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In recent years, fire disasters caused by charging electric bicycles/moped (e-bikes) have been increasing, causing catastrophic loss of life and property; Worse still, existing fire warning systems are costly to install and maintain, and they work after the accident occurs. Some existing works propose using power meters or similar sensors in the power grid to monitor e-bike charging health. However, the use of additional equipment makes them challenging to deploy. Others can use the sound or electromagnetic signals emitted by e-bikes for monitoring, but they suffer from limited monitoring distance. To solve this problem, we propose SingMonitor, a scheme to remotely monitor e-bike charging status using mobile devices’ microphones. The charging e-bike generates a unique current signal, which is then transmitted through the power grid and drives the mobile devices’ power supply to generate sound, which is then captured by a microphone. Based on this principle and the proposed template matching method, SingMonitor can identify the e-bike charging status. Experiments show SingMonitor achieves an F1 score of 0.94 in identifying 10 e-bikes’ charging status, with a detection distance of 9m+.

PurposeTo develop a bounding box (BBOX)-based radiomics model for the preoperative diagnosis of occult peritoneal metastasis (OPM) in advanced gastric cancer (AGC) patients.Materials and Methods599 AGC patients from 3 centers were retrospectively enrolled and were divided into training, validation, and testing cohorts. The minimum circumscribed rectangle of the ROIs for the largest tumor area (R_BBOX), the nonoverlapping area between the tumor and R_BBOX (peritumoral area; PERI) and the smallest rectangle that could completely contain the tumor determined by a radiologist (M_BBOX) were used as inputs to extract radiomic features. Multivariate logistic regression was used to construct a radiomics model to estimate the preoperative probability of OPM in AGC patients.ResultsThe M_BBOX model was not significantly different from R_BBOX in the validation cohort [AUC: M_BBOX model 0.871 (95% CI, 0.814–0.940) vs. R_BBOX model 0.873 (95% CI, 0.820–0.940); p = 0.937]. M_BBOX was selected as the final radiomics model because of its extremely low annotation cost and superior OPM discrimination performance (sensitivity of 85.7% and specificity of 82.8%) over the clinical model, and this radiomics model showed comparable diagnostic efficacy in the testing cohort.ConclusionsThe BBOX-based radiomics could serve as a simpler reliable and powerful tool for the preoperative diagnosis of OPM in AGC patients. And M_BBOX-based radiomics is simpler and less time consuming.

ObjectiveThis study assessed the association between body composition and prognosis of patients with upper urinary tract urothelial carcinoma (UTUC) patients treated by radical nephroureterectomy.MethodsWe retrospectively collected baseline data on age, sex, body mass index (BMI), hypertension, diabetes, and tumor-related factors. Computed tomography (CT) scans were performed to measure body composition parameters such as muscle attenuation (MA), total abdominal muscle area (TAMA), visceral fat area (VFA), intermuscular fat area (IMF), and lateral/posterior perirenal fat thickness (L/P PNF), visceral fat density (VD), and subcutaneous fat density (SD). Patient follow-up was conducted via telephone or in the clinic. The endpoints of follow-up were all-cause death, local progression or distant metastasis. Survival analysis was analyzed using the Kaplan-Meier method, and risk factors associated with prognosis were identified using univariate and multivariate Cox proportional hazard analyses.ResultsAmong the 273 UTUC patients (median age, 68 years) enrolled in our study, 102 had a BMI > 24.0, 100 suffered from diabetes, and 120 had hypertension. A large proportion of patients (189) had high grade tumors. Across all patients, 1- and 3-year rates for overall survival were 86.45% and 75.55%; local progression-free survival, 92.11% and 89.67%; and distant metastasis-free survival, 85.23% and 80.17%. Based on the Cox regression analysis, MA, IMF, TAMA, TPA, TPT, APT, SMI and PMI significantly reduced the risk of local progression (p < 0.05), while PPNF = 1 point reduced the risk of distant metastasis (p < 0.05). Overall survival was significantly associated with MA, TAMA, and SMI (p < 0.05).ConclusionOur findings illustrate that body composition parameters can act as independent predictors of prognosis in UTUC patients who underwent RNU. These results can help improve stratification of patients and optimize postoperative treatment.

In this paper, linearity improved Doherty power amplifier (DPA) is proposed for linearity improvement by modifying the input capacitor value of peaking amplifier. The non-foster circuits are substituted for the conventional λ/4 transmission line in front of peaking amplifier, which the 90° phase shifter and impedance matching can be performed by the proposed structure. Furthermore, the input capacitor value of peaking amplifier FET can be modified through modifying bias voltage and match resistance of non-foster circuits. Therefore, the AM-PM distortion of peak amplifier is suppressed, and the linearity of total DPA is improved. Compared to a conventional DPA, the third-order inter-modulation distortion of proposed DPA is reduced by 12 dB without cutting efficiency.