Miracle-dz/UniFAR-FTUs huggingface dataset:
Estimación de la vida útil de almacenamiento de carne de res y de cerdo con diferente contenido graso

Antecedentes: La estimación acertada de la vida útil del material cárnico es de gran relevancia para que las empresas definan su logística de abastecimiento. Conocer el efecto de la temperatura, la composición y el tiempo de almacenamiento sobre sus características fisicoquímicas, microbiológicas y sensoriales, permiten optimizar la gestión del almacenamiento y por lo tanto reducir pérdidas durante las etapas de suministro industrial. Objetivos: En el presente estudio se evaluó la vida útil de material cárnico de cerdo y res, en función de la temperatura, el tiempo de almacenamiento y su contenido graso. Métodos: Se desarrollaron modelos de superficie de respuesta para describir los efectos controlados de la temperatura y el tiempo de almacenamiento, sobre variables microbiológicas, fisicoquímicas y sensoriales, de carne deshuesada de res y cerdo con diferente contenido de grasa; el tiempo post-sacrificio fue evaluado como una covariable. Muestras con bajo contenido de grasa (entre 0 y 10%) y alto contenido de grasa (entre 20 y 30%), fueron empacadas en condiciones aeróbicas y almacenadas a temperaturas de -4, -2, 0, 4 y 8°C, entre 6 y 50 días, según la temperatura. Durante el tiempo de almacenamiento, se realizaron mediciones de variables fisicoquímicas (pH, croma C*ab, tono hab, malondialdehído (MDA)), microbiológicas (recuento de microorganismos mesófilos aerobios totales y enterobacterias) y sensoriales (olor y apariencia general). Resultados: El tiempo y la temperatura de almacenamiento fueron los factores más significantes, seguidos del contenido de grasa y el tiempo post sacrificio. Las variables microbiológicas y sensoriales presentaron los cambios más significativos y se usaron para estimar la vida útil de las muestras evaluadas. A altas temperaturas de almacenamiento (8 y 4°C), no se encontraron diferencias significativas entre la estabilidad de la carne de res y cerdo, sin embargo, a bajas temperaturas (0, -2 y -4°C) la carne de cerdo fue mucho más estable que la de res. Conclusiones: Los abusos de temperatura (por encima de 4°C) disminuyen considerablemente la vida útil de la carne deshuesada; a bajas temperaturas de refrigeración (entre 0 y -4°C) es posible alcanzar tiempos de vida útil mayores, incluso superiores a 50 días de almacenamiento.
Independent multichannel wavefront modulation for angle and polarization multiplexing through planar metasurfaces

The rapidly growing demand for miniaturized multifunctional devices and systems constantly brings new structural challenges to the research community. The planar metamaterials (metasurfaces) provide a revolutionary way for wavefront manipulation of electromagnetic waves and promise the achievement of superior degrees of freedom. Compared to conventional refractive optical elements, a distinct advantage of metasurfaces can be accomplished by embedding multi-functionality through encoding various sets of information into a single metasurface. Herein, we proposed a unique angle multiplexing strategy that provides wavefront modulation independently in a flat metasurface by controlling the incidence angle of the input light. The fundamental unit cell of the meta-platform of the proposed structure includes a rectangular tetrad with distinct structural geometry. Due to the proposed geometry's geometrical characteristics, the angle multiplexed phenomena are induced in the structure by the mutual coupling of tetrads made of hydrogenated amorphous silicon (a-Si:H). For the proposed metasurface, we exploited the meta-platform comprised of two Meta-atoms, one for positive incidence angle and another for negative incidence angle of light. To increase the applicability of the metasurface, we proposed a single multifunctional meta-device that will project the image of one information for one angle while another information for the opposite angle of the input light. The angle multiplexing phenomena designed for a wavelength can open promising avenues for real-time applications in switchable multifunctional devices, holography, information encryption, and security purposes.
New Solid Forms of Nitrofurantoin and 4-Aminopyridine Salt: Influence of Salt Hydration Level on Crystal Packing and Physicochemical Properties

The crystallization of the poorly soluble drug nitrofurantoin (NFT) with 4-aminopyridine (4AmPy) resulted in three multicomponent solid forms with different hydration levels: anhydrous salt [NFT+4AmPy] (1:1), salt monohydrate [NFT+4AmPy+H2O] (1:1:1), and salt tetrahydrate [NFT+4AmPy+H2O] (1:1:4). Each salt was selectively prepared by liquid-assisted grinding in the presence of acetonitrile or ethanol/water mixture at a specific composition. The NFT hydrated salts were characterized using single crystal X-ray diffraction. The [NFT+4AmPy+H2O] salt (1:1:1) crystallized as an isolated site hydrate, while the [NFT+4AmPy+H2O] salt (1:1:4) crystallized as a channel hydrate. The dehydration processes of the NFT salt hydrates were investigated using differential scanning calorimetry and thermogravimetric analysis. A powder dissolution experiment was carried out for all NFT multicomponent solid forms in pH 7.4 phosphate buffer solution at 37 °C.
A Class of Transformation Matrices and Its Applications

This paper studies a class of transformation matrices and its applications. Firstly, we introduce a class of transformation matrices between two different vector operators and give some important properties of it. Secondly, we consider its two applications. The first one is to improve Qian Jiling's formula. And the second one is to deal with the observability of discrete-time stochastic linear systems with Markovian jump and multiplicative noises. A new necessary and sufficient condition for the weak observability will be given in the second application.
Neural representation and modulation of volitional motivation in response to escalating efforts

Task‐dependent volitional control of the selected neural activity in the cortex is critical to neuroprosthetic learning to achieve reliable and robust control of the external device. The volitional control of neural activity is driven by a motivational factor (volitional motivation), which directly reinforces the target neurons via real‐time biofeedback. However, in the absence of motor behaviour, how do we evaluate volitional motivation? Here, we defined the criterion (ΔF/F) of the calcium fluorescence signal in a volitionally controlled neural task, then escalated the efforts by progressively increasing the number of reaching the criterion or holding time after reaching the criterion. We devised calcium‐based progressive threshold‐crossing events (termed ‘Calcium PTE’) and calcium‐based progressive threshold‐crossing holding‐time (termed ‘Calcium PTH’) for quantitative assessment of volitional motivation in response to progressively escalating efforts. Furthermore, we used this novel neural representation of volitional motivation to explore the neural circuit and neuromodulator bases for volitional motivation. As with behavioural motivation, chemogenetic activation and pharmacological blockade of the striatopallidal pathway decreased and increased, respectively, the breakpoints of the ‘Calcium PTE’ and ‘Calcium PTH’ in response to escalating efforts. Furthermore, volitional and behavioural motivation shared similar dopamine dynamics in the nucleus accumbens in response to trial‐by‐trial escalating efforts. In general, the development of a neural representation of volitional motivation may open a new avenue for smooth and effective control of brain–machine interface tasks.
Non-Steady State NMR Effect and Application on Time-Varying Magnetic Field Measurement

The measurement of a time-varying magnetic field is different from a constant magnetic field, due to its field intensity variation with time. Usually, the time-varying magnetic field measurement converts the solution of the magnetic induction intensity into the calculation of the induced electromotive force (EMF); then, the magnetic induction intensity is obtained by the time integration of the EMF, but the process is vulnerable to external interference. In this paper, a non-steady state nuclear magnetic resonance (NSS-NMR) scheme for the measurement of a time-varying magnetic field is proposed. In a time-varying magnetic field environment, an RF excitation signal with a certain frequency bandwidth is applied to excite the nuclear spin system. The NSS-NMR signal, which varies with time in the frequency range corresponding to the frequency bandwidth of the RF excitation, could finally be obtained after a series of processing of the probe output signal. During the NSS-NMR experiment, an orthogonal dual-coil probe is adopted to synchronously generate the RF excitation and induce the probe output signal. Moreover, a directional coupler that utilized in the experiment outputs a reference signal from the coupling port for the subsequent signal processing. The experimental results show that the weak NSS-NMR signal is indeed observed. The longitudinal time-varying magnetic field ranges from 0.576 T to 0.582 T, which is inverted by the Larmor precession relationship, have been successfully detected based on the so-called NSS-NMR effect.
Epidemiology and costs of dengue in Thailand: A systematic literature review

Background Dengue is the fastest-spreading vector-borne viral disease worldwide. In Thailand, dengue is endemic and is associated with a high socioeconomic burden. A systematic literature review was conducted to assess and describe the epidemiological and economic burden of dengue in Thailand. Methods Epidemiological and economic studies published in English and Thai between 2011–2019 and 2009–2019, respectively, were searched in MEDLINE, Embase, and Evidence-Based Medicines reviews databases. Reports published by the National Ministry of Public Health (MoPH) and other grey literature sources were also reviewed. Identified studies were screened according to predefined inclusion and exclusion criteria. Extracted data were descriptively summarised and reported following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Results A total of 155 publications were included in the review (39 journal articles and 116 grey literature). Overall, dengue incidence varied yearly, with the highest rates per 100,000 population in 2013 (dengue fever (DF) 136.6, dengue haemorrhagic fever (DHF) 100.9, dengue shock syndrome (DSS) 3.58) and 2015 (DF 133.1, DHF 87.4, DSS 2.14). Peak incidence coincided with the monsoon season, and annual mortality was highest for DSS, particularly in the age group 15–24-year-olds. The highest dengue incidence rates were reported in children (10–14-year-olds) and young adults (15-24-year-olds), irrespective of dengue case definition. Economic and societal burdens are extensive, with the average cost per case ranging from USD 41 to USD 261, total cost per year estimated at USD 440.3 million, and an average of 7.6 workdays lost for DHF and 6.6 days for DF. Conclusions The epidemiological, economic, and societal burden of dengue in Thailand is high and underreported due to gaps in national surveillance data. The use of expansion factors (EFs) is recommended to understand the true incidence of dengue and cost-benefit of control measures. Furthermore, as dengue is often self-managed and underreported, lost school and workdays result in substantial underestimation of the true economic and societal burden of dengue. The implementation of integrated strategies, including vaccination, is critical to reduce the disease burden and may help alleviate health disparities and equity challenges posed by dengue.
Artificial Intelligence in Emergency Radiology: Where Are We Going?

Emergency Radiology is a unique branch of imaging, as rapidity in the diagnosis and management of different pathologies is essential to saving patients’ lives. Artificial Intelligence (AI) has many potential applications in emergency radiology: firstly, image acquisition can be facilitated by reducing acquisition times through automatic positioning and minimizing artifacts with AI-based reconstruction systems to optimize image quality, even in critical patients; secondly, it enables an efficient workflow (AI algorithms integrated with RIS–PACS workflow), by analyzing the characteristics and images of patients, detecting high-priority examinations and patients with emergent critical findings. Different machine and deep learning algorithms have been trained for the automated detection of different types of emergency disorders (e.g., intracranial hemorrhage, bone fractures, pneumonia), to help radiologists to detect relevant findings. AI-based smart reporting, summarizing patients’ clinical data, and analyzing the grading of the imaging abnormalities, can provide an objective indicator of the disease’s severity, resulting in quick and optimized treatment planning. In this review, we provide an overview of the different AI tools available in emergency radiology, to keep radiologists up to date on the current technological evolution in this field.
Nuclear S6K1 Enhances Oncogenic Wnt Signaling by Inducing Wnt/β-Catenin Transcriptional Complex Formation

Ribosomal protein S6 kinase 1 (S6K1), a key downstream effector of the mammalian target of rapamycin (mTOR), regulates diverse functions, such as cell proliferation, cell growth, and protein synthesis. Because S6K1 was previously known to be localized in the cytoplasm, its function has been mainly studied in the cytoplasm. However, the nuclear localization and function of S6K1 have recently been elucidated and other nuclear functions are expected to exist but remain elusive. Here, we show a novel nuclear role of S6K1 in regulating the expression of the Wnt target genes. Upon activation of the Wnt signaling, S6K1 translocated from the cytosol into the nucleus and subsequently bound to β-catenin and the cofactors of the Wnt/β-catenin transcriptional complex, leading to the upregulation of the Wnt target genes. The depletion or repression of S6K1 downregulated the Wnt target gene expression by inhibiting the formation of the Wnt/β-catenin transcriptional complex. The S6K1-depleted colon cancer cell lines showed lower transcription levels of the Wnt/β-catenin target genes and a decrease in the cell proliferation and invasion compared to the control cell lines. Taken together, these results indicate that nuclear S6K1 positively regulates the expression of the Wnt target genes by inducing the reciprocal interaction of the subunits of the transcriptional complex.
Multimode-Multilevel Bidirectional Converter for Target Battery Equalization in a Tandem Connected Battery Units

Battery equalizers are crucial for improving battery life, performance and provides safety in long run. The paper proposes a Multimode-Multilevel Bidirectional Converter (MMBE), for active battery voltage equalization that can operate multiple legs at a time along with its auxiliary circuits for achieving faster and targeted balancing. Switching algorithm of the converter is effectively controlled by setting the thresholds of voltage difference and enabling multiple current loops to target the energy flow towards the weakest cells with customized gradients. The proposed topology and control are implemented on the MATLAB platform, and the results confirm the effectiveness and quick dynamics of the proposed equalization system.
Highly efficient terahertz wave modulators by photo-excitation of organics/silicon bilayers

Using hybrid bilayer systems comprising a molecular organic semiconductor and silicon, we achieve optically controllable active terahertz (THz) modulators that exhibit extremely high modulation efficiencies. A modulation efficiency of 98% is achieved from thermally annealed C60/silicon bilayers, due to the rapid photo-induced electron transfer from the excited states of the silicon onto the C60 layer. Furthermore, we demonstrate the broadband modulation of THz waves. The cut-off condition of the system that is determined by the formation of efficient charge separation by the photo-excitation is highly variable, changing the system from insulating to metallic. The phenomenon enables an extremely high modulation bandwidth and rates of electromagnetic waves of interest. The realization of near-perfect modulation efficiency in THz frequencies opens up the possibilities of utilizing active modulators for THz spectroscopy and communications.
AI-assisted Synthesis in Next Generation EDA: Promises, Challenges, and Prospects

Despite the great advance achieved by electronic design automation (EDA) tools, there is still a long way towards hardware agile development, whose ultimate goal is to reduce chip development cycles from years to months or even weeks. Hardware development typically involves many optimization-evaluation iterations, indicating that (1) fast and accurate quality-of-result (QoR) evaluation and (2) efficient optimization, either independently or integrally, will conspicuously improve the development efficiency. Specifically, targeting high-level synthesis and logic synthesis, we investigate (1) the power of exploiting graph neural networks (GNNs) for generalizable and accurate performance predictions, (2) the efficacy of applying reinforcement learning (RL) for design exploration, and (3) the superiority of combining GNN and RL to solve EDA problems. Experimental results demonstrate the promises of infusing intelligence into design synthesis and EDA tools. On top of current endeavors, we summarize the challenges in the respective EDA contexts and the prospects toward next generation EDA tools.
The Madden–Julian Oscillation Observed during the TOGA COARE IOP: Global View

Abstract During the TOGA COARE Intensive Observing Period (November 1992–February 1993), two pronounced Madden–Julian oscillation (MJO) events associated with super cloud clusters and westerly wind bursts were observed. This paper presents a global view of the MJOs including the origin of the super clusters in the Indian Ocean, their migration into the Maritime Continent and the TOGA COARE large-scale soundings array (LSA) in the western equatorial Pacific, and their rapid decay over cold water of the eastern Pacific. The structure and evolution of the MJO are examined with emphasis on the coupling between large-scale motion and convection. Because of differences in propagation speeds, the positions of maximum zonal wind perturbations relative to deep convection undergo systematic changes during the travel of the MJO. However, the centers of deep convection always coincide with those of large-scale ascent. The super cloud cluster accompanies a wide area of warm air in the upper troposphere. Over the warm .
Design of Advanced Multi-loop PI Controller for Multi-delay Processes

An analytical method for robust design of the multi-loop proportional-integral (PI) controller is proposed for various types of multi-delay processes. On the basis of the direct synthesis and generalized IMC-PID approach, the analytical tuning rules of the multi-loop PI controller are firstly derived for achieving the desired closed-loop response, and the structured singular value synthesis is then utilized for the tradeoffs between the robust stability and performance by adjusting only one design parameter (i.e., the closed-loop time constant). To verify the superiority of the proposed method, the simulation studies have been conducted on a wide variety of multivariable processes. The multi-loop PI controller designed by the proposed method shows a fast, well-balanced and robust response with the minimum integral absolute error (IAE) in compared with other renowned methods.
Machine Learning-Based Anomaly Detection for Multivariate Time Series With Correlation Dependency

Recent advances in data collection facilitate the acquisition of large quantities of multivariate time series (MTS) data from various real-world systems. Anomaly detection in high-dimensional MTS data is essential to improving the productivity and safety of such systems; however, capturing the complex intercorrelations between different pairs of time series related to anomalous patterns is challenging. In this study, two different anomaly detection problems—mean shift and structural change—were defined based on the correlation dependency of MTS. Existing algorithms were experimentally analyzed and compared based on their correlation dependency encoding methods using synthetic datasets, with the results revealing that the explicit encoding of correlation dependency improves the predictive performance of anomaly detection in MTS data.
Review of the Treatments for Central Neuropathic Pain

Central neuropathic pain (CNP) affects millions worldwide, with an estimated prevalence of around 10% globally. Although there are a wide variety of treatment options available, due to the complex and multidimensional nature in which CNP arises and presents symptomatically, many patients still experience painful symptoms. Pharmaceutical, surgical, non-invasive, cognitive and combination treatment options offer a generalized starting point for alleviating symptoms; however, a more customized approach may provide greater benefit. Here, we comment on the current treatment options that exist for CNP and further suggest the need for additional research regarding the use of biomarkers to help individualize treatment options for patients.
Empirical likelihood intervals for conditional Value‐at‐Risk in ARCH/GARCH models

Value‐at‐Risk (VaR) is a simple, but useful measure in risk management. When some volatility model is employed, conditional VaR is of importance. As autoregressive conditional heteroscedastic (ARCH) and generalized ARCH (GARCH) models are widely used in modelling volatilities, in this article, we propose empirical likelihood methods to obtain an interval estimation for the conditional VaR with the volatility model being an ARCH/GARCH model.
RTS-GAT: Spatial Graph Attention-Based Spatio-Temporal Flow Prediction for Big Data Retailing

Intelligent logistics is a crucial element in the era of innovative retailing. The retail industry may benefit from more sophisticated logistics forecasting, management, and collaboration to lower costs, boost efficiency, and improve service standards. It is vital to intelligent retail’s optimization of logistics and retail product transportation. Decoupling the retail road network traffic flow big data from the temporal or spatial dimension and creating strong correlations are the keys to effective modeling since it is a high-dimensional, Spatio-temporal sequence. While the spatial dimension does not correlate with the temporal dimension of the retail road network traffic flow, the temporal dimension varies continuously. This feature allows us to separate the spatial-temporal sequence of retail road network traffic flow into spatial graph data and model it primarily using the spatial correlation paradigm. In this study, we examine the variables that affect the retail road network’s traffic flow based on the demand for human travel. And we suggest a unique deep learning-based representation and fusion method of temporal, spatial, and traffic flow features as features of the spatial graph nodes of the shared spatial graph neural network framework RTS-GAT. Individual primitive features are restricted to be simultaneously learned and optimized in the feature space associated with each other in the modeling context of shared model parameters, and efficient eigenfeature representations are learned. After rigorous and comprehensive experimental validation, the RTS-GAT model achieves the best performance to date on multiple datasets.