TP53 patients live less than mutant patients and both groups respond similarly to treatment. No differences in age, sex, race, TP53 levels, and MGMT methylation status were observed between the groups. Gene expression analyses revealed that MAPK8, a p53 activator, negatively correlates with TP53 in the wt group. Moreover, ~21% of p53 regulators are differentially expressed between wt and mutant patients. Among these, we selected SIRT3, a p53 repressor, for experimental validation. We verified the p53 genetic status of PDCs through western blot and confirmed Sirt3 expression in wt and mutant TP53 PDCs. By CRISPR-Cas9-based genomic editing, we are currently evaluating the impact of SIRT3 knockout in GBM-PDC and patient-derived xenografts. We expect that SIRT3 knockout will restore p53 function in the wt group. In conclusion, our results suggest that p53 function is impaired in wt patients and provide insights into mechanisms affecting its activity.

M5C related-regulator-mediated methylation modification patterns and prognostic significance in breast cancer

5-Methylcytosine (m5C) is closely associated with cancer. However, the role of m5C in breast cancer(BC)remains unclear. This study combined single-cell RNA sequencing (scRNA-Seq) and transcriptomics datasets to screen m5C regulators associated with BC progression and analyze their clinical values. Firstly, This study elucidates the mechanisms of the m5C landscape and the specific roles of m5C regulators in BC patients. we found that the dysregulation of m5C regulators with m5Cscore play the essential role of the carcinogenesis and progression in epithelial cells and myeloid cells of BC at single cell level. External validation was conducted using an independent scRNA-Seq datasets. Then, three distinct m5C modification patterns were identified by transcriptomics datasets. Based on the m5C differentially expressed regulators, the m5Cscore was constructed, and used to divide patients with BC into high and low m5Cscore groups. Patients with a high m5Cscore had more abundant immune cell infiltration, stronger antitumor immunity, and better prognoses. Finally, Quantitative real-time (PCR) and immunohistochemistry were used for the in vitro experimental validation, which had extensive prognostic value. In this study, we aimed to assess the expression of m5C regulators involved in BC and investigate their correlation with the tumor microenvironment, clinicopathological characteristics, and prognosis of BC. The m5C regulators could be used to effectively assess the cell specific regulation prognosis of patients with BC and develop more effective immunotherapy strategies.

Energy‐Efficient Hardware Implementation of Spiking‐Restricted Boltzmann Machines Using Pseudo‐Synaptic Sampling

Stochastic sampling is performed to reduce hardware energy consumption and prevent overfitting by reducing parameters, because not all data are required for learning. In this study, a new approach, pseudo‐synaptic sampling (PS2) method, which approximates the conventional synaptic sampling machine (S2M) method through a hardware‐friendly implementation while demonstrating superior efficiency, is introduced. By sampling in front of neurons rather than at each synapse, the PS2 method improves hardware energy efficiency and ensures scalability. Furthermore, it improves energy and area efficiency by eliminating the additional circuit required by other techniques, such as the random walk (RW) method previously used which requires an additional circuit to frequently charge/discharge the membrane potential. Herein, the average firing rate equation for the S2M method is modified to suit the experimental conditions of this study. Through this numerical simulations, it is confirmed that the activation function of the PS2 method aligns with that of the S2M method and verified that the PS2 method can implement stochasticity for restricted Boltzmann machine (RBM) neurons. Experimental validation of the PS2 method, compared to the RW method, for Modified National Institute of Standards and Technology database (MNIST) training and inference on field‐programmable‐gate‐array‐implemented spiking RBM chips reveals promising results. In an MNIST 100‐handwritten digit experiment, the PS2 method exhibits on‐chip training accuracy (92%) comparable to that of the RW method (93%). Furthermore, in the energy consumption analysis, it is shown that the PS2 method reduces power consumption by 94.94% compared to the RW method, highlighting its enhanced power efficiency due to a reduced number of circuit elements. In the investigation into the impact of increasing the frequency at which random bits are generated, it is shown that the RW method experiences accuracy degradation even with slight increases, whereas the PS2 method maintains accuracy over significantly longer periods. This enables further power reduction by allowing for a longer period during random bit generation. In this study, a foundation is laid for maximizing the energy efficiency of spiking neural network processors by optimizing internal noise generation mechanisms.

The Methodology of Adaptive Levels of Interval for Laser Speckle Imaging

A methodology is proposed for use in the laser speckle imaging field. This methodology modified the graphical and numerical speckle pattern imaging methods to improve their extraction and discrimination capabilities when processing the embedded temporal activity in the images of laser speckle patterns. This is through enabling these methods to adapt the levels of speckle images’ interval during processing to speed up the process and overcome the lack of discrimination when they deal with a complex scattering medium having regions of various scales of activity. The impact of using the new methodology on the imaging methods’ performance was evaluated using graphical and numerical evaluation tests, in addition, an exceptional laser speckle imaging system was designed and implemented to undertake a series of experimental validation tests on this methodology. The evaluation and experimental validation tests show the effectiveness of this methodology on the extraction and discrimination capabilities for the standard imaging speckle pattern methods and prove its ability to provide high performance with the real images of speckle patterns. The results also show an improvement in the processing speed for both graphical and numerical methods when the adaptive levels methodology is applied to them, which reaches 78% for the graphical and 87% for the numerical speckle processing methods.

B cell epitope prediction by capturing spatial clustering property of the epitopes using graph attention network.

Knowledge of B cell epitopes is critical to vaccine design, diagnostics, and therapeutics. As experimental validation for epitopes is time-consuming and costly, many in silico tools have been developed to computationally predict the B cell epitopes. While most methods show poor performance, deep learning methods in recent years have shown promising results. We developed a method called EpiGraph that outperformed previous methods, including those that showed a significant improvement in performance in recent years. Our model's performance can be attributed to the following factors: (1) a combination of structure and sequence feature embeddings obtained from pretrained ESM-IF1 and ESM-2 models could capture the structural and evolutionary features of B cell epitopes, (2) a graph attention network could learn the spatial proximity of B cell epitopes with high graph homophily, and (3) residual connections in the model framework mitigate the over-smoothing problem in the graph neural network. Our model achieved the highest performance on an independent benchmark dataset. The results were also consistent on a different dataset. The datasets and source codes are available at https://github.com/sj584/EpiGraph . A user-friendly web server is freely available at http://epigraph.kaist.ac.kr .

Impact of Geometric Attributes on Abdominal Aortic Aneurysm Rupture Risk: An InVivo FSI-Based Study.

Reported in this paper is a cutting-edge computational investigation into the influence of geometric characteristics on abdominal aortic aneurysm (AAA) rupture risk, beyond the traditional measure of maximum aneurysm diameter. A Comprehensive fluid-structure interaction (FSI) analysis was employed to assess risk factors in a range of patient scenarios, with the use of three-dimensional (3D) AAA models reconstructed from patient-specific aortic data and finite element method. Wall shear stress (WSS), and its derivatives such as time-averaged WSS (TAWSS), oscillatory shear index (OSI), relative residence time (RRT) and transverse WSS (transWSS) offer insights into the force dynamics acting on the AAA wall. Emphasis is placed on these WSS-based metrics and seven key geometric indices. By correlating these geometric discrepancies with biomechanical phenomena, this study highlights the novel and profound impact of geometry on risk prediction. This study demonstrates the necessity of a multidimensional assessment approach, future efforts should complement these findings with experimental validations for an applicable approach for clinical use.

Integrating transcriptomic data and digital pathology for NRG-based prediction of prognosis and therapy response in gastric cancer

Background Cancer is characterized by its ability to resist cell death, and emerging evidence suggests a potential correlation between non-apoptotic regulated cell death (RCD), tumor progression, and therapy response. However, the prognostic significance of non-apoptotic RCD-related genes (NRGs) and their relationships with immune response in gastric cancer (GC) remain unclear. Methods In this study, RNA-seq gene expression and clinical information of GC patients were acquired from The Cancer Genome Atlas and the Gene Expression Omnibus databases. Cox and LASSO regression analyses were used to construct the NRG signature. Moreover, we developed a deep learning model based on ResNet50 to predict the NRG signature from digital pathology slides. The expression of signature hub genes was validated using real-time quantitative PCR and single-cell RNA sequencing data. Results We identified 13 NRGs as signature genes for predicting the prognosis of patients with GC. The high-risk group, characterized by higher NRG scores, demonstrated a shorter overall survival rate, increased immunosuppressive cell infiltration, and immune dysfunction. Moreover, associations were observed between the NRG signature and chemotherapeutic drug responsiveness, as well as immunotherapy effectiveness in GC patients. Furthermore, the deep learning model effectively stratified GC patients based on the NRG signature by leveraging morphological variances, showing promising results for the classification of GC patients. Validation experiments demonstrated that the expression level of SERPINE1 was significantly upregulated in GC, while the expression levels of GPX3 and APOD were significantly downregulated. Conclusion The NRG signature and its deep learning model have significant clinical implications, highlighting the importance of individualized treatment strategies based on GC subtyping. These findings provide valuable insights for guiding clinical decision-making and treatment approaches for GC.

Optimization of Supercritical Extraction of Cannabidiol Using Response Surface Methodology

Hemp, also known as Cannabis sativa L., contains over 80 cannabinoids, with cannabidiol (CBD) being the primary neuroactive component. CBD possesses various pharmacological properties and is considered a non-psychoactive compound, making it a promising component for various applications, such as pharmaceuticals, cosmetics, and nutraceuticals. The aim of this study was to identify the optimal conditions for extracting CBD from hemp using supercritical fluid extraction (SFE). Response surface methodology (RSM) was employed to optimize the SFE conditions. The Box–Behnken design and the central composite design were utilized to refine the extraction parameters, including extraction time, temperature, and pressure. The statistical significance and reliability of the optimized conditions were confirmed by the significant influence of these independent variables on CBD yield. The extracted CBD was purified to a high level of purity and converted from cannabidiolic acid (CBDA) through heat treatment and then analyzed using high-performance liquid chromatography (HPLC). The following extraction conditions were considered optimal and led to a CBD yield of approximately 70.46 g/kg: pressure of 48.3 MPa, temperature of 60 °C, and extraction time of 109.2 min. Validation experiments confirmed the accuracy of the model, with experimental values closely matching the predicted values (69.93 ± 0.88 g/kg). This study demonstrates that SFE is an efficient method for obtaining high-purity CBD from hemp, highlighting its potential for industrial applications. The findings suggest that optimizing SFE conditions through RSM can significantly enhance the efficiency and yield of CBD extraction, providing a robust framework for industrial-scale production.

Peptide‐Guided Self‐Assembly: Fabrication of Tailored Spiral‐Like Nanostructures for Precise Inorganic Templating

AbstractSelf‐assembling peptides (SAPs) are versatile building blocks in the creation of hierarchical nanostructures. Although SAPs promise precise control over assembled morphology and dimensions, experimental validation is still crucial. In this sense, recent efforts have focused on nanospiral structures, which mimic natural architectures and hold potential for biomedical and nanotechnological applications. Here, it is demonstrated that SARS‐CoV‐2 fusion peptides are able to form specific spiral‐like structures using interfacial assembly as a fabrication methodology, along with fine‐tuning of the spiral ensembles through an imposed interfacial flow due to the uniaxial compression in a Langmuir–Blodgett trough. Molecular characterization by atomic force microscopy, neutron reflectometry, interfacial shear rheology, and infrared spectroscopy, highlighted how variations in fusion peptide sequences influence assembly, highlighting the importance of molecular design. These spiral structures are subsequently modified to serve as templates for metallic replicas, expanding the potential of peptide‐guided self‐assembly in fabricating tailored nanostructured surfaces with sub‐10 nm dimensions over cm2 areas.

Investigating the Morphology of a Free-Falling Jet with an Accurate Finite Element and Level Set Modeling

This study investigates the morphology of a free-falling liquid jet by using a computational approach with an experimental validation. Numerical simulations are developed by means of the Finite Element Method (FEM) for solving the viscous fluid flow and the level set method in order to track the interface between the fluid and air. Experiments are conducted in order to capture the shape of a free-falling jet of viscous fluid via circular orifice, where the shape is measured optically. The numerical results are found to be in agreement with the experimental data, demonstrating the validity of the proposed approach. Furthermore, we analyze the role of the surface tension by implementing linear as well as nonlinear surface energy models. All computational codes are developed with the aid of open-source packages from FEniCS and made publicly available. The combination of experimental and numerical techniques provides a comprehensive understanding of the morphology of free-falling jets and may be extended to multiphysics problems rather in a straightforward manner.

Revisiting huntingtin activity and localization signals in the context of protein structure

Protein localization signals and activity motifs have been defined within huntingtin since 2003. Advances in technology in protein structure determination by cryo-electron microscopy (EM) have led to 2.6 Å resolution structures of huntingtin and HAP40 for the majority of the protein, although structure of the amino terminus with the polyglutamine expansion remains elusive in the context of full-length huntingtin. Recent advances in protein modeling using neural network algorithms trained on a database of known protein structures has resulted in structure predictions that are useful for researchers but need experimental validation. Here, we use both structures solved by cryo-EM as well as modeling centered around experimental structural data to retrospectively revisit huntingtin protein localization signals identified prior to the cryo-EM and AI-enabled structural revolutions. We interrogate these models as well as put forward testable hypotheses of allosteric changes in huntingtin and how they could be affected by polyglutamine expansion. We also extended this methodology to another polyglutamine disease protein, ataxin-1, expanded in Spinocerebellar Ataxia Type 1 (SCA1).

Kallmann Syndrome: Functional Analysis of a CHD7 Missense Variant Shows Aberrant RNA Splicing

Kallmann syndrome is a rare disorder characterized by hypogonadotropic hypogonadism and an impaired sense of smell (anosmia or hyposmia) caused by congenital defects in the development of the gonadotropin-releasing hormone (GnRH) and olfactory neurons. Mutations in several genes have been associated with Kallmann syndrome. However, genetic testing of this disorder often reveals variants of uncertain significance (VUS) that remain uninterpreted without experimental validation. The aim of this study was to analyze the functional consequences of a heterozygous missense VUS in the CHD7 gene (c.4354G>T, p.Val1452Leu), in a patient with Kallmann syndrome with reversal of hypogonadism. The variant, located in the first nucleotide of exon 19, was analyzed using minigene assays to determine its effect on ribonucleic acid (RNA) splicing. These showed that the variant generates two different transcripts: a full-length transcript with the missense change (p.Val1452Leu), and an abnormally spliced transcript lacking exon 19. The latter results in an in-frame deletion (p.Val1452_Lys1511del) that disrupts the helicase C-terminal domain of the CHD7 protein. The variant was reclassified as likely pathogenic. These findings demonstrate that missense variants can exert more extensive effects beyond simple amino acid substitutions and underscore the critical role of functional analyses in VUS reclassification and genetic diagnosis.

Linarin Identified as a Bioactive Compound of Lycii Cortex Ameliorates Insulin Resistance and Inflammation Through the c-FOS/ARG2 Signaling Axis.

Insulin resistance (IR) is a central pathophysiological process underlying numerous chronic metabolic disorders, including type 2 diabetes and obesity. Lycii Cortex, a widely used traditional Chinese herb, has demonstrated potential benefits in preventing and managing diabetes and IR. Whereas, the specific bioactive compounds responsible for these protective effects and their underlying mechanisms of action remain elusive. This study aimed to identify the bioactive components within Lycii Cortex that contribute to its anti-diabetic effects and to elucidate the molecular mechanisms underlying its beneficial actions on insulin resistance. Network pharmacology and molecular docking analyses were employed to identify the potential active compounds in Lycii Cortex and their corresponding target proteins. An invitro model of IR was established using palmitic acid (PA)-treated HepG2 cells. Cell viability was assessed using the CCK-8 assay, while glucose uptake was evaluated by 2-NBDG staining and extracellular glucose measurement. To validate the invitro findings, an invivo model of obesity-induced IR was established using high-fat diet (HFD)-fed mice. The network pharmacology analysis preliminarily identified 13 candidate chemicals and 10 hub LyC and IR-related genes (LIRRGs). Molecular docking analysis demonstrates that Linarin as the potential active component exhibits the greatest potential to target c-FOS for preventing obesity-induced IR. Enrichment analysis suggested that Linarin-targeted pathways are correlated with inflammation. Invitro experimental validation demonstrated that Linarin was capable of protecting against PA-induced IR in HepG2 cells evidenced by improving glucose uptake ability and reducing extracellular glucose content. Additionally, we found that Linarin ablated PA-induced increase in the expression of c-FOS and inflammatory cytokines. Furthermore, in PA-treated cells, silencing c-FOS markedly improved glucose consumption, and reduced inflammation and Arginase 2 (ARG2) expression. Similarly, as exposure to PA, silencing ARG2 also ameliorated glucose uptake and inflammation, while not affecting c-FOS expression. Invivo experiments further showed that Linarin administration remarkably improved glucose tolerance and insulin sensitivity, and reduced the fat mass and body weight in HFD-induced obese mice. In this study, Linarin has been identified as the bioactive compound of Lycii Cortex to ameliorate obesity-related IR and inflammation through the c-FOS/ARG2 signaling cascade. These findings underscore the therapeutic potential of Linarin and provide valuable insights into developing novel intervention strategies for type 2 diabetes therapy.

Enhancing Oxygen-Dissolving Capacity of Rotary Drum Food Waste Composting: Tumbling Process Optimization and Experimental Validation with Discrete and Finite Element Methods

An optimized tumbling process can significantly improve the oxygen dissolving capacity of composting and fertilizer quality: by increasing the fluffiness of the lower layer of the pile, localized anaerobic fermentation can be avoided, thereby enhancing compost quality. This paper presents a method for improving the oxygen dissolving capacity of rotary drum food waste composting through a combination of simulation optimization and experimental validation. First, the discrete element method was used to optimize the key parameters of the tumbling process. The response surface method was then employed to analyze the composting test results and determine the optimal conditions. To ensure the reliability of the equipment under this method, failure risk analysis was conducted using the finite element method. The simulation optimization results showed that with a rotary drum reactor speed of 3.5 r/min, a horizontal angle of inclination of 2.5°, a mixing blade angle of inclination of 43°, and a blade pitch of 580 mm, the fluffiness of the lower layer of the pile increased by 8.515%, achieving the best tumbling and indirectly enhancing oxygen dissolving capacity. The maximum deformation of the load-bearing components was only 0.0548 mm, and the minimum safety factor was 4.408 (≥1 is considered safe). A 14-day composting experiment was conducted to validate the optimized parameters. The results showed that the maximum temperature of the compost pile reached 68.34 °C (lasting 7 days), with the pH, moisture content, C/N ratio, humus substances, humic acid, and fulvic acid contents of the fertilizer all meeting or exceeding the levels recommended by Chinese national standards. These findings indicate that the optimized tumbling device effectively improved the stability and dissolved oxygen efficiency of food waste composting, providing valuable practical insights and a research foundation for enhancing oxygen efficiency in the composting of other organic wastes.

Trajectory Planning and Adaptive Fuzzy PID Control for Precision in Robotic Vertebral Plate Cutting: Addressing Force Dynamics and Deformation Challenges

This study explores trajectory planning and cutting force control for spinal surgical robots, focusing on managing cutting deformation and vibration during vertebral plate cutting—a critical challenge in robotic spinal surgery. Through theoretical analysis, simulation, and experimental validation, the research addresses the complexities of cutting force dynamics and the associated deformations. A dynamic equation for vertebral plate cutting is established, providing a theoretical foundation for trajectory planning. A new trajectory planning method using B-spline curves and an interpolation point constraint formula is introduced, enabling precise robot trajectory control. Additionally, an adaptive fuzzy PID control strategy with force feedback is proposed to dynamically adjust the feed rate, effectively suppressing cutting deformation and vibration in real time. Simulations and experiments validate the effectiveness of these methods in reducing force fluctuations and enhancing control stability. The findings offer valuable guidance for improving the performance and safety of spinal surgery robots, optimizing their trajectories, incorporating dynamic sensing, and enhancing safety controls. Future research should focus on refining control strategies for more complex surgical scenarios and validating their applicability under conditions closer to actual surgical environments. This work provides theoretical and practical insights into advancing robotic spinal surgery, contributing to better surgical outcomes and patient safety.

Dynamic Analysis and Vibration Control of Additively Manufactured Thin-Walled Polylactic Acid Polymer (PLAP) and PLAP Composite Beam Structures: Numerical Investigation and Experimental Validation

This study primarily presents a numerical investigation of the dynamic behavior and vibration control in thin-walled, additively manufactured (AM) beam structures, validated through experimental results. Vibration control in thin-walled structures has gained significant attention recently because vibrations can severely affect structural integrity. Therefore, it is necessary to minimize these vibrations or keep them within acceptable limits to ensure the structure’s integrity. In this study, the AM beam structures were made of polylactic acid polymer (PLAP), short carbon fiber reinforced in PLAP (SCFR|PLAP), and continuous carbon fiber reinforced in PLAP (CCFR|PLAP), with 0°|0° layer orientations. The finite element modeling (FEM) of the AM beam structures integrated with macro fiber composite (MFC) was carried out in Abaqus. The initial four modal frequencies of bending modes (BMs) and their respective modal shapes were acquired through numerical simulation. It is crucial to highlight the numerical findings that reveal discrepancies in the 1st modal frequencies of the beams, ranging up to 1.5% compared to their respective experimental values. For the 2nd, 3rd, and 4th modal frequencies, the discrepancies are within 10%. Subsequently, frequency response analysis (FRA) was carried out to observe the frequency-dependent vibration amplitude spectrum at the initial four BM frequencies. Despite discrepancy in the amplitude values between the numerical and experimental datasets, there was consistency in the overall amplitude behavior as frequency varied. THz spectroscopy was performed to identify voids or misalignment errors in the actual beam models. Finally, vibration amplitude control using MFC (M8507-P2) was examined in each kinematically excited numerical beam structure. After applying a counterforce with the MFC, the controlled vibration amplitudes for the PLAP, SCFR|PLAP, and CCFR|PLAP configurations were approximately ±19 µm, ±16 µm, and ±13 µm, respectively. The trend in the controlled amplitudes observed in the numerical findings was consistent with the experimental results. The numerical findings of the study reveal valuable insights for estimating trends related to vibration control in AM beam structures.