Twins:Device-free Object Tracking using Passive Tags

Without requiring objects to carry any transceiver, device-free based object tracking provides a promising solution for many localization and tracking systems to monitor non-cooperative objects such as intruders. However, existing device-free solutions mainly use sensors and active RFID tags, which are much more expensive compared to passive tags. In this paper, we propose a novel motion detection and tracking method using passive RFID tags, named Twins. The method leverages a newly observed phenomenon called critical state caused by interference among passive tags. We contribute to both theory and practice of such phenomenon by presenting a new interference model that perfectly explains this phenomenon and using extensive experiments to validate it. We design a practical Twins based intrusion detection scheme and implement a real prototype with commercial off-the-shelf reader and tags. The results show that Twins is effective in detecting the moving object, with low location error of 0.75m in average

Machine-learning prediction of BMI change among doctors and nurses in North China during the COVID-19 pandemic

ObjectiveThe COVID-19 pandemic has become a major public health concern over the past 3 years, leading to adverse effects on front-line healthcare workers. This study aimed to develop a Body Mass Index (BMI) change prediction model among doctors and nurses in North China during the COVID-19 pandemic, and further identified the predicting effects of lifestyles, sleep quality, work-related conditions, and personality traits on BMI change.MethodsThe present study was a cross-sectional study conducted in North China, during May-August 2022. A total of 5,400 doctors and nurses were randomly recruited from 39 COVID-19 designated hospitals and 5,271 participants provided valid responses. Participants’ data related to social-demographics, dietary behavior, lifestyle, sleep, personality, and work-related conflicts were collected with questionnaires. Deep Neural Network (DNN) was applied to develop a BMI change prediction model among doctors and nurses during the COVID-19 pandemic.ResultsOf participants, only 2,216 (42.0%) individuals kept a stable BMI. Results showed that personality traits, dietary behaviors, lifestyles, sleep quality, burnout, and work-related conditions had effects on the BMI change among doctors and nurses. The prediction model for BMI change was developed with a 33-26-20-1 network framework. The DNN model achieved high prediction efficacy, and values of R2, MAE, MSE, and RMSE for the model were 0.940, 0.027, 0.002, and 0.038, respectively. Among doctors and nurses, the top five predictors in the BMI change prediction model were unbalanced nutritional diet, poor sleep quality, work-family conflict, lack of exercise, and soft drinks consumption.ConclusionDuring the COVID-19 pandemic, BMI change was highly prevalent among doctors and nurses in North China. Machine learning models can provide an automated identification mechanism for the prediction of BMI change. Personality traits, dietary behaviors, lifestyles, sleep quality, burnout, and work-related conditions have contributed to the BMI change prediction. Integrated treatment measures should be taken in the management of weight and BMI by policymakers, hospital administrators, and healthcare workers

Haplotype-Contained PCR Products Analysis by Sequencing with Selective Restriction of Primer Extension

We develop a strategy for haplotype analysis of PCR products that contained two adjacent heterozygous loci using sequencing with specific primers, allele-specific primers, and ddNTP-blocked primers. To validate its feasibility, two sets of PCR products, including two adjacent heterozygous SNPs, UGT1A1⁎6 (rs4148323) and UGT1A1⁎28 (rs8175347), and two adjacent heterozygous SNPs, K1637K (rs11176013) and S1647T (rs11564148), were analyzed. Haplotypes of PCR products, including UGT1A1⁎6 and UGT1A1⁎28, were successfully analyzed by Sanger sequencing with allele-specific primers. Also, haplotypes of PCR products, including K1637K and S1647T, could not be determined by Sanger sequencing with allele-specific primers but were successfully analyzed by pyrosequencing with ddNTP-blocked primers. As a result, this method is able to effectively haplotype two adjacent heterozygous PCR products. It is simple, fast, and irrespective of short read length of pyrosequencing. Overall, we fully hope it will provide a new promising technology to identify haplotypes of conventional PCR products in clinical samples

Feasibility of Isolation Remediation Technology for Heavy Metal Contaminated Soil

This paper discussed current situations of researches about the isolation remediation technology “soil barrier and landfill technology” and “physical isolation remediation technology” for heavy metal contaminated soil in mining areas. In view of defects of current technologies, it introduced a new isolation remediation technology, of which the new isolation materials were mixed by slaked lime, soil, find sand, and clay mineral in certain proportion. The new isolation remediation technology is expected to realize isolation remediation of heavy metal combined pollution of soil through chemical passivation of slaked lime and physical adsorption function of clay minerals or activated carbons

A semi-consensus strategy toward multi-functional hybrid energy storage system in DC microgrids

This paper proposes a semi-consensus strategy for multi-functional hybrid energy storage systems (HESSs) in DC microgrids. Batteries in a HESS are regulated by conventional V-P droops and supercapacitors (SCs) are with integral droops (ID). Only batteries are assigned with local distributed compensators which exchange information through sparse communication links. Those SCs are exempted from data exchange process, which would save system investment costs. Within the semi-consensus scheme, the most essential function is the cooperation of V-P droop and ID that helps to naturally allocate low frequency components of load power to batteries and high frequency components to SCs, thus prolonging the overall life time of HESS. In addition to the transient power allocation function, there are other three functions endowed by the proposed strategy, which are autonomous DC bus voltage recovery to its nominal level, spontaneous SC state of charge (SOC) restoration, autonomous power sharing and SOC balancing among batteries. It is the simultaneous realization of above four functions with limit communications that makes up the main contributions in this paper. A generic mathematical modeling of HESS with the semi-consensus strategy is established. The model allows for dynamic analyses to theoretically validate the effectiveness of proposed method in both frequency and time domains. In-house experimental results are shown fully consistent with the dynamic analyses and also effectively corroborate the intended HESS multi-functional operations

A Fine-Grained Unsupervised Domain Adaptation Framework for Semantic Segmentation of Remote Sensing Images

Unsupervised domain adaptation (UDA) aims at adapting a model from the source domain to the target domain by tackling the issue of domain shift. Cross-domain segmentation of remote sensing images (RSIs) remains a big challenge due to the unique properties of RSIs. On the one hand, the divergence of data distribution in different local regions leads to negative transfer by directly applying the global alignment method in RSIs. On the other hand, the underlying category-level structure in the target domain is often ignored, which confuses the decision of semantic boundaries on the dispersed category features caused by large intraclass variance and small interclass variance in RSIs. In this study, we propose a novel fine-grained adaptation framework combining two stages of global-local alignment and category-level alignment to solve the above-mentioned problems. In the first stage of global-local adaptation, an attention map is derived from an intermediate discriminator and focuses on hard-to-align regions to mitigate negative transfer due to global adversarial learning. In the second stage of category-level adaptation, the category feature compact module is utilized to address the issue of dispersed features in the target domain attained by the cross-domain network, which will facilitate the fine-grained alignment of categories. Experiments under various scenarios, including geographic location variation and spectral band composition variation, demonstrate that the local adaptation and category-level adaptation of RSIs are complementary in the cross-domain segmentation, and the integrated framework helps achieve outstanding performance for UDA semantic segmentation of RSIs

Prognostic analysis of patients with stage IIIC1p cervical cancer treated by surgery

Abstract Background Cervical cancer (CC) is one of the most common gynaecologic malignancies. The prognosis of stage IIIC1p cervical cancer patients treated by surgery is heterogeneous. Therefore, the aim of this study was to analyse the factors influencing the prognosis in such patients. Methods From January 2012 to December 2017, 102 patients with cervical cancer who underwent surgical treatment in the Department of Gynaecology and Tumours, Changzhou Maternal and Child Health Hospital, and had pelvic lymph node metastasis confirmed by pathology were analysed retrospectively. All patients underwent radical hysterectomy with/without oophorectomy with pelvic lymphadenectomy with/without para-aortic lymphadenectomy. Clinical data was collected including age, surgical method, ovarian status, intraoperative blood loss, perioperative complications, tumour size, pathological type, depth of stromal invasion (DSI), whether the lymphatic vascular space was infiltrated, number of pelvic lymph node metastases, location of pelvic lymph node metastases, total number of lymph nodes resected, lymph node ratio (LNR), nature of vaginal margin, whether parametrium was involved, postoperative adjuvant therapy, preoperative neutrophil–lymphocyte ratio (NLR) and prognostic information of patients. Survival curves for overall survival (OS) and disease-free survival (DFS) were plotted using the Kaplan–Meier method, and the difference between the survival curves was tested using the log-rank test. Univariate and multivariate COX regression models were used to assess the factors associated with overall survival and disease-free survival in patients with stage IIIC1p cervical cancer. Nomogram plots were constructed to predict OS and DFS, and the predictive accuracy of the nomograms was measured by Harrell’s C-index and calibration curves. Results A total of 102 patients with stage IIIC1p cervical cancer were included in the study, and the median follow-up time was 63 months (range from 6 to 130 months). The 5-year OS was 64.7%, and the 5-year DFS was 62.7%. Multivariate analysis showed that no postoperative adjuvant therapy, LNR > 0.3 and NLR > 3.8 were independent risk factors for OS and DFS in patients with stage IIIC1p cervical cancer. Conclusions Patients with stage IIIC1p cervical cancer have a poor prognosis. Lower OS and DFS were associated with no postoperative adjuvant therapy, LNR > 0.3 and NLR > 3.8

Structure design and performance analysis of aerostatic thrust bearing with compound restrictors

Aerostatic thrust bearing compensated by multi-orifices and porous material restrictor simultaneously is proposed to improve the static performance of the bearing. Load Carrying Capacity (LCC), stiffness and the flow field characteristics of the bearing are obtained by Computational Fluid Dynamic (CFD) simulation. The influences of supply pressure, orifice number, orifice diameter, orifice distribution, porous material thickness and permeability coefficient on the bearing performance are analysed. It is indicated that LCC and stiffness of the bearing with compound restrictors are much higher than those of the bearing with porous material restrictor or multi-orifice restrictor if gas film thickness is in rational range. The bearing with compound restrictors has better stability than that of the bearing with multi-orifice restrictor. Moreover, the optimum bearing parameters with compound restrictors are given to improving the performance of aerostatic thrust bearing

An integral droop for transient power allocation and output impedance shaping of hybrid energy storage system in DC microgrid

Power allocation in hybrid energy storage systems (HESSs) is an important issue for dc microgrids. In this paper, an integral droop (ID), inspired by the electrical characteristics of capacitor charging/discharging process, is proposed and applied to a cluster of energy storages (ESs) with high ramp rates. Through the coordination of the ID and conventional V-P droop, the transient power allocation in HESSs can be intrinsically realized in a decentralized manner. The high-frequency components of power demand can be compensated by the ESs with ID, whereas the ESs with V-P droop respond to the smooth change of load power. Additionally, the ID coefficient can be designed according to the nominal ramp rate of the ESs with slow response, which helps to extend the lifespan of the HESS. On the other hand, to easily assess the stability of the system feeding constant power loads, a minimum relative impedance criterion (MRIC) is developed. Based on MRIC, it is revealed that the proposed ID can shape the output impedance of the HESS and stabilize the entire system. The feasibility and effectiveness of ID are verified by both simulations and experimental results.Accepted versio