Miracle-dz/UniFAR-FTUs huggingface dataset:
Reinforcement learning and its application to control

Learning control involves modifying a controller's behavior to improve its performance as measured by some predefined index of performance (IP). If control actions that improve performance as measured by the IP are known, supervised learning methods, or methods for learning from examples, can be used to train the controller. But when such control actions are not known a priori, appropriate control behavior has to be inferred from observations of the IP. One can distinguish between two classes of methods for training controllers under such circumstances. Indirect methods involve constructing a model of the problem's IP and using the model to obtain training information for the controller. On the other hand, direct, or model-free, methods obtain the requisite training information by observing the effects of perturbing the controlled process on the IP. Despite its reputation for inefficiency, we argue that for certain types of problems the latter approach, of which reinforcement learning is an example, can yield faster, more reliable learning. Using several control problems as examples, we illustrate how the complexity of model construction can often exceed that of solving the original control problem using direct reinforcement learning methods, making indirect methods relatively inefficient. These results indicate the importance of considering direct reinforcement learning methods as tools for learning to solve control problems. We also present several techniques for augmenting the power of reinforcement learning methods. These include (1) the use of local models to guide assigning credit to the components of a reinforcement learning system, (2) implementing a procedure from experimental psychology called "shaping" to improve the efficiency of learning, thereby making more complex problems amenable to solution, and (3) implementing a multi-level learning architecture designed for exploiting task decomposability by using previously-learned behaviors as primitives for learning more complex tasks.
Organization of histo-hematic barriers of the liver in Anglo-Nubian goat

The aim of this research was to establish features of the liver histo-hematic barriers ultrastructural organization of the Anglo-Nubian goat. The liver of an adult Anglo-Nubian goat was used as the material. The work was carried out using the electron microscopic method. Liver parenchymal tissue fragments were selected. These samples were fixed in a 2.0% glutaraldehyde solution on a cacodylate buffer for two hours. They were then washed in three portions of the same buffer and post-fixed in a 1.0% solution of osmium tetrachloride for one hour. The samples were then dehydrated in alcohols of ascending concentration and absolute acetone. The subsequent filling of the fragments was carried out in Epon-812. Ultrathin sections were obtained on an ultramicrotome, contrasted with a 2.0% aqueous solution of uranyl acetate and a solution of lead citrate. The ultrathin sections were photographed with a Jem-1011 electron microscope at magnifications of 2500-3000. Two histo-hematic barriers are detected in the liver of the studied animals hemato-hepatic and hepatobiliary. The hemato-hepatic barrier is formed by the plasmalemma of the apical end of the hepatocyte, covered by the glycocalyx, the perisinusoidal space of the Disse, the endotheliocyte of the sinusoid capillary, as well as Kupfer cells located in the lumen of the latter. The hepatobiliary includes all of the above structures, with the exception of Kupfer cells, as well as the plasmalemma of the basal end of the hepatocyte. All of the above structures in their organization have characteristic species features for Anglo-Nubian goats.
A novel frequency warping scale for speech emotion recognition

We investigate an optimised non-linear frequency warping scale for speech emotion recognition (SER). The proposed scale maps the speech spectrogram onto another time-frequency domain which is invariant to speaker-specific variations. Generally, the famous mel-scale designed on human audio perception is considered the de facto standard of frequency warping. However, designed mainly for speech recognition, the gener-alisability of mel on other speech processing tasks is debatable. Our experiments show that an emotion-specific scale designed on an SER database outperforms the standard mel-scale. Along with performance improvement, the proposed approach also provides insight into the emotion-relevant frequency regions for SER. Despite the database-dependent design of our approach, we find that the scale obtained from our experiments also shows SER performance improvement when tested on two other databases.
Time-Triggered Reservation for Cooperative Random Access in Wireless LANs

In this paper, a novel time-triggered reservation scheme is proposed to improve the performance of IEEE 802.11 medium access control (MAC) protocol. The proposed Reservation-based Distributed Channel Access (RDCA) protocol leverages the overhearing feature of the shared wireless channel and allows reservation-based data transmission with collision resolution. The reservation is realized in a time-triggered manner by setting extra timers on mobile stations. Through time-triggered reservation, a collision-free transmission scheduling can be achieved in a collaborative way without the assistance from the access point (AP). RDCA substantially reduces the delay and can function as the MAC layer for the wireless extension of time-sensitive networking (TSN). Meanwhile, the throughput of the network is increased due to the reduction of collisions. Moreover, RDCA also maintains backward compatibility with the legacy IEEE 802.11 terminals. The analysis of saturated throughput is provided and the performance gain in terms of delay and throughput is evaluated by extensive simulations. Compared to legacy IEEE 802.11, the throughput in RDCA increases up to 50% and the delay reduces up to 70%.
An Overview of Smart Irrigation Management for Improving Water Productivity under Climate Change in Drylands

Global drylands, covering about 41% of Earth’s surface and inhabited by 38% of the world’s population, are facing the stark challenges of water scarcity, low water productivity, and food insecurity. This paper highlights the major constraints to agricultural productivity, traditional irrigation scheduling methods, and associated challenges, efforts, and progress to enhance water use efficiency (WUE), conserve water, and guarantee food security by overviewing different smart irrigation approaches. Widely used traditional irrigation scheduling methods (based on weather, plant, and soil moisture conditions) usually lack important information needed for precise irrigation, which leads to over- or under-irrigation of fields. On the other hand, by using several factors, including soil and climate variation, soil properties, plant responses to water deficits, and changes in weather factors, smart irrigation can drive better irrigation decisions that can help save water and increase yields. Various smart irrigation approaches, such as artificial intelligence and deep learning (artificial neural network, fuzzy logic, expert system, hybrid intelligent system, and deep learning), model predictive irrigation systems, variable rate irrigation (VRI) technology, and unmanned aerial vehicles (UAVs) could ensure high water use efficiency in water-scarce regions. These smart irrigation technologies can improve water management and accelerate the progress in achieving multiple Sustainable Development Goals (SDGs), where no one gets left behind.
MmWave Vehicular Beam Alignment Leveraging Online Learning

Beam alignment accuracy is critical for millimeter wave communications employing beamforming techniques. However, due to the high cost and complexity of the traditional solutions, especially beam sweeping, learning-based methods that use contextual data to select optimum beam orientations are emerging as a desirable technology for mobility applications such as vehicle to everything. However, the majority of available systems involve supervised learning, in which the training data is acquired ahead of time. In this study, we create an online learning algorithm for beam pair selection using a multi-armed bandit framework. The learning machine recognizes beam directions in a specified codebook separated by 3 dB beamwidths and makes recommendations for a set of beam combinations to complete beam training. In vehicular communication applications, the learning algorithm’s robustness to location inaccuracy provides a trade-off between overhead and alignment efficiency.
Temporomandibular Joint Space Changes in Skeletal Class III Malocclusion Patients with Orthognathic Surgery

The purpose of this retrospective study was to evaluate changes in the temporomandibular joint spaces (TMJSs) in skeletal Class III adult patients with orthognathic surgery using cone-beam computed tomography (CBCT). CBCT images taken from 26 orthognathic surgery adult patients (15 females, 11 males, average 19.6 ± 2.8 years at pretreatment, range 15.8–26.8 years) with skeletal Class III malocclusion (ANB < 1°) were used for this study. TMJSs (AS, anterior space; SS, superior space; PS, posterior space; MS, medial space; CS, central space; LS, lateral space) were measured at each stage of treatment (T0, pretreatment; T1, presurgery; T2, postsurgery; T3, posttreatment, and T4, retention) and were compared according to gender, side, vertical skeletal pattern, number of surgery sites, and amount of mandibular setback. At T0, TMJSs were significantly greater in SS than in AS and PS. The ratio of AS to SS to PS was 1.0 to 1.5 to 1.1. TMJSs were significantly greater in MS and CS than in LS. The ratio of MS to CS to LS was 1.0 to 1.0 to 0.8. All TMJSs in males were significantly greater than in females except in PS. TMJSs on the left side were significantly greater than on the right side only in PS. TMJSs were not significantly different depending on the SN-MP, number of surgery sites, and amount of setback. From T0 to T4, there were no significant changes in TMJSs or their ratios according to gender, side, sella to nasion (SN), mandibular plane (MP), number of surgery sites, and amount of setback. Exceptionally, at T4, SS and CS were significantly greater in the small amount of setback group than in the large amount of setback group. There were no statistical changes in TMJSs throughout all stages when skeletal Class III patients were treated with surgery.
Optical Dual Gas Sensor for Simultaneous Detection of Nitric Oxide and Oxygen

This work presents a new optical dual sensor based on PtTFPP-containing electrospun fibers and CsPbBr3 perovskite quantum dots (PQDs) for simultaneous detection of oxygen (O2) and nitric oxide (NO) gases, wherein PtTFPP-containing electrospun fibers for O2 sensing was based on electrospinning process fabricated by platinum(II) meso-tetrakis (pentafluorophenyl) porphyrin (PtTFPP) complex immobilized in cellulose acetate (CA) matrix. CsPbBr3 PQDs were used as NO-sensitive material and coated on the surface of PtTFPP-containing electrospun fibers. Both materials were excited by a UV LED with a central wavelength of 380 nm, and the fluorescence intensities of sensing materials were recorded and analyzed with a spectrometer. The experiment results show that the optical NO and O2 sensors have linear Stern–Volmer plots, and the sensitivities are around 2.7 and 10.7, respectively. The response and recovery times of the optical NO sensor are 71 and 109 s, respectively. For optical O2, response and recovery times are 60 and 65 s, respectively. The optical dual sensor with a new method based on fluorescent dye containing electrospun fibers and coated with CsPbBr3 PQDs has been successfully developed to detect NO and O2 gases simultaneously. The optical dual gas sensor provides great potential for practical applications with low cost and ease of fabrication.
Design of Double-Cladding Heterogeneous 2LP-Mode 6-Core Fiber with Two-Ring Layout

Double-cladding heterogeneous 2LP-mode 6-core fibers (2LP-6CFs) with a two-ring layout are investigated. By optimizing core parameters in $125-\mu\mathrm{m}$ cladding diameter, crosstalk in 2LP-6CF can be suppressed compared with that in heterogeneous 2LP-6CFs without double-cladding layer.
Supervised Contrastive Learning with Nearest Neighbor Search for Speech Emotion Recognition

Speech Emotion Recognition (SER) is a challenging task due to limited data and blurred boundaries of certain emotions. In this paper, we present a comprehensive approach to improve the SER performance throughout the model lifecycle, including pre-training, fine-tuning, and inference stages. To address the data scarcity issue, we utilize a pre-trained model, wav2vec2.0. During fine-tuning, we propose a novel loss function that combines cross-entropy loss with supervised contrastive learning loss to improve the model's discriminative ability. This approach increases the inter-class distances and decreases the intra-class distances, mitigating the issue of blurred boundaries. Finally, to leverage the improved distances, we propose an interpolation method at the inference stage that combines the model prediction with the output from a k-nearest neighbors model. Our experiments on IEMOCAP demonstrate that our proposed methods outperform current state-of-the-art results.
Modified Taylor Impact Tests with Profiled Copper Cylinders: Experiment and Optimization of Dislocation Plasticity Model

Current progress in numerical simulations and machine learning allows one to apply complex loading conditions for the identification of parameters in plasticity models. This possibility expands the spectrum of examined deformed states and makes the identified model more consistent with engineering practice. A combined experimental-numerical approach to identify the model parameters and study the dynamic plasticity of metals is developed and applied to the case of cold-rolled OFHC copper. In the experimental part, profiled projectiles (reduced cylinders or cones in the head part) are proposed for the Taylor impact problem for the first time for material characterization. These projectiles allow us to reach large plastic deformations with true strains up to 1.3 at strain rates up to 105 s−1 at impact velocities below 130 m/s. The experimental results are used for the optimization of parameters of the dislocation plasticity model implemented in 3D with the numerical scheme of smoothed particle hydrodynamics (SPH). A Bayesian statistical method in combination with a trained artificial neural network as an SPH emulator is applied to optimize the parameters of the dislocation plasticity model. It is shown that classical Taylor cylinders are not enough for a univocal selection of the model parameters, while the profiled cylinders provide better optimization even if used separately. The combination of different shapes and an increase in the number of experiments increase the quality of optimization. The optimized numerical model is successfully validated by the experimental data about the shock wave profiles in flyer plate experiments from the literature. In total, a cheap, simple, but efficient route for optimizing a dynamic plasticity model is proposed. The dislocation plasticity model is extended to estimate grain refinement and volume fractions of weakened areas in comparison with experimental observations.
Calificación y optimización para la generación de un láser aleatorio con nano emisores

En este trabajo se estudia el efecto láser sin espejos, el láser aleatorio de percolación en 3D. La diferencia con láser convencional es que el láser aleatorio contiene estructuras desordenadas que definen la frecuencia y la dirección en las que el láser aleatorio emite la luz y la emisión de este láser es coherente y estable. Se estudia el sistema semiclásico no lineal de las ecuaciones de Maxwell en 3D acoplado con ecuaciones de polarización y ecuaciones de cuatro niveles de los puntos cuánticos. Tal sistema es considerablemente no lineal, es complejo y no existe ninguna solución analítica. Por eso es de interés la generación del campo de láser numéricamente con el método de Diferencias Finitas en el Dominio del Tiempo (FDTD) 3D y con ayuda de los paquetes numéricos modernos microsoft visual studio (Visual C#). Se calcula de manera detallada el espectro de los modos ópticos localizados en el láser aleatorio de percolación.
Physical Activity and Cardiovascular Disease Prevention: Current Recommendations

Epidemiologic studies support an inverse and robust relationship between physical activity (PA) and mortality risk, even after adjusting for potential confounding factors. The authors have reported 13% lower mortality risk for every 1-MET (metabolic equivalent) increase in exercise capacity. For those with an exercise capacity >7 METs, the mortality risk was approximately 50% to 70% lower when compared with those achieving <5 METs. The risk reduction is, at least in part, attributed to the favorable effect of PA on the cardiovascular risk factors. Increased PA lowers blood pressure in hypertensive individuals, increases high-density lipoprotein cholesterol in a dose—response fashion, and reduces the incidence of diabetes. The health benefits of PA can be realized by engaging in moderate-intensity physical activity (brisk walk) for at least 30 minutes per day, 5 days per week or vigorous activity (jogging) for 20 or more minutes, 3 days per week. Combinations of the 2 types of activity can also be performed.
Improving Joint Speech and Emotion Recognition Using Global Style Tokens

Automatic speech recognition (ASR) and speech emotion recognition (SER) are closely related in that the acoustic features of speech, such as pitch, tone, and intensity, can vary according to the speaker’s emotional state. Our study focuses on a joint ASR and SER task, in which an emotion token is tagged and recognized along with the text. To further improve the joint recognition performance, we propose a novel training method that adopts the global style tokens (GSTs). The style embedding is extracted from the GSTs module to enhance the joint ASR and SER model to capture emotional information from speech. Specifically, a conformer-based joint ASR and SER model pre-trained on a large-scale dataset is jointly fine-tuned with style embedding to improve both ASR and SER. The experimental results on the IEMOCAP dataset showed that the proposed model achieves a word error rate of 15.8% and four emotion classification weighted and unweighted accuracy of 75.1% and 76.3%, respectively.
Joint Beamforming and Metasurface Reflection: A Lightweight Design for Energy Efficiency via Deep Reinforcement Learning

Intelligent reflecting surfaces (IRSs) continue to gain a growing research interest for their potential to support next-generation wireless communications without incurring additional power consumption. In this work, we propose a deep reinforcement learning (DRL)-driven and IRS-aided active/passive beamforming solution for multi-user multiple-input single-output (MISO) settings in beyond 5G networks, which is both lightweight and energy-efficient. The proposed solution is based on a hybrid finely-engineered design that leverages two Twin-Delayed DDPG (TD3) agents. Compared to classical optimization techniques, our numerical evaluation shows that the proposed DRL approach achieves 60% reduction in online computation complexity at the expense of only 1 dB higher power consumption.
Building resilience to extreme weather and climate events in the rural water and wastewater sectors

Extreme weather and climate events pose significant risks to rural water and wastewater systems. We examine the vulnerability of the water sector to weather and climate extremes in rural, predominantly Indigenous and underserved coastal areas and analyze how networks support resilience. Drawing on the analysis of 39 web‐based questionnaire responses and 19 interviews with rural water and wastewater managers and service providers in southern Louisiana and western Alaska, this article reports a range of interrelated historical, environmental, and social factors that influence vulnerability to extreme weather events. Formal and informal social networks serve multiple roles in building resilience. These roles include building technical and financial capacities, supporting emergency response and operational‐ to long‐term planning, fostering data collection and monitoring, supporting information sharing and innovative research, and providing institutional support. Results from this research enrich our understanding of the social, relational, and networking processes that condition community resilience to extreme weather events.
Prognostic and clinicopathological role of RACK1 for cancer patients: a systematic review and meta-analysis

Background The receptor for activated C kinase 1 (RACK1) expression is associated with clinicopathological characteristics and the prognosis of various cancers; however, the conclusions are controversial. As a result, this study aimed to explore the clinicopathological and prognostic values of RACK1 expression in patients with cancer. Methodology PubMed, Embase, Web of Science, Cochrane Library, and Scopus were comprehensively explored from their inception to April 20, 2023, for selecting studies on the clinicopathological and prognostic role of RACK1 in patients with cancer that met the criteria for inclusion in this review. Pooled hazard ratios (HRs) and 95% confidence intervals (CIs) were used to assess the prognosis-predictive value of RACK1 expression, while pooled odds ratios (ORs) and 95% CIs were used to evaluate the correlation between RACK1 expression and the clinicopathological characteristics of patients with cancer. The quality of the included studies was evaluated using the Newcastle-Ottawa Scale. Results Twenty-two studies (13 on prognosis and 20 on clinicopathological characteristics) were included in this systematic review and meta-analysis. The findings indicated that high RACK1 expression was significantly associated with poor overall survival (HR = 1.62; 95% CI, 1.13–2.33; P = 0.009; I2 = 89%) and reversely correlated with disease-free survival/recurrence-free survival (HR = 1.87; 95% CI, 1.22–2.88; P = 0.004; I2 = 0%). Furthermore, increased RACK1 expression was significantly associated with lymphatic invasion/N+ stage (OR = 1.74; 95% CI, 1.04–2.90; P = 0.04; I2 = 79%) of tumors. Conclusions RACK1 may be a global predictive marker of poor prognosis in patients with cancer and unfavorable clinicopathological characteristics. However, further clinical studies are required to validate these findings.