Solutions of the Yang-Mills-Higgs equations in 2+1 dimensional anti-de Sitter space-time

The solutions of the Bogomolny equation in anti-de Sitter space-time are obtained by using Darboux transformations with both constant spectral parameters and variable "spectral parameters". These solutions give the Yang-Mills-Higgs fields in anti-de Sitter space-time. Some examples in SU(2) case are considered and qualitative asymptotic behaviors of the solutions as t tends to infinity are discussed in detail.Comment: LaTeX, 18 pages, 11 PS figure

An Investigation of Indoor Positioning Systems and their Applications

PhDActivities of Daily Living (ADL) are important indicators of both cognitive and physical well-being in healthy and ill humans. There is a range of methods to recognise ADLs, each with its own limitations. The focus of this research was on sensing location-driven activities, in which ADLs are derived from location sensed using Radio Frequency (RF, e.g., WiFi or BLE), Magnetic Field (MF) and light (e.g., Lidar) measurements in three different environments. This research discovered that different environments can have different constraints and requirements. It investigated how to improve the positioning accuracy and hence how to improve the ADL recognition accuracy. There are several challenges that need to be addressed in order to do this. First, RF location fingerprinting is affected by the heterogeneity smartphones and their orientation with respect to transmitters, increasing the location determination error. To solve this, a novel Received Signal Strength Indication (RSSI) ranking based location fingerprinting methods that use Kendall Tau Correlation Coefficient (KTCC) and Convolutional Neural Networks (CNN) are proposed to correlate a signal position to pre-defined Reference Points (RPs) or fingerprints, more accurately, The accuracy has increased by up to 25.8% when compared to using Euclidean Distance (ED) based Weighted K-Nearest Neighbours Algorithm (WKNN). Second, the use of MF measurements as fingerprints can overcome some additional RF fingerprinting challenges, as MF measurements are far more invariant to static and dynamic physical objects that affect RF transmissions. Hence, a novel fast path matching data algorithm for an MF sensor combined with an Inertial Measurement Unit (IMU) to determine direction was researched and developed. It can achieve an average of 1.72 m positioning accuracy when the user walks far fewer (5) steps. Third, a device-free or off-body novel location-driven ADL method based upon 2D Lidar was investigated. An innovative method for recognising daily activities using a Seq2Seq model to analyse location data from a low-cost rotating 2D Lidar is proposed. It provides an accuracy of 88% when recognising 17 targeted ADLs. These proposed methods in this thesis have been validated in real environments.Chinese Scholarship Counci

Biogeography-based Optimization in Noisy Environments

Biogeography-based optimization (BBO) is a new evolutionary optimization algorithm that is based on the science of biogeography. In this paper, BBO is applied to the optimization of problems in which the fitness function is corrupted by random noise. Noise interferes with the BBO immigration rate and emigration rate, and adversely affects optimization performance. We analyse the effect of noise on BBO using a Markov model. We also incorporate re-sampling in BBO, which samples the fitness of each candidate solution several times and calculates the average to alleviate the effects of noise. BBO performance on noisy benchmark functions is compared with particle swarm optimization (PSO), differential evolution (DE), self-adaptive DE (SaDE) and PSO with constriction (CPSO). The results show that SaDE performs best and BBO performs second best. In addition, BBO with re-sampling is compared with Kalman filter-based BBO (KBBO). The results show that BBO with re-sampling achieves almost the same performance as KBBO but consumes less computational tim

Hybrid biogeography-based evolutionary algorithms

Hybrid evolutionary algorithms (EAs) are effective optimization methods that combine multiple EAs. We propose several hybrid EAs by combining some recently-developed EAs with a biogeography-based hybridization strategy. We test our hybrid EAs on the continuous optimization benchmarks from the 2013 Congress on Evolutionary Computation (CEC) and on some real-world traveling salesman problems. The new hybrid EAs include two approaches to hybridization: (1) iteration-level hybridization, in which various EAs and BBO are executed in sequence; and (2) algorithm-level hybridization, which runs various EAs independently and then exchanges information between them using ideas from biogeography. Our empirical study shows that the new hybrid EAs significantly outperforms their constituent algorithms with the selected tuning parameters and generation limits, and algorithm-level hybridization is generally better than iteration-level hybridization. Results also show that the best new hybrid algorithm in this paper is competitive with the algorithms from the 2013 CEC competition. In addition, we show that the new hybrid EAs are generally robust to tuning parameters. In summary, the contribution of this paper is the introduction of biogeography-based hybridization strategies to the EA community

Proteins Identified from Saliva and Salivary Glands of the Chinese Gall Aphid Schlechtendalia chinensis

Aphid saliva plays an essential role in the interaction between aphids and their host plants. Several aphid salivary proteins have been identified but none from galling aphids. Here the salivary proteins from the Chinese gall aphid are analyzed, Schlechtendalia chinensis, via an LC-MS/MS analysis. A total of 31 proteins are identified directly from saliva collected via an artificial diet, and 141 proteins are identified from extracts derived from dissected salivary glands. Among these identified proteins, 17 are found in both collected saliva and dissected salivary glands. In comparison with salivary proteins from ten other free-living Hemipterans, the most striking feature of the salivary protein from S. chinensis is the existence of high proportion of proteins with binding activity, including DNA-, protein-, ATP-, and iron-binding proteins. These proteins maybe involved in gall formation. These results provide a framework for future research to elucidate the molecular basis for gall induction by galling aphids

Finite-dimensional integrable systems associated with Davey-Stewartson I equation

For the Davey-Stewartson I equation, which is an integrable equation in 1+2 dimensions, we have already found its Lax pair in 1+1 dimensional form by nonlinear constraints. This paper deals with the second nonlinearization of this 1+1 dimensional system to get three 1+0 dimensional Hamiltonian systems with a constraint of Neumann type. The full set of involutive conserved integrals is obtained and their functional independence is proved. Therefore, the Hamiltonian systems are completely integrable in Liouville sense. A periodic solution of the Davey-Stewartson I equation is obtained by solving these classical Hamiltonian systems as an example.Comment: 18 pages, LaTe

A Novel Immune Classification for Predicting Immunotherapy Responsiveness in Patients With Adamantinomatous Craniopharyngioma

Adamantinomatous craniopharyngioma (ACP) is the most common tumor of the sellar region in children. The aggressive behavior of ACP challenges the treatment for it. However, immunotherapy is rarely studied in ACP. In this research, we performed unsupervised cluster analysis on the 725 immune-related genes and arrays of 39 patients with ACP patients in GSE60815 and GSE94349 databases. Two novel immune subtypes were identified, namely immune resistance (IR) subtype and immunogenic (IG) subtype. Interestingly, we found that the ACPs with IG subtype (34.78%, 8/23) were more likely to respond to immunotherapy than the ACPs with IR subtype (6.25%, 1/16) via tumor immune dysfunction and exclusion (TIDE) method. Simultaneously, the enrichment analysis indicated that the differentially expressed genes (DEGs) (p < 0.01, FDR < 0.01) of the IG subtype were chiefly involved in inflammatory and immune responses. However, the DEGs of the IR subtype were mainly involved in RNA processing. Next, immune infiltration analysis revealed a higher proportion of M2 macrophage in the IG subtype than that in the IR subtype. Compared with the IR subtype, the expression levels of immune checkpoint molecules (PD1, PDL1, PDL2, TIM3, CTLA4, Galectin9, LAG3, and CD86) were significantly upregulated in the IG subtype. The ssGSEA results demonstrated that the biofunction of carcinogenesis in the IG subtype was significantly enriched, such as lymphocyte infiltration, mesenchymal phenotype, stemness maintenance, and tumorigenic cytokines, compared with the IR subtype. Finally, a WDR89 (the DEG between IG and IR subtype)-based nomogram model was constructed to predict the immune classification of ACPs with excellent performance. This predictive model provided a reliable classification assessment tool for clinicians and aids treatment decision-making in the clinic

Leveraging Prototype Patient Representations with Feature-Missing-Aware Calibration to Mitigate EHR Data Sparsity

Electronic Health Record (EHR) data frequently exhibits sparse characteristics, posing challenges for predictive modeling. Current direct imputation such as matrix imputation approaches hinge on referencing analogous rows or columns to complete raw missing data and do not differentiate between imputed and actual values. As a result, models may inadvertently incorporate irrelevant or deceptive information with respect to the prediction objective, thereby compromising the efficacy of downstream performance. While some methods strive to recalibrate or augment EHR embeddings after direct imputation, they often mistakenly prioritize imputed features. This misprioritization can introduce biases or inaccuracies into the model. To tackle these issues, our work resorts to indirect imputation, where we leverage prototype representations from similar patients to obtain a denser embedding. Recognizing the limitation that missing features are typically treated the same as present ones when measuring similar patients, our approach designs a feature confidence learner module. This module is sensitive to the missing feature status, enabling the model to better judge the reliability of each feature. Moreover, we propose a novel patient similarity metric that takes feature confidence into account, ensuring that evaluations are not based merely on potentially inaccurate imputed values. Consequently, our work captures dense prototype patient representations with feature-missing-aware calibration process. Comprehensive experiments demonstrate that designed model surpasses established EHR-focused models with a statistically significant improvement on MIMIC-III and MIMIC-IV datasets in-hospital mortality outcome prediction task. The code is publicly available at \url{https://github.com/yhzhu99/SparseEHR} to assure the reproducibility

A comprehensive benchmark for COVID-19 predictive modeling using electronic health records in intensive care

The COVID-19 pandemic highlighted the need for predictive deep-learning models in health care. However, practical prediction task design, fair comparison, and model selection for clinical applications remain a challenge. To address this, we introduce and evaluate two new prediction tasks?outcome-specific length-of-stay and early-mortality prediction for COVID-19 patients in intensive care?which better reflect clinical realities. We developed evaluation metrics, model adaptation designs, and open-source data preprocessing pipelines for these tasks while also evaluating 18 predictive models, including clinical scoring methods and traditional machine-learning, basic deep-learning, and advanced deep-learning models, tailored for electronic health record (EHR) data. Benchmarking results from two real-world COVID-19 EHR datasets are provided, and all results and trained models have been released on an online platform for use by clinicians and researchers. Our efforts contribute to the advancement of deep-learning and machine-learning research in pandemic predictive modeling

Cellular network based multistatic integrated sensing and communication systems

A novel multistatic integrated sensing and communication (ISAC) system based on cellular network is proposed. It can make use of widespread base stations (BSs) to perform cooperative sensing in wide area. This system is important since the deployment of sensing function can be achieved upon the mobile communication network at low complexity and cost without modifying the architecture of BSs for full duplexing. In this work, the topology of sensing cell is first provided, which can be duplicated to seamlessly cover the cellular network. Each sensing cell consists of a single central BS transmitting signals and multiple neighboring BSs receiving reflected signals from sensing objects. Then an estimating approach is described for obtaining position and velocity of sensing objects that locate in the sensing cell. Joint data processing with an efficient optimization method is also provided. In addition, key issues in the cellular network based multistatic ISAC system are analyzed. Simulation results show that the multistatic ISAC system can reduce interference power by over 10 dBm and significantly improve position and velocity estimation accuracy of objects when compared with the monostatic ISAC system, demonstrating the effectiveness and promise of implementing the proposed system in the mobile network