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Abstract This article introduces a novel approach named HBA‐dHoSMO, which combines a continuous decentralized higher‐order sliding mode controller‐based observer (dHoSMO) with the honey badger algorithm (HBA), specifically designed for load frequency control in multi‐area power systems (MAPSs). Traditional sliding mode controllers (SMCs) employed in load frequency control of MAPSs often face challenges related to chattering and oscillations, leading to decreased robustness and stability. Additionally, tuning the parameters for these SMC designs to achieve optimal performance in MAPSs can be challenging. The HBA‐dHoSMO is proposed to address the issues of chattering and oscillations, while the optimal parameters for SMC design are obtained using HBA. The stability analysis of the entire system is conducted using linear matrix inequality and the Lyapunov stability theory, affirming the reliability and feasibility of the approach. A comprehensive set of case studies is performed under various configurations and conditions. Additionally, particle swarm optimization and tuna swarm optimization, in conjunction with SMC‐based and proportional–integral–derivative controllers, are examined for performance comparison. Simulation results demonstrate the superior performance of the proposed controller across all case studies. This is evidenced by the lowest integral time absolute error values recorded as 0.0133, 6.45 × 10−4, and 0.0167 for single‐, two‐, and three‐area power systems, respectively.

RNA N4-acetylcytidine (ac4C) is a highly conserved RNA modification that plays a crucial role in controlling mRNA stability, processing, and translation. Consequently, accurate identification of ac4C sites across the genome is critical for understanding gene expression regulation mechanisms. In this study, we have developed ac4C-AFL, a bioinformatics tool that precisely identifies ac4C sites from primary RNA sequences. In ac4C-AFL, we identified the optimal sequence length for model building and implemented an adaptive feature representation strategy that is capable of extracting the most representative features from RNA. To identify the most relevant features, we proposed a novel ensemble feature importance scoring strategy to rank features effectively. We then used this information to conduct the sequential forward search, which individually determine the optimal feature set from the 16 sequence-derived feature descriptors. Utilizing these optimal feature descriptors, we constructed 176 baseline models using 11 popular classifiers. The most efficient baseline models were identified using the two-step feature selection approach, whose predicted scores were integrated and trained with the appropriate classifier to develop the final prediction model. Our rigorous cross-validations and independent tests demonstrate that ac4C-AFL surpasses contemporary tools in predicting ac4C sites. Moreover, we have developed a publicly accessible web server at https://balalab-skku.org/ac4C-AFL/.

In this article, a broadband 1-D high-gain beam steering solution to steer sum and difference patterns for tracking the optimum common scatter volume of troposcatter links at ${X}$ -band is presented. The novel steering arrangement consists of a $D = 70$ cm offset parabolic cylindrical (OPC) reflector fed by a triple-mode circular waveguide (TM-CW) horn antenna integrated with a two-element dielectric wedge-based Risley prism and a parabolic phase correcting dielectric lens (PCDL). The proposed hybrid beam steering reflector-feed obviates the need for complex 3-axes based gimbals to mechanically steer the conventional reflector-feed assembly. In the proposed hybrid reflector-feed arrangement, steering is achieved using a simple axial rotation of smaller Risley prism components at the feed. The broadband high-gain attribute required for an efficient troposcatter link is achieved by the proposed hybrid reflector-feed with much smaller and compact moving parts compared to traditional axial rotation-based mechanical steering arrangements. The simulated gain of the proposed antenna operating in TE11 mode at broadside is 25.7 dBi. A prototype of the proposed beam steering solution is built and tested in an anechoic chamber. The OPC reflector prototype is made of lightweight composite carbon fiber/fiber glass material thus reducing the overall antenna assembly weight. Experimental results show close agreement with the simulated ones. All three modes of TM-CW horn are steered by ±19° with TE11 mode patterns showing scan losses lower than 1 dB. The measured null depths for TM01 and TE21 modes are lower than −23 dB at 10 GHz. The peak of the sum and the nulls of the difference patterns are separated by a minimum difference of 25 dB at 10 GHz.

ABSTRACTSpeech emotion recognition (SER) has several applications, such as e-learning, human-computer interaction, customer service, and healthcare systems. Although researchers have investigated lots of techniques to improve the accuracy of SER, it has been challenging with feature extraction, classifier schemes, and computational costs. To address the aforementioned problems, we propose a new set of 1D features extracted by using an overlapping sliding window (OSW) technique for SER in this study. In addition, a deep neural network-based classifier scheme called the deep Pattern Recognition Network (PRN) is designed to categorize emotional states from the new set of 1D features. We evaluate the proposed method on the Emo-DB and the AESSD datasets that contain several different emotional states. The experimental results show that the proposed method achieves an accuracy of 98.5% and 87.1% on the Emo-DB and AESSD datasets, respectively. It is also more comparable with accuracy to and better than the state-of-the-art and current approaches that use 1D features on the same datasets for SER. Furthermore, the SHAP (SHapley Additive exPlanations) analysis is employed for interpreting the prediction model to assist system developers in selecting the optimal features to integrate into the desired system.

2'-O-methylation (2-OM or Nm) is a widespread RNA modification observed in various RNA types like tRNA, mRNA, rRNA, miRNA, piRNA, and snRNA, which plays a crucial role in several biological functional mechanisms and innate immunity. To comprehend its modification mechanisms and potential epigenetic regulation, it is necessary to accurately identify 2-OM sites. However, biological experiments can be tedious, time-consuming, and expensive. Furthermore, currently available computational methods face challenges due to inadequate datasets and limited classification capabilities. To address these challenges, we proposed Meta-2OM, a cutting-edge predictor that can accurately identify 2-OM sites in human RNA. In brief, we applied a meta-learning approach that considered eight conventional machine learning algorithms, including tree-based classifiers and decision boundary-based classifiers, and eighteen different feature encoding algorithms that cover physicochemical, compositional, position-specific and natural language processing information. The predicted probabilities of 2-OM sites from the baseline models are then combined and trained using logistic regression to generate the final prediction. Consequently, Meta-2OM achieved excellent performance in both 5-fold cross-validation training and independent testing, outperforming all existing state-of-the-art methods. Specifically, on the independent test set, Meta-2OM achieved an overall accuracy of 0.870, sensitivity of 0.836, specificity of 0.904, and Matthew's correlation coefficient of 0.743. To facilitate its use, a user-friendly web server and standalone program have been developed and freely available at http://kurata35.bio.kyutech.ac.jp/Meta-2OM and https://github.com/kuratahiroyuki/Meta-2OM.

The plasmonic antenna probe is constructed using a silver rod embedded in a modified Mach-Zehnder interferometer (MZI) ad-drop filter. Rabi antennas are formed when space-time control reaches two levels of system oscillation and can be used as human brain sensor probes. Photonic neural networks are designed using brain-Rabi antenna communication, and transmissions are connected via neurons. Communication signals are carried by electron spin (up and down) and adjustable Rabi frequency. Hidden variables and deep brain signals can be obtained by external detection. A Rabi antenna has been developed by simulation using computer simulation technology (CST) software. Additionally, a communication device has been developed that uses the Optiwave program with Finite-Difference Time-Domain (OptiFDTD). The output signal is plotted using the MATLAB program with the parameters of the OptiFDTD simulation results. The proposed antenna oscillates in the frequency range of 192 THz to 202 THz with a maximum gain of 22.4 dBi. The sensitivity of the sensor is calculated along with the result of electron spin and applied to form a human brain connection. Moreover, intelligent machine learning algorithms are proposed to identify high-quality transmissions and predict the behavior of transmissions in the near future. During the process, a root mean square error (RMSE) of 2.3332(±0.2338) was obtained. Finally, it can be said that our proposed model can efficiently predict human mind, thoughts, behavior as well as action/reaction, which can be greatly helpful in the diagnosis of various neuro-degenerative/psychological diseases (such as Alzheimer's, dementia, etc.) and for security purposes.

Abstract Monitoring the conditions of machines is vital in the manufacturing industry. Early detection of faulty components in machines for stopping and repairing the failed components can minimize the downtime of the machine. In this article, we present a method for detecting failures in drill machines using drill sounds in Valmet AB, a company in Sundsvall, Sweden that supplies equipment and processes for the production of pulp, paper, and biofuels. The drill dataset includes two classes: anomalous sounds and normal sounds. Detecting drill failure effectively remains a challenge due to the following reasons. The waveform of drill sound is complex and short for detection. Furthermore, in realistic soundscapes, both sounds and noise exist simultaneously. Besides, the balanced dataset is small to apply state-of-the-art deep learning techniques. Due to these aforementioned difficulties, sound augmentation methods were applied to increase the number of sounds in the dataset. In this study, a convolutional neural network (CNN) was combined with a long-short-term memory (LSTM) to extract features from log-Mel spectrograms and to learn global representations of two classes. A leaky rectified linear unit (Leaky ReLU) was utilized as the activation function for the proposed CNN instead of the ReLU. Moreover, an attention mechanism was deployed at the frame level after the LSTM layer to pay attention to the anomaly in sounds. As a result, the proposed method reached an overall accuracy of 92.62% to classify two classes of machine sounds on Valmet’s dataset. In addition, an extensive experiment on another drilling dataset with short sounds yielded 97.47% accuracy. With multiple classes and long-duration sounds, an experiment utilizing the publicly available UrbanSound8K dataset obtains 91.45%. Extensive experiments on our dataset as well as publicly available datasets confirm the efficacy and robustness of our proposed method. For reproducing and deploying the proposed system, an open-source repository is publicly available at https://github.com/thanhtran1965/DrillFailureDetection_SciRep2022 .

A microstrip circuit is designed, constructed, and tested based on the nest microstrip add-drop filters (NMADF). The multi-level system oscillation is generated by the wave-particle behaviors of AC driven along the microstrip ring circular path. The continuous successive filtering is applied via the device input port. The higher-order harmonic oscillations can be filtered, from which the two-level system known as a Rabi oscillation is achieved. The outside microstrip ring energy is coupled to the inside rings, from which the multiband Rabi oscillations can be formed within the inner rings. The resonant Rabi frequencies can be applied for multi-sensing probes. The relationship between electron density and Rabi oscillation frequency of each microstrip ring output can be obtained and used for multi-sensing probe applications. The relativistic sensing probe can be obtained by the warp speed electron distribution at the resonant Rabi frequency respecting the resonant ring radii. These are available for relativistic sensing probe usage. The obtained experimental results have shown that there are 3-center Rabi frequencies obtained, which can be used for 3-sensing probes simultaneously. The sensing probe speeds of 1.1c, 1.4c, and 1.5c are obtained using the microstrip ring radii of 14.20, 20.12, and 34.49 mm, respectively. The best sensor sensitivity of 1.30 ms is achieved. The relativistic sensing platform can be used for many applications.

This paper presents a passive 2-D beam steering solution comprising of a triple-mode circular waveguide horn antenna operating at Ka-band frequency of 30 GHz, a pair of identical dielectric lenses employing Risley prism and a parabolic phase correcting surface. The circular waveguide horn antenna generates ${\mathrm {TE}}_{11}, {\mathrm {TM}}_{01}$ and ${\mathrm {TE}}_{21}$ modes independently. The pair of identical dielectric lenses employing the Risley prism concept are axially rotated individually and/or simultaneously to steer the radiated beam. The parabolic phase correcting structure is used to further improve the performance of the steered patterns. Overall, this antenna structure steers ${\mathrm {TE}}_{11}, {\mathrm {TM}}_{01}$ and ${\mathrm {TE}}_{21}$ modes patterns which makes it an attracting solution for tracking and communication applications. The proposed antenna system has been prototyped and experimentally verified in a compact antenna test range (CATR) chamber. The measured conical scan angles for ${\mathrm {TE}}_{11}$ mode is found to be around $\pm 40{^\circ }$ with gain deviation less than 3 dB. The ${\mathrm {TM}}_{01}$ and ${\mathrm {TE}}_{21}$ modes are steered with a scan angle covering $\pm 30{^\circ }$ .

Surface frustrated Lewis pairs (SFLPs) provide a unique class of active sites that enable efficient gas-phase CO2 photocatalysis. How to tailor the reactivity of the SFLPs represents a major challenge, which the authors address here by single-site Bi3+ ion substitution of the SFLPs.

Abstract Introduction Chronic obstructive pulmonary disease (COPD) is the third leading cause of death worldwide. COPD results from chronic inflammation of the lungs. Current treatments, including physical and chemical therapies, provide limited results. Stem cells, particularly mesenchymal stem cells (MSCs), are used to treat COPD. Here, we evaluated the safety and efficacy of umbilical cord-derived (UC)-MSCs for treating COPD. Methods Twenty patients were enrolled, 9 at stage C and 11 at stage D per the Global Initiative for Obstructive Lung Disease (GOLD) classification. Patients were infused with 106 cells/kg of expanded allogeneic UC-MSCs. All patients were followed for 6 months after the first infusion. The treatment end-point included a comprehensive safety evaluation, pulmonary function testing (PFT), and quality-of-life indicators including questionnaires, the 6-min walk test (6MWT), and systemic inflammation assessments. All patients completed the full infusion and 6-month follow-up. Results No infusion-related toxicities, deaths, or severe adverse events occurred that were deemed related to UC-MSC administration. The UC-MSC-transplanted patients showed a significantly reduced Modified Medical Research Council score, COPD assessment test, and number of exacerbations. However, the forced expiratory volume in 1 s, C-reactive protein, and 6MWT values were nonsignificantly reduced after treatment (1, 3, and 6 months) compared with those before the treatment. Conclusion Systemic UC-MSC administration appears to be safe in patients with moderate-to-severe COPD, can significantly improve their quality of life, and provides a basis for subsequent cell therapy investigations. Trial registration ISRCTN, ISRCTN70443938 . Registered 06 July 2019

The coastline of the Ca Mau and Kien Giang provinces in the Vietnamese Mekong Delta has been severely eroded in recent decades. Pile–Rock Breakwaters (PRBWs) are among the most widely adopted structures for controlling shoreline erosion in this region. These structures are effective for wave energy dissipation, stimulating sediment accumulation, and facilitating the restoration of mangrove forests. These breakwaters are generally considered to be the best-engineering practice; however, there is currently insufficient scientific evidence with regard to specific structural design aspects. This can lead to PRBW structures being compromised when deployed in the field. This study used a physical model of a PRBW in a laboratory to investigate several design parameters, including crest width and working states (i.e., submerged, transition, and emerged), and investigated their relationship with the wave transmission coefficient, wave reflection coefficient, and wave energy dissipation. To investigate these relationships further, empirical formulas were derived for PRBWs under different sea states and crest widths to aid the design process. The results showed that the PRBW width had a significant influence on the wave energy coefficients. The findings revealed that the crest width of the breakwater was inversely proportional to the wave transmission coefficient (Kt) under the emerged state. The crest width was also proportional to the wave reduction efficiency and wave energy dissipation in both working states (i.e., the submerged and emerged states). The front wave disturbance coefficient (Kf) was found to be proportional to the wave reflection coefficient, and the wave height in front of the structure was found to increase by up to 1.4 times in the emerged state. The wave reflection coefficient requires special consideration to reduce the toe erosion in the structure. Lastly, empirical equations including linear and non-linear formulas were compared with previous studies for different classes of breakwaters. These empirical equations will be useful for understanding the wave transmission efficiency of PRBWs. The findings of this study provide important guidance for PRBW design in the coastal area of the Mekong Delta.

BACKGROUND: Long-lasting insecticidal hammocks (LLIHs) are being evaluated as an additional malaria prevention tool in settings where standard control strategies have a limited impact. This is the case among the Ra-glai ethnic minority communities of Ninh Thuan, one of the forested and mountainous provinces of Central Vietnam where malaria morbidity persist due to the sylvatic nature of the main malaria vector An. dirus and the dependence of the population on the forest for subsistence--as is the case for many impoverished ethnic minorities in Southeast Asia. METHODS: A social science study was carried out ancillary to a community-based cluster randomized trial on the effectiveness of LLIHs to control forest malaria. The social science research strategy consisted of a mixed methods study triangulating qualitative data from focused ethnography and quantitative data collected during a malariometric cross-sectional survey on a random sample of 2,045 study participants. RESULTS: To meet work requirements during the labor intensive malaria transmission and rainy season, Ra-glai slash and burn farmers combine living in government supported villages along the road with a second home at their fields located in the forest. LLIH use was evaluated in both locations. During daytime, LLIH use at village level was reported by 69.3% of all respondents, and in forest fields this was 73.2%. In the evening, 54.1% used the LLIHs in the villages, while at the fields this was 20.7%. At night, LLIH use was minimal, regardless of the location (village 4.4%; forest 6.4%). DISCUSSION: Despite the free distribution of insecticide-treated nets (ITNs) and LLIHs, around half the local population remains largely unprotected when sleeping in their forest plot huts. In order to tackle forest malaria more effectively, control policies should explicitly target forest fields where ethnic minority farmers are more vulnerable to malaria.