Experimental Validation Research Articles

An Intent Recognition Method for Aerial Swarm Based on Attention Pooling Mechanism

In the realm of unmanned aerial vehicle (UAV) swarm intent recognition, conventional approaches predominantly focus on the attributes derived from singular targets at discrete instances. This trend leads to a significant limitation: the inability to effectively harness and capture the collective feature information of the entire swarm over temporal sequences. To address this gap, this study introduces a comprehensive end-to-end UAV swarm intent recognition approach. Initially, this method utilizes the distance threat coefficient and angular threat coefficient between UAVs to construct the graphical structural representation of the UAV swarm. Subsequently, an innovative deep learning framework, designated as attention-pool based on graph attention network and long short-term memory, which integrates a graph attention network, a novel graph pooling strategy, and a long short-term memory, is developed. This architecture can process the graphically structured data derived from the swarm modeling and accurately deduce the collective intent. Through experimental validation and analyses against existing methodologies, as well as ablation studies, it is evidenced that the model outperforms state-of-the-art methods in terms of accuracy of intent recognition.

Elucidating role of long non-coding RNAs of Tamarindus indica Linn. in post-transcriptional gene regulation

Tamarindus indica Linn., commonly known as tamarind, is a rich source of carbohydrates, proteins, lipids, fatty acids, vitamins, minerals and bioactive compounds. It is well established that long non-coding RNAs (lncRNAs) plays an important role in transcriptional, post-transcriptional and epigenetic regulation. Despite the availability of tamarind genome information, a handful of studies have been done on its non-coding genome, especially lncRNAs. In this study, we have computationally predicted lncRNAs from the coding DNA sequences of T. indica and analysed the sequences. We experimentally validated seven randomly chosen lncRNAs by performing quantitative Real Time Polymerase Chain Reaction (qRT-PCR). 320 lncRNAs have been predicted and sequence analysis of these predicted, lncRNAs reveals the presence of different motifs and tandem repeats. Along with the experimental validation of 7 randomly chosen lncRNAs, functional analysis of the predicted lncRNAs and their targets elucidated their roles in various biological pathways. We believe prediction and validation of the lncRNAs along with their interaction with mRNAs will enhance our knowledge about the non-coding genome of tamarind and their involvement in post transcriptional gene regulation, medicinal properties, metabolic engineering, stress tolerance and genome editing.

2-DOF fractional-order control for delay-dominant industrial processes with experimental validation

2-DOF fractional-order control for delay-dominant industrial processes with experimental validation

Expression and prognostic value of ferritinophagy-related NCOA4 gene in low-grade glioma: integration of bioinformatics and experimental validation

BackgroundLow-grade glioma (LGG) is a primary brain tumor with relatively low malignancy. NCOA4 is a key regulator of ferritinophagy-related processes and is involved in the occurrence and development of many cancers. However, the role of NCOA4 in LGG remains poorly understood.MethodsThis study comprehensively analyzed several mainstream bioinformatics databases to explore the expression, diagnostic efficacy, clinical pathological features, immune infiltration, prognostic value, and biological functions of NCOA4 in LGG. Immunohistochemistry experiments were conducted using LGG tissue samples collected from our hospital to validate the bioinformatics analysis results.ResultsNCOA4 expression was significantly elevated in LGG (p < 0.05), with an Area Under the Receiver Operating Characteristic Curve (AUC) of 0.973, suggesting it as a potential diagnostic marker. High NCOA4 expression was associated with younger age (21–40 years), lower malignancy (oligodendroglioma), and better prognosis (IDHmut-non-codel and IDHmut-codel subtypes) (all p < 0.05) in LGG. Kaplan-Meier survival curves from three databases showed that high NCOA4-expressing LGG patients had better prognosis (all p < 0.05). NCOA4 correlated weakly with B cells, CD8 + T cells, macrophages, and dendritic cells infiltration (all with correlation coefficients r < 0.3, and p < 0.05) in LGG. Multivariate Cox regression identified NCOA4, age, CD8 T cells, and macrophages as LGG independent prognostic factors (all p < 0.05). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses indicated that NCOA4’s primary function in LGG is related to autophagy processes (all p < 0.05).ConclusionOur findings suggest that NCOA4 could be a potential prognostic marker and therapeutic target in LGG.

Nanoscale Viscometry Reveals an Inherent Mucus Defect in Cystic Fibrosis.

The abnormally viscous and thick mucus is a hallmark of cystic fibrosis (CF). How the mutated CF gene causes abnormal mucus remains an unanswered question of paramount interest. Mucus is produced by the hydration of gel-forming mucin macromolecules that are stored in intracellular granules prior to release. Current understanding of mucin/mucus structure before and after secretion remains limited, and contradictory models exist. Here, we used a molecular viscometer and fluorescence lifetime imaging of human bronchoepithelial cells (Normal and CF) to measure nanometer-scale viscosity. We found significantly elevated intraluminal nanoviscosity in a population of CF mucin granules, indicating an intrinsic, presecretory mucin defect. Nanoviscosity influences protein conformational dynamics and function. Its elevation along the protein secretory pathway could arise from molecular overcrowding, impacting mucin's post-translational processing, hydration, and mucus rheology after release. The nanoviscosity of secreted CF mucus was elevated compared to that of non-CF. Interestingly, it was higher after release than in granules. Validation experiments indicate that reduced mobility of water hydrating mucin macromolecules may contribute to the high nanoviscosity in mucus and mucin granules. This suggests that mucins have a weakly ordered state in granules but adopt a highly ordered, nematic crystalline structure when secreted. This challenges the traditional view of mucus as a porous agarose-like gel and suggests an alternative model for mucin organization before and after secretion. Our study also indicates that endoplasmic reticulum stress due to molecular overcrowding could contribute to mucus pathogenesis in CF cells. It encourages the development of therapeutics that target presecretory mechanisms in CF and other muco-obstructive lung diseases.

Efficient optimisation of physical reservoir computers using only a delayed input

Reservoir computing is a machine learning algorithm for processing time dependent data which is well suited for experimental implementation. Tuning the hyperparameters of the reservoir is a time-consuming task that limits is applicability. Here we present an experimental validation of a recently proposed optimisation technique in which the reservoir receives both the input signal and a delayed version of the input signal. This augments the memory of the reservoir and improves its performance. It also simplifies the time-consuming task of hyperparameter tuning. The experimental system is an optoelectronic setup based on a fiber delay loop and a single nonlinear node. It is tested on several benchmark tasks and reservoir operating conditions. Our results demonstrate the effectiveness of the delayed input method for experimental implementation of reservoir computing systems.

Molecular Strain Accelerates Electron Transfer for Enhanced Oxygen Reduction.

Fe-N-C materials are emerging catalysts for replacing precious platinum in the oxygen reduction reaction (ORR) for renewable energy conversion. However, their potential is hindered by sluggish ORR kinetics, leading to a high overpotential and impeding efficient energy conversion. Using iron phthalocyanine (FePc) as a model catalyst, we elucidate how the local strain can enhance the ORR performance of Fe-N-Cs. We use density functional theory to predict the reaction mechanism for the four-electron reduction of oxygen to water. Several key differences between the reaction mechanisms for curved and flat FePc suggest that molecular strain accelerates the reductive desorption of *OH by decreasing the energy barrier by ∼60 meV. Our theoretical predictions are substantiated by experimental validation; we find that strained FePc on single-walled carbon nanotubes attains a half-wave potential (E1/2) of 0.952 V versus the reversible hydrogen electrode and a Tafel slope of 35.7 mV dec-1, which is competitive with the best-reported Fe-N-C values. We also observe a 70 mV change in E1/2 and dramatically different Tafel slopes for the flat and curved configurations, which agree well with the calculated energies. When integrated into a zinc-air battery, our device affords a maximum power density of 350.6 mW cm-2 and a mass activity of 810 mAh gZn-1 at 10 mA cm-2. Our results indicate that molecular strain provides a compelling tool for modulating the ORR activities of Fe-N-C materials.

An extended state repetitive observer formulation for periodic/polynomial signal rejection in feedback control schemes

Repetitive control is a renowned method for managing periodic disturbances and references in control systems. Despite its effectiveness, performance issues arise when confronting disturbances like polynomial or sinusoidal signals. To enhance its capability, we introduce an extended state repetitive observer by incorporating internal models of periodic, sinusoidal, and polynomial signals into a novel observer framework. This design empowers the observer to adeptly reject and track both periodic and combined sinusoidal+polynomial signals. Beyond addressing the limitations of traditional repetitive control, our observer offers precise disturbance estimation for further analysis and feedback implementation. The observer’s performance is demonstrated through experimental validation within a control setup across various scenarios, focusing on its ability to reject periodic disturbances and track periodic signals effectively.

Dual-Stage Stacking Machine Learning Method Considering Virtual Sample Generation for the Prediction of ZIF-8' BET Specific Surface Area with Experimental Validation.

The widespread application of metal-organic frameworks (MOFs) in wastewater and gas treatment has created an increasing demand for accurate and rapid assessment of their BET specific surface area. However, experimental methods for acquiring sufficient statistical data are often costly and time-consuming. Therefore, this study proposes a dual-stage stacking model with Gaussian mixture model-virtual sample generation (GMM-VSG) technology for the BET specific surface area prediction. In this study, 90 real samples were selected from the MOF database and 300 virtual samples were generated. The performance on both real and virtual samples was evaluated by using four machine learning models, including Bayesian regression (Bayes), adaptive boosting (AdaBoost), random forest (RF), and extreme gradient boosting (XGBoost). Subsequently, three best-performing models and a linear regression model were selected for constructing a two-stage stacking model, with R2 value of 0.974. Finally, experimental conditions were adjusted based on feature importance analysis during the validation process, and the result shows that the prediction accuracy of the BET specific surface area is 0.943. This study contributes to the development of more efficient and accurate evaluation methods.

Computational Methods for Predicting Chemical Reactivity of Covalent Compounds.

In recent decades, covalent inhibitors have emerged as a promising strategy for therapeutic development, leveraging their unique mechanism of forming covalent bonds with target proteins. This approach offers advantages such as prolonged drug efficacy, precise targeting, and the potential to overcome resistance. However, the inherent reactivity of covalent compounds presents significant challenges, leading to off-target effects and toxicities. Accurately predicting and modulating this reactivity have become a critical focus in the field. In this work, we compiled a data set of 419 cysteine-targeted covalent compounds and their reactivity through an extensive literature review. Employing machine learning, deep learning, and quantum mechanical calculations, we evaluated the intrinsic reactivity of the covalent compounds. Our FP-Stack models demonstrated robust Pearson and Spearman correlations of approximately 0.80 and 0.75 on the test set, respectively. This empowers rapid and accurate reactivity predictions, significantly reducing computational costs and streamlining structural handling and experimental procedures. Experimental validation on acrylamide compounds underscored the predictive efficacy of our model. This study presents an efficient computational tool for the reactivity prediction of covalent compounds and is expected to offer valuable insights for guiding covalent drug discovery and development.

Sensorized laparoscopic surgical grasper with integrated capacitive force sensor for robot-assisted minimally invasive surgery

Purpose This paper aims to introduce a sensorized surgical grasper with a novel flexible capacitive tactile force sensor integrated within the surgical grasper for minimally invasive surgery (MIS) and robot-assisted MIS (RMIS) procedures. Design/methodology/approach The proposed sensor offers a unique configuration of sensing electrodes with one top excitation electrode and three bottom electrodes enabling the measurement of normal and shear forces without incorporating any complex decoupling algorithms. The design of the sensor is optimized using finite-element method simulations, ensuring efficiency and reliability. Findings Experimental validation, real-time sensor response and application in lump detection through stiffness assessment demonstrate the decoupled force response (0–5 N normal range and 0–2 N shear range) with high sensitivity 0.0124/N, repeatability and hysteresis response with 5.65% and 4.7% errors respectively. Originality/value The compact design of the sensor makes it compliant with surgical graspers and therefore enhances the overall efficiency of robotic surgical procedures. The sensorized surgical grasper is fabricated using conventional machining and rapid prototyping techniques, presenting a cost-effective solution for adoption.

Dynamic Decoupled Current Control for Smooth Torque of the Open-Winding Variable Flux Reluctance Motor Using Integrated Torque Harmonic Extended State Observer

Variable Flux Reluctance Machines (VFRMs) face multiple interconnected challenges that limit their performance, particularly in high-performance applications such as electric vehicles (EVs), where smooth torque output and robust operation are critical. Chief among these challenges are complex inter-axis couplings, including cross-coupling in the dq-axis, differential term coupling in the d0-axis, and disturbances propagating from the 0-axis to the q-axis. Additionally, harmonic disturbances associated with torque ripple exacerbate performance issues, resulting in degraded dynamic behavior. These challenges hinder current loop controllers, preventing effective management of winding impedance voltage drops and inter-axis coupling terms without advanced decoupling strategies. To address these challenges, this paper proposes a novel integrated torque harmonic extended state observer (ITHESO) within a decoupled current control designed to ensure fast and accurate current tracking, system stability, and torque ripple reduction. The ITHESO identifies and compensates for total current disturbances, including harmonic components, through feed-forward compensation within the current loop. Furthermore, the influence of control parameters and the effects of parameter mismatches on stability, torque ripple reduction, and disturbance rejection are thoroughly analyzed. Experimental validations demonstrate that the proposed strategy significantly enhances torque dynamics and reduces torque ripple, outperforming the conventional Active Disturbance Rejection Control (ADRC), which does not explicitly address disturbances associated with torque ripple. These advancements position the VFRM with the ITHESO as a competitive option for high-performance EV propulsion systems, offering smooth operation, noise reduction, and reliable performance under varying speeds and loads.

Superconductivity and high hardness in scandium-borides under pressure.

Exploration of new superconducting or superhard transition-metal borides has attracted extensive interest in the past few decades. In this study, we conducted comprehensive theoretical investigations in the scandium-boron binary system by employing a structural search method based upon first-principles density functional theory. Among the six predicted superconducting scandium-borides, ScB14 (Pm3̄) has the highest superconducting transition temperature Tc = 12.3 K and a Vickers hardness of 12.6 GPa at ambient pressure. The superconductor ScB4 (C2/m) has Tc = 3.6 K and a high Vickers hardness of 25.5 GPa at ambient pressure. Further analysis indicates that the previously synthesized ScB15 (P41) is of superhardness. Our discoveries not only enrich the phase diagram of scandium-borides, but also pave the way for future experimental validations and potential applications of superconducting scandium-borides in industry.

Measurement and Analysis of Optical Transmission Characteristics of the Human Skull.

The brain, as a vital part of central nervous system, receives approximately 25% of body's blood supply, making accurate monitoring of cerebral blood flow essential. While fNIRS is widely used for measuring brain physiology, complex tissue structure affects light intensity, spot size, and detection accuracy. Many studies rely on simulations with limited experimental validation. In this study, we used real adult skulls and agar to create a mimic model, building a transmission optical system with 13 wavelength filters and varying agar thicknesses. Peak intensity of transmitted light and size of scattered spot were measured at different wavelengths, and transmittance, total attenuation coefficient, and spot diameter enlargement of cranial mimics at different wavelengths were obtained. Results showed wavelengths below 550 nm struggled to penetrate the skull, while those above 700 nm penetrated deeper and diffused more. This suggests that short wavelengths capture epidermal PPG signals, whereas longer wavelengths detect both epidermal and intracranial signals.

Small Molecular Oligopeptides Adorned with Tryptophan Residues as Potent Antitumor Agents: Design, Synthesis, Bioactivity Assay, Computational Prediction, and Experimental Validation.

Tryptophan participates in important life activities and is involved in various metabolic processes. The indole and aromatic binuclear ring structure in tryptophan can engage in diverse interactions, including π-π, π-alkyl, hydrogen bonding, cation-π, and CH-π interactions with other side chains and protein targets. These interactions offer extensive opportunities for drug development. In this letter, we have designed and synthesized a series of linear oligopeptides adorned with tryptophan residues and identified their potential targets through artificial intelligence-assisted technology and experimental verification. In vitro bioactivity assays revealed that the oligopeptides containing Gly-Pro-Trp residues exhibited promising antitumor activity by inducing autophagy and apoptosis. The PharmMapper pharmacophore mapping approach, molecular docking, and molecular dynamics simulations together identified poly(ADP-ribose) polymerase 1 (PARP1), an enzyme associated with chromatin regulation, as the potential target for the designed compounds. Experimental biolayer interferometry (BLI) and enzyme-linked immunosorbent assay (ELISA) have verified that the oligopeptides could bind with PARP1 and influence PARP1 expression levels. A quantitative structure-activity relationship has been established between the chemical structures of the prepared compounds and their IC50 values. In summary, the research presents a feasible approach for exploring oligopeptide-based antitumor agents.

Computational Approaches for Multitarget Drug Design in Alzheimer's Disease: A Comprehensive Review.

Alzheimer's disease (AD) is a chronic and progressive neurodegenerative brain disorder, primarily affecting the elderly. Its socio-economic impact and mortality rate are alarming, necessitating innovative approaches to drug discovery. Unlike single-target diseases, Alzheimer's multifactorial nature makes single-target approaches less effective. To address this challenge, researchers are turning to drug design strategies targeting multiple disease pathways simultaneously. This approach has led to the promising identification of dual or multiple-target inhibitors, offering new perspectives for improving disease management. Computer-Aided Drug Design (CADD) such as virtual screening, docking, QSAR, molecular dynamics, ADMET prediction, etc., are valuable tools for designing and identifying new multi target directed ligands (MTDLs). These methods enable efficient screening of extensive compound libraries and accurate prediction of pharmacokinetic profiles, optimizing development costs and time. Challenges such as model accuracy, simulation complexity, and data integration persist. Addressing these issues requires advances in in silico modeling, high-performance computing, and experimental validation. In this regard, this review highlights recent advances using various computational methods to screen and identify new candidate compounds containing different heterocyclic motifs that could serve as potential bases for designing ligands targeting multiple targets for Alzheimer's disease.

VITA: A Multi-modal LLM-based System for Longitudinal, Autonomous, and Adaptive Robotic Mental Well-being Coaching

Recently, several works have explored if and how robotic coaches can promote and maintain mental well-being in different settings. However, findings from these studies revealed that these robotic coaches are not ready to be used and deployed in real-world settings due to several limitations that span from technological challenges to coaching success. To overcome these challenges, this paper presents VITA, a novel multi-modal LLM-based system that allows robotic coaches to autonomously adapt to the coachee's multi-modal behaviours (facial valence and speech duration) and deliver coaching exercises in order to promote mental well-being in adults. We identified five objectives that correspond to the challenges in the recent literature, and we show how the VITA system addresses these via experimental validations that include one in-lab pilot study ( \(N\) = 4) that enabled us to test different robotic coach configurations (pre-scripted, generic, and adaptive models) and inform its design for using it in the real world, and one real-world study ( \(N\) = 17) conducted in a workplace over 4 weeks. Our results show that: (i) coachees perceived the VITA adaptive and generic configurations more positively than the pre-scripted one, and they felt understood and heard by the adaptive robotic coach, (ii) the VITA adaptive robotic coach kept learning successfully by personalising to each coachee over time and did not detect any interaction ruptures during the coaching, (iii) coachees had significant mental well-being improvements via the VITA-based robotic coach practice. The code for the VITA system is openly available via https://github.com/Cambridge-AFAR/VITA-system .

Forward/Backward Decomposition for Dispersive Wave Propagation Measurements

Two complicating features commonly found in wave propagation applications include dispersion, i.e., frequency-dependent propagation velocity, and reflections, which introduce coherent noise. In this work, we present a signal processing technique which can be applied in a variety of applications to decompose signals into their forward- and backward-propagating components. The theory is presented, along with algorithmic implementation and experimental validation on a Timoshenko beam. The implications and potential utility of the method are briefly discussed.

Investigating the molecular mechanism of epimedium herb in treating rheumatoid arthritis through network pharmacology, molecular docking, and experimental validation.

This study attempted to explore the molecular mechanism of Epimedium herb (EH) on rheumatoid arthritis (RA) treatment. We employed network pharmacology, molecular docking, and HPLC analysis to investigate the molecular mechanisms underlying the efficacy of EH in treating RA. To assess the efficacy of EH intervention, RA fibroblast-like synoviocytes (RA-FLS) and collagen-induced arthritis (CIA) mouse models were utilized. Ultimately, the active compounds icariin, luteolin, quercetin, and kaempferol were identified, with interleukin-1β (IL-1β), IL-6, tumor necrosis factor-alpha (TNF-α), and matrix metalloproteinase-9 (MMP-9) emerging as key targets of EH for RA. These targets were found to be downregulated in both in vitro and in vivo experiments following EH intervention. Furthermore, EH treatment induced apoptosis, reduced metastasis and invasion in RA-FLS, and ameliorated arthritis-related symptoms while regulating Th17 and Treg cells in CIA mice.

Design, Modeling, and Validation of a Compact, Energy-Efficient Mixing Screw for Sustainable Polymer Processing

This study presents the design, modeling, and validation of a mixing screw for energy-efficient single-screw extrusion. The screw features a short length-to-diameter (L/D) ratio of 8:1 and incorporates double flights with variable pitch and counter-rotating mixing slots. These features promote enhanced plastication by breaking up the solid bed and improving thermal homogeneity through backflow mechanisms relieving a 3.75 compression ratio. Non-isothermal, non-Newtonian simulations modeled the thermal and flow behavior for high-impact polystyrene (HIPS) and recycled polypropylene (rPP) under various operating conditions. Experimental validation was conducted using a 20 mm pilot-scale extruder with screw speeds ranging from 10 to 40 RPM and barrel temperatures of 220 °C and 240 °C. Results showed a strong linear dependence of mass output on screw speed, with maximum mass throughputs of 0.58 kg/h for HIPS and 0.74 kg/h for rPP at 40 RPM. Specific energy consumption (SEC) was calculated as 0.264 kWh/kg for HIPS and 0.344 kWh/kg for rPP, corresponding to efficiencies of 31.5% and 56.5% relative to theoretical minimum energy requirements. Compared to traditional general-purpose and barrier screws with L/D ratios of 27:1, the mixing screw demonstrated improved energy efficiency and reduced residence time distributions. These findings suggest the potential of the mixing screw for compact extrusion systems, including 3D printing and other sustainable polymer and bioplastics processing applications.