Miracle-dz/UniFAR-FTUs huggingface dataset:
Load Mismatch Compensation of Doherty Power Amplifier Using Dual-Input and Mode Reconfiguration Techniques

Doherty power amplifier (DPA) has shown great potentials in wireless communication systems. However, the output power and efficiency of a DPA are very susceptible to load mismatch. This paper presents the load mismatch analysis and compensation of coupler-based DPAs leveraging on dual-input and mode reconfiguration techniques. A comprehensive analysis, which takes voltage clipping into consideration, is conducted on a coupler-based dual-input DPA (CBDI-DPA) under load mismatch condition. It is illustrated that the parallel Doherty mode could recover the saturation and back-off drain efficiencies (DEs) for the load mismatch in the left and right half parts of the Smith chart, respectively. And the serial Doherty mode could recover the back-off and saturation DEs for the load mismatch in the left and right half parts of the Smith chart, respectively. As a validation, a CBDI-DPA operating at 2.4 GHz is fabricated and measured in this paper. Under the optimal load impedance condition, the fabricated CBDI-DPA delivers a saturation power of more than 43.5 dBm, a saturation DE of more than 70% and a 6 dB back-off DE of more than 60% in both the parallel and the serial Doherty modes. Meanwhile, the simulation and experimental results demonstrate the proposed method can recover the output power and efficiency of a DPA when the load mismatch is inside the 2:1 VSWR circle.
Improved continuous wavelet analysis of variation in the dominant period of hydrological time series

Abstract Two key issues of continuous wavelet transform (CWT), the choice of wavelet basis function and the determination of analytic procedure of CWT, were studied and approaches to solve them proposed. Then, the improved CWT method was used to reveal the periodic characteristics of several typical hydrological series, including runoff and precipitation data measured at diverse sampling rates. Finally, the results of periods identified by three methods were compared, and the variation of the first main period (FMP) with length of the annual hydrological series was investigated. The results indicate that hydrological time series show both global and local characteristics. Comparatively, the latter are more complicated and difficult to describe, because of their frequent manifestations of irregular phenomena. Moreover, the variation of FMP just reflects the complicated local characteristics of the hydrological series. In summary, this study improves the understanding of complicated hydrological processes, whereas description and simulation of the local characteristics of hydrological series should be the focus of future research. Editor D. Koutsoyiannis Citation Sang, Y.F. , Wang, D. , Wu, J.-C. , Zhu, Q. -P., and Wang, L. , 2013. Improved continuous wavelet analysis of variation in the dominant period of hydrological time series. Hydrological Sciences Journal, 58 (1), 1–15.
Application of linear-scale differential analysis in phase correlation method of image stitching

A robust method of creation of panoramic images which does not consume much processing resources is proposed and investigated. The phase correlation (PC) method is taken as a basic one because of simplicity of its algorithm and low computing time due to application of FFT technique. Standard PC procedure is modified by preprocessing of source frames of panoramic images in spatial domain. Preprocessing comprises Linear-Scale Differential Analysis (LSDA) with sequent content-dependent thresholding of intensity gradients. Method is proved for artificially blurred and noise corrupted images. It is shown that new robust algorithm allows to increase the productivity of creation of panoramic images keeping the probability of successive stitching close to maximum even for low-quality source frames.
Numerical and Experimental Study on Dielectrophoretic and Electrohydrodynamic Traps using Micro-Particles on an Interdigitated Electrode Array System

Dielectrophoretic and electrohydrodynamic traps are presented that can localize microparticles on specified positions of an interdigitated electrode array. For the generation of the traps, an interdigitated electrode array chip covered by an oxide layer with 10 μηι χ 10 μιη opening windows is designed and fabricated, then an AC signal with varying frequency is applied to the array, the traps are finally created on the positions between two opening windows or inside the windows by dielectrophoretic and electrohydrodynamic effects. In addition, the dielectrophoretic and electrohydrodynamic forces are analyzed numerically using a finite element method.
Source, Contamination Assessment and Risk Evaluation of Heavy Metals in the Stream Sediments of Rivers around Olode Area SW, Nigeria

In order to investigate the source, contamination, and risk of heavy metals such as Pb, Zn, Cu, Ni, Co, Fe, Mn, and Cr, twelve (12) stream sediments and ten (10) rock samples were collected from pegmatite mining sites at Olode and its environs inside Ibadan, Southwestern Nigeria. The average values and order of abundance obtained followed the pattern: Mn (595.09) > Ba (80) > Cr (50.82) > V (45.09) > Zn (29.73) > Cu (13.82) > Co (13.82) > Sr (10.46) > Ni (9.73) > Pb (9.09) > Fe (1.59). These were greater than the background values, indicating that mining has a negative impact on the study area, as indicated by the high coefficient of variation and correlation values (> 0.6) for Copper-Lead (0.929), Copper-Vanadium (0.970), Copper-Chromium (0.815), Lead-Vanadium (0.884), and others. On the basis of the enrichment factor (EF), the Olode sediments show extremely high enrichment for Mn and Ba in the research region. Cu and Ni are most likely to blame for the elevated contamination levels, according to CF values. The degree of contamination (CD), pollution load index (PLI), pollution index (PI), and modified pollution index (MPI) all revealed high levels of contamination in all stream sediment samples, whereas Igeo shows that the Olode stream sediments are “practically uncontaminated to extremely contaminated by Ni, Co, and Mn”. Ni and Cu are the major regulating factors that are most likely causing the possible Eri. As a result, these findings give important information for conducting appropriate ecological management research.
Subtractive clustering Takagi-Sugeno position tracking for humans by low-cost inertial sensors and velocity classification

In this work, open-loop position tracking using low-cost inertial measurement units is aided by Takagi-Sugeno velocity classification using the subtractive clustering algorithm to help generate the fuzzy rule base. Using the grid search approach, a suitable window of classified velocity vectors was obtained and then integrated to generate trajectory segments. Using publicly available experimental data, the reconstruction accuracy of the method is compared against four competitive pedestrian tracking algorithms. The comparison on selected test data, has demonstrated more competitive relative and absolute trajectory error metrics. The proposed method in this paper is also verified on an independent experimental data set. Unlike the methods which use deep learning, the proposed method has shown to be transparent (fuzzy rule base). Lastly, a sensitivity analysis of the velocity classification models to perturbations from the training orientation at test time is investigated, to guide developers of such data-driven algorithms on the granularity required in an ensemble modeling approach. The accuracy and transparency of the approach may positively influence applications requiring low-cost inertial position tracking such as augmented reality headsets for emergency responders.
Cellular senescence-related gene signature as a valuable predictor of prognosis in hepatocellular carcinoma

Background: Hepatocellular carcinoma (HCC) is a lethal tumor. Its prognosis prediction remains a challenge. Meanwhile, cellular senescence, one of the hallmarks of cancer, and its related prognostic genes signature can provide critical information for clinical decision-making. Method: Using bulk RNA sequencing and microarray data of HCC samples, we established a senescence score model via multi-machine learning algorithms to predict the prognosis of HCC. Single-cell and pseudo-time trajectory analyses were used to explore the hub genes of the senescence score model in HCC sample differentiation. Result: A machine learning model based on cellular senescence gene expression profiles was identified in predicting HCC prognosis. The feasibility and accuracy of the senescence score model were confirmed in external validation and comparison with other models. Moreover, we analyzed the immune response, immune checkpoints, and sensitivity to immunotherapy drugs of HCC patients in different prognostic risk groups. Pseudo-time analyses identified four hub genes in HCC progression, including CDCA8, CENPA, SPC25, and TTK, and indicated related cellular senescence. Conclusions: This study identified a prognostic model of HCC by cellular senescence-related gene expression and insight into novel potential targeted therapies.
Magnetic transitions and site-disordered induced weak ferromagnetism in (1-x)BiFeO3-xBaTiO3

3m space group. It is shown that the local symmetry of the average cubic compositions of BF-xBT for 0.35 <x< 0.55 is broken due to off-centering of Bi 3+ in the (1-10) plane and O 2− along the � 110� direction from their special Wyckoff positions at (0,0,0) and (1/2,1/2,0), respectively. The local O 2− disorder is shown to be equivalent to local antiferrodistortive rotation, leading to deviation of the Fe 3+ -O 2− -Fe 3+ bond angle from 180° that allows spin canting due to Dzyaloshinskii-Moriya interaction − D i,j · ( − S i × − S j), which is otherwise irreconcilable with the ideal cubic symmetry. The magnetization and neutron powder diffraction measurements confirm the absence of magnetic ordering at room temperature for x 0.55.
Nurses in Public Health: A profession in continuous evolution

Today, nurses play a fundamental role in Public Health. In the last few decades the health profession has seen strong professional growth, in the areas of Clinical practice, Management and Education. In particular, this growth has been also informed by the progress being made in nursing research. In fact, nursing research has contributed to the establishment of support tools to facilitate and evaluate the daily work done by nurses, contributing significantly to the improvement of their professional performance.
Cyclic circular balanced and strongly balanced crossover designs through integer programming

ABSTRACT This article proposes a new linear integer programming approach to obtain cyclic circular balanced and strongly balanced crossover designs for a given number of treatments v, number of periods p, and number of units n. The linear integer programming approach has been used for generating a sequence of treatments to be assigned to the units. Using this approach, cyclic circular balanced and strongly balanced crossover designs for v < 30, p < 5, and λ ⩽ 4 or λ* ⩽ 4 have been generated, where λ (λ*) refers to the number of times each treatment is preceded by every other treatment excluding itself (including itself) depending on whether it is circular balanced or circular strongly balanced. The designs obtained are uniform over periods. A catalogue of designs for v < 30, p < 5, λ ⩽ 4 or λ* ⩽ 4, p < v with n ⩽ 100 is given. The designs obtained are universally optimal over the class of all connected designs with a fixed number of treatments, number of periods, and number of sequences.
Glucocorticoid Receptor and β-Catenin Interact in Prostate Cancer Cells and Their Co-Inhibition Attenuates Tumorsphere Formation, Stemness, and Docetaxel Resistance

Therapy resistance hinders the efficacy of anti-androgen therapies and taxane-based chemotherapy for advanced prostate cancer (PCa). Glucocorticoid receptor (GR) signaling mediates resistance to androgen receptor signaling inhibitors (ARSI) and has also been recently implicated in PCa resistance to docetaxel (DTX), suggesting a role in therapy cross-resistance. Like GR, β-catenin is upregulated in metastatic and therapy-resistant tumors and is a crucial regulator of cancer stemness and ARSI resistance. β-catenin interacts with AR to promote PCa progression. Given the structural and functional similarities between AR and GR, we hypothesized that β-catenin also interacts with GR to influence PCa stemness and chemoresistance. As expected, we observed that treatment with the glucocorticoid dexamethasone promotednuclear accumulation of GR and active β-catenin in PCa cells. Co-immunoprecipitation studies showed that GR and β-catenin interact in DTX-resistant and DTX-sensitive PCa cells. Pharmacological co-inhibition of GR and β-catenin, using the GR modulator CORT-108297 and the selective β-catenin inhibitor MSAB, enhanced cytotoxicity in DTX-resistant PCa cells grown in adherent and spheroid cultures and decreased CD44+/CD24– cell populations in tumorspheres. These results indicate that GR and β-catenin influence cell survival, stemness, and tumorsphere formation in DTX-resistant cells. Their co-inhibition could be a promising therapeutic strategy to overcome PCa therapy cross-resistance.
E-Learning Pricing Model Policy for Higher Education

The technological advancement in learning has made it possible for students to study beyond space and time restrictions, known as online learning. Impacted by the pandemic for an extended period, most students have adapted to online learning and, even more, have realized the vast benefits of online learning despite all the minor disadvantages. As a result, worldwide institutions, including Indonesia, are now offering online degrees and courses. Previous studies have shown contradictory results of cost factor effects on online courses, from the least important to the most critical factor for student achievements. Therefore, deciding the online course rates has been a major concern for online course providers. This research aims to answer the fundamental question of designing costs for online learning by analyzing online course preferences and ensuring sustainability by proposing a framework for the E-learning Pricing Model Policy in Higher Education using literature studies and qualitative approaches. The results show four main phases: the preparation phase, which conducts market research to understand consumer demand and behavior; the implementation phase, which includes marketing expenses and tutor fees; evaluation phase, which includes the course content material and video production revisions for further implementation. In addition, the infrastructure phase as the Learning Management System’s virtual space is added with the Cloud Expenses. However, as a limitation of this research, countless factors influence online course rates, and no exact number can determine those rates. Nevertheless, the course cost can be estimated by considering the factors that affect the overall cost and the number of learners who take the course. As a result, this framework acts as an essential foundation for institutions to determine sustainable online course rates.
Dynamical Downscaling for Climate Projection with High-Resolution MRI AGCM-RCM

High-resolution downscaling is vital to project climate extremes and their future changes by resolving fine topography reasonably well, which is a key to represent local climatology and impacts of weather extremes. A direct dynamical downscaling with a regional climate model (RCM) embedded within an atmosphere–ocean coupled general circulation model (AOGCM) is commonly used but is subject to systematic biases in their present-day simulations of AOGCM, which may cause unexpected effects on future projections and lead to difficult interpretation of climate change. In a high-resolution atmospheric general circulation model (AGCM)–RCM system, the present-day climate in AGCM is forced by observed sea surface temperature (SST) and sea-ice distribution. Then, the future climate is calculated with the “future” boundary conditions (SST and sea-ice), which are created by adding their future changes projected by AOGCM to the observed present-day values, besides the future radiative forcing. This system is one of methods to minimize the effects of such biases. A Meteorological Research Institute AGCM with 20-km grids is successfully applied to project future changes in weather extremes such as tropical cyclones and rain systems that cause heavy rainfall and strong winds. Regional downscaling with 5-km mesh RCM is then performed over certain area to investigate local extreme rainfall events and their future changes. In this paper, we review various downscaling methods and try to rationalize a use of high-resolution AGCM–RCM system.
Hand Movement Recognition and Salient Tremor Feature Extraction With Wearable Devices in Parkinson’s Patients

Tremor is one of the earliest signs of Parkinson’s disease (PD), which seriously disrupts patients’ daily lives. It is important to study upper limb tremors quantitatively to control PD progression. In this study, surface electromyography (sEMG) signals from wearable devices are used to recognize rest, posture, and kinetic tremor action from six upper limb clinical actions and to quantify features of tremors. A multivariable time-series classification model (MTSCM) based on fully convolutional networks and a long short-term memory network is proposed to recognize tremor actions. MTSCM achieves a high degree of accuracy both on the left-hand and right-hand data sets for tremor actions. An improved Hilbert–Huang transform (HHT) method is proposed to decompose the inertial signals of tremor actions to obtain tremor components. Using the improved HHT, tremor and motion components can be decomposed effectively. In addition, 53 features are extracted from inertial and sEMG signals, and a canonical correlation analysis is used to determine the correlation between features and movement disorder society unified PD rating scale (MDS-UPDRS) scores. Several of the relevant characteristics are related to MDS-UPDRS scores, notably the dominant frequency and amplitude of the tremor component are significantly correlated ( ${p}$ < 0.01) with tremor scores. Detecting upper limb clinical tremors in PD patients using wearable sensors is feasible according to our findings.
Secreted Amyloid Precursor Protein Alpha (sAPPα Regulates the Cellular Proteome and Secretome of Mouse Primary Astrocytes

Secreted amyloid precursor protein alpha (sAPPα), processed from a parent mammalian brain protein, amyloid precursor protein, can modulate learning and memory. Recently it has been shown to modulate the transcriptome and proteome of human neurons, including proteins with neurological functions. Here, we analysed whether the acute administration of sAPPα facilitated changes in the proteome and secretome of mouse primary astrocytes in culture. Astrocytes contribute to the neuronal processes of neurogenesis, synaptogenesis and synaptic plasticity. Cortical mouse astrocytes in culture were exposed to 1 nM sAPPα, and changes in both the whole-cell proteome (2 h) and the secretome (6 h) were identified with Sequential Window Acquisition of All Theoretical Fragment Ion Spectra–Mass Spectrometry (SWATH-MS). Differentially regulated proteins were identified in both the cellular proteome and secretome that are involved with neurologically related functions of the normal physiology of the brain and central nervous system. Groups of proteins have a relationship to APP and have roles in the modulation of cell morphology, vesicle dynamics and the myelin sheath. Some are related to pathways containing proteins whose genes have been previously implicated in Alzheimer’s disease (AD). The secretome is also enriched in proteins related to Insulin Growth Factor 2 (IGF2) signaling and the extracellular matrix (ECM). There is the promise that a more specific investigation of these proteins will help to understand the mechanisms of how sAPPα signaling affects memory formation.
Threshold-Based Routing-Topology Co-Design for Optical Data Center

Despite the bandwidth scaling limit of electrical switching and the high cost of building Clos data center networks (DCNs), the adoption of optical DCNs is still limited. There are two reasons. First, existing optical DCN designs usually face high deployment complexity. Second, these designs are not full-optical and the performance benefit over the non-blocking Clos DCN is not clear. After exploring the design tradeoffs of the existing optical DCN designs, we propose TROD (Threshold Routing based Optical Datacenter), a low-complexity optical DCN with superior performance than other optical DCNs. There are two novel designs in TROD that contribute to its success. First, TROD performs robust topology optimization based on the recurring traffic patterns and thus does not need to react to every traffic change, which lowers deployment and management complexity. Second, TROD introduces tVLB (threshold-based Valiant Load Balance), which can avoid network congestion as much as possible even under unexpected traffic bursts. We conduct simulation based on both Facebook’s real DCN traces and our synthesized highly bursty DCN traces. TROD reduces flow completion time (FCT) by about 1.15-<inline-formula> <tex-math notation="LaTeX">$2.16\times $ </tex-math></inline-formula> compared to Google’s Jupiter DCN, at least <inline-formula> <tex-math notation="LaTeX">$2\times $ </tex-math></inline-formula> compared to other optical DCN designs, and about 2.4-<inline-formula> <tex-math notation="LaTeX">$3.2\times $ </tex-math></inline-formula> compared to expander graph DCN. Compared with the non-blocking Clos, TROD reduces the hop count of the majority packets by one, and could even outperform the non-blocking Clos with proper bandwidth over-provision at the optical layer. Note that TROD can be built with commercially available hardware and does not require host modifications.
What is “Where”: Physical Reasoning Informs Object Location

Abstract A central puzzle the visual system tries to solve is: “what is where? ” While a great deal of research attempts to model object recognition (“what”), a comparatively smaller body of work seeks to model object location (“where”), especially in perceiving everyday objects. How do people locate an object, right now, in front of them? In three experiments collecting over 35,000 judgements on stimuli spanning different levels of realism (line drawings, real images, and crude forms), participants clicked “where” an object is, as if pointing to it. We modeled their responses with eight different methods, including both human response-based models (judgements of physical reasoning, spatial memory, free-response “click anywhere” judgements, and judgements of where people would grab the object), and image-based models (uniform distributions over the image, convex hull, saliency map, and medial axis). Physical reasoning was the best predictor of “where,” performing significantly better than even spatial memory and free-response judgements. Our results offer insight into the perception of object locations while also raising interesting questions about the relationship between physical reasoning and visual perception.