Optimal Control of Semi-Active Suspension for Agricultural Tractors Using Linear Quadratic Gaussian Control.

In this study, a semi-active suspension based on a hydro-pneumatic mechanism was designed to minimize the ride vibration using a suspension control algorithm. The performance of the algorithm was critical for controlling the characteristics of the target tractor. A linear-quadratic-Gaussian (LQG) optimal control algorithm was designed as a semi-active suspension control algorithm. The plant model for developing this algorithm was based on the parameters of an actual tractor. The rear suspension deflection was represented by a Kalman-filter-based state observer feedback to estimate the state variables that were difficult to measure. The designed state observer of the LQG controller was validated in terms of an accuracy index. The estimated vertical velocity and acceleration accuracies of the cabin were 83% and 79%, respectively. The performance of the designed controller was validated in terms of a performance index by comparing the performance of a tractor equipped with a rear rubber mount with that of one equipped with a semi-active suspension. The peak and root-mean-square values of the vertical acceleration of the cabin were reduced by up to 48.97% and 47.06%, respectively. This study could serve as a basis for the application of the control algorithm to systems with similar characteristics, thereby reducing system costs.

Bioessential Inorganic Molecular Wire-Reinforced 3D-Printed Hydrogel Scaffold for Enhanced Bone Regeneration.

Materials with physicochemical properties and biological activities similar to those of the natural extracellular matrix are in high demand in tissue engineering. In particular, Mo3 Se3 - inorganic molecular wire (IMW) is a promising material composed of bioessential minerals and possess nanometer-scale diameters, negatively charged surfaces, physical flexibility, and nanotopography characteristics, which are essential for interactions with cell membrane proteins. Here, an implantable 3D Mo3 Se3 - IMW enhanced gelatin-GMA/silk-GMA hydrogel (IMW-GS hydrogel) is developed for osteogenesis and bone formation, followed by biological evaluations. The mechanical properties of the 3D printed IMW-GS hydrogel are improved by noncovalent interactions between the Mo3 Se3 - IMWs and the positively charged residues of the gelatin molecules. Long-term biocompatibility with primary human osteoblast cells (HOBs) is confirmed using the IMW-GS hydrogel. The proliferation, osteogenic gene expression, collagen accumulation, and mineralization of HOBs improve remarkably with the IMW-GS hydrogel. In in vivo evaluations, the IMW-GS hydrogel implantation exhibits a significantly improved new bone regeneration of 87.8 ± 5.9% (p < 0.05) for 8 weeks, which is higher than that from the gelatin-GMA/silk-GMA hydrogel without Mo3 Se3 - IMW. These results support a new improved strategy with in vitro and in vivo performance of 3D IMW enhanced scaffolds in tissue engineering.

Argyrodite configuration determination for DFT and AIMD calculations using an integrated optimization strategy.

When constructing a partially occupied model structure for use in density functional theory (DFT) and ab initio molecular dynamics (AIMD) calculations, the selection of appropriate configurations has been a vexing issue. Random sampling and the ensuing low-Coulomb-energy entry selection have been routine. Here, we report a more efficient way of selecting low-Coulomb-energy configurations for a representative solid electrolyte, Li6PS5Cl. Metaheuristics (genetic algorithm, particle swarm optimization, cuckoo search, and harmony search), Bayesian optimization, and modified deep Q-learning are utilized to search the large configurational space. Ten configuration candidates that exhibit relatively low Coulomb energy values and thereby lead to more convincing DFT and AIMD calculation results are pinpointed along with computational cost savings by the assistance of the above-described optimization algorithms, which constitute an integrated optimization strategy. Consequently, the integrated optimization strategy outperforms the conventional random sampling-based selection strategy.

Optimal Composition of Li Argyrodite with Harmonious Conductivity and Chemical/Electrochemical Stability: Fine-Tuned Via Tandem Particle Swarm Optimization.

A tandem (two-step) particle swarm optimization (PSO) algorithm is implemented in the argyrodite-based multidimensional composition space for the discovery of an optimal argyrodite composition, i.e., with the highest ionic conductivity (7.78 mS cm-1 ). To enhance the industrial adaptability, an elaborate pellet preparation procedure is not used. The optimal composition (Li5.5 PS4.5 Cl0.89 Br0.61 ) is fine-tuned to enhance its practical viability by incorporating oxygen in a stepwise manner. The final composition (Li5.5 PS4.23 O0.27 Cl0.89 Br0.61 ), which exhibits an ionic conductivity (σion ) of 6.70 mS cm-1 and an activation barrier of 0.27 eV, is further characterized by analyzing both its moisture and electrochemical stability. Relative to the other compositions, the exposure of Li5.5 PS4.23 O0.27 Cl0.89 Br0.61 to a humid atmosphere results in the least amount of H2 S released and a negligible change in structure. The improvement in the interfacial stability between the Li(Ni0.9 Co0.05 Mn0.05 )O2 cathode and Li5.5 PS4.23 O0.27 Cl0.89 Br0.61 also results in greater specific capacity during fast charge/discharge. The structural and chemical features of Li5.5 PS4.5 Cl0.89 Br0.61 and Li5.5 PS4.23 O0.27 Cl0.89 Br0.61 argyrodites are characterized using synchrotron X-ray diffraction, Raman spectroscopy, and X-ray photoelectron spectroscopy. This work presents a novel argyrodite composition with favorably balanced properties while providing broad insights into material discovery methodologies with applications for battery development.

Unravelling the Nature of the Intrinsic Complex Structure of Binary-Phase Na-Layered Oxides.

The layered sodium transition metal oxide, NaTMO2 (TM = transition metal), with a binary or ternary phases has displayed outstanding electrochemical performance as a new class of strategy cathode materials for sodium-ion batteries (SIBs). Herein, an in-depth phase analysis of developed Na1-x TMO2 cathode materials, Na0.76 Ni0.20 Fe0.40 Mn0.40 O2 with P2- and O3-type phases (NFMO-P2/O3) is offered. Structural visualization on an atomic scale is also provided and the following findings are unveiled: i) the existence of a mixed-phase intergrowth layer distribution and unequal distribution of P2 and O3 phases along two different crystal plane indices and ii) a complete reversible charge/discharge process for the initial two cycles that displays a simple phase transformation, which is unprecedented. Moreover, first-principles calculations support the evidence of the formation of a binary NFMO-P2/O3 compound, over the proposed hypothetical monophasic structures (O3, P3, O'3, and P2 phases). As a result, the synergetic effect of the simultaneous existence of P- and O-type phases with their unique structures allows an extraordinary level of capacity retention in a wide range of voltage (1.5-4.5 V). It is believed that the insightful understanding of the proposed materials can introduce new perspectives for the development of high-voltage cathode materials for SIBs.

Dendritic-Linear Copolymer and Dendron Lipid Nanoparticles for Drug and Gene Delivery.

Polymers constitute a diverse class of macromolecules that have demonstrated their unique advantages to be utilized for drug or gene delivery applications. In particular, polymers with a highly ordered, hyperbranched structure─"dendrons"─offer significant benefits to the design of such nanomedicines. The incorporation of dendrons into block copolymer micelles can endow various unique properties that are not typically observed from linear polymer counterparts. Specifically, the dendritic structure induces the conical shape of unimers that form micelles, thereby improving the thermodynamic stability and achieving a low critical micelle concentration (CMC). Furthermore, through a high density of highly ordered functional groups, dendrons can enhance gene complexation, drug loading, and stimuli-responsive behavior. In addition, outward-branching dendrons can support a high density of nonfouling polymers, such as poly(ethylene glycol), for serum stability and variable densities of multifunctional groups for multivalent cellular targeting and interactions. In this paper, we review the design considerations for dendron-lipid nanoparticles and dendron micelles formed from amphiphilic block copolymers intended for gene transfection and cancer drug delivery applications. These technologies are early in preclinical development and, as with other nanomedicines, face many obstacles on the way to clinical adoption. Nevertheless, the utility of dendron micelles for drug delivery remains relatively underexplored, and we believe there are significant and dramatic advancements to be made in tumor targeting with these platforms.

A Novel High-Performance TiO2-x /TiO1-y Ny Coating Material for Silicon Anode in Lithium-Ion Batteries.

Protective surface coatings on Si anodes are promising for improving the electrochemical performance of lithium-ion batteries (LIBs). Nevertheless, most coating materials have severe issues, including low initial coulombic efficiency, structural fracture, morphology control, and complicated synthetic processing. In this study, a multifunctional TiO2- x /TiO1- y Ny (TTN) formed via a facile and scalable synthetic process is applied as a coating material for Si anodes. A thin layer of amorphous TiO2 is uniformly coated onto Si nanoparticles by a simple sol-gel method and then converted into a two phase TiO2- x /TiO1- y Ny via nitridation. The lithiated TiO2-x provides high ionic and electrical conductivity, while TiO1-y Ny can improve mechanical strength that alleviates volume change of Si to address capacity fading issue. Owing to these synergetic advantages, TiO2- x /TiO1- y Ny -coated Si (Si@TTN) exhibits excellent electrochemical properties, including a high charge capacity of 1650 mA h g-1 at 0.1 A g-1 and 84% capacity retention after 100 cycles at 1 A g-1 . Moreover, a significantly enhanced rate performance can be achieved at a high current density. This investigation presents a facile and effective coating material to use as the high-capacity silicon anode in the emerging Si anode technology in LIBs.

Natural bone-mimicking nanopore-incorporated hydroxyapatite scaffolds for enhanced bone tissue regeneration.

BACKGROUND: A considerable number of studies has been carried out to develop alloplastic bone graft materials such as hydroxyapatite (HAP) that mimic the hierarchical structure of natural bones with multiple levels of pores: macro-, micro-, and nanopores. Although nanopores are known to play many essential roles in natural bones, only a few studies have focused on HAPs containing them; none of those studies investigated the functions of nanopores in biological systems. METHOD: We developed a simple yet powerful method to introduce nanopores into alloplastic HAP bone graft materials in large quantities by simply pressing HAP nanoparticles and sintering them at a low temperature. RESULTS: The size of nanopores in HAP scaffolds can be controlled between 16.5 and 30.2 nm by changing the sintering temperature. When nanopores with a size of ~ 30.2 nm, similar to that of nanopores in natural bones, are introduced into HAP scaffolds, the mechanical strength and cell proliferation and differentiation rates are significantly increased. The developed HAP scaffolds containing nanopores (SNPs) are biocompatible, with negligible erythema and inflammatory reactions. In addition, they enhance the bone regeneration when are implanted into a rabbit model. Furthermore, the bone regeneration efficiency of the HAP-based SNP is better than that of a commercially available bone graft material. CONCLUSION: Nanopores of HAP scaffolds are very important for improving the bone regeneration efficiency and may be one of the key factors to consider in designing highly efficient next-generation alloplastic bone graft materials.

Detection of Testicular Torsion With Preserved Intratesticular Flow By Point of Care Ultrasound in the Emergency Department: A Case Report.

BACKGROUND: Ultrasonography is an effective diagnostic tool for testicular torsion (TT), which is typically characterized by the absence of blood flow in the affected testicle on color Doppler mode. However, there are a few reported cases of TT with symmetrical preserved flow. We report a case of TT with the preserved intratesticular flow on color Doppler ultrasound. CASE REPORT: A 14-year-old boy was admitted due to sudden-onset right scrotal pain. Point-of-care ultrasound (POCUS) revealed that the right testicle was larger than the left. The intratesticular flow in both testicles was preserved. Radiology-performed ultrasound confirmed the preserved intratesticular flow observed on POCUS, but also demonstrated a whirlpool sign of the right spermatic cord. TT was confirmed surgically. WHY SHOULD AN EMERGENCY PHYSICIAN BE AWARE OF THIS?: Emergency physicians should investigate the presence of intratesticular blood flow and the whirlpool sign of the spermatic cord or other ultrasound features suggestive of TT, even if testicular blood flow is preserved. Suspicion of TT from POCUS findings warrants further evaluation to preserve the patient's fertility.

Bio-essential Inorganic Molecular Nanowires as a Bioactive Muscle Extracellular-Matrix-Mimicking Material.

Many physiochemical properties of the extracellular matrix (ECM) of muscle tissues, such as nanometer scale dimension, nanotopography, negative charge, and elasticity, must be carefully reproduced to fabricate scaffold materials mimicking muscle tissues. Hence, we developed a muscle tissue ECM-mimicking scaffold using Mo6S3I6 inorganic molecular wires (IMWs). Composed of bio-essential elements and having a nanofibrous structure with a diameter of ∼1 nm and a negative surface charge with high stability, Mo6S3I6 IMWs are ideal for mimicking natural ECM molecules. Once Mo6S3I6 IMWs were patterned on a polydimethylsiloxane surface with an elasticity of 1877.1 ± 22.2 kPa, that is, comparable to that of muscle tissues, the proliferation and α-tubulin expression of myoblasts enhanced significantly. Additionally, the repetitive one-dimensional patterns of Mo6S3I6 IMWs induced the alignment and stretching of myoblasts with enhanced α-tubulin expression and differentiation into myocytes. This study demonstrates that Mo6S3I6 IMWs are promising for mimicking the ECM of muscle tissues.

Simultaneous Stabilization and Functionalization of Gold Nanoparticles via Biomolecule Conjugation: Progress and Perspectives.

Gold nanoparticles (AuNPs) are used in various biological applications because of their small surface area-to-volume ratios, ease of synthesis and modification, low toxicity, and unique optical properties. These properties can vary significantly with changes in AuNP size, shape, composition, and arrangement. Thus, the stabilization of AuNPs is crucial to preserve the properties required for biological applications. In recent years, various polymer-based physical and chemical methods have been extensively used for AuNP stabilization. However, a new stabilization approach using biomolecules has recently attracted considerable attention. Biomolecules such as DNA, RNA, peptides, and proteins are representative of the biomoieties that can functionalize AuNPs. According to several studies, biomolecules can stabilize AuNPs in biological media; in addition, AuNP-conjugated biomolecules can retain certain biological functions. Furthermore, the presence of biomolecules on AuNPs significantly enhances their biocompatibility. This review provides a representative overview of AuNP functionalization using various biomolecules. The strategies and mechanisms of AuNP functionalization using biomolecules are comprehensively discussed in the context of various biological fields.

Bioinspired Adenosine Triphosphate as an "All-In-One" Green Flame Retardant via Extremely Intumescent Char Formation.

The development of eco-friendly flame retardants is crucial due to the hazardous properties of most conventional flame retardants. Herein, adenosine triphosphate (ATP) is reported to be a highly efficient "all-in-one" green flame retardant as it consists of three essential groups, which lead to the formation of char with extreme intumescence, namely, three phosphate groups, providing an acid source; one ribose sugar, working as a char source; and one adenine, acting as a blowing agent. Polyurethane foam was used as a model flammable material to demonstrate the exceptional flame retardancy of ATP. The direct flammability tests have clearly shown that the ATP-coated polyurethane (PU) foam almost did not burn upon exposure to the torch flame. Importantly, ATP exhibits an extreme volume increase, whereas general phosphorus-based flame retardants show a negligible increase in volume. The PU foam coated with 30 wt % of ATP (PU-ATP 30 wt %) exhibits a significant reduction in the peak heat release rate (94.3%) with a significant increase in the ignition time, compared to bare PU. In addition, PU-ATP 30 wt % exhibits a high limiting oxygen index (LOI) value of 31% and HF-1 rating in the UL94 horizontal burning foamed material test. Additionally, we demonstrated that ATP's flame retardancy is sufficient for other types of matrices such as cotton, as confirmed from the results of the standardized ASTM D6413 test; cotton-ATP 30 wt % exhibits an LOI value of 32% and passes the vertical flame test. These results strongly suggest that ATP has great potential to be used as an "all-in-one" green flame retardant.

A machine-learning-based alloy design platform that enables both forward and inverse predictions for thermo-mechanically controlled processed (TMCP) steel alloys.

Predicting mechanical properties such as yield strength (YS) and ultimate tensile strength (UTS) is an intricate undertaking in practice, notwithstanding a plethora of well-established theoretical and empirical models. A data-driven approach should be a fundamental exercise when making YS/UTS predictions. For this study, we collected 16 descriptors (attributes) that implicate the compositional and processing information and the corresponding YS/UTS values for 5473 thermo-mechanically controlled processed (TMCP) steel alloys. We set up an integrated machine-learning (ML) platform consisting of 16 ML algorithms to predict the YS/UTS based on the descriptors. The integrated ML platform involved regularization-based linear regression algorithms, ensemble ML algorithms, and some non-linear ML algorithms. Despite the dirty nature of most real-world industry data, we obtained acceptable holdout dataset test results such as R2 > 0.6 and MSE < 0.01 for seven non-linear ML algorithms. The seven fully trained non-linear ML models were used for the ensuing 'inverse design (prediction)' based on an elitist-reinforced, non-dominated sorting genetic algorithm (NSGA-II). The NSGA-II enabled us to predict solutions that exhibit desirable YS/UTS values for each ML algorithm. In addition, the NSGA-II-driven solutions in the 16-dimensional input feature space were visualized using holographic research strategy (HRS) in order to systematically compare and analyze the inverse-predicted solutions for each ML algorithm.

Cyan-Light-Emitting Chalcogenometallate Phosphor, KGaS2:Eu2+, for Phosphor-Converted White Light-Emitting Diodes.

A novel KGaS2 phosphor host that emits a cyan light was discovered to fill the cyan gap in the visible spectrum of phosphor-converted white light-emitting diodes (pc-wLEDs). KGaS2, belonging to the chalcogenometallates of the type ABQ2, was synthesized via a solid-state route with compositions optimized to achieve a phosphor host that would achieve the best photoluminescence (PL) properties. The activation with Eu2+ gave rise to PL in the cyan region of the spectrum with a PL maximum at ∼498 nm, as measured under the near-UV (420 nm) and blue (450 nm) excitations. The PL properties at the near-UV excitation are found to be much better, as compared to those obtained at the blue excitation. The Rietveld analysis, using high resolution synchrotron X-ray diffraction calibrated at a wavelength of 1.522 Å and selected area electron diffraction (SAED) pattern analysis of the composition optimized with the highest PL intensity, revealed a centrosymmetric monoclinic structure in the C2/c space group. The stoichiometry of the optimized composition, as estimated using Rietveld refinement, was revealed as KGa0.921S1.882:Eu2+. The decay curve measurement, using time-resolved spectroscopy, yielded a 10% decay time of 0.41 µs, which is much smaller compared with the decay time of the commercially available ß-SIALON phosphor that has a 10% decay time of 1.71 µs. The white pc-LED, fabricated with a cyan phosphor, had a higher value on the color rendering index and a lower value for color correlated temperatures, as compared with the version fabricated without a cyan phosphor, which makes this novel phosphor suitable for applications as a pc-wLED.

A study on the bio-applicability of aqueous-dispersed van der Waals 1-D material Nb2Se9 using poloxamer.

In this research, dispersion of a new type of one-dimensional inorganic material Nb2Se9, composed of van der Waals bonds, in aqueous solution for bio-application study were studied. To disperse Nb2Se9, which exhibits hydrophobic properties in water, experiments were carried out using a block copolymer (poloxamer) as a dispersant. It was confirmed that PPO, the hydrophobic portion of Poloxamer, was adsorbed onto the surface of Nb2Se9, and PEO, the hydrophilic portion, induced steric hinderance to disperse Nb2Se9 to a size of 10 nm or less. To confirm the adaptability of muscle cells C2C12 to the dispersed Nb2Se9 using poloxamer 188 as dispersant, a MTT assay and a live/dead assay were performed, demonstrating improvement in the viability and proliferation of C2C12 cells.

Statistical Characterization of the Morphologies of Nanoparticles through Machine Learning Based Electron Microscopy Image Analysis.

Although transmission electron microscopy (TEM) may be one of the most efficient techniques available for studying the morphological characteristics of nanoparticles, analyzing them quantitatively in a statistical manner is exceedingly difficult. Herein, we report a method for mass-throughput analysis of the morphologies of nanoparticles by applying a genetic algorithm to an image analysis technique. The proposed method enables the analysis of over 150,000 nanoparticles with a high precision of 99.75% and a low false discovery rate of 0.25%. Furthermore, we clustered nanoparticles with similar morphological shapes into several groups for diverse statistical analyses. We determined that at least 1,500 nanoparticles are necessary to represent the total population of nanoparticles at a 95% credible interval. In addition, the number of TEM measurements and the average number of nanoparticles in each TEM image should be considered to ensure a satisfactory representation of nanoparticles using TEM images. Moreover, the statistical distribution of polydisperse nanoparticles plays a key role in accurately estimating their optical properties. We expect this method to become a powerful tool and aid in expanding nanoparticle-related research into the statistical domain for use in big data analysis.

Dirty engineering data-driven inverse prediction machine learning model.

Most data-driven machine learning (ML) approaches established in metallurgy research fields are focused on a build-up of reliable quantitative models that predict a material property from a given set of material conditions. In general, the input feature dimension (the number of material condition variables) is much higher than the output feature dimension (the number of material properties of concern). Rather than such a forward-prediction ML model, it is necessary to develop so-called inverse-design modeling, wherein required material conditions could be deduced from a set of desired material properties. Here we report a novel inverse design strategy that employs two independent approaches: a metaheuristics-assisted inverse reading of conventional forward ML models and an atypical inverse ML model based on a modified variational autoencoder. These two unprecedented approaches were successful and led to overlapped results, from which we pinpointed several novel thermo-mechanically controlled processed (TMCP) steel alloy candidates that were validated by a rule-based thermodynamic calculation tool (Thermo-Calc.). We also suggested a practical protocol to elucidate how to treat engineering data collected from industry, which is not prepared as independent and identically distributed (IID) random data.

Does Combining Biomarkers and Brain Images Provide Improved Prognostic Predictive Performance for Out-Of-Hospital Cardiac Arrest Survivors before Target Temperature Management?

We examined whether combining biomarkers measurements and brain images early after the return of spontaneous circulation improves prognostic performance compared with the use of either biomarkers or brain images for patients with cardiac arrest following target temperature management (TTM). This retrospective observational study involved comatose out-of-hospital cardiac arrest survivors. We analyzed neuron-specific enolase levels in serum (NSE) or cerebrospinal fluid (CSF), grey-to-white matter ratio by brain computed tomography, presence of high signal intensity (HSI) in diffusion-weighted imaging (DWI), and voxel-based apparent diffusion coefficient (ADC). Of the 58 patients, 33 (56.9%) had poor neurologic outcomes. CSF NSE levels showed better prognostic performance (area under the curve (AUC) 0.873, 95% confidence interval (CI) 0.749-0.950) than serum NSE levels (AUC 0.792, 95% CI 0.644-0.888). HSI in DWI showed the best prognostic performance (AUC 0.833, 95% CI 0.711-0.919). Combining CSF NSE levels and HSI in DWI had better prognostic performance (AUC 0.925, 95% CI 0.813-0.981) than each individual method, followed by the combination of serum NSE levels and HSI on DWI and that of CSF NSE levels and the percentage of voxels of ADC (AUC 0.901, 95% CI 0.792-0.965; AUC 0.849, 95% CI 0.717-0.935, respectively). Combining CSF/serum NSE levels and HSI in DWI before TTM improved the prognostic performance compared to either each individual method or other combinations.

Changes in peak inspiratory flow rate and peak airway pressure with endotracheal tube size during chest compression.

BACKGROUND: Adequate airway management plays an important role in high-quality cardiopulmonary resuscitation (CPR). Airway management is usually performed using an endotracheal tube (ETT) during CPR. However, no study has assessed the effect of ETT size on the flow rate and airway pressure during CPR. METHODS: We measured changes in peak inspiratory flow rate (PIFR), peak airway pressure (Ppeak), and mean airway pressure (Pmean) according to changes in ETT size (internal diameter 6.0, 7.0, and 8.0 mm) and with or without CPR. A tidal volume of 500 mL was supplied at a rate of 10 times per minute using a mechanical ventilator. Chest compressions were maintained at a constant compression depth and speed using a mechanical chest compression device (LUCAS2, mode: active continuous, chest compression rate: 102±2/minute, chest compression depth 2-2.5 inches). RESULTS: The median of several respiratory physiological parameters during CPR was significantly different according to the diameter of each ETT (6.0 vs. 8.0 mm): PIFR (32.1 L/min [30.5-35.3] vs. 28.9 L/min [27.5-30.8], P<0.001), Ppeak (48.84 cmH2O [27.46-52.11] vs. 27.45 cmH2O [22.53-52.57], P<0.001), and Pmean (18.34 cmH2O [14.61-21.66] vs.13.66 cmH2O [8.41-19.24], P<0.001). CONCLUSION: The changes in PIFR, Ppeak, and Pmean were related to the internal diameter of ETT, and these values tended to decrease with an increase in ETT size. Higher airway pressures were measured in the CPR group than in the no CPR group.