Anomalous Correlation between Thermal Conductivity and Elastic Modulus in Two-Dimensional Hybrid Metal Halide Perovskites.

Device-level implementation of soft materials for energy conversion and thermal management demands a comprehensive understanding of their thermal conductivity and elastic modulus to mitigate thermo-mechanical challenges and ensure long-term stability. Thermal conductivity and elastic modulus are usually positively correlated in soft materials, such as amorphous macromolecules, which poses a challenge to discover materials that are either soft and thermally conductive or hard and thermally insulative. Here, we show anomalous correlations of thermal conductivity and elastic modulus in two-dimensional (2D) hybrid organic-inorganic perovskites (HOIP) by engineering the molecular interactions between organic cations. By replacing conventional alkyl-alkyl and aryl-aryl type organic interactions with mixed alkyl-aryl interactions, we observe an enhancement in elastic modulus with a reduction in thermal conductivity. This anomalous dependence provides a route to engineer thermal conductivity and elastic modulus independently and a guideline to search for better thermal management materials. Further, introducing chirality into the organic cation induces a molecular packing that leads to the same thermal conductivity and elastic modulus regardless of the composition across all half-chiral 2D HOIPs. This finding provides substantial leeway for further investigations in chiral 2D HOIPs to tune optoelectronic properties without compromising thermal and mechanical stability.

Anti-Inflammatory Effect of Chestnut Honey and Cabbage Mixtures Alleviates Gastric Mucosal Damage.

Gastritis, one of the most common gastrointestinal disorders, damages the stomach lining as it causes a disproportion between the protective and ruinous factors of the gastric system. Cabbage (CB) is widely used to treat gastric lesions but requires the addition of natural sweeteners to counteract its distinct bitter taste. Therefore, this study sought to determine whether the combination of chestnut honey (CH)-which is known for its dark brown color and high kynurenic acid (KA) content-or KA-increased CH (KACH) with CB (CH + CB or KACH + CB) exerts synergistic effects for improving both taste and efficacy. Before confirming the gastroprotective effects in indomethacin (INDO)-induced rats, the anti-inflammatory activities of CH + CB and KACH + CB were assessed in lipopolysaccharide (LPS)-stimulated RAW 264.7 macrophages. As a result, treatment with either CH + CB or KACH + CB downregulated pro-inflammatory cytokine levels in LPS-stimulated RAW 264.7 macrophages by regulating the translocation of nuclear factor kappa B. Furthermore, both CH + CB and KACH + CB not only enhanced the levels of antioxidant enzymes but also triggered the activation of nuclear factor erythroid-related factor 2. Based on these effects, CH + CB or KACH + CB effectively protected the gastric mucosa in INDO-induced rats. Therefore, this study suggests that CH + CB and KACH + CB exert stronger gastroprotective effects when used together.

Changes in Revision Cochlear Implantation and Device Failure Profiles.

OBJECTIVES: As cochlear implantation (CI) experiences rapid innovations and its indications expand, the characteristics of revision CI (RCI) are evolving. This study investigated changes in the RCI profile and explored their clinical implications. METHODS: A retrospective chart review was conducted of all CIs performed at a tertiary medical institution between October 2001 and January 2023. The rates of and reasons for RCI were evaluated in relation to the manufacturer and device model. Kaplan-Meier analysis was employed to examine cumulative and device survival curves. Cumulative and device survival rates were additionally analyzed based on age group, period of primary CI, and manufacturer. A Cox proportional hazards model was employed to evaluate the association between RCI and the device manufacturer. RESULTS: Among 1,430 CIs, 73 (5.1%) required RCI. The predominant reason for RCI was device failure (40 of 73 RCIs [54.8%]), with an overall device failure rate of 2.8%. This was followed by flap-associated problems and migration (nine of 73 RCIs each [12.3%]). Flap retention issues emerged as a new cause in three cases (two involving the CI 632 and one involving the SYNCHRONY 2 implant), and six instances of electrode tip fold-over arose (four for the CI 600 series and two for the CI 500 series). The overall 10-year cumulative and device survival rates were 93.4% and 95.8%, respectively. After excluding models with recall issues, significant differences in cumulative (P =0.010) and device (P =0.001) survival rates were observed across manufacturers. CONCLUSION: While the overall CI survival rate is stable, device failure persists as the predominant reason for RCI. Moreover, the types of complications leading to revision (including issues with flap retention and electrode tip fold-over) have shifted, particularly for newer implant models. Given the clinical importance of device failure and subsequent reoperation, clinicians should remain informed about and responsive to these trends.

Synthesis and Optical Properties of One Year Air-Stable Chiral Sb(III) Halide Semiconductors.

Chiral hybrid metal-halide semiconductors (MHS) pose as ideal candidates for spintronic applications owing to their strong spin-orbit coupling (SOC), and long spin relaxation times. Shedding light on the underlying structure-property relationships is of paramount importance for the targeted synthesis of materials with an optimum performance. Herein, we report the synthesis and optical properties of 1D chiral (R-/S-THBTD)SbBr5 (THBTD = 4,5,6,7-tetrahydro-benzothiazole-2,6-diamine) semiconductors using a multifunctional ligand as a countercation and a structure directing agent. (R-/S-THBTD)SbBr5 feature direct and indirect band gap characteristics, exhibiting photoluminescence (PL) light emission at RT that is accompanied by a lifetime of a few ns. Circular dichroism (CD), second harmonic generation (SHG), and piezoresponse force microscopy (PFM) studies validate the chiral nature of the synthesized materials. Density functional theory (DFT) calculations revealed a Rashba/Dresselhaus (R/D) spin splitting, supported by an energy splitting (ER) of 23 and 25 meV, and a Rashba parameter (αR) of 0.23 and 0.32 eV·Å for the R and S analogs, respectively. These values are comparable to those of the 3D and 2D perovskite materials. Notably, (S-THBTD)SbBr5 has been air-stable for a year, a record performance among chiral lead-free MHS. This work demonstrates that low-dimensional, lead-free, chiral semiconductors with exceptional air stability can be acquired, without compromising spin splitting and manipulation performance.

Effects of hole transporting PEDOT:PSS on the photoemission of upconverted hot electron in Mn-doped CdS/ZnS quantum dots.

In this work, we investigated the effect of hole transporting poly(3,4-ethylenedioxythiophene):polystyrene sulfonate (PEDOT:PSS) interfacing with Mn-doped CdS/ZnS quantum dots (QDs) deposited on an indium tin oxide (ITO) substrate on the photoemission of upconverted hot electrons under weak continuous wave photoexcitation in a vacuum. Among the various factors that can influence the photoemission of the upconverted hot electrons, we studied the role of PEDOT:PSS in facilitating the hole transfer from QDs and altering the energy of photoemitted hot electrons. Compared to hot electrons emitted from QDs deposited directly on the ITO substrate, the addition of the PEDOT:PSS layer between the QD and ITO layers increased the energy of the photoemitted hot electrons. The increased energy of the photoemitted hot electrons is attributed in part to the reduced steady-state positive charge on the QDs under continuous photoexcitation, which reduces the energy required to eject the electron from the conduction band.

An Automated Cell Detection Method for TH-positive Dopaminergic Neurons in a Mouse Model of Parkinson's Disease Using Convolutional Neural Networks.

Quantification of tyrosine hydroxylase (TH)-positive neurons is essential for the preclinical study of Parkinson's disease (PD). However, manual analysis of immunohistochemical (IHC) images is labor-intensive and has less reproducibility due to the lack of objectivity. Therefore, several automated methods of IHC image analysis have been proposed, although they have limitations of low accuracy and difficulties in practical use. Here, we developed a convolutional neural network-based machine learning algorithm for TH+ cell counting. The developed analytical tool showed higher accuracy than the conventional methods and could be used under diverse experimental conditions of image staining intensity, brightness, and contrast. Our automated cell detection algorithm is available for free and has an intelligible graphical user interface for cell counting to assist practical applications. Overall, we expect that the proposed TH+ cell counting tool will promote preclinical PD research by saving time and enabling objective analysis of IHC images.

Unveiling the Fatigue Behavior of 2D Hybrid Organic-Inorganic Perovskites: Insights for Long-Term Durability.

2D hybrid organic-inorganic perovskites (HOIPs) are commonly found under subcritical cyclic stresses and suffer from fatigue issues during device operation. However, their fatigue properties remain unknown. Here, the fatigue behavior of (C4 H9 -NH3 )2 (CH3 NH3 )2 Pb3 I10 , the archetype 2D HOIP, is systematically investigated by atomic force microscopy (AFM). It is found that 2D HOIPs are much more fatigue resilient than polymers and can survive over 1 billion cycles. 2D HOIPs tend to exhibit brittle failure at high mean stress levels, but behave as ductile materials at low mean stress levels. These results suggest the presence of a plastic deformation mechanism in these ionic 2D HOIPs at low mean stress levels, which may contribute to the long fatigue lifetime, but is inhibited at higher mean stresses. The stiffness and strength of 2D HOIPs are gradually weakened under subcritical loading, potentially as a result of stress-induced defect nucleation and accumulation. The cyclic loading component can further accelerate this process. The fatigue lifetime of 2D HOIPs can be extended by reducing the mean stress, stress amplitude, or increasing the thickness. These results can provide indispensable insights into designing and engineering 2D HOIPs and other hybrid organic-inorganic materials for long-term mechanical durability.

Spatiotemporally controlled drug delivery via photothermally driven conformational change of self-integrated plasmonic hybrid nanogels.

BACKGROUND: Spatiotemporal regulation is one of the major considerations for developing a controlled and targeted drug delivery system to treat diseases efficiently. Light-responsive plasmonic nanostructures take advantage due to their tunable optical and photothermal properties by changing size, shape, and spatial arrangement. RESULTS: In this study, self-integrated plasmonic hybrid nanogels (PHNs) are developed for spatiotemporally controllable drug delivery through light-driven conformational change and photothermally-boosted endosomal escape. PHNs are easily synthesized through the simultaneous integration of gold nanoparticles (GNPs), thermo-responsive poly (N-isopropyl acrylamide), and linker molecules during polymerization. Wave-optic simulations reveal that the size of the PHNs and the density of the integrated GNPs are crucial factors in modulating photothermal conversion. Several linkers with varying molecular weights are inserted for the optimal PHNs, and the alginate-linked PHN (A-PHN) achieves more than twofold enhanced heat conversion compared with others. Since light-mediated conformational changes occur transiently, drug delivery is achieved in a spatiotemporally controlled manner. Furthermore, light-induced heat generation from cellular internalized A-PHNs enables pinpoint cytosolic delivery through the endosomal rupture. Finally, the deeper penetration for the enhanced delivery efficiency by A-PHNs is validated using multicellular spheroid. CONCLUSION: This study offers a strategy for synthesizing light-responsive nanocarriers and an in-depth understanding of light-modulated site-specific drug delivery.

A Critical Involvement of Glutamatergic Neurons in the Anterior Insular Cortex for Subdiaphragmatic Vagotomy-induced Analgesia.

Subdiaphragmatic vagotomy (SDV) is known to produce analgesic effect in various pain conditions including not only visceral pain but also somatic pain. We aimed to determine brain mechanisms by which SDV induces analgesic effect in somatic pain condition by using formalin-induced acute inflammatory pain model. We identified brain regions that mediate SDV-induced analgesic effect on acute inflammatory pain by analyzing c-Fos expression in the whole brain. We found that c-Fos expression was specifically increased in the anterior insular cortex (aIC) among subregions of the insular cortex in acute inflammatory pain, which was reversed by SDV. These results were not mimicked in female mice, indicating sexual-dimorphism in SDV-induced analgesia. SDV decreased c-Fos expressions more preferentially in glutamatergic neurons rather than GABAergic neurons in the aIC, and pharmacological activation of glutamatergic neurons with NMDA in the aIC inhibited SDV-induced analgesic effect. Furthermore, chemogenetic activation of glutamatergic neurons in the aIC reversed SDV-induced analgesia. Taken together, our results suggest that the decrease in the neuronal activity of glutamatergic neurons in the aIC mediates SDV-induced analgesic effect, potentially serving as an important therapeutic target to treat inflammatory pain.

Covalent Scrambling in Porous Polyarylthioethers through a Stepwise SN Ar for Tunable Bandgap and Porosity.

Porous poly(aryl thioether)s offer stability and electronic tunability by robust sulfur-aryl conjugated architecture, but synthetic access is hindered due to limited control over the nucleophilic nature of sulfides and the air sensitivity of aromatic thiols. Here, we report a simple, one-pot, inexpensive, regioselective synthesis of highly porous poly(aryl thioether)s through polycondensation of perfluoroaromatic compounds with sodium sulfide. The unprecedented temperature-dependent para-directing formation of thioether linkages leads to a stepwise transition of the polymer extension into a network, thereby allowing fine control of the porosity and optical band gaps. The obtained porous organic polymers with ultra-microporosity (<1 nm) and sulfur as the surface functional groups show size-dependent separation of organic micropollutants and selective removal of mercury ions from water. Our findings offer easy access to poly(aryl thioether)s with accessible sulfur functionalities and higher complexity, which will help in realizing advanced synthetic designs in applications such as adsorption, (photo)catalysis, and (opto)electronics.

Photoactive MOF-Derived Bimetallic Silver and Cobalt Nanocomposite with Enhanced Antibacterial Activity.

Conventional antibiotic-based treatment of bacterial infections remains one of the most difficult challenges in medicine because of the threat of multidrug resistance caused by indiscriminate abuse. To solve these problems, it is essential to develop an effective antibacterial agent that can be used at a small dose while minimizing the occurrence of multiple resistance. Metal-organic frameworks (MOFs), which are hyper-porous hybrid materials containing metal ions linked by organic ligands, have recently attracted attention because of their strong antibacterial activity through metal-ion release, unlike conventional antibiotics. In this study, we developed a photoactive MOF-derived cobalt-silver bimetallic nanocomposite (Ag@CoMOF) by simply depositing silver nanoparticles on a cobalt-based MOF through nanoscale galvanic replacement. The nanocomposite structure continuously releases antibacterial metal ions (i.e., Ag and Co ions) in the aqueous phase and exhibits a strong photothermal conversion effect of Ag nanoparticles, accompanied by a rapid temperature increase of 25-80 °C under near-infrared (NIR) irradiation. Using this MOF-based bimetallic nanocomposite, superior antibacterial activities were achieved by 22.1-fold for Escherichia coli and 18.3-fold for Bacillus subtilis enhanced inhibition of bacterial growth in a liquid culture environment compared with the generally used chemical antibiotics. In addition, we confirmed the synergistic enhancement of the antibacterial ability of the bimetallic nanocomposite induced by NIR-triggered photothermal heating and bacterial membrane disruption even when using a small amount of the nanocomposites. We envision that this novel antibacterial agent using MOF-based nanostructures will replace traditional antibiotics to circumvent multidrug resistance and present a new approach to antibiotic development.

Abnormal In-Plane Thermomechanical Behavior of Two-Dimensional Hybrid Organic-Inorganic Perovskites.

The implementation of two-dimensional (2D) hybrid organic-inorganic perovskites (HOIPs) in semiconductor device applications will have to accommodate the co-existence of strain and temperature stressors and requires a thorough understanding of the thermomechanical behavior of 2D HOIPs. This will mitigate thermomechanical stability issues and improve the durability of the devices, especially when one considers the high susceptibility of 2D HOIPs to temperature due to their soft nature. Here, we employ atomic force microscopy (AFM) stretching of suspended membranes to measure the temperature dependence of the in-plane Young's modulus (E∥) of model Ruddlesden-Popper 2D HOIPs with a general formula of (CH3(CH2)3NH3)2(CH3NH3)n-1PbnI3n+1 (here, n = 1, 3, or 5). We find that E∥ values of these 2D HOIPs exhibit a prominent non-monotonic dependence on temperature, particularly an abnormal thermal stiffening behavior (nearly 40% change in E∥) starting around the order-disorder transition temperature of the butylammonium spacer molecules, which is significantly different from the thermomechanical behavior expected from their 3D counterpart (CH3NH3PbI3) or other low-dimensional material systems. Further raising the temperature eventually reverses the trend to thermal softening. The magnitude of the thermally induced change in E∥ is also much higher in 2D HOIPs than in their 3D analogs. Our results can shed light on the structural origin of the thermomechanical behavior and provide needed guidance to design 2D HOIPs with desired thermomechanical properties to meet the application needs.

Tackling prediction uncertainty in machine learning for healthcare.

Predictive machine-learning systems often do not convey the degree of confidence in the correctness of their outputs. To prevent unsafe prediction failures from machine-learning models, the users of the systems should be aware of the general accuracy of the model and understand the degree of confidence in each individual prediction. In this Perspective, we convey the need of prediction-uncertainty metrics in healthcare applications, with a focus on radiology. We outline the sources of prediction uncertainty, discuss how to implement prediction-uncertainty metrics in applications that require zero tolerance to errors and in applications that are error-tolerant, and provide a concise framework for understanding prediction uncertainty in healthcare contexts. For machine-learning-enabled automation to substantially impact healthcare, machine-learning models with zero tolerance for false-positive or false-negative errors must be developed intentionally.

MONITORING OF PULMONARY EDEMA USING ULTRASOUND RADIOFREQUENCY SIGNAL.

ABSTRACT: Objectives: Excessive accumulation of extravascular lung water impairs respiratory gas exchange and results in respiratory distress. Real-time radiofrequency signals of ultrasound can continuously and quantitatively monitor excessive lung water. This study aims to evaluate the availability of continuous real-time quantitative pulmonary edema monitoring using ultrasound radiofrequency signals and compare it with Pa o2 (partial pressure of arterial oxygen)/F io2 (fraction of inspired oxygen) (PF) ratio, conventional lung ultrasound, and the Hounsfield unit of chest computed tomography. Methods: Male Yorkshire pigs (40.5 ± 0.5 kg) were anesthetized and mechanically ventilated. A balanced crystalloid was administered to induce hydrostatic pulmonary edema. Three different infusion rates of 2, 4, and 6 mL/kg per minute were tested to determine the infusion rate for the appropriate swine model. The chest computed tomography and ultrasonography with radiofrequency signals were taken every 5 min during the full inspiration. The ultrasonography scans with radiofrequency signals were measured at the intercostal space where the line crossing the two armpits and the right anterior axillary line intersected. Results: The infusion rate of fluid for the pulmonary edema model was determined to be 6 mL/kg per minute, and a total of four pigs were tested at an injection rate of 6 mL/kg. The adjusted R2 values of regression analysis between the radiofrequency signal and computer tomography Hounsfield score were 0.990, 0.993, 0.988, and 0.993 (all P values <0.05). All radiofrequency signal changes preceded changes in PF ratio or lung ultrasound changes. The area under the receiver operating characteristic curve of the radiofrequency signal for predicting PF ratio <300 was 0.88 (95% confidence interval, 0.82-0.93). Conclusion: We evaluated ultrasound radiofrequency signals to assess pulmonary edema in a swine model that can worsen gradually and showed that quantitative ultrasound radiofrequency signal analysis could assess pulmonary edema and its progression before PF ratio or lung ultrasound changes.

Prediction of oxygen requirement in patients with COVID-19 using a pre-trained chest radiograph xAI model: efficient development of auditable risk prediction models via a fine-tuning approach.

Risk prediction requires comprehensive integration of clinical information and concurrent radiological findings. We present an upgraded chest radiograph (CXR) explainable artificial intelligence (xAI) model, which was trained on 241,723 well-annotated CXRs obtained prior to the onset of the COVID-19 pandemic. Mean area under the receiver operating characteristic curve (AUROC) for detection of 20 radiographic features was 0.955 (95% CI 0.938-0.955) on PA view and 0.909 (95% CI 0.890-0.925) on AP view. Coexistent and correlated radiographic findings are displayed in an interpretation table, and calibrated classifier confidence is displayed on an AI scoreboard. Retrieval of similar feature patches and comparable CXRs from a Model-Derived Atlas provides justification for model predictions. To demonstrate the feasibility of a fine-tuning approach for efficient and scalable development of xAI risk prediction models, we applied our CXR xAI model, in combination with clinical information, to predict oxygen requirement in COVID-19 patients. Prediction accuracy for high flow oxygen (HFO) and mechanical ventilation (MV) was 0.953 and 0.934 at 24 h and 0.932 and 0.836 at 72 h from the time of emergency department (ED) admission, respectively. Our CXR xAI model is auditable and captures key pathophysiological manifestations of cardiorespiratory diseases and cardiothoracic comorbidities. This model can be efficiently and broadly applied via a fine-tuning approach to provide fully automated risk and outcome predictions in various clinical scenarios in real-world practice.

Organizing Reliable Polymer Electrode Lines in Flexible Neural Networks via Coffee Ring-Free Micromolding in Capillaries.

With an increase in the demand for smart wearable systems, artificial synapse arrays for flexible neural networks have received considerable attention. A synaptic device with a two-terminal configuration is promising for complex neural networks because of its ability to scale to a crossbar array architecture. To realize practical crossbar arrays with a high density, it is essential to achieve reliable electrode lines that act as signal terminals. However, an effective method to develop intrinsically flexible signal lines in artificial neural networks has not been developed. In this study, we achieved reliable polymer signal lines for flexible neural networks using coffee ring-free micromolding in capillaries (MIMIC). In a typical MIMIC, the outward convective flow of the polymer solution inherently deteriorates the pattern fidelity. To achieve reliable conducting polymer (CP) lines, we precisely controlled the flow of the polymer solution in the MIMIC by inducing the Marangoni force. When the convective and Marangoni flows for the solution were balanced in the MIMIC, the CP line patterns were reliably produced with high fidelity. The developed CP lines exhibited superior conductivity and high mechanical flexibility. Moreover, flexible memristor arrays consisting of CP signal lines demonstrated a high potential for realizing practical neuromorphic systems linked to artificial intelligence.

Systematic Modulation of Thiol Functionalities in Inexpensive Porous Polymers for Effective Mercury Removal.

Through accumulation, mercury contamination in aquatic systems still poses serious health risks despite the strict regulations on drinking water and industrial discharge. One effective strategy against this is adsorptive removal, in which a suitably functionalized porous material is added to water treatment protocols. Thiol (SH) group-grafted structures perform commendably; however, insufficient attention is paid to the cost, scalability, and reusability or how the arrangement of sulfur atoms could affect the HgII binding strength. We used an inexpensive and scalable porous covalent organic polymer (COP-130) to systematically introduce thiol functional groups with precise chain lengths and sulfur content. Thiol-functionalized COP-130 demonstrates enhanced wettability and excellent HgII uptake of up to 936 mg g-1 , with fast kinetics and exceptionally high selectivity. These Hg adsorbents are easily regenerated with HCl and can be used at least six times without loss of capacity even after treatment with strong acid, a rare performance in the domain of Hg-removal research.

The Nature of Noradrenergic Volume Transmission From Locus Coeruleus to Brainstem Mesencephalic Trigeminal Sensory Neurons.

Noradrenergic neurons in the locus coeruleus (LC) release noradrenaline (NA) that acts via volume transmission to activate extrasynaptic G-protein coupled receptors (GPCRs) in target cells throughout the brain. As the closest projection, the dorsal LC laterally adjoins the mesencephalic trigeminal nucleus (MTN), in which proprioceptive primary sensory neurons innervating muscle spindles of jaw-closing muscles are exceptionally located. MTN neurons express α2-adrenergic receptors (α2-ARs) and display hyperpolarization-activated cyclic nucleotide-gated (HCN) currents (Ihs), which is downregulated by α2-AR activation. To quantify the activity-dependent outcome of volume transmission of NA from LC to MTN, we investigated how direct LC activation inhibits Ih in MTN neurons by performing dual whole-cell recordings from LC and MTN neurons. Repetition of 20 Hz spike-train evoked with 1-s current-pulse in LC neurons every 30 s resulted in a gradual decrease in Ih evoked every 30 s, revealing a Hill-type relationship between the number of spike-trains in LC neurons and the degree of Ih inhibition in MTN neurons. On the other hand, when microstimulation was applied in LC every 30 s, an LC neuron repeatedly displayed a transient higher-frequency firing followed by a tonic firing at 5-10 Hz for 30 s. This subsequently caused a similar Hill-type inhibition of Ih in the simultaneously recorded MTN neuron, but with a smaller Hill coefficient, suggesting a lower signal transduction efficacy. In contrast, 20 Hz activity induced by a 1-s pulse applied every 5-10 s caused only a transient facilitation of Ih inhibition followed by a forced termination of Ih inhibition. Thus, the three modes of LC activities modulated the volume transmission to activate α2-adrenergic GPCR to differentially inhibit Ih in MTN neurons.

Anomaly Detection with Feature Extraction Based on Machine Learning Using Hydraulic System IoT Sensor Data.

Hydraulic systems are advanced in function and level as they are used in various industrial fields. Furthermore, condition monitoring using internet of things (IoT) sensors is applied for system maintenance and management. In this study, meaningful features were identified through extraction and selection of various features, and classification evaluation metrics were presented through machine learning and deep learning to expand the diagnosis of abnormalities and defects in each component of the hydraulic system. Data collected from IoT sensor data in the time domain were divided into clusters in predefined sections. The shape and density characteristics were extracted by cluster. Among 2335 newly extracted features, related features were selected using correlation coefficients and the Boruta algorithm for each hydraulic component and used for model learning. Linear discriminant analysis (LDA), logistic regression, support vector classifier (SVC), decision tree, random forest, XGBoost, LightGBM, and multi-layer perceptron were used to calculate the true positive rate (TPR) and true negative rate (TNR) for each hydraulic component to detect normal and abnormal conditions. Valve condition, internal pump leakage, and hydraulic accumulator data showed TPR performance of 0.94 or more and a TNR performance of 0.84 or more. This study's findings can help to determine the stable and unstable states of each component of the hydraulic system and form the basis for engineers' judgment.