Miracle-dz/UniFAR-FTUs huggingface dataset:
Numerical Computations of Non-Newtonian Fluid Flow in Hexagonal Cavity With a Square Obstacle: A Hybrid Mesh–Based Study

Thermal flow phenomena in a double lid–driven enclosure have many potential applications in numerous engineering domains. The present article theoretically investigates the heat transfer analysis of power-law fluid in a hexagonal cavity embedded with a square obstacle. The upper and lower lid walls of the cavity are considered heated along with the walls of the centered embedded square, while the rest of the cavity walls are thermally insulated. In addition, the horizontal lid walls of the cavity are uniformly moving in opposite horizontal directions. The mathematical modeling of the nonlinear fluid has been developed considering the continuity, momentum, and energy equations subjected to the appropriate boundary conditions. Due to the nonlinearity of the governing partial differential equations and of the viscosity models, we use the numerical scheme based on the finite element method The stable finite element pair ( ℙ 2 / ℙ 1 ) has been selected for the discretization purpose. The discretized nonlinear system is solved with the Newton method in conjunction with a direct linear solver in the inner iterations. The thermal flow features are exposed via streamlines and temperature contours for a set of governing parameters such as Reynolds number (Re), Prandtl number ( Pr ) , and Grashof number (Gr) along with the power-law index (n). The values of local and average Nusselt numbers are calculated for the involved parameters. It is noted that the square obstacle has a strong impact for the formulation of streamlines and isotherms. Moreover, the power-law index has a strong impact on the values of the average Nusselt number and kinetic energy. The kinetic energy of the system increases with an increasing value of Gr and n, while Reynolds number has opposite effects on kinetic energy.
Allostatic Load in Gambel’s White Crowned Sparrow, Zonotrichia leucophrys gambelii: Relationships With Glucocorticoids

Regulation of energetic expenditure in a changing environment, considered here as allostatic load, is central to organism-environment interactions. The value of responses that modify behavior or physiology in coping strategies is often measured in terms of energetic benefits. In this study, the total energetic cost incurred by Gambel’s white-crowned sparrows, Zonotrichia leucophrys gambelii, was assessed using heart-rate transmitters. The use of heart rate was validated as a proxy for metabolic rate via flow-through respirometry. Applying heart rate as an indicator of allostatic load, we confirmed that ambient temperature under wintering conditions influences allostatic load. However, baseline corticosterone, proposed to mediate physiological responses to variation in allostatic load, does not appear to vary with heart rate or temperature in captivity, or with temperature under ambient conditions in the field. The relationship between allostatic load and plasma corticosterone levels was also investigated by manipulating feeding effort for captive Gambel’s white-crowned sparrows using a sand-excavation challenge that approximated a type of foraging work that these birds normally perform in the wild. This experiment was designed to test the hypothesis that experimentally increased allostatic load induces elevation in baseline corticosteroids. We did not find support for this hypothesis. We suggest that the adrenocortical response to increased allostatic load may be limited to overload or environmental conditions that meaningfully threaten energy imbalance, indicating new targets for further research.
Pseudomonas aeruginosa Detection Using Conventional PCR and Quantitative Real-Time PCR Based on Species-Specific Novel Gene Targets Identified by Pangenome Analysis

Mining novel specific molecular targets and establishing efficient identification methods are significant for detecting Pseudomonas aeruginosa, which can enable P. aeruginosa tracing in food and water. Pangenome analysis was used to analyze the whole genomic sequences of 2017 strains (including 1,000 P. aeruginosa strains and 1,017 other common foodborne pathogen strains) downloaded from gene databases to obtain novel species-specific genes, yielding a total of 11 such genes. Four novel target genes, UCBPP-PA14_00095, UCBPP-PA14_03237, UCBPP-PA14_04976, and UCBPP-PA14_03627, were selected for use, which had 100% coverage in the target strain and were not present in nontarget bacteria. PCR primers (PA1, PA2, PA3, and PA4) and qPCR primers (PA12, PA13, PA14, and PA15) were designed based on these target genes to establish detection methods. For the PCR primer set, the minimum detection limit for DNA was 65.4 fg/μl, which was observed for primer set PA2 of the UCBPP-PA14_03237 gene. The detection limit in pure culture without pre-enrichment was 105 colony-forming units (CFU)/ml for primer set PA1, 103 CFU/ml for primer set PA2, and 104 CFU/ml for primer set PA3 and primer set PA4. Then, qPCR standard curves were established based on the novel species-specific targets. The standard curves showed perfect linear correlations, with R2 values of 0.9901 for primer set PA12, 0.9915 for primer set PA13, 0.9924 for primer set PA14, and 0.9935 for primer set PA15. The minimum detection limit of the real-time PCR (qPCR) assay was 102 CFU/ml for pure cultures of P. aeruginosa. Compared with the endpoint PCR and traditional culture methods, the qPCR assay was more sensitive by one or two orders of magnitude. The feasibility of these methods was satisfactory in terms of sensitivity, specificity, and efficiency after evaluating 29 ready-to-eat vegetable samples and was almost consistent with that of the national standard detection method. The developed assays can be applied for rapid screening and detection of pathogenic P. aeruginosa, providing accurate results to inform effective monitoring measures in order to improve microbiological safety.
Uncommon Electromagnetic Radiations Related to Extra-High Voltage/Ultra-High Voltage Power Projects in China

Based on the high-resolution electromagnetic field intensity observed by the DEMETER satellite, the harmonic electromagnetic radiations in the ionosphere closely above the extra-high voltage and ultra-high voltage power projects in the Chinese power grid have been detected. In the time-frequency spectrograms, these radiations are characterized by parallel spectral lines with spacings of 50, 100, 150, or 600 Hz. The highest frequency can reach approximately 8,850 Hz. The radiations are probably attributed to the nonlinear devices such as converters or geomagnetic disturbance. The harmonic radiations over alternating current-to-direct current (AC-to-DC) substations of power system are generally more diverse and stronger than those over AC-to-AC substations. The measurements on the ground demonstrate that the weak magnetic radiations at several kilohertz with an intensity in the order of nT do exist around substations. A simple physical model is established to understand the characteristics of harmonic radiations propagating from the Earth’s surface to the ionosphere.
Diversity and Diversification: Ecosystem Services Derived From Underutilized Crops and Their Co-benefits for Sustainable Agricultural Landscapes and Resilient Food Systems in Africa

There are growing calls to adopt more sustainable forms of agriculture that balance the need to increase production with environmental, human health, and wellbeing concerns. Part of this conversation has included a debate on promoting and mainstreaming neglected and underutilized crop species (NUS) because they represent a more ecologically friendly type of agriculture. We conducted a systematic review to determine the ecosystem services derived from NUS and assess their potential to promote functional ecological diversity, food and nutritional security, and transition to more equitable, inclusive, sustainable and resilient agricultural landscapes and food systems in Africa. Our literature search yielded 35 articles for further analysis. The review showed that NUS provide various provisioning, regulating, cultural, and supporting ecosystem services and several environmental and health co-benefits, dietary diversity, income, sustainable livelihood outcomes, and economic empowerment, especially for women. Importantly, NUS address the three pillars of sustainable development- ecological, social, and economic. Thus, NUS may provide a sustainable, fit-for-purpose transformative ecosystem-based adaptation solution for Africa to transition to more sustainable, healthy, equitable, and resilient agricultural landscapes and food systems.
Artificial Intelligence and Feature Identification Based Global Perception of Power Consumer: Definition, Structure, and Applications

Excessive use of energy makes environmental problems increasingly prominent and gradually affects people’s production and life. Therefore, wind energy, solar energy, and other renewable new energy sources are gradually rising in the application of the power system, but these new energy sources are vulnerable to the environment, and the operation stability is poor, which seriously reduces the power quality, and cannot work well with the power grid, which restricts its development (Tu et al., 2018; Ren et al., 2020; Guo et al., 2021). With the overall improvement of family living standards, electric vehicles, electricity replacement, smart homes, and the popularity of new energy equipment, power load also presents rapid growth. Effective load monitoring can provide timely feedback on residents’ electricity situation, and then the network regulation to save electricity can effectively reduce the waste of energy (Zhang et al., 2019; Luo et al., 2020; Zheng et al., 2020). As an efficient and practical tool, artificial intelligence plays an important role in constructing the global perception of terminal electrical quantity feature recognition (Gao et al., 2018; Chahla et al., 2019; Liang et al., 2019). At present, many scholars conduct non-invasive load research through artificial intelligence-related technologies and use cloud terminals to build a cloud network of the power system. The overall steps can be seen in Figure 1. However, the practical application of the global perception of terminal power consumption will face many challenges. First, the current method used is only suitable for highfrequency sampling data, with high hardware requirements for the acquisition device. Second, intraclass differences between loads lead to insufficient generalization ability of existing load labels, and non-invasive load identification across datasets and regions is still difficult to achieve ideal results. This study clarifies the above issues and brings some views on different electrical feature identifications.
Sprouting Angiogenesis in Human Pituitary Adenomas

Introduction Angiogenesis in pituitary tumors is not fully understood, and a better understanding could help inform new pharmacologic therapies, particularly for aggressive pituitary tumors. Materials and Methods 219 human pituitary tumors and 12 normal pituitary glands were studied. Angiogenic genes were quantified by an angiogenesis qPCR array and a TaqMan probe-based absolute qPCR. Angiogenesis inhibition in pituitary tumors was evaluated in vitro with the endothelial tube formation assay and in vivo in RbΔ19 mice. Results 71 angiogenic genes, 40 of which are known to be involved in sprouting angiogenesis, were differentially expressed in pituitary tumors. Expression of endothelial markers CD31, CD34, and ENG was significantly higher in pituitary tumors, by 5.6, 22.3, and 8.2-fold, respectively, compared to in normal pituitary tissue. There was no significant difference in levels of the lymphatic endothelial marker LYVE1 in pituitary tumors compared with normal pituitary gland tissue. Pituitary tumors also expressed significantly higher levels of angiogenesis growth factors, including VEGFA (4.2-fold), VEGFB (2.2), VEGFC (19.3), PGF (13.4), ANGPT2 (9.2), PDGFA (2.7), PDGFB (10.5) and TGFB1 (3.8) compared to normal pituitary tissue. Expression of VEGFC and PGF was highly correlated with the expression of endothelial markers in tumor samples, including CD31, CD34, and ENG (endoglin, a co-receptor for TGFβ). Furthermore, VEGFR inhibitors inhibited angiogenesis induced by human pituitary tumors and prolonged survival of RbΔ19 mice. Conclusion Human pituitary tumors are characterized by more active angiogenesis than normal pituitary gland tissue in a manner consistent with sprouting angiogenesis. Angiogenesis in pituitary tumors is regulated mainly by PGF and VEGFC, not VEGFA and VEGFB. Angiogenesis inhibitors, such as the VEGFR2 inhibitor cabozantinib, may merit further investigation as therapies for aggressive human pituitary tumors.
Abnormal Visual Evoked Responses to Emotional Cues Correspond to Diagnosis and Disease Severity in Fibromyalgia

Background Chronic pain disorders are often associated with cognitive-emotional dysregulation. However, the relations between such dysregulation, underlying brain processes, and clinical symptom constellations, remain unclear. Here, we aimed to characterize the abnormalities in cognitive-emotional processing involved in fibromyalgia syndrome (FMS) and their relation to disease severity. Methods Fifty-eight participants, 39 FMS patients (35F), and 19 healthy control subjects (16F) performed an EEG-based paradigm assessing attention allocation by extracting steady-state visually evoked potentials (ssVEP) in response to affective distractors presented during a cognitive task. Patients were also evaluated for pain severity, sleep quality, depression, and anxiety. Results EEG ssVEP measurement indicated that, compared to healthy controls, FMS patients displayed impaired affective discrimination, and sustained attention to negative distractors. Moreover, patients displayed decreased task-related fronto-occipital EEG connectivity. Lack of adaptive attentional discrimination, measured via EEG, was predictive of pain severity, while impairments in fronto-occipital connectivity were predictive of impaired sleep. Conclusions FMS patients display maladaptive affective attention modulation, which predicts disease symptoms. These findings support the centrality of cognitive-emotional dysregulation in the pathophysiology of chronic pain.
Coverage and Latency Analysis for NB-IoT in Uplink Transmission

Narrowband internet of things (NB-IoT) is still in its infancy but gaining momentum compared to other existing Low power wide area (LPWA) technologies to capture the future IoT market. NB-IoT has attracted much attention because of providing low device cost, low energy consumption, comprehensive-area cellular connectivity. In uplink transmission, providing comprehensive area coverage with low latency and increased data rate is challenging when millions of devices are communicated simultaneously. However, to resolve this problem, the proposed work developed an uplink repetition-dominated model that provides enhanced coverage compared to existing technologies. Secondly, it increases the data rate by utilizing higher resource units with the same transmission time. The simulation results show that low latency can enhance data rate and coverage even in poor channel conditions.
Stress detection using non-semantic speech representation

In today's world, stress has become a prominent cause for many ailments. Automatic detection of stress from speech using state-of-the-art machine learning algorithms can facilitate early detection and prevention of stress. Artificial intelligence agents involved in affective computing and human-machine spoken interaction (HMI) might benefit from the capacity to identify human stress automatically. Despite the fact that several different methods have been established for stress detection, it is still unclear which auditory features should be considered for training a deep neural network (DNN) model. In this study, we propose to investigate the performance of traditional and modern auditory features for stress classification using the StressDat database. The StressDat database is a collection of acted speech recordings in Slovak realizing sentences within stress-prone situations in three different levels of stress. The per-formance of traditional auditory features such as Mel-Frequency Cepstral Coefficients (MFCC) and Perceptual Linear Prediction (PLP) are compared with modern auditory non-semantic speech representation such as x-vectors and TRIpLet Loss network (TRILL) vectors. As a benchmark, Low-level descriptors (LLD) auditory features are extracted using the OpenSMILE toolkit. We evaluated performance of four different automatic classification algorithms: support vector machine (SVM), multilayer perceptron (MLP), convolutional neural network (CNN), and long short-term memory (LSTM). The results reveal that TRILL vectors trained on CNN provide the highest accuracy (81.86 %).
Impact of Electrical Defects located at Transistor Periphery on Analog and RTN Device Performance

In this work, analog and RTN performance of transistors operating in low current conditions were reported. In subthreshold regime the RTN signal is driven by defects located at the STI edges. Experimental data show that reducing the amount of electrical impurities present at the device periphery, lead to RTN improvement without changing neither the transistor targeting nor its reliability. Furthermore, the transistor noise sensitivity to temperature and body bias are decreased. Empirical correlation between RTN and intrinsic gain is also discussed, which can be employed for fast inline process monitoring of defects present at the STI edges.
Influence of SiC epitaxial wafer quality on yield of 1.2kV SiC-DMOSFETs

This work presents the influence of SiC epitaxial wafer quality on yield of 1.2kV SiC-DMOSFETs. Various wafer quality indicators were estimated by an integrated wafer quality evaluation system. SiC-DMOSFETs were fabricated on six 100 mm 4H-SiC epitaxial wafers. We show that the yield of SiC-DMOSFETs is related to threading screw dislocation density, defect density and bunched-steps length density evaluated by an integrated wafer quality evaluation system. A one-to-one analysis of multiple inspection data for SiC epitaxial defects and wafer-level electrical test results reveals the main factors in the yield of SiC- MOSFETs.
Fast Measurement of BTI on 28nm Fully Depleted Silicon-On-Insulator MOSFETs at Cryogenic Temperature down to 4K

We report preliminary results on bias temperature instabilities (BTI) on 28FDSOI MOS transistors from 300K to 4K with ultra-fast measurements. Common models describe BTI as a temperature-activated effect. Consequently, BTI is expected to be suppressed at cryogenic temperature. However, our measurements show that some BTI degradation remains even at 4K, underlying another degradation mechanism that becomes predominant at low temperature for both PBTI and NBTI. We offer insight into its characteristic temperature and time scales. We study the effect of the back-gate voltage, and show that its use can mitigate BTI degradation. Finally, an anomalous signal is observed in small PMOS devices, which can be caused by a trapping from the gate that is revealed at low temperature.
Towards efficiency of a subjective evaluation for music source separation

In this paper we present a subjective evaluation of harmonic sound separation algorithms. Five primitive blind source separation algorithms with low-computational latency were chosen for the separation of harmonic parts of 20 songs. 10-second samples were chosen and evaluated by principles of MUSHRA methodology. We propose new guidelines for choosing percipients and recommendations for musical source separation assessment, based on statistical evaluation and background experience of listeners who participated in the subjective test.
Fe-Traps Influence on Time-dependent Breakdown Voltage in 0.1-μm GaN HEMTs for 5G Applications

Scaled (LG = 0.1 μm) GaN HEMT technology is currently pursued for high-frequency applications (such as 5G), requiring high current/speed and blocking capability. However, traps introduced with intentional Fe doping yield time-dependent breakdown voltage (VBR), seriously affecting reliability. Here, we investigate the role of Fe traps by pulsed I-V characterization performed at different pulse durations (TOFF). A TOFF-dependent VBR is observed on tested devices and is ascribed to the time-dependent occupancy of deep acceptors in the buffer layer. More specifically, the decrease in VBR for short pulses is attributed to the increased leakage due to the reduced ionization of Fe-traps. This interpretation is supported by 2D numerical simulations.
Middle-of-the-Line Reliability Characterization of Recessed-Diffusion-Contact Adopted sub-5nm Logic Technology

In this paper, we report middle-of-the-line (MOL) reliability characterization of recessed-diffusion-contacts adopted sub-5nm logic technology. The intrinsic MOL reliability including voltage-ramp-to-breakdown (Vramp) and time-dependent-dielectric-breakdown (TDDB) shows a notable improvement. MOL breakdown voltage (Vbd) is also comparable when a gate contact is placed next to a recessed-diffusion-contact. MOL Vramp related process parameters, such as critical dimension and height, are explained. MOL Vbd as an early diagnosis tool for process maturity, such as contact misalignment and dynamic voltage screening (DVS), on sub-5nm logic technology is addressed.
Monolithic 3D Integration of Oxide Semiconductor FETs and Memory Devices for AI Acceleration (Invited)

IoT edge devices are expected to have high-capacity and low-power storage memory, as well as smart data processing capability such as machine learning and AI, to fully utilize big-data by co-working with cloud computing system. Such edge intelligence requires energy-efficient data movement and computation, which opens new challenges and opportunities to integrated devices, circuits and systems. In this paper, we introduce monolithic 3D integration technologies of (1) high-density embedded memory and (2) in-memory computing, both of which are enabled by oxide semiconductor FETs and memory devices, for AI acceleration. We address key reliability issues regarding manufacturability for each technology.
A new approach to fast fault detection in power systems

This paper presents an integrated approach to the problem of power system fast fault detection and isolation. The idea is to use concepts from signal processing and wavelet theory to create fast and sensitive fault indicators. The indicators can then be analyzed by standard statistical hypothesis testing or by artificial neural nets to create intelligent decision rules. The approach described in this paper does not depend on the availability of an accurate mathematical model. Hence it is expected to be robust and of wide applicability. Results of the analysis of computer simulated faulty transmission lines using filter banks is included.