An Ensemble Multilabel Classification for Disease Risk Prediction

It is important to identify and prevent disease risk as early as possible through regular physical examinations. We formulate the disease risk prediction into a multilabel classification problem. A novel Ensemble Label Power-set Pruned datasets Joint Decomposition (ELPPJD) method is proposed in this work. First, we transform the multilabel classification into a multiclass classification. Then, we propose the pruned datasets and joint decomposition methods to deal with the imbalance learning problem. Two strategies size balanced (SB) and label similarity (LS) are designed to decompose the training dataset. In the experiments, the dataset is from the real physical examination records. We contrast the performance of the ELPPJD method with two different decomposition strategies. Moreover, the comparison between ELPPJD and the classic multilabel classification methods RAkEL and HOMER is carried out. The experimental results show that the ELPPJD method with label similarity strategy has outstanding performance

Proprioceptive Learning with Soft Polyhedral Networks

Proprioception is the "sixth sense" that detects limb postures with motor neurons. It requires a natural integration between the musculoskeletal systems and sensory receptors, which is challenging among modern robots that aim for lightweight, adaptive, and sensitive designs at a low cost. Here, we present the Soft Polyhedral Network with an embedded vision for physical interactions, capable of adaptive kinesthesia and viscoelastic proprioception by learning kinetic features. This design enables passive adaptations to omni-directional interactions, visually captured by a miniature high-speed motion tracking system embedded inside for proprioceptive learning. The results show that the soft network can infer real-time 6D forces and torques with accuracies of 0.25/0.24/0.35 N and 0.025/0.034/0.006 Nm in dynamic interactions. We also incorporate viscoelasticity in proprioception during static adaptation by adding a creep and relaxation modifier to refine the predicted results. The proposed soft network combines simplicity in design, omni-adaptation, and proprioceptive sensing with high accuracy, making it a versatile solution for robotics at a low cost with more than 1 million use cycles for tasks such as sensitive and competitive grasping, and touch-based geometry reconstruction. This study offers new insights into vision-based proprioception for soft robots in adaptive grasping, soft manipulation, and human-robot interaction.Comment: 20 pages, 10 figures, 2 tables, submitted to the International Journal of Robotics Research for revie

Knowledge Graph Driven Recommendation System Algorithm

In this paper, we propose a novel graph neural network-based recommendation model called KGLN, which leverages Knowledge Graph (KG) information to enhance the accuracy and effectiveness of personalized recommendations. We first use a single-layer neural network to merge individual node features in the graph, and then adjust the aggregation weights of neighboring entities by incorporating influence factors. The model evolves from a single layer to multiple layers through iteration, enabling entities to access extensive multi-order associated entity information. The final step involves integrating features of entities and users to produce a recommendation score. The model performance was evaluated by comparing its effects on various aggregation methods and influence factors. In tests over the MovieLen-1M and Book-Crossing datasets, KGLN shows an Area Under the ROC curve (AUC) improvement of 0.3% to 5.9% and 1.1% to 8.2%, respectively, which is better than existing benchmark methods like LibFM, DeepFM, Wide&Deep, and RippleNet

Crystal Structure Transformation and Dielectric Properties of Polymer Composites Incorporating Zinc Oxide Nanorods

Zinc oxide (ZnO) nanorods were synthesized using a modified wet chemical method. Poly(vinylidene fluoride-co-hexafluoropropylene), P(VDF-HFP), nanocomposites with different ZnO nanorods loadings were prepared via a solution blend route. Field emission scanning electron microscopic (FE-SEM), X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR) were used to investigate the structure and morphology of the nanocomposites. XRD and FTIR data indicate that the incorporation of ZnO nanorods promote the crystalline structure transformation of P(VDF-HFP). As the content of ZnO nanorods increases, the β phase structure increases while the α phase decreases. In addition, the dielectric properties of the P(VDF-HFP) and its composites were systematically studied

Reservoir Characterization during Underbalanced Drilling of Horizontal Wells Based on Real-Time Data Monitoring

In this work, a methodology for characterizing reservoir pore pressure and permeability during underbalanced drilling of horizontal wells was presented. The methodology utilizes a transient multiphase wellbore flow model that is extended with a transient well influx analytical model during underbalanced drilling of horizontal wells. The effects of the density behavior of drilling fluid and wellbore heat transfer are considered in our wellbore flow model. Based on Kneissl’s methodology, an improved method with a different testing procedure was used to estimate the reservoir pore pressure by introducing fluctuations in the bottom hole pressure. To acquire timely basic data for reservoir characterization, a dedicated fully automated control real-time data monitoring system was established. The methodology is applied to a realistic case, and the results indicate that the estimated reservoir pore pressure and permeability fit well to the truth values from well test after drilling. The results also show that the real-time data monitoring system is operational and can provide accurate and complete data set in real time for reservoir characterization. The methodology can handle reservoir characterization during underbalanced drilling of horizontal wells

In vitro antioksidacijska, citotoksična i antidijabetička aktivnost hidrolizata proteina iz Reevesove barske kornjače (Chinemys reevesii)

Research background. Cardiovascular diseases and diabetes are the biggest causes of death globally. Bioactive peptides derived from many food proteins using enzymatic proteolysis and food processing have a positive impact on the prevention of these diseases. The bioactivity of Chinese pond turtle muscle proteins and their enzymatic hydrolysates has not received much attention, thus this study aims to investigate their antioxidant, antidiabetic and cytotoxic activities. Experimental approach. Chinese pond turtle muscles were hydrolysed using four proteolytic enzymes (Alcalase, Flavourzyme, trypsin and bromelain) and the degrees of hydrolysis were measured. High-performance liquid chromatography (HPLC) was conducted to explore the amino acid profiles and molecular mass distribution of the hydrolysates. The antioxidant activities were evaluated using various in vitro tests, including 1,1-diphenyl-2-picrylhydrazyl (DPPH) and 2,2’-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS), hydroxyl radical scavenging activity, reducing capacity, chelating Fe2+ and lipid peroxide inhibition activity. Antidiabetic activity was evaluated using α-amylase inhibition and α-glucosidase inhibition assays. Besides, cytotoxic effect of hydrolysates on human colon cancer (HT-29) cells was assessed. Results and conclusions. The amino acid composition of the hydrolysates revealed higher mass fractions of glutamic, aspartic, lysine, hydroxyproline and hydrophobic amino acids. Significantly highest inhibition of lipid peroxidation was achieved when hydrolysate obtained with Alcalase was used. Protein hydrolysate produced with Flavourzyme had the highest radical scavenging activity measured by DPPH (68.32%), ABTS (74.12%) and FRAP (A700 nm=0.300) assays, α-glucosidase (61.80%) inhibition and cytotoxic effect (82.26%) on HT-29 cell line at 550 µg/mL. Hydrolysates obtained with trypsin and bromelain had significantly highest (p<0.05) hydroxyl radical scavenging (92.70%) and Fe2+ metal chelating (63.29%) activities, respectively. The highest α-amylase (76.89%) inhibition was recorded when using hydrolysates obtained with bromelain and Flavourzyme. Novelty and scientific contribution. Enzymatic hydrolysates of Chinese pond turtle muscle protein had high antioxidant, cytotoxic and antidiabetic activities. The findings of this study indicated that the bioactive hydrolysates or peptides from Chinese pond turtle muscle protein can be potential ingredients in pharmaceuticals and functional food formulations.Pozadina istraživanja. Kardiovaskularne bolesti i dijabetes najčešći su uzroci smrti na svijetu. Bioaktivni peptidi dobiveni proteolizom i preradom hrane imaju pozitivan učinak na prevenciju tih bolesti. Biološka aktivnost proteina iz mišića Reevesove barske kornjače i njihovih hidrolizata nije dovoljno istražena, stoga je svrha ovoga rada bila ispitati njihovu antioksidacijsku, antidijabetičku i citotoksičnu aktivnost. Eksperimentalni pristup. Mišići Reevesove barske kornjače hidrolizirani su pomoću proteolitičkih enzima (Alcalase, Flavourzyme, tripsin i bromelain), te su mjereni stupnjevi hidrolize proteina. Aminokiselinski sastav i distribucija molekularne mase hidrolizata ispitani su pomoću visokodjelotvorne tekućinske kromatografije. Antioksidacijska aktivnost određena je različitim testovima in vitro, uključujući sposobnost uklanjanja 1,1-difenil-2-pikrilhidrazila (DPPH), 2,2’-azino-bis(3-etilbenzotiazolin-6-sumporne kiseline) (ABTS) i hidroksil radikala, keliranja Fe2+ i inhibicije lipidne peroksidacije. Antidijabetička aktivnost ispitana je testovima inhibicije α-amilaze i α-glukozidaze. Osim toga, analiziran je citotoksični učinak hidrolizata na stanice tumora debelog crijeva (HT-29). Rezultati i zaključci. Analizom aminokiselinskog sastava hidrolizata pronađeni su veći maseni udjeli glutaminske i asparaginske kiseline, lizina, hidroksiprolina te hidrofobnih aminokiselina od onih u nehidroliziranim proteinima. Hidrolizat proteina dobiven pomoću proteolitičkog enzima Alcalase bitno je inhibirao peroksidaciju lipida. Pri koncentraciji od 550 µg/mL, hidrolizat proteina dobiven pomoću enzima Flavourzyme imao je najveću sposobnost uklanjanja slobodnih radikala mjerenu pomoću DPPH (68,32 %), ABTS (74,12 %) i FRAP (A700 nm=0,300) metoda, inhibicije α-glukozidaze (61,80 %) te najveći citotoksični učinak na stanične linije HT-29 (82.26 %). Hidrolizat proteina dobiven pomoću tripsina imao je znatnu (p<0,05) aktivnost uklanjanja hidroksilnih radikala (92,70 %), a onaj dobiven pomoću bromelaina najveću aktivnost keliranja Fe2+ (63,29 %). Najveća inhibicija α-amilaze postignuta je pomoću hidrolizata proteina dobivenih djelovanjem bromelaina i enzima Flavourzyme. Novina i znanstveni doprinos. Hidrolizati proteina mišića Reevesove barske kornjače dobiveni enzimskom hidrolizom imali su veliku antioksidacijsku, citotoksičnu i antidijabetičku aktivnost. Rezultati istraživanja pokazuju da se ti hidrolizati ili peptidi zbog svojih bioaktivnih svojstava mogu upotrijebiti kao sastojak u farmaceutskim i funkcionalnim prehrambenim proizvodima

NMD-12: A New Machine-Learning Derived Screening Instrument to Detect Mild Cognitive Impairment and Dementia

Introduction Using machine learning techniques, we developed a brief questionnaire to aid neurologists and neuropsychologists in the screening of mild cognitive impairment (MCI) and dementia. Methods With the reduction of the survey size as a goal of this research, feature selection based on information gain was performed to rank the contribution of the 45 items corresponding to patient responses to the specified questions. The most important items were used to build the optimal screening model based on the accuracy, practicality, and interpretability. The diagnostic accuracy for discriminating normal cognition (NC), MCI, very mild dementia (VMD) and dementia was validated in the test group. Results The screening model (NMD-12) was constructed with the 12 items that were ranked the highest in feature selection. The receiver-operator characteristic (ROC) analysis showed that the area under the curve (AUC) in the test group was 0.94 for discriminating NC vs. MCI, 0.88 for MCI vs. VMD, 0.97 for MCI vs. dementia, and 0.96 for VMD vs. dementia, respectively. Discussion The NMD-12 model has been developed and validated in this study. It provides healthcare professionals with a simple and practical screening tool which accurately differentiates NC, MCI, VMD, and dementia

Black-box Attack Algorithm for SAR-ATR Deep Neural Networks Based on MI-FGSM

The field of Synthetic Aperture Radar Automatic Target Recognition (SAR-ATR) lacks effective black-box attack algorithms. Therefore, this research proposes a migration-based black-box attack algorithm by combining the idea of the Momentum Iterative Fast Gradient Sign Method (MI-FGSM). First, random speckle noise transformation is performed according to the characteristics of SAR images to alleviate model overfitting to the speckle noise and improve the generalization performance of the algorithm. Second, an AdaBelief-Nesterov optimizer is designed to rapidly find the optimal gradient descent direction, and the attack effectiveness of the algorithm is improved through a rapid convergence of the model gradient. Finally, a quasihyperbolic momentum operator is introduced to obtain a stable model gradient descent direction so that the gradient can avoid falling into a local optimum during the rapid convergence and to further enhance the success rate of black-box attacks on adversarial examples. Simulation experiments show that compared with existing adversarial attack algorithms, the proposed algorithm improves the ensemble model black-box attack success rate of mainstream SAR-ATR deep neural networks by 3%～55% and 6.0%～57.5% on the MSTAR and FUSAR-Ship datasets, respectively; the generated adversarial examples are highly concealable

Enhanced Interfacial Electronic Transfer of BiVO4 Coupled with 2D g‐C3N4 for Visible‐light Photocatalytic Performance

A BiVO4/2D g‐C3N4 direct dual semiconductor photocatalytic system has been fabricated via electrostatic self‐assembly method of BiVO4 microparticle and g‐C3N4 nanosheet. According to experimental measurements and first‐principle calculations, the formation of built‐in electric field and the opposite band bending around the interface region in BiVO4/2D g‐C3N4 as well as the intimate contact between BiVO4 and 2D g‐C3N4 will lead to high separation efficiency of charge carriers. More importantly, the intensity of bulid‐in electric field is greatly enhanced due to the ultrathin nanosheet structure of 2D g‐C3N4. As a result, BiVO4/2D g‐C3N4 exhibits excellent photocatalytic performance with the 93.0% Rhodamine B (RhB) removal after 40 min visible light irradiation, and the photocatalytic reaction rate is about 22.7 and 10.3 times as high as that of BiVO4 and 2D g‐C3N4, respectively. In addition, BiVO4/2D g‐C3N4 also displays enhanced photocatalytic performance in the degradation of tetracycline (TC). It is expected that this work may provide insights into the understanding the significant role of built‐in electric field in heterostructure and fabricating highly efficient direct dual semiconductor systems