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Wind speed affects aviation performance, clean energy production, and other applications. By accurately predicting wind speed, operational delays and accidents can be avoided, while the efficiency of wind energy production can also be increased. This paper initially overviews the definition, characteristics, sensors capable of measuring the feature, and the relationship between this feature and wind speed for all Quality Indicators (QIs). Subsequently, the feature importance of each QI relevant to wind-speed prediction is assessed, and all QIs are employed to predict horizontal wind speed. In addition, we conduct a comparison between the performance of traditional point-wise machine learning models and temporally correlated deep learning ones. The results demonstrate that the Bidirectional Long Short-Term Memory (BiLSTM) neural network yielded the highest level of accuracy across three metrics. Additionally, the newly proposed set of QIs outperformed the previously utilised QIs to a significant degree.

Abstract Background The ovarian reserve is a reservoir for reproductive potential. In clinical practice, early detection and treatment of premature ovarian decline characterized by abnormal ovarian reserve tests is regarded as a critical measure to prevent infertility. However, the relevant data are typically stored in an unstructured format in a hospital’s electronic medical record (EMR) system, and their retrieval requires tedious manual abstraction by domain experts. Computational tools are therefore needed to reduce the workload. Methods We presented RegEMR, an artificial intelligence tool composed of a rule-based natural language processing (NLP) extractor and a knowledge-based disease scoring model, to automatize the screening procedure of premature ovarian decline using Chinese reproductive EMRs. We used regular expressions (REs) as a text mining method and explored whether REs automatically synthesized by the genetic programming-based online platform RegexGenerator + + could be as effective as manually formulated REs. We also investigated how the representativeness of the learning corpus affected the performance of machine-generated REs. Additionally, we translated the clinical diagnostic criteria into a programmable disease diagnostic model for disease scoring and risk stratification. Four hundred outpatient medical records were collected from a Chinese fertility center. Manual review served as the gold standard, and fivefold cross-validation was used for evaluation. Results The overall F-score of manually built REs was 0.9444 (95% CI 0.9373 to 0.9515), with no significant difference (paired t test p > 0.05) compared with machine-generated REs that could be affected by training set sizes and annotation portions. The extractor performed effectively in automatically tracing the dynamic changes in hormone levels (F-score 0.9518–0.9884) and ultrasonographic measures (F-score 0.9472–0.9822). Applying the extracted information to the proposed diagnostic model, the program obtained an accuracy of 0.98 and a sensitivity of 0.93 in risk screening. For each specific disease, the automatic diagnosis in 76% of patients was consistent with that of the clinical diagnosis, and the kappa coefficient was 0.63. Conclusion A Chinese NLP system named RegEMR was developed to automatically identify high risk of early ovarian aging and diagnose related diseases from Chinese reproductive EMRs. We hope that this system can aid EMR-based data collection and clinical decision support in fertility centers.

Abstract None-Line-of-Sight (NLOS) signals denote Global Navigation Satellite System (GNSS) signals received indirectly from satellites and could result in unacceptable positioning errors. To meet the high mission-critical transportation and logistics demand, NLOS signals received in the built environment should be detected, corrected, and excluded. This paper proposes a cost-effective NLOS impact mitigation approach using only GNSS receivers. By exploiting more signal Quality Indicators (QIs), such as the standard deviation of pseudorange, Carrier-to-Noise Ratio (C/N0), elevation and azimuth angle, this paper compares machine-learning-based classification algorithms to detect and exclude NLOS signals in the pre-processing step. The probability of the presence of NLOS is predicted using regression algorithms. With a pre-defined threshold, the signals can be classified as Line-of-Sight (LOS) or NLOS. The probability of the occurrence of NLOS is also used for signal subset selection and specification of a novel weighting scheme. The novel weighting scheme consists of both C/N0 and elevation angle and NLOS probability. Experimental results show that the best LOS/NLOS classification algorithm is the random forest. The best QI set for NLOS classification is the first three QIs mentioned above and the difference of azimuth angle. The classification accuracy obtained from this proposed algorithm can reach 93.430%, with 2.810% false positives. The proposed signal classifier and weighting scheme improved the positioning accuracy by 69.000% and 40.700% in the horizontal direction, 79.361% and 75.322% in the vertical direction, and 75.963% and 67.824% in the 3D direction.

Conclusion: Treatment preferences of hospitalized diabetes patients are mainly influenced by antihyperglycemic effectiveness, adverse reactions, healthcare providers, and individual characteristics. Comprehensive consideration and an individualized therapy strategy should be given when constructing a hospital-wide glycemic control programme.

Objective. To study the risk factors for granulocytopenia caused by antithyroid drugs. Methods. Patients who were diagnosed with Graves’ hyperthyroidism and regularly treated with antithyroid drugs (ATDs) from January 2010 to July 2022 at Nanjing Drum Tower Hospital, aged >18 years, were selected for general information and laboratory tests and divided into two groups according to the occurrence of granulocytopenia. Independent risk factors for the development of granulocytopenia in patients treated with ATDs were analyzed using one-way and multiway logistic regression analyses, and the predictive value of each index was evaluated using the receiver operating characteristic (ROC) curve and the area under the curve (AUC). Results. A total of 818 patients were enrolled, of which 95 developed granulocytopenia. Univariate analysis revealed that sex, white blood cell (WBC) counts, neutrophil-to-lymphocyte ratio (NLR), glutamic-pyruvic transaminase (ALT), aspartate transaminase (AST), free triiodothyronine (FT3), free thyroxine (FT4), and thyroid stimulating hormone (TSH) before medication were risk factors for ATD-induced granulocytopenia (P

This study theoretically proposed a novel surface plasmon resonance biosensor by incorporating emerging two dimensional material blue phosphorus and graphene layers with plasmonic gold film. The excellent performances employed for biosensing can be realized by accurately tuning the thickness of gold film and the number of blue phosphorus interlayer. Our proposed plasmonic biosensor architecture designed by phase modulation is much superior to angular modulation, providing 4 orders of magnitude sensitivity enhancement. In addition, the optimized stacked configuration is 42 nm Au film/2-layer blue phosphorus /4-layer graphene, which can produce the sharpest differential phase of 176.7661 degrees and darkest minimum reflectivity as low as 5.3787 × 10−6. For a tiny variation in local refractive index of 0.0012 RIU (RIU, refractive index unit) due to the binding interactions of aromatic biomolecules, our proposed biosensor can provide an ultrahigh detection sensitivity up to 1.4731 × 105 °/RIU, highly promising for performing ultrasensitive biosensing application.

In pursuit of widespread adoption of renewable energy and the realization of decarbonization objectives, this study investigates an innovative system known as a wind-solar-hydrogen multi-energy supply (WSH-MES) system. This system seamlessly integrates a wind farm, photovoltaic power station, solar thermal power station, and hydrogen energy network at the power grid level. Central to the study is the introduction of a bi-level collaborative optimization model—an innovative algorithmic framework specifically tailored for complex multi-energy systems. This model co-optimizes both the capacity planning of essential system components and their annual load distribution, adeptly navigating the complexities of optimizing capacity and annual load distribution under uncertain energy sources and load conditions. A layered methodology synergistically combines linear programming with an advanced version of non-dominated sorting genetic algorithm-II. When applied to a real-world case study in Zhangbei, China, this approach identifies an optimal system capacity, leading to annual green power generation of 201.56 GW and a substantial reduction of over 173,703 tons of CO2 emissions. An economic analysis further reveals that each 1% reduction in CO2 emissions corresponds to a modest 1.7% increase in the system’s levelized cost of energy. Moreover, a comprehensive exploration of the impacts of various capacity parameters on the WSH-MES system’s performance is conducted. These insights offer invaluable guidance for the large-scale advancement of efficient renewable energy utilization and the attainment of decarbonization targets.