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Accurate prediction of scoliotic curve progression is crucial for guiding treatment decisions in adolescent idiopathic scoliosis (AIS). Traditional methods of assessing the likelihood of AIS progression are limited by variability and rely on static measurements. This study developed and validated machine learning models for classifying progressive and non-progressive scoliotic curves based on gait analysis using wearable inertial sensors. Gait data from 38 AIS patients were collected using seven inertial measurement unit (IMU) sensors, and hip–knee (HK) cyclograms representing inter-joint coordination were generated. Various machine learning algorithms, including support vector machine (SVM), random forest (RF), and novel deep convolutional neural network (DCNN) models utilizing multi-plane HK cyclograms, were developed and evaluated using 10-fold cross-validation. The DCNN model incorporating multi-plane HK cyclograms and clinical factors achieved an accuracy of 92% in predicting curve progression, outperforming SVM (55% accuracy) and RF (52% accuracy) models using handcrafted gait features. Gradient-based class activation mapping revealed that the DCNN model focused on the swing phase of the gait cycle to make predictions. This study demonstrates the potential of deep learning techniques, and DCNNs in particular, in accurately classifying scoliotic curve progression using gait data from wearable IMU sensors.

Gait disturbance is a common sequela of stroke. Conventional gait analysis has limitations in simultaneously assessing multiple joints. Therefore, we investigated the gait characteristics in stroke patients using hip-knee cyclograms, which have the advantage of simultaneously visualizing the gait kinematics of multiple joints. Stroke patients (n = 47) were categorized into two groups according to stroke severity, and healthy controls (n = 32) were recruited. An inertial measurement unit sensor-based gait analysis system, which requires placing seven sensors on the dorsum of both feet, the shafts of both tibias, the middle of both femurs, and the lower abdomen, was used for the gait analysis. Then, the hip-knee cyclogram parameters (range of motion, perimeter, and area) were obtained from the collected data. The coefficient of variance of the cyclogram parameters was obtained to evaluate gait variability. The cyclogram parameters differed between the stroke patients and healthy controls, and differences according to stroke severity were also observed. The gait variability parameters mainly differed in patients with more severe stroke, and specific visualized gait patterns of stroke patients were obtained through cyclograms. In conclusion, the hip-knee cyclograms, which show inter-joint coordination and visualized gait cycle in stroke patients, are clinically significant.

Inter-joint coordination and gait variability in knee osteoarthritis (KOA) has not been well investigated. Hip-knee cyclograms can visualize the relationship between the hip and knee joint simultaneously. The aim of this study was to elucidate differences in inter-joint coordination and gait variability with respect to KOA severity using hip-knee cyclograms. Fifty participants with KOA (early KOA, n = 20; advanced KOA, n = 30) and 26 participants (≥ 50 years) without KOA were recruited. We analyzed inter-joint coordination by hip-knee cyclogram parameters including range of motion (RoM), center of mass (CoM), perimeter, and area. Gait variability was assessed by the coefficient of variance (CV) of hip-knee cyclogram parameters. Knee RoM was significantly reduced and total perimeter tended to be decreased with KOA progression. KOA patients (both early and advanced) had reduced stance phase perimeter, swing phase area, and total area than controls. Reduced knee CoM and swing phase perimeter were observed only in advanced KOA. Both KOA groups had a greater CV for CoM, knee RoM, perimeter (stance phase, swing phase and total) and swing phase area than the controls. Increased CV of hip RoM was only observed in advanced KOA. These results demonstrate that hip-knee cyclograms can provide insights into KOA patient gait.

This study aims to explore the utility of artificial intelligence (AI) in classifying dementia severity based on gait analysis data and to examine how machine learning (ML) can address the limitations of conventional statistical approaches. The study included 34 individuals with mild cognitive impairment (MCI), 25 with mild dementia, 26 with moderate dementia, and 54 healthy controls. A support vector machine (SVM) classifier was employed to categorize dementia severity using gait parameters. As complexity and high dimensionality of gait data increase, traditional statistical methods may struggle to capture subtle patterns and interactions among variables. In contrast, ML techniques, including dimensionality reduction methods such as principal component analysis (PCA) and gradient-based feature selection, can effectively identify key gait features relevant to dementia severity classification. This study shows that ML can complement traditional statistical analyses by efficiently handling high-dimensional data and uncovering meaningful patterns that may be overlooked by conventional methods. Our findings highlight the promise of AI-based tools in advancing our understanding of gait characteristics in dementia and supporting the development of more accurate diagnostic models for complex or large datasets.

The microstructures and mechanical properties of cobalt-chromium (Co-Cr) alloys produced by three CAD/CAM-based processing techniques were investigated in comparison with those produced by the traditional casting technique. Four groups of disc- (microstructures) or dumbbell- (mechanical properties) specimens made of Co-Cr alloys were prepared using casting (CS), milling (ML), selective laser melting (SLM), and milling/post-sintering (ML/PS). For each technique, the corresponding commercial alloy material was used. The microstructures of the specimens were evaluated via X-ray diffractometry, optical and scanning electron microscopy with energy-dispersive X-ray spectroscopy, and electron backscattered diffraction pattern analysis. The mechanical properties were evaluated using a tensile test according to ISO 22674 (n = 6). The microstructure of the alloys was strongly influenced by the manufacturing processes. Overall, the SLM group showed superior mechanical properties, the ML/PS group being nearly comparable. The mechanical properties of the ML group were inferior to those of the CS group. The microstructures and mechanical properties of Co-Cr alloys were greatly dependent on the manufacturing technique as well as the chemical composition. The SLM and ML/PS techniques may be considered promising alternatives to the Co-Cr alloy casting process.

This study evaluates CaCl2-modified electric arc furnace (EAF) slag for fluoride removal from synthetic hydrofluoric acid (HF) wastewater. Adsorption performance was assessed under different particle sizes (850 μm–1.7 mm, 250–850 μm, and <250 μm), temperatures (25–45 °C), and initial pH values (2–11), using oxidized (EOS) and reduced (ERS) slags in raw and modified (C1, C2) forms. Characterization included isotherm modeling (Langmuir and Freundlich), X-ray diffraction (XRD), and inductively coupled plasma mass spectrometry (ICP-MS). The CaCl2-modified slags (particularly EOS-C2 and ERS-C2) demonstrated stable performance under all conditions. ERS-C2 achieved the maximum adsorption capacity of 16.13 mg/g at 600 mg F−/L. EOS-C2 maintained capacities above 8.0 mg/g across pH 2–11, whereas unmodified slag showed a decline in performance above pH 5, with residual concentrations exceeding 250 mg F−/L and capacities dropping to 1.14–2.14 mg/g. XRD analysis indicated increased amorphization and enhancement of dicalcium silicate and brownmillerite phases after modification. Isotherm fitting showed better agreement with the Freundlich model, suggesting multilayer adsorption. Leaching tests confirmed that Cr, Cu, and As concentrations were within safe limits, while Pb and Cd were not detected. These results demonstrate the strong potential of CaCl2-modified EAF slag as an efficient, pH-stable, and environmentally safe adsorbent for treating HF-containing industrial wastewater.

Core/shell (C/S)-structured upconversion nanophosphor (UCNP)-incorporated polymer waveguide-based flexible transparent displays are demonstrated. Bright green- and blue-emitting Li(Gd,Y)F 4 :Yb,Er and Li(Gd,Y)F 4 :Yb,Tm UCNPs are synthesized via solution chemical route. Their upconversion luminescence (UCL) intensities are enhanced by the formation of C/S structure with LiYF 4 shell. The Li(Gd,Y)F 4 :Yb,Er/LiYF 4 and Li(Gd,Y)F 4 :Yb,Tm/LiYF 4 C/S UCNPs exhibit 3.3 and 2.0 times higher UCL intensities than core counterparts, respectively. In addition, NaGdF 4 :Yb,Tm/NaGdF 4 :Eu C/S UCNPs are synthesized and they show red emission via energy transfer and migration of Yb 3+  → Tm 3+  → Gd 3+  → Eu 3+ . The C/S UCNPs are incorporated into bisphenol A ethoxylate diacrylate which is used as a core material of polymer waveguides. The fabricated stripe-type polymer waveguides are highly flexible and transparent (transmittance > 90% in spectral range of 443–900 nm). The polymer waveguides exhibit bright blue, green, and red luminescence, depending on the incorporated UCNPs into the polymer core, under coupling with a near infrared (NIR) laser. Moreover, patterned polymer waveguide-based display devices are fabricated by reactive ion etching process and they realize bright blue-, green-, and red-colored characters under coupling with an NIR laser.

Textile dyeing wastewater is one of the most challenging industrial effluents to treat due to its high concentrations of persistent organic compounds and nitrogenous substances. Conventional treatment methods often fall short in achieving both sufficient removal efficiency and environmental safety. In this study, we aimed to remove the total nitrogen (T-N) and total organic carbon (TOC) of dyeing wastewater from an industrial complex in D City, Korea, by applying bipolar and packed bipolar electrolysis using aluminum (Al) electrodes and activated carbon (AC). The system was operated for 60 min under varying conditions of applied voltage (5–15 V), electrolyte type and concentration (non-addition, NaCl 5 mM, NaCl 10 mM, Na2SO4 5 mM, Na2SO4 10 mM), and AC packing amount (non-addition or 100 g/L). The highest T-N and TOC removal efficiencies were observed at 15 V, reaching 69.53% and 63.68%, respectively. Electrolyte addition significantly improved initial treatment performance, with NaCl 10 mM showing the best results. However, Al leaching also increased, from 549.83 mg/L (non-addition) to 623.06 mg/L (NaCl 10 mM). When AC was used without electrolysis (control experiment), the T-N and TOC removal efficiencies were limited to 30.24% and 29.86%, respectively. In contrast, AC packing combined with 15 V electrolysis under non-addition achieved 86.04% T-N and 77.98% TOC removal, while also reducing Al leaching by 40.12%. These results suggested that electrochemical treatment with AC packing under non-addition conditions offers the best balance between high treatment efficiency and low environmental impact. These findings demonstrate that the synergistic use of packed activated carbon and electrochemical treatment under additive-free conditions can overcome the limitations of conventional methods. This study contributes to the development of more sustainable and effective technologies for treating high-strength industrial wastewater.

This work presents the comparative study among pole-placement (PP), optimal-control using output-feedback (OCOF), linear-quadratic regulator (LQR), and PID controllers for the twin rotor multi-input multi-output system (TRMS). The pitch and yaw are key attributes for stabilizing the TRMS MIMO system and control of flight. The main objective of this study is to use these classical controller techniques to monitor the pitch and yaw angles of TRMS and show the result of these techniques. Simulation results depicts the actual performance and reveals how PP outperforms the other techniques.

Direct interspecies electron transfer (DIET) between electroactive microorganisms (EAMs) offers significant potential to enhance methane production, necessitating research for its practical implementation. This study investigated enhanced methane production through DIET in an anaerobic digester bio-augmented with EAMs. A horizontal anaerobic digester (HAD) operated for 430 days as a testbed to validate the benefits of bioaugmentation with EAMs. Anaerobic digestate slurry, discharged from the HAD, was enriched with EAMs in a bioelectrochemical auxiliary reactor (BEAR) under an electric field. This slurry enriched with EAMs was then recirculated into the HAD. Results showed bio-augmentation with EAMs led to an increase in volatile solids removal from 56.2% to 77.5%, methane production rate from 0.59 to 1.00 L/L.d, methane yield from 0.26 to 0.34 L/g CODr, and biogas methane content from 59.9% to 71.6%. It suggests that bio-augmentation enhances DIET, promoting the conversion of volatile fatty acids to methane and enhancing resilience against kinetic imbalances. The enrichment of EAMs reached optimal efficacy under an electric field intensity of 2.07 V/cm with a mean exposure time of 2.53 days to the electric field in the BEAR. Bio-augmentation with externally enriched EAMs is a feasible and effective strategy to optimize anaerobic digestion processes.