Miracle-dz/UniFAR-FTUs huggingface dataset:
ANALISIS KUALITAS SISTEM INFORMASI KEGIATAN SEKOLAH BERBASIS MOBILE WEB DI SMK NEGERI 2 YOGYAKARTA

Tujuan dari penelitian ini adalah menjamin tingkat kualitas Sistem Informasi Kegiatan Sekolah berbasis mobile web agar tidak terjadi error serta fitur dan fugsionalitasnya sesuai yang dijanjikan dengan melakukan pengujian menggunakan standar ISO 25010. Pengujian dilakukan menggunakan 4 karakteristik pada standar ISO 25010 yaitu functional suitability , compatibility , usability , dan performance efficiency . Instrumen yang digunakan yaitu black-box testing, USE Questionnaire, Yslow tools dan Pingdom Website Speed Test. Hasil dari penelitian ini adalah Sistem Informasi Kegiatan Sekolah telah memenuhi standar ISO 25010 pada karakteristik functional suitability dengan nilai 100% (sangat tinggi), karakteristik compatibility sebesar 100% , karakteristik usability sebesar 82% dengan nilai alpha cronbach sebesar 0.981 (sempurna), dan karakteristik performance efficiency diperoleh nilai rata-rata overall performance score 94.2 dan rata-rata kecepatan akses sebesar 0.9305 detik setiap halaman (baik).
Azacitidine and donor lymphocyte infusion for patients with relapsed acute myeloid leukemia and myelodysplastic syndromes after allogeneic hematopoietic stem cell transplantation: A meta-analysis

Background For patients with relapsed acute myeloid leukemia (AML) and myelodysplastic syndromes (MDS) after allogeneic hematopoietic stem cell transplantation (allo-HSCT), azacitidine with donor lymphocyte infusion (DLI) is a feasible option to perform a preemptive or salvage treatment. However, its efficacy lacked comprehensive analysis, and this study aimed to fill this gap. Methods We searched potential studies in PUBMED, EMBASE, and the Cochrane Central Register of Controlled Trials. Thirteen studies involving 811 patients were analyzed. The inverse variance method was used to calculate the pooled proportion and 95% confidence interval (CI). Subgroup analysis was performed to explore the source of heterogeneity. Results The rate of pooled complete remission + partial remission (CR + PR), CR, and 2-year overall survival (OS) were 30% (95% CI: 22%–39%), 21% (95% CI: 16%–28%), and 31% (95% CI: 27%–35%), respectively. The pooled acute graft-versus-host disease (GvHD) and chronic GvHD rates were 15% (95% CI: 9%–23%) and 14% (95% CI: 8%–23%), respectively. Adverse cytogenetics and a higher percentage of bone marrow (BM) blasts at relapse were correlated with worse CR + PR and CR (interaction p < 0.05). Higher 2-year OS was found in patients with lower BM blasts at relapse or a longer time from allo-HSCT to relapse (interaction p < 0.05). Furthermore, the preemptive treatment for molecular relapse/minimal residual disease positivity resulted in much better outcomes than that for hematological relapse, both in terms of CR and 2-year OS (interaction p < 0.001). Conclusion The regimen of azacitidine and DLI could safely improve the outcomes of relapsed AML/MDS after allo-HSCT, especially in those with signs of early relapse. The administration of targeted medicines in azacitidine-based therapies may further improve the outcomes of relapsed AML/MDS.
Design of a Reconfigurable Receiver for Nanophotonic Interconnects

As silicon photonics is becoming a mature technology, it is now possible to envision nanophotonic interconnects replacing electrical interconnects. This transition can improve the chip-scale communication bandwidth, latency, and energy efficiency. This paper presents a digitally controlled reconfigurable receiver that can adapt its gain based on the input optical power. The receiver power consumption can thus be reduced to the minimum required to reach the targeted BER (Bit Error Rate). For this purpose, we propose a design method to configure the receivers according to both system-level and technological level parameters. Compared to the conventional receiver, the proposed receiver reduces power consumption by 9% on average and up to 25% for short-range communications.
Federated Reinforcement Learning at the Edge: Exploring the Learning-Communication Tradeoff

Modern cyber-physical architectures use data col-lected from systems at different physical locations to learn appropriate behaviors and adapt to uncertain environments. However, an important challenge arises as communication exchanges at the edge of networked systems are costly due to limited resources. This paper considers a setup where multiple agents need to communicate efficiently in order to jointly solve a reinforcement learning problem over time-series data collected in a distributed manner. This is posed as learning an approximate value function over a communication network. An algorithm for achieving communication efficiency is proposed, supported with theoretical guarantees, practical implementations, and numerical evaluations. The approach is based on the idea of communicating only when sufficiently informative data is collected.
Computer-aided diagnosis of cervical dysplasia using colposcopic images

Background computer-aided diagnosis of medical images is becoming more significant in intelligent medicine. Colposcopy-guided biopsy with pathological diagnosis is the gold standard in diagnosing CIN and invasive cervical cancer. However, it struggles with its low sensitivity in differentiating cancer/HSIL from LSIL/normal, particularly in areas with a lack of skilled colposcopists and access to adequate medical resources. Methods the model used the auto-segmented colposcopic images to extract color and texture features using the T-test method. It then augmented minority data using the SMOTE method to balance the skewed class distribution. Finally, it used an RBF-SVM to generate a preliminary output. The results, integrating the TCT, HPV tests, and age, were combined into a naïve Bayes classifier for cervical lesion diagnosis. Results the multimodal machine learning model achieved physician-level performance (sensitivity: 51.2%, specificity: 86.9%, accuracy: 81.8%), and it could be interpreted by feature extraction and visualization. With the aid of the model, colposcopists improved the sensitivity from 53.7% to 70.7% with an acceptable specificity of 81.1% and accuracy of 79.6%. Conclusion using a computer-aided diagnosis system, physicians could identify cancer/HSIL with greater sensitivity, which guided biopsy to take timely treatment.
The effect of magnesium alone or its combination with other supplements on the markers of inflammation, OS and metabolism in women with polycystic ovarian syndrome (PCOS): A systematic review

The objective of this systematic review is to synthesize the available evidence on the effectiveness of magnesium supplements on the markers of inflammation, oxidative stress (OS), and metabolism in PCOS patients and to provide a basis for its clinical treatment. Electronic databases (PubMed, Cochrane Library databases, Embase, Web of science, CMB, CNKI, VIP, Wan Fang and ClinicalTrials.gov) were searched from their inception until January 2022. Randomized controlled trials (RCTs) for PCOS undergoing therapy with magnesium supplementation alone or in combination with other agents. The primary outcomes were the markers of blood glucose and OS.363 patients from nine RCTs were included in the current systematic review. Four of the nine studies reported the effects of magnesium supplementation alone on OS or metabolic markers in women with PCOS. Whilemagnesium supplementation alone did not show any significant improvement in the markers of inflammation, OS or metabolism in PCOS, seven of the nine articles reported the effect of magnesium co-supplementation on OS or metabolic markers in PCOS patients. Magnesium combined with vitamin E or zinc-calcium-vitamin D significantly improved glucose and lipid metabolism in PCOS patients. Magnesium intake alone did not lead to a significant improvement in the markers of OS, blood glucose, or serum lipids in PCOS. However, magnesium combined with other supplements (vitamin E, zinc, zinc-calcium-vitamin D) significantly improved serum hs-CRP, insulin, HOMA-IR, TG, TC levels, and the improvement in OS markers was inconclusive. The effect of magnesium and melatonin supplementation on the markers of metabolism needs to be further verified. System Review Registration PROSPERO https://www.crd. york.ac. uk/PROSPERO/#myprospero, CRD42022303410.
Risk of cardiovascular disease among cancer survivors: Protocol of a pooled analysis of population-based cohort studies

Introduction Cancer and cardiovascular disease remain leading causes of death and disability worldwide, which places a heavy burden on public health systems and causes widespread suffering. Because these entities have highly overlapping risk factors, including hyperlipidemia, hypertension, diabetes, obesity, smoking and other lifestyle factors, many studies have reported that they have similar etiological mechanisms. Accumulating evidence indicates that there is an increased risk of cardiovascular disease among cancer survivors compared with the general population. However, whether cancer is associated with an increased risk of cardiovascular disease remains controversial. Methods and analysis We will conduct and report the meta-analysis strictly based on the Cochrane Handbook for Systematic Reviews and the Meta-analysis of Observational Studies in Epidemiology guidelines combined with the Preferred Reporting Items for Systematic Reviews and Meta-analysis for Protocols (PRISM-P). This meta-analysis was registered with PROSPERO (registration number CRD42022307056). We will search for studies published from database inception to December 1, 2021, regardless of language or date, in three electronic databases (PubMed, EMBASE, and Cochrane Library) to identify and appraise cohort studies examining the relationship between cancer and subsequent cardiovascular disease risk. The literature screening, inclusion and data extraction will be conducted independently by two investigators using pre-designed standardized data extraction forms. A senior investigator will be consulted in cases of disagreement. We will assess risk of bias in the included cohort studies using the Newcastle–Ottawa Scale (NOS). Quantitative synthesis will be conducted using a random-effects model. To explore potential sources of heterogeneity, we will carry out multiple sensitivity analysis, meta-regression and subgroup analysis according to baseline characteristics. Publication bias will be evaluated through visual inspection of funnel plot asymmetry as well as by Begg's rank correlation test and Egger's weighted linear regression test.
PHA-Based Feedback Control of a Biomimetic AUV for Diver Following: Design, Simulations and Real-Time Experiments

This paper deals with the use of a passive hy-drophone array (PHA) for following non-stationary underwater objects with a miniature biomimetic autonomous underwater vehicle. Current acoustic underwater localization systems are large and expensive. They are not well suited for using on miniature, low-cost underwater vehicles. However, compact size and low cost are necessary when developing autonomous vehicles that can be widely used by recreational and professional divers. As an alternative, a simple hydrophone array is developed for a biomimetic 4-flipper U-CAT AUV (Autonomous Underwater Vehicle). The array is used to measure the bearing of a single acoustic beacon. Two control approaches are proposed for navigating the vehicle towards the beacon. The whole system is validated both in a simulator and in a natural environment using two scenarios: 1) navigating towards a stationary beacon and 2) navigating towards a beacon mounted on a diver. The results show that the proposed control system is a viable approach to be used for diver following.
Inhibitory of active dual cancer targeting 5-Fluorouracil nanoparticles on liver cancer in vitro and in vivo

The chitosan (CS) material as the skeleton nano-drug delivery system has the advantages of sustained release, biodegradability, and modifiability, and has broad application prospects. In the previous experiments, biotin (Bio) was grafted onto CS to synthesize biotin-modified chitosan (Bio-CS), and it was confirmed that it has liver cancer targeting properties. Single-targeted nanomaterials are susceptible to pathological and physiological factors, resulting in a state of ineffective binding between ligands and receptors, so there is still room for improvement in the targeting of liver cancer. Based on the high expression of folate (FA) receptors on the surface of liver cancers, FA was grafted onto Bio-CS by chemical synthesis to optimize the synthesis of folic acid-modified biotinylated chitosan (FA-CS-Bio), verified by infrared spectroscopy and hydrogen-1 nuclear magnetic resonance spectroscopy. The release of FA-CS-Bio/fluorouracil (5-FU) had three obvious stages: fast release stage, steady release stage, and slow release stage, with an obvious sustained release effect. Compared with Bio-CS, FA-CS-Bio could promote the inhibition of the proliferation and migration of liver cancer by 5-FU, and the concentration of 5-FU in hepatoma cells was significantly increased dose-dependently. Laser confocal experiments confirmed that FA-CS-Bio caused a significant increase in the fluorescence intensity in liver cancer cells. In terms of animal experiments, FA-CS-Bio increased the concentration of 5-FU in liver cancer tissue by 1.6 times on the basis of Bio-CS and the number of monophotons in liver cancer tissue by in vivo dynamic imaging experiments was significantly stronger than that of Bio-CS, indicating that the targeting ability of FA-CS-Bio was further improved. Compared with Bio-CS, FA-CS-Bio can significantly prolong the survival time of 5-FU in the orthotopic liver cancer transplantation model in mice, and has a relieving effect on liver function damage and bone marrow suppression caused by 5-FU. In conclusion, FA-CS-Bio nanomaterials have been optimized for synthesis. In vivo and in vitro experiments confirmed that FA-CS-Bio can significantly improve the targeting of liver cancer compared with Bio-CS. FA-CS-Bio/5-FU nanoparticles can improve the targeted inhibition of the proliferation and migration of liver cancer cells, prolong the survival period of tumor-bearing mice, and alleviate the toxic and side effects.
An averaged model for switched systems with state jumps applicable for PWM descriptor systems

Switched descriptor systems with pulse width modulation are characterized by modes whose dynamics are described by differential algebraic equations; this type of models can be viewed as switched impulsive systems, i.e. switched systems with ordinary differential equations as modes dynamics and state jumps at the switching time instants. The presence of possible jumps in the state makes the application of the classical averaging technique nontrivial. In this paper we propose an averaged model for switched impulsive systems. The state trajectory of the proposed averaged model is shown to approximate the one of the original system with an error of order of the switching period. The model reduces to the classical averaged model when there are no jumps in the state. The practical interest of the theoretical averaging result is demonstrated through numerical simulations of a switched capacitor electrical circuit.
Pattern Recognition Facilities of Extended Kalman Filtering in Stochastic Neural Fields

In mathematical neuroscience, a special interest is paid to a working memory mechanism in the neural tissue modeled by the Dynamic Neural Field (DNF) in the presence of model uncertainties. The working memory facility implies that the neurons' activity remains self-sustained after the external stimulus removal due to the recurrent interactions in the networks and allows the system to cope with missing sensors' information. In our previous works, we have developed two reconstruction methods of the neural membrane potential from incomplete data available from the sensors. The methods are derived within the Extended Kalman filtering approach by using the Euler-Maruyama method and the Ito-Taylor expansion of order 1.5. It was shown that the Ito-Taylor EKF -based restoration process is more accurate than the Euler-Maruyama-based alternative. It improves the membrane potential reconstruction quality in case of incomplete sensors information. The aim of this paper is to investigate their pattern recognition facilities, i.e. the quality of the pattern formation reconstruction in case of model uncertainties and incomplete information. The numerical experiments are provided for an example of the stochastic DNF with multiple active zones arisen in a neural tissue.
An Expression Recognition Method based on Improved Convolutional Network

In facial expression recognition tasks, traditional machine learning methods are based on manual feature extraction, which has high time complexity and poor stability. However, the existing convolutional network model has some problems such as insufficient feature extraction and large number of parameters. To solve these problems, this paper adds deep detachable convolutional network into the ordinary convolutional layer to reduce the number of parameters and operation cost. We add Squeeze - and - Excitation block to the outputs of the deeply separable convolution layer to reallocate the weights of the output channels according to the importance. What’s more, we introduce the residual mechanism in Resnet network to reduce the disappearance of gradient. The designed model was trained in Fer2013 data set. The nerual network proposed in this paper achieves 70% accuracy on the test set after training when the number of parameters is much smaller than many traditional convolutional networks, and achieves more accurate and lightweight facial expression recognition.
Hardware aware modeling of mixed-signal spiking neural network

The work presents a hardware aware modeling and learning rule for mixed-signal Spiking Neural Network (SNN). The Python-based models are compared with transistor simulation in 65nm CMOS technology. The general approach to training and inferring with mixed-signal SNN is to learn the weights offline and then deploy them on neuromorphic hardware. However, in the case of analog or mixed-signal hardware, the circuits employed for computation are non-linear and have significant variability. This work is an initial proof-of-concept towards fully modeling this non-linearity and learning with these circuits. The study specifically employs Floating-Gate (FG) transistor-based synapses for storing the weights of the synapses and adaptive Leaky-Integrate and Fire circuit for modeling the neurons in the SNN. Additionally, the study develops a localized gradient-based algorithm to learn the FG voltage of the analog synapses of SNN. This approach enables integrating the non-linearity of analog synapses into the learning framework. Using these models and learning algorithm the work shows system-level classification using multiple layers, neurons and time-steps.
A 208μW PPG-based Glucose Monitoring SoC using Ensembled Boosted Trees

A noninvasive glucose monitoring system-on-chip (SoC) based on near-infrared (NIR) Photoplethysmography (PPG) is proposed in this paper. The proposed SoC includes a PPG-readout circuit, and a glucose estimation processor (GPP). Six different temporal and spectral features are extracted from the PPG signal after motion artifact and baseline removal from the PPG signal. The GPP utilizes Ensembled Boosted Trees to predict blood glucose levels. The SoC is implemented in 180um CMOS and consumes 208μW with an area of 4.5mm2. It achieves a mean absolute relative difference (mARD) of 5.83% (mARD) verified on 200 subjects. This improves (accuracy/power) figure of merit (FoM) by 5.5% compared to the state-of-the-artwork.
Aliasing’s Study on Bandpass Sampling

The increase in the complexity of algorithms for digital processing of RF signals is limited by the need to increase the sampling rate. Sub-Nyquist sampling, in particular bandpass sampling, allows us to reduce the sampling rate, determined by the Nyquist-Shannon sampling theorem, and turn to baseband digital processing. In a large number of publications, the issues of choosing the sampling frequency, which makes it possible to eliminate aliasing, are considered. At the same time, there are works devoted to the restoration of signals distorted by aliasing. Therefore, digital signal processing algorithms are of interest, in which the presence of aliasing is allowed. These algorithms make it possible to further reduce the sampling rate and thus make it easier to process wideband signals or narrowband signals over a wider bandwidth. This paper shows that with a fixed sampling rate and a tunable bandwidth of the processed signals, various configurations of the relative position of the alias of the bandpass signal transferred to the first Nyquist zone and its mirror image are possible. The features of aliasing are considered for various configurations of the down-transferred spectrum and for various orders of aliasing.
Discrete-Time Dynamic Disturbance Rejection for the Modified Linear Extended State Observer

In this paper, a time discretization of the Modified Linear Extended State Observer (MLESO) is proposed to improve performance for large sample times. Stemming from Model Reference Adaptive Control (MRAC) and L1-adaptive control schemes, the goal of MLESO control is to approximately track a reference model. This is achieved by interpreting all deviations from the reference model as an effect of disturbances, estimating the disturbances, and rejecting the estimates by using a suitable control law. Fast sampling, which is favorable for accurate estimation and compensation, is often limited by hardware. To reduce the required sample time, the continuous-time MLESO is discretized in this paper. After discretizing the plant, state-space dead-beat methods are used to derive a discrete-time control law. Parts of the method can be used for general discrete-time dynamic disturbance rejection. The effectiveness of the proposed discretization scheme is demonstrated in simulation on a hydraulic system.
Optimal Power Allocation in MIMO Channel

In this paper, we introduce a spatial MIMO channel for modern communication systems. Parameters required in the spatial MIMO channel modeling are studied and the channel is simulated for two types of modeling named “un-polarized” and “cross-polarized” antennas. Then, the channel capacity is computed at different signal to noise ratio. Besides, the channel capacity is analyzed for two different scenarios including high SNR and low SNR. To reach the maximum capacity, the optimal power allocation on antennas is also investigated. The simulation results show that, at low SNR, the power allocation to the sub-channel corresponding to maximum singular value is optimal and equal power allocation to the sub-channels is optimal at high SNR.
Robust Control of a Deformable Mirror for Adaptive Optics Systems

To improve the robust stability of adaptive optics system, H synthesis has been introduced to design the controller. H synthesis has advantages to deal with unmodelled dynamic of plant and to ensure the optimal performance in tracking the desired input. To simplify the design of controller, Singular Value Decomposition is adopted to uncouple the channels. This paper first analyzes the frequency responses of the H synthesis and integrator in uncoupled channels. The results indicate that H synthesis can increase the phase margin, which explain that the stability of adaptive optics is improved. Then the simulation of atmospheric turbulence correction is analyzed to testify the performance of adaptive optics. The results show that both integrator and H synthesis can obtain a nearly same satisfying performance, but the former achieves a lower control voltage to correct wavefront than the latter. It validates that H synthesis has a better robustness to deal with the disturbance than integrator. That is to say, H synthesis is able to improve the stability of adaptive optics system. KeywordsH synthesis; adaptive optics; atmospheric correction