Reactive power planning for regional power grids based on active and reactive power adjustments of DGs

To deal with extreme overvoltage scenarios with small probabilities in regional power grids, the traditional reactive power planning model requires a huge VAR compensator investment. Obviously, such a decision that makes a large investment to cope with a small probability event is not economic. Therefore, based on the scenario analysis of power outputs of distributed generations and load consumption, a novel reactive power planning model considering the active and reactive power adjustments of distributed generations is proposed to derive the optimal allocation of VAR compensators and ensure bus voltages within an acceptable range under extreme overvoltage scenarios. The objective of the proposed reactive power planning model is to minimize the VAR compensator investment cost and active power adjustment cost of distributed generations. Moreover, since the proposed reactive power planning model is formulated as a mixed-integer nonlinear programming problem, a primal-dual interior point method-based particle swarm optimization algorithm is developed to effectively solve the proposed model. Simulation results were conducted with the modified IEEE 30-bus system to verify the effectiveness of the proposed reactive power planning model

Retaining Image Feature Matching Performance Under Low Light Conditions

Poor image quality in low light images may result in a reduced number of feature matching between images. In this paper, we investigate the performance of feature extraction algorithms in low light environments. To find an optimal setting to retain feature matching performance in low light images, we look into the effect of changing feature acceptance threshold for feature detector and adding pre-processing in the form of Low Light Image Enhancement (LLIE) prior to feature detection. We observe that even in low light images, feature matching using traditional hand-crafted feature detectors still performs reasonably well by lowering the threshold parameter. We also show that applying Low Light Image Enhancement (LLIE) algorithms can improve feature matching even more when paired with the right feature extraction algorithm.Comment: Accepted in ICCAS 2020 - 20th International Conference on Control, Robotics, and System

A Comparison of the Severity of Tinnitus in Patients with and without Hearing Loss Using the Tinnitus Functional Index (TFI)

Background: Tinnitus is a disturbing symptom present in approximately 15% of the world population and between 2-7% of tinnitus sufferers seek medical help because of the chronic distress caused. Although well established that tinnitus can be present with and without hearing loss the different characteristics in terms of severity are still not completely known and studied. Aim: The objective of this study was to compare the severity of tinnitus in tinnitus patients with and without hearing loss. Materials and Methods: 73 tinnitus patients were included in this study at an audiology clinic in Amman, Jordan. Participants were assigned to two groups according to hearing status. The severity of tinnitus was evaluated using the Tinnitus Functional Index questionnaire. All participants were interviewed, followed by otoscopic examination, pure tone audiometry and tests for admittance and tinnitus matching. Results: The normal hearing group included 34 participants (46.6%) whose TFI scores were divided as follow: mild annoyance (17), significant annoyance (14) and severe annoyance (3). The sensorineural loss group included 39 participants (53.4%) with mild annoyance (11), significant annoyance (12) and severe annoyance (16). A statistically significant association was found between hearing status and the severity of tinnitus using a Chi Squared test (x2=0.487, p=0.007). There was no association between tinnitus severity and age or gender. Conclusion: Tinnitus severity was significantly worse in tinnitus patients with a hearing loss than tinnitus patients with normal hearing thresholds. This should be taken in consideration when clinicians are planning counselling and management protocols for individual patients

Making Large Language Models Better Reasoners with Alignment

Reasoning is a cognitive process of using evidence to reach a sound conclusion. The reasoning capability is essential for large language models (LLMs) to serve as the brain of the artificial general intelligence agent. Recent studies reveal that fine-tuning LLMs on data with the chain of thought (COT) reasoning process can significantly enhance their reasoning capabilities. However, we find that the fine-tuned LLMs suffer from an \textit{Assessment Misalignment} problem, i.e., they frequently assign higher scores to subpar COTs, leading to potential limitations in their reasoning abilities. To address this problem, we introduce an \textit{Alignment Fine-Tuning (AFT)} paradigm, which involves three steps: 1) fine-tuning LLMs with COT training data; 2) generating multiple COT responses for each question, and categorizing them into positive and negative ones based on whether they achieve the correct answer; 3) calibrating the scores of positive and negative responses given by LLMs with a novel constraint alignment loss. Specifically, the constraint alignment loss has two objectives: a) Alignment, which guarantees that positive scores surpass negative scores to encourage answers with high-quality COTs; b) Constraint, which keeps the negative scores confined to a reasonable range to prevent the model degradation. Beyond just the binary positive and negative feedback, the constraint alignment loss can be seamlessly adapted to the ranking situations when ranking feedback is accessible. Furthermore, we also delve deeply into recent ranking-based alignment methods, such as DPO, RRHF, and PRO, and discover that the constraint, which has been overlooked by these approaches, is also crucial for their performance. Extensive experiments on four reasoning benchmarks with both binary and ranking feedback demonstrate the effectiveness of AFT.Comment: Large Language Models; Reasoning; Alignmen

Contributions and limitations of using machine learning to predict noise-induced hearing loss

Purpose Noise-induced hearing loss (NIHL) is a global issue that impacts people’s life and health. The current review aims to clarify the contributions and limitations of applying machine learning (ML) to predict NIHL by analyzing the performance of different ML techniques and the procedure of model construction. Methods The authors searched PubMed, EMBASE and Scopus on November 26, 2020. Results Eight studies were recruited in the current review following defined inclusion and exclusion criteria. Sample size in the selected studies ranged between 150 and 10,567. The most popular models were artificial neural networks (n = 4), random forests (n = 3) and support vector machines (n = 3). Features mostly correlated with NIHL and used in the models were: age (n = 6), duration of noise exposure (n = 5) and noise exposure level (n = 4). Five included studies used either split-sample validation (n = 3) or ten-fold cross-validation (n = 2). Assessment of accuracy ranged in value from 75.3% to 99% with a low prediction error/root-mean-square error in 3 studies. Only 2 studies measured discrimination risk using the receiver operating characteristic (ROC) curve and/or the area under ROC curve. Conclusion In spite of high accuracy and low prediction error of machine learning models, some improvement can be expected from larger sample sizes, multiple algorithm use, completed reports of model construction and the sufficient evaluation of calibration and discrimination risk

Arginine Alters miRNA Expression Involved in Development and Proliferation of Rat Mammary Tissue

This study was designed to determine the effects of dietary arginine on development and proliferation in rat mammary tissue through changes in miRNA profiles. Twelve pregnant Wistar rats were allocated randomly to two groups. A basal diet containing arginine or the control diet containing glutamate on an equal nitrogen basis as the arginine supplemented diet were used. The experiment included a pre-experimental period of four days before parturition and an experimental period of 17 days after parturition. Mammary tissue was collected for histology, RNA extraction and high-throughput sequencing analysis. The greater mammary acinar area indicated that arginine supplementation enhanced mammary tissue development (p < 0.01). MicroRNA profiling indicated that seven miRNA (miR-206-3p, miR-133a-5p, miR-133b-3p, miR-1-3p, miR-133a-3p, miR-1b and miR-486) were differentially expressed in response to Arginine when compared with the glutamate-based control group. In silico gene ontology enrichment and KEGG pathway analysis revealed between 240 and 535 putative target genes among the miRNA. Further verification by qPCR revealed concordance with the differential expression from the sequencing results: 17 of 28 target genes were differentially expressed (15 were highly expressed in arginine and 2 in control) and 11 target genes did not have significant difference in expression. In conclusion, our study suggests that arginine may potentially regulate the development of rat mammary glands through regulating miRNAs

FindVehicle and VehicleFinder: A NER dataset for natural language-based vehicle retrieval and a keyword-based cross-modal vehicle retrieval system

Natural language (NL) based vehicle retrieval is a task aiming to retrieve a vehicle that is most consistent with a given NL query from among all candidate vehicles. Because NL query can be easily obtained, such a task has a promising prospect in building an interactive intelligent traffic system (ITS). Current solutions mainly focus on extracting both text and image features and mapping them to the same latent space to compare the similarity. However, existing methods usually use dependency analysis or semantic role-labelling techniques to find keywords related to vehicle attributes. These techniques may require a lot of pre-processing and post-processing work, and also suffer from extracting the wrong keyword when the NL query is complex. To tackle these problems and simplify, we borrow the idea from named entity recognition (NER) and construct FindVehicle, a NER dataset in the traffic domain. It has 42.3k labelled NL descriptions of vehicle tracks, containing information such as the location, orientation, type and colour of the vehicle. FindVehicle also adopts both overlapping entities and fine-grained entities to meet further requirements. To verify its effectiveness, we propose a baseline NL-based vehicle retrieval model called VehicleFinder. Our experiment shows that by using text encoders pre-trained by FindVehicle, VehicleFinder achieves 87.7\% precision and 89.4\% recall when retrieving a target vehicle by text command on our homemade dataset based on UA-DETRAC. The time cost of VehicleFinder is 279.35 ms on one ARM v8.2 CPU and 93.72 ms on one RTX A4000 GPU, which is much faster than the Transformer-based system. The dataset is open-source via the link https://github.com/GuanRunwei/FindVehicle, and the implementation can be found via the link https://github.com/GuanRunwei/VehicleFinder-CTIM

Estimation of Fracture Size and Probability Density Function by Setting Scanlines in Rectangular Sampling Window

Rock masses are very important materials in geotechnical engineering. In engineering rock mass, fracture is the relatively weak part of mechanical strength in rock mass and is the most important factor controlling the deformation, damage, and permeability of rock mass. Therefore, investigating fractures is very important for characterizing rock mass. This paper proposed a new approach by using uniformly equidistant orthogonal scanlines. Within the study context, the solution formula of fracture size is derived by establishing the space intersection model of arbitrary fracture and scanline, rectangular window, and a rectangular box with a rectangular window. Then, fractures were randomly generated in a certain size cube and compared with the traditional Kulatilake trace length integral evaluation method. The study results have shown that the proposed method is more reasonable and accurate. Then, this method was applied to an adit of Songta Hydropower Station. Finally, a new fracture diameter probability density estimation method was proposed, the fracture diameter of the normal distribution was verified, and the parameters of the probability density function obtained by the scanlines method were in agreement with the initial set parameters. In summary, the proposed scanlines method can well estimate the mean value of the fracture diameter and the probability density function of the fracture size

Degradable mesoporous semimetal antimony nanospheres for near-infrared II multimodal theranostics.

Metallic and semimetallic mesoporous frameworks are of great importance owing to their unique properties and broad applications. However, semimetallic mesoporous structures cannot be obtained by the traditional template-mediated strategies due to the inevitable hydrolytic reaction of semimetal compounds. Therefore, it is yet challenging to fabricate mesoporous semimetal nanostructures, not even mention controlling their pore sizes. Here we develop a facile and robust selective etching route to synthesize monodispersed mesoporous antimony nanospheres (MSbNSs). The pore sizes of MSbNSs are tunable by carefully controlling the partial oxidation of Sb nuclei and the selective etching of the as-formed Sb2O3. MSbNSs show a wide absorption from visible to second near-infrared (NIR-II) region. Moreover, PEGylated MSbNSs are degradable and the degradation mechanism is further explained. The NIR-II photothermal performance of MSbNSs is promising with a high photothermal conversion efficiency of ~44% and intensive NIR-II photoacoustic signal. MSbNSs show potential as multifunctional nanomedicines for NIR-II photoacoustic imaging guided synergistic photothermal/chemo therapy in vivo. Our selective etching process would contribute to the development of various semimetallic mesoporous structures and efficient multimodal nanoplatforms for theranostics