Search

Purpose: This review article reviews the contemporary studies of localization ability for different populations in different listening environments and provides possible future research directions. Conclusions: The ability to accurately localize a sound source relying on three cues (interaural time difference, interaural level difference, and spectral cues) is important for communication, learning, and safety. Confounding effects including noise and reverberation, which exist in common listening environments, mask or alter localization cues and negatively affect localization performance. Hearing loss, a common public health issue, also affects localization accuracy. Although hearing devices have been developed to provide excellent audibility of speech signals, less attention has been paid to preserving and replicating crucial localization cues. Unique challenges are faced by users of various hearing devices, including hearing aids, bone-anchored hearing instruments, and cochlear implants. Hearing aids have failed to consistently improve localization performance and, in some cases, significantly impair sound localization. Bone-conduction hearing instruments show little to no benefit for sound localization performance in most cases, although some improvement is seen in binaural users. Although cochlear implants provide great hearing benefit to individuals with severe-to-profound sensorineural hearing loss, cochlear implant users have significant difficulty localizing sound, even with two implants. However, technologies in each of these areas are advancing to reduce interference with desired sound signals and preserve localization cues to help users achieve better hearing and sound localization in real-life environments.

Medulloblastoma (MB) is the most frequent malignant brain tumor in pediatrics. Since the current standard of care for MB consisting of surgery, cranio‑spinal irradiation and chemotherapy often leads to a high morbidity rate, a number of patients suffer from long‑term sequelae following treatment. Targeted therapies hold the promise of being more effective and less toxic. Therefore, the present study aimed to identify hub genes with an upregulated expression in MB and to search for potential therapeutic targets from these genes. For this purpose, gene expression profile datasets were obtained from the Gene Expression Omnibus database and processed using R 3.6.0 software to screen differentially expressed genes (DEGs) between MB samples and normal brain tissues. A total of 282 upregulated and 436 downregulated DEGs were identified. Functional enrichment analysis revealed that the upregulated DEGs were predominantly enriched in the cell cycle, DNA replication and cell division. The top 10 hub genes were identified from the protein‑protein interaction network of upregulated genes, and one identified hub gene [PDZ binding kinase (PBK)] was selected for further investigation due to its possible role in the pathogenesis of MB. The aberrant expression of PBK in MB was verified in additional independent gene expression datasets. Survival analysis demonstrated that a higher expression level of PBK was significantly associated with poorer clinical outcomes in non‑Wingless MBs. Furthermore, targeting PBK with its inhibitor, HI‑TOPK‑032, impaired the proliferation and induced the apoptosis of two MB cell lines, with the diminished phosphorylation of downstream effectors of PBK, including ERK1/2 and Akt, and the activation of caspase‑3. Hence, these results suggest that PBK may be a potential prognostic biomarker and a novel candidate of targeted therapy for MB.

This paper proposes a new singular value decomposition-based adaptive robust unscented Kalman filter (SVD-ARUKF) for power system dynamic state estimation. Specifically, a suboptimal fading factor is introduced into the predictive covariance matrix to improve the tracking performance for sudden state variables. Unknown system noise is adapted through the covariance matching method. To avoid the numerical sensitivity, SVD is performed on the covariance matrix. Simulated results demonstrate the filtering stability and accuracy of the proposed SVD-ARUKF algorithm on the IEEE 30-node system.

Since the subsidies of solar energy photovoltaic power may be eliminated in the short run, it is inappropriate to neglect the cost of photovoltaic power in economic dispatch of thermal-photovoltaic power system. In this day-ahead economical schedule of photovoltaic-thermal power units, the cost of photovoltaic (PV) power generation be added in objective function. In such a way, a new objective function of economic dispatch is constructed to be compatible with two parts, i.e., thermal power units expense and PV cost. Power balance of this system, the rotate reserve, the output power range, the open and close time of thermal power units, etc. are taken as the constraints of economic dispatch. Unit commitment (UC) problem can use the genetic algorithm (GA) based on real matrix coding be solved. Finally, two study cases are conducted to verify that the total cost can be reduced by reasonable utilization of PV generation.

This paper investigates the rotor angular velocity state estimation of permanent magnet linear synchronous motor (PMLSM) sensorless system. The velocity feedback is an indispensable link to realize the accurate speed control of PMLSM. Accurate rotor information acquisition is the essential problem of the whole control system, since it is impossible to obtain rotor information by traditional mechanical sensors in the non-position sensor control of PMLSM drive system. To work out this problem, this paper raises a non-position sensor control method based on unscented particle filter (UPF). Firstly, the nonlinear dynamic model is established for PMLSM. In the framework of particle filter (PF), the latest measurement information is to generate the predicted particles. Finally, a numerical example and simulated results show that UPF can accurately estimate the rotor angular velocity.

This paper is dedicated to investigate the stability problem of multi-area networked power systems with load frequency control (LFC) and event-triggered communication. To save limited network resources and improve the data transmission efficiency, an ACE-dependent time-delay model is constructed to describe the multi-area networked power system via event-triggering mechanism. By utilizing the Lyapunov theory, a delay-dependent stability criterion for networked power systems is presented in terms of linear matrix inequality. Finally, a three-area power system based on MATLAB is provided to show the superiority and effectiveness of the proposed method.

Objective Using three-dimensional (3D) printing to create individualized patient models of the skull base, the optic chiasm and facial nerve can be previsualized to help identify and protect these structures during tumor removal surgery. Methods Preoperative imaging data for 2 cases of sellar tumor and 1 case of acoustic neuroma were obtained. Based on these data, the cranial nerves were visualized using 3D T1-weighted turbo field echo sequence and diffusion tensor imaging–based fiber tracking. Mimics software was used to create 3D reconstructions of the skull base regions surrounding the tumors, and 3D solid models were printed for use in simulation of the basic surgical steps. Results The 3D printed personalized skull base tumor solid models contained information regarding the skull, brain tissue, blood vessels, cranial nerves, tumors, and other associated structures. The sphenoid sinus anatomy, saddle area, and cerebellopontine angle region could be visually displayed, and the spatial relationship between the tumor and the cranial nerves and important blood vessels was clearly defined. The models allowed for simulation of the operation, prediction of operative details, and verification of accuracy of cranial nerve reconstruction during the operation. Questionnaire assessment showed that neurosurgeons highly valued the accuracy and usefulness of these skull base tumor models. Conclusions 3D printed models of skull base tumors and nearby cranial nerves, by allowing for the surgical procedure to be simulated beforehand, facilitate preoperative planning and help prevent cranial nerve injury.

Objective Temporal lobe epilepsy (TLE) is a common type of acquired epilepsy refractory to medical treatment. As such, establishing animal models of this disease is critical to developing new and effective treatment modalities. Because of their small head size, rodents are not suitable for comprehensive electroencephalography (EEG) evaluation via scalp or subdural electrodes. Therefore, a larger primate model that closely recapitulates signs of TLE is needed; here we describe a rhesus monkey model resembling chronic TLE. Methods Eight monkeys were divided into two groups: kainic acid (KA) group (n = 6) and saline control group (n = 2). Intra-amygdala KA injections were performed biweekly via an Ommaya device until obvious epileptiform discharges were recorded. Video-EEG recording was conducted intermittently throughout the experiment using both scalp and subdural electrodes. Brains were then analyzed for Nissl and glial fibrillary acid protein (GFAP) immunostaining. Results After 2–4 injections of KA (approximately 1.2-2.4 mg, 0.12-0.24 mg/kg), interictal epileptiform discharges (IEDs) were recorded in all KA-treated animals. Spontaneous recurrent seizures (SRSs) accompanied by symptoms mimicking temporal lobe absence (undetectable without EEG recording), but few mild motor signs, were recorded in 66.7% (four of six) KA-treated animals. Both IEDs and seizures indicated a primary epileptic zone in the right temporal region and contralateral discharges were later detected. Segmental pyramidal cell loss and gliosis were detected in the brain of a KA-treated monkey. Conclusions Through a modified protocol of unilateral repetitive intra-amygdala KA injections, a rhesus monkey model with similar behavioral and brain electrical features as TLE was developed.

This paper describes the on-going development of a comprehensive integrated guidance and control architecture featuring an on-line aerodynamic identification scheme that produces a global estimate of a damaged vehicle's aerodynamic characteristics. This estimate is used to derive updated constraints reflecting the vehicle's degraded maneuverability that are incorporated into computationally efficient algorithms for on-line trajectory reshaping/retargeting. This paper presents recent progress on a Multiple-Model Adaptive Estimation approach to the global aerodynamic estimation process. The scheme utilizes diagnostic information assumed to be provided by a vehicle health monitor to generate a bank of aerodynamic models in which each model is created by an aerodynamic prediction code for a vehicle configuration reflecting a particular type of damage. The individual models are blended through weights that are adapted on-line using measured data.

An approach based on an algebraic Riccati equation for designing an observer-based H/sub /spl infin// reduced-order controller is provided. The advantage of this approach is that through pole-zero cancellation, we only need to solve one algebraic Riccati equation during the design process. The scheme can guarantee that the closed-loop system is stable and the closed-loop transfer function satisfies a prescribed H/sub /spl infin// norm bound.