Kumoh National Institute of Technology

This paper presents a continuum arm architecture that enables repeatable and load-bearing manipulation by employing a gear-synchronized backbone. Although conventional continuum arms provide excellent compliance in confined spaces, their inconsistent motion and low stiffness hinder applications requiring robustness and precision. To address these limitations, we propose a modular structure that passively synchronizes joint motion using interlinked gear mechanisms. This internal synchronization ensures uniform angular displacement across all target joints, allowing predictable and mechanically stable arm bending without additional sensors or actuators. The compact actuation system simplifies integration and reduces hardware complexity. The proposed system is validated through a dual-arm grasp-and-store task, which uses two mechanically coordinated arms to sequentially grasp and store an object. Experimental results on the human-scale prototype show high repeatability and structural robustness: the system achieves a maximum positional RMSE of <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><inline-formula><tex-math notation="LaTeX">$\text{5.18}\,\text{mm}$</tex-math></inline-formula></b> across <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">15</b> trials, with the end-effector tracking error consistently within <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><inline-formula><tex-math notation="LaTeX">$5.83\%$</tex-math></inline-formula></b> of the total arm length. The arm also reliably supports payloads up to <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><inline-formula><tex-math notation="LaTeX">$\text{1}\,\text{kg}$</tex-math></inline-formula></b> during dynamic motion, confirming its capability for load-bearing manipulation in semi-structured environments.

Background/Objectives: Type 2 diabetes (T2D) develops gradually over many years through a prolonged preclinical phase, yet traditional static risk scores often fail to capture these dynamic metabolic trajectories. We propose a longitudinal deep learning framework to predict the 10-year risk of Type 2 diabetes onset defined by comprehensive ADA criteria by modeling the physiological acceleration of routine clinical biomarkers. Methods: Utilizing an 18-year longitudinal dataset from the community-based Korean Genome and Epidemiology Study (KoGES) cohort, we selected N=4354 participants with complete follow-up records, ensuring high data integrity without requiring synthetic data augmentation. We constructed a 3-dimensional tensor of 21 non-invasive clinical variables spanning a 6-year observation window. To resolve the inherent precision-recall trade-offs of individual models, we developed a stacking ensemble that integrates Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) architectures via a logistic regression meta-learner. To evaluate the added value of longitudinal modeling, we compared this dynamic framework against a static XGBoost baseline that only saw the most recent data. Results: Evaluated on an independent test set (n=874), the ensemble significantly outperformed baseline models, achieving an overall accuracy of 0.90 (95% CI: 0.88-0.92) and an AUROC of 0.94 (95% CI: 0.93-0.95). By harmonizing LSTM's sensitivity and GRU's precision, the model yielded an exceptional Positive Predictive Value (PPV) of 0.97, a sensitivity of 0.80, and a specificity of 0.98. Conclusions: This framework provides a highly accurate, resource-efficient triage instrument for T2D screening, thereby reducing unnecessary clinical alerts and improving screening efficiency.

Protonic Ceramic Fuel Cells In their Research Article (10.1002/adfm.202525258), Ho-Il Ji, Sihyuk Choi, and co-workers present a rationally designed cathode microstructure for protonic ceramic fuel cells, featuring a thermomechanically compatible backbone decorated with electrochemically active nanocatalysts. This synergistic cathode design effectively mitigates thermal expansion mismatch while expanding electrochemically active sites, thereby delivering high power output and outstanding thermal cycling stability in fuel cell operation.

Soft Electronics In article number (e11832), Jin Kon Kim, Chungryong Choi, and co-workers demonstrated a sustainable route from biomass-derived soft electronics to reusable raw materials through one-pot closed-loop chemical recycling. Devices made with bio-based components can be fully disassembled and recovered for re-fabrication without loss of performance, providing a scalable pathway toward zero e-waste soft electronic systems.

Advanced oxidation processes using porphyrin-based heterogeneous catalysts hold promise for removing hazardous pollutants from wastewater. Their high visible-light absorption coefficients enable absorption of light from the solar spectrum. Moreover, their conjugated aromatic skeletons and intrinsic electronic properties facilitate the delocalization of photogenerated electrons during photodegradation. Delaying the recombination of photogenerated electron–hole pairs by introducing specific materials increases efficiency, as separated charges have more time to participate in redox reactions, boosting photocatalytic activities. However, applying these photocatalysts for wastewater treatment is challenging owing to facile agglomeration, deactivation, and recovery of the photocatalyst for reuse, which can significantly increase the overall cost. Therefore, new photocatalytic systems comprising porphyrin molecules must be developed. For this purpose, porphyrins can be conjugated to nanomaterials to create hybrid materials with photocatalytic efficiencies superior to those of free-standing starting porphyrins. Various transition metal oxides (TiO2, ZnO, and Fe3O4) nanoparticles, main-group-element oxides (Al2O3 and SiO2) nanoparticles, metal plasmons (silver nanoparticles), carbon-based platforms (graphene, graphene oxide, and g-C3N4), and polymer matrices have been used as nanostructured solid supports for the successful fabrication of porphyrin-conjugated hybrid materials. The conjugation of porphyrin molecules to solid supports improves the photocatalytic degradation activity in terms of visible-light conversion ability, recyclability, active porous sites, substrate mobility, separation of photogenerated charge species, recovery for reuse, and chemical stability, along with preventing the generation of secondary pollution. This review discusses the ongoing development of porphyrin-conjugated hybrid nanomaterials for the heterogeneous photocatalytic degradation of organic dyes, pharmaceutical pollutants, heavy metals, pesticides, and human care in water. Several important results and advancements in the field allow for a more efficient wastewater remediation process.