Experimental Study on Shear Behavior and Acoustic Emission Characteristics of Nonpersistent Joints

The shear behavior of rock discontinuities controls the stability of rock masses to a great extent. In this paper, laboratory shear tests were performed on rock-like materials with different cracks to study the effect of nonpersistent joints on the shear behavior of rock masses. The results show that the variation trends of the shear stress-displacement curves of specimens with different cracks are generally similar and have the same stage characteristics. When the crack length is relatively short, the elastic stage is prolonged, the peak shear strength decreases, and the shear displacement corresponding to the peak shear strength and the residual shear strength increases with the increase of the crack length. When the crack length is relatively long, the elastic stage is shortened, the peak shear strength decreases, and the shear displacement corresponding to the peak shear strength increases with the increase of the crack length. The peak shear stress gradually decreases with the increase of the crack length. The shear strength of the specimens with unilateral cracks is much higher than that of the specimens with bilateral cracks. The shear strength of the specimens is affected not only by the crack length but also by the crack distribution. The acoustic emission (AE) count peak occurs when the shear stress drops sharply and has an inverse "S"-type variation trend with the increase of the crack length. The inclination angle of the fracture decreases, the roughness of the fracture surface decreases, and the proportion of the wear area on the fracture surface increases gradually with the increase of the crack length. The AE source decreases with the increase of the crack length, and their locations are obviously asymmetric. This work can greatly contribute to the insight into the shear failure mechanism of rock discontinuities with nonpersistent joints

Synergetic Thermal Therapy for Cancer: State-of-the-Art and the Future

As a safe and minimal-invasive modality, thermal therapy has become an effective treatment in cancer treatment. Other than killing the tumor cells or destroying the tumor entirely, the thermal modality results in profound molecular, cellular and biological effects on both the targeted tissue, surrounding environments, and even the whole body, which has triggered the combination of the thermal therapy with other traditional therapies as chemotherapy and radiation therapy or new therapies like immunotherapy, gene therapy, etc. The combined treatments have shown encouraging therapeutic effects both in research and clinic. In this review, we have summarized the outcomes of the existing synergistic therapies, the underlying mechanisms that lead to these improvements, and the latest research in the past five years. Limitations and future directions of synergistic thermal therapy are also discussed

A Multidirectional Forearm Electromagnetic Generator Designed via Numerical Simulations

Harvesting biomechanical energy from daily human body motions provides a promising and sustainable power solution for wearable electronics, whose current power supplies, i.e., batteries, have unsatisfactory capacity and durability due to volume, shape, and flexibility constraints. Electromagnetic generators (EMGs) are favorable energy transducers because of their high energy-conversion efficiency, low dependence on frequencies, and long-term stability. However, an EMG that can effectively harvest energy from multi-directional arm motions at aperiodic low frequencies are yet to be created. Here, we introduce a unique EMG configuration by combining a linear and a helix frame into a monolithic unit (EMG-LH), enabling the EMG to scavenge energy from all kinds of arm motions up to 6 degrees of freedom (DOFs) (movement along XYZ axes and forearm rotations). The EMG frame geometry is designed and optimized according to numerical simulations. To clarify the working mechanism and maximize the power output, the copper coils’ winding pattern, the magnets’ velocity profiles, and the resulting voltage output are numerically simulated and then experimentally verified. Our EMG-LH outperforms linear EMGs (EMG-Ls) and helix EMGs (EMG-Hs) in harvesting energy from all arm motions. This work explicitly presents a forearm-wearable energy harvester as a sustainable power source for wearable electronics

Mulberry Leaf Compounds and Gut Microbiota in Alzheimer’s Disease and Diabetes: A Study Using Network Pharmacology, Molecular Dynamics Simulation, and Cellular Assays

Recently, studies have reported a correlation that individuals with diabetes show an increased risk of developing Alzheimer’s disease (AD). Mulberry leaves, serving as both a traditional medicinal herb and a food source, exhibit significant hypoglycemic and antioxidative properties. The flavonoid compounds in mulberry leaf offer therapeutic effects for relieving diabetic symptoms and providing neuroprotection. However, the mechanisms of this effect have not been fully elucidated. This investigation aimed to investigate the combined effects of specific mulberry leaf flavonoids (kaempferol, quercetin, rhamnocitrin, tetramethoxyluteolin, and norartocarpetin) on both type 2 diabetes mellitus (T2DM) and AD. Additionally, the role of the gut microbiota in these two diseases’ treatment was studied. Using network pharmacology, we investigated the potential mechanisms of flavonoids in mulberry leaves, combined with gut microbiota, in combating AD and T2DM. In addition, we identified protein tyrosine phosphatase 1B (PTP1B) as a key target for kaempferol in these two diseases. Molecular docking and molecular dynamics simulations showed that kaempferol has the potential to inhibit PTP1B for indirect treatment of AD, which was proven by measuring the IC50 of kaempferol (279.23 μM). The cell experiment also confirmed the dose-dependent effect of kaempferol on the phosphorylation of total cellular protein in HepG2 cells. This research supports the concept of food–medicine homology and broadens the range of medical treatments for diabetes and AD, highlighting the prospect of integrating traditional herbal remedies with modern medical research

Adsorption of Organic Constituents from Reverse Osmosis Concentrate in Coal Chemical Industry by Coking Coal

To solve the unwieldy problem of coal chemical wastewater reverse osmosis concentrate (ROC), a novel treatment method in which coking coal was used to adsorb the organic from ROC and the adsorption mechanism involved was investigated. The results showed that the organic components in the ROC of coal chemical industry can be effectively absorbed by the coking coal and the total organic carbon, UV254 and chromaticity of treated ROC reduced by 70.18%, 70.15% and 59.55%, respectively, at the coking coal dosage of 80 g/L. The isothermal adsorption data were fitted to the Langmuir model well. The kinetics were expressed well by the quasi-second-order kinetic model. The intragranular diffusion model and the BET (Acronym for three scientists: Brunauer–Emmett–Teller) test showed that the adsorption occurred mainly on the surface of the coking coal and its macropores and mesopores. When the pollutants further diffused to the mesopores and micropores, the adsorption rate decreased. The result of X-ray photoelectron spectroscopy and fourier transform infrared spectroscopy spectra showed that the coking coal mainly adsorbed the nitrogen and oxygen species and the halogenated hydrocarbon organic compounds in the ROC. The chlorinated hydrocarbons and heterocyclic organics in ROC are adsorbed on the surface of coking coal

Prognostic Value and Quantitative CT Analysis in RANKL Expression of Spinal GCTB in the Denosumab Era: A Machine Learning Approach

The receptor activator of the nuclear factor kappa B ligand (RANKL) is the therapeutic target of denosumab. In this study, we evaluated whether radiomics signature and machine learning analysis can predict RANKL status in spinal giant cell tumors of bone (GCTB). This retrospective study consisted of 107 patients, including a training set (n = 82) and a validation set (n = 25). Kaplan-Meier survival analysis was used to validate the prognostic value of RANKL status. Radiomic feature extraction of three heterogeneous regions (VOIentire, VOIedge, and VOIcore) from pretreatment CT were performed. Followed by feature selection using Selected K Best and least absolute shrinkage and selection operator (LASSO) analysis, three classifiers (random forest (RF), support vector machine, and logistic regression) were used to build models. The area under the curve (AUC), accuracy, F1 score, recall, precision, sensitivity, and specificity were used to evaluate the models’ performance. Classification of 75 patients with eligible follow-up based on RANKL status resulted in a significant difference in progression-free survival (p = 0.035). VOIcore-based RF classifier performs best. Using this model, the AUCs for the training and validation cohorts were 0.880 and 0.766, respectively. In conclusion, a machine learning approach based on CT radiomic features could discriminate prognostically significant RANKL status in spinal GCTB, which may ultimately aid clinical decision-making