Miracle-dz/UniFAR-FTUs huggingface dataset:
Deterministic Performance Space Exploration of Analog Integrated Circuits considering Process Variations and Operating Conditions

During analog integrated circuit design, a compromise between different competing circuit performances must be found. The so-called Pareto front shows all optimal compromises between the circuit performances. Novel deterministic methods for the efficient computation of the Pareto front are presented in this work. A new parallel optimization algorithm based on Sequential Quadratic Programming is described, which features an exchange of solutions between search runs for different points on the Pareto front and a special treatment of optimization constraints. The Pareto front is computed in a new iterative manner by increasing the number of considered performances step-wise. This leads to a better coverage of peripheral parts of high-dimensional Pareto fronts. Finally, the work presents a general problem formulation and efficient optimization approaches for the computation of a specification Pareto front that considers process variations and operating conditions.
Topologically Organized Networks in the Claustrum Reflect Functional Modularization

Using genetic strategies and viral-based directional tracers, we investigated the topological location and output networks of claustrum (CLA) neuron populations projecting to either the retrosplenial cortex, primary motor cortex, or basolateral amygdala. We found that all three CLA neuron populations clearly reside in distinct topological locations within the CLA complex and project broadly to multiple downstream targets. Each neuron population projects to different targets, suggesting that each CLA subzone coordinates a unique set of brain-wide functions. Our findings establish that the claustrum complex encompasses at least three minimally overlapping networks that are compartmentalized into different topological subzones. Such modularity is likely to be important for CLA function.
Prognostic value and clinicopathological correlation of the tumor regression grade in neoadjuvant chemotherapy for gastric adenocarcinoma: a retrospective cohort study.

Background Neoadjuvant chemotherapy (NACT) and radical gastrectomy are the gold standard treatments for resectable advanced gastric cancer (GC). However, the prognostic value of the pathological tumor regression grade (TRG) of NACT remains controversial. This retrospective study aimed to investigate the correlation between the TRG after NACT and clinicopathological features as well as its prognostic value in advanced GC. Methods In total, 551 patients with GC who received NACT combined with surgical resection at the Zhejiang Cancer Hospital from April 2004 to December 2019 were included. The demographic characteristics, treatment response, tumor characteristics, treatment regimens, and survival data were reviewed from the medical records of all patients. The Chi-square test was used to analyze the correlation between TRG and clinicopathological factors. Kaplan-Meier univariate analysis and Cox regression multivariate analysis were used to determine the independent risk factors affecting the prognosis of GC patients. Results Among the 551 patients with advanced GC who accepted NACT treatment, 14 were determined to be in TRG 0, 98 in TRG 1, 257 in TRG 2, and 182 in TRG 3. Also, TRG was significantly correlated with the cT stage (P=0.015), ypT stage (P<0.001), ypN stage (P<0.001), ypTNM stage (P<0.001), vascular tumor thrombus (P<0.001), Borrmann classification (P=0.042), and lymph node ratio (LNR) (P<0.001). Furthermore, patients who had a good pathological response to NACT had a better prognosis, with a 3-year overall survival (OS) of 70.9% versus 48.8% in patients who had a poor pathological response. We also found that TRG (P=0.042, HR =1.65) was an independent prognostic factor affecting the OS of GC patients. Conclusions TRG plays a significant role in the prognostic value in neoadjuvant chemotherapy for gastric adenocarcinoma. Patients with higher cT stage, higher levels of pre-CA199 and pre-CA125 may have worse pathological response.
Penerapan Sistem Kendali XY-Stage dan Modulasi Laser Pada Tomografi Fotoakustik Menggunakan Arduino

Photoacoustic tomography is a system of utilization of the photoacoustic effect is used to obtain image-processing with high resolution and contrast. The purpose of studies is to design a mechanics xy-stage and implement control systems in laser modulation. The system design consists of designing hardware and software. Hardware design includes the design of optical devices and the design of electronic devices using the Arduino microcontroller. Software design using Arduino IDE, Audacity and Matlab. Recording test results showed that the photoacoustic signal peaks on the recording with the samples was higher than the recording unsampled. Based on the test mileage xy-stage mechanical movement using a type of bipolar stepper motor Nema 17, earned mileage (4.046 ± 0.005) mm for the motor movement for one revolution and it takes 25 steps to obtain the distance as far (0.50 ± 0.05) mm used on scanning process. Image-processing has been able to show the results of the image corresponding to the scanning area, the process is carried out on the object-imaging soft tissues such as the pancreas with cell tumor tissue.
Size‐reduction balanced UWB BPF design using meander slotline and its mirror structure with the microstrip feed

The contribution of this work is to propose a size‐reduction design of a balanced UWB BPF using a meander slotline together with its mirror structure. The size reduction is accomplished by meandering the slotline. The proposed circuit is excited by a vertically oriented feeding microstrip. The meander slotline combined its mirror one exhibits a wideband response as well as the one of an ordinary slotline. However, the former together with the microstrip feed, which perfectly preserves the structural symmetry, has a much smaller circuit size. This structural symmetry of the circuit renders itself suitable in the balanced circuit design. Thus, a smallest balanced UWB BPF in the slotline‐alike structure category is realized.
On the predictability of optical rogue waves in a semiconductor laser with optical injection

Extreme events and rogue waves are ubiquitous in complex systems [1] and a lot of efforts are nowadays focused in developing reliable analysis techniques for their detection and prediction [2]. Laser systems displaying extreme pulses in their output intensity are ideal candidates for performing laboratory controlled experiments that allow testing novel diagnostic tools to detect warning signals of upcoming extreme events.
Comparison of Vault Measurements Using a Swept-Source OCT-Based Optical Biometer and Anterior Segment OCT

Background To newly describe the vault measurement by using a widely used swept-source OCT-based optical biometer (IOLMaster700) and accessd the accuracy of vault measurement. Methods This was a retrospective, cross-sectional study. All patients underwent implantable Collamer lens (ICL) implantation surgery without complications. IOLMaster700 and AS-OCT analyses were conducted for each eye on the same day in the same condition. Measurements of anterior chamber depth (ACD), corneal-ICL (C-ICL), and vault values were made and recorded. The repeatability of the IOL Master700 measurements was quantified based upon intraclass correlation coefficient (ICC) values. Correlations between IOL Master700 and AS-OCT measurements made with these different analytical approaches were assessed. The agreement of instruments was evaluated using Bland-Altman plots. Results The IOLMaster700 instrument yielded highly reliable measurements of vault, C-ICL, and ACD (ICC = 0.996, 0.995, 0.995, respectively). Vault, C-ICL and ACD values as measured using the IOLMaster700, was slightly smaller than that measured via AS-OCT, but these differences were not significant (p = 0.652, p = 0.121 and p = 0.091, respectively). The vault, C-ICL, and ACD measurements by these two instruments were strongly correlated (r = 0.971, r = 0.944, and r = 0.963, respectively; all p < 0.001). The 95% limits of agreement for vault, C-ICL, and ACD measurements between the two devices were−0.08 to 0.08 mm,−0.14 to 0.11 mm, and−0.13 to 0.10 mm, respectively. Conclusions The IOLMasrer700 can measure implanted ICL vault with a high degree of accuracy and repeatability. Good correlations and agreement were observed between IOLMaster700 and AS-OCT in measuring vault, C-ICL, and ACD measurements.
Roles of ATP and SERCA in the Regulation of Calcium Turnover in Unloaded Skeletal Muscles: Current View and Future Directions

A decrease in skeletal muscle contractile activity or its complete cessation (muscle unloading or disuse) leads to muscle fibers’ atrophy and to alterations in muscle performance. These changes negatively affect the quality of life of people who, for one reason or another, are forced to face a limitation of physical activity. One of the key regulatory events leading to the muscle disuse-induced changes is an impairment of calcium homeostasis, which leads to the excessive accumulation of calcium ions in the sarcoplasm. This review aimed to analyze the triggering mechanisms of calcium homeostasis impairment (including those associated with the accumulation of high-energy phosphates) under various types of muscle unloading. Here we proposed a hypothesis about the regulatory mechanisms of SERCA and IP3 receptors activity during muscle unloading, and about the contribution of these mechanisms to the excessive calcium ion myoplasmic accumulation and gene transcription regulation via excitation–transcription coupling.
Photo-Oxygenation as a New Therapeutic Strategy for Neurodegenerative Proteinopathies by Enhancing the Clearance of Amyloid Proteins

Alzheimer disease (AD) is associated with the aggregation of two amyloid proteins: tau and amyloid-β (Aβ). The results of immunotherapies have shown that enhancing the clearance and suppressing the aggregation of these two proteins are effective therapeutic strategies for AD. We have developed photocatalysts that attach oxygen atoms to Aβ and tau aggregates via light irradiation. Photo-oxygenation of these amyloid aggregates reduced their neurotoxicity by suppressing their aggregation both in vitro and in vivo. Furthermore, photo-oxygenation enhanced the clearance of Aβ in the brain and microglial cells. Here, we describe the effects of photo-oxygenation on tau and Aβ aggregation, and the potential of photo-oxygenation as a therapeutic strategy for AD, acting via microglial clearance.
An Object Detection and Pose Estimation Approach for Position Based Visual Servoing

Abstract In this paper, an object recognition method and a pose estimation approach using stereo vision is presented. The proposed approach was used for position based visual servoing of a 6 DoF manipulator. The object detection and recognition method was designed with the purpose of increasing robustness. A RGB color-based object descriptor and an online correction method is proposed for object detection and recognition. Pose was estimated by using the depth information derived from stereo vision camera and an SVD based method. Transformation between the desired pose and object pose was calculated and later used for position based visual servoing. Experiments were carried out to verify the proposed approach for object recognition. The stereo camera was also tested to see whether the depth accuracy is adequate. The proposed object recognition method is invariant to scale, orientation and lighting condition which increases the level of robustness. The accuracy of stereo vision camera can reach 1 mm. The accuracy is adequate for tasks such as grasping and manipulation.
Detecting cracks in concrete structures with the baseline model of the visual characteristics of images

A method based on the baseline model of the visual characteristics of images (BMVCI) is proposed to detect cracks in concrete structures. BMVCI refers to the model, which consists of images of the noncrack areas of a concrete structure with cracks or images of the noncrack state of a concrete structure. Compared with the performance of edge detection (ED) methods for detecting cracks in concrete structures, this baseline model expands the quasi‐distance between the edges of cracks and the image background; thus, the crack detection accuracy is effectively improved. Additionally, the discriminative threshold of cracks is quantitatively determined with BMVCI, which avoids the influence of artificial interference when determining the abovementioned threshold used for ED methods. Meanwhile, compared with the methods based on artificial intelligence, such as deep learning (DL), the calculating efficiency of the proposed method is higher because the proposed method converts the high‐dimensional image data into low‐dimensional digital features for training. With the same small size set of training samples, the accuracy of the crack detection of the proposed method is higher than that of the methods based on the framework of DL. In this study, Gaussian convolution is applied to generate the visual characteristics of images, and then a kernel principal component analysis‐based method is implemented to establish the BMVCI. The basic idea of novelty detection is applied to detect cracks in concrete structures. Finally, an experiment on concrete structures is designed and applied to demonstrate the effectiveness of the proposed method.
Water Uptake Tradeoffs of Dominant Shrub Species in the Coastal Wetlands of the Yellow River Delta, China

Tamarix chinensis and Ziziphus jujuba are two dominant shrub species on Chenier Island in the Yellow River Delta, China. Water is a restrictive factor determining the plant growth, vegetation composition, and community succession in this coastal zone. We investigated how water uptake tradeoffs of the two shrub species responded to soil water fluctuations caused by seasonal variations of precipitation. The soil water content, salinity and δ18O values of potential water sources (soil water in 0–20, 20–40, 40–60, and 60–100 cm soil layers, and groundwater) and plant xylem water were measured in wet (July 2013) and dry (July 2014) seasons. The IsoSource model was employed to calculate the contributions of different water sources to plant xylem water. The results showed that δ18O values of soil water decreased significantly with soil depth in the dry season, while increased significantly with soil depth in the wet season. In the wet season, when the soil water was abundant, Z. jujuba mostly used the soil water from the 60–100 cm layer, while T. chinensis took up a mixture of groundwater and soil water from the 60–100 cm layer. In the dry season, when the soil water was depleted because of low precipitation, Z. jujuba mainly took up a mixture of the soil water from 20 to 100 cm soil layers, while T. chinensis mainly used groundwater. T. chinensis and Z. jujuba showed different ecological amplitudes of water sources during dry and wet seasons. The niche differentiation of major water sources for T. chinensis and Z. jujuba demonstrated their adaptabilities to the fluctuations of soil moisture in water-limited ecosystems. Water niche differentiations of coexisting shrub species were expected to minimize their competition for limited water sources, contributing to successful coexistence and increasing the resilience of the coastal wetland ecosystem to drought.
ROC-Tree Algorithm for Stratification of Binary Classifier Sets with Varied Discrimination Threshold

Binary classifier systems are used in multiple practical situations. Evaluation of diagnostic ability of a binary classifier, as its discrimination threshold is varied, often requires data transformation by performing aggregation operations. One of the most used aggregation methods is division by percentiles which divides the data set at the equal by size subgroups blindly, independently from the structure of data. We developed a ROC-tree algorithm for selection of threshold values, which is a recursive downwards splitting of each group at the two subgroups (branches) by cut-off point of ROC curve. We showed that suggested ROC-tree algorithm allows to define optimal (natural) boundaries and number of groups. Two methods of data aggregation (percentiles and ROC-tree algorithms) were tested using the dataset ‘Credit Card Fraud Detection’ (https://www.kaggle.com/mlg-ulb/creditcardfraud). The results of one-vs-one reduction for the assessment of the multiclass classifications were presented as macro-average of hybrid threshold performance metrics. The macro-averages of metrics like Youden index, accuracy, optimized precision, and geometric mean were significantly different between used aggregation algorithms. The differences between macro-average of metrics ROC-tree and quartiles algorithms of stratification were preserved during 10-fold stratified cross-validation procedure. Using algorithm sensitive to the distribution patterns, e.g., ROC-tree algorithm showed adequate stratification at groups by natural cut-off points determined by the data set composition. This method provides effective aggregation for summarizing or analyzing data in a various field of sciences. In health care described algorithm allows effective evaluation of mortality causes and quality control specialized medical care by hospitals.
Corn Tiller Yield Contributions are Dependent on Environment: A 17 Site-Year Kansas Study

Abstract Historic breeding efforts in corn (Zea mays L.) have resulted in uniform, single-stalked phenotypes with limited potential for environmental plasticity. Therefore, plant density is a critical yield component for corn, as corn is unable to successfully compensate for a deficit of plants. Other grass crop species can overcome plant density deficits via vegetative branching (tillering), but this trait is historically undesirable in corn. Improving corn flexibility across plant densities has potential benefits, particularly considering diverse yield environments and seasonal weather uncertainties due to climate change. The present study evaluated tiller presence with two hybrids in a range of plant densities across the state of Kansas to identify yield impacts and potential usefulness of this plasticity trait in corn. Tiller presence was identified as neutral or additive to final yields, but fine-tuning plant density was confirmed as key to maximizing grain yields. Tillers have potential to stabilize yields across plant densities in productive environments. This capability may offer a source of production stability for growers when deficits develop in plant density after planting. Summary Historic breeding efforts in corn ( Zea mays L.) have resulted in uniform, single-stalked phenotypes with limited potential for environmental plasticity. Therefore, plant density is a critical yield component for corn, as corn is unable to successfully compensate for a deficit of plants. Other grass crop species can overcome plant density deficits via vegetative branching (tillering), but this trait is historically undesirable in corn. Improving corn flexibility across plant densities has potential benefits, particularly considering diverse yield environments and seasonal weather uncertainties due to climate change. The present study evaluated tiller presence with two hybrids in a range of plant densities across the state of Kansas to identify yield impacts and potential usefulness of this plasticity trait in corn. Tiller presence was identified as neutral or additive to final yields, but fine-tuning plant density was confirmed as key to maximizing grain yields. Tillers have potential to stabilize yields across plant densities in productive environments. This capability may offer a source of production stability for growers when deficits develop in plant density after planting.
Seamless Operation of Master–Slave Organized AC Microgrid With Robust Control, Islanding Detection, and Grid Synchronization

In this article, islanding detection, control, grid synchronization, and power share techniques have been considered for the seamless operation of ac microgrid in grid-connected mode, islanded mode, and during the transition between both modes. A master–slave organization coordinates the power distribution among microgrid sources. The control scheme of each source is designed with a nested loop robust linear quadratic regulator and mixed $H_{2}/H_{\infty }$ optimal controller. The islanding detection algorithm is developed with synchro-extraction transform and optimal support vector machine (OSVM). An optimal linear Kalman filter-based synchronous reference frame phase locked loop (OLKF-SRFPLL) method is considered for the reconnecting of the microgrid to the utility grid. For optimal parameter tuning in the OSVM and LKF-SRFPLL methods, a particle swarm optimization technique is used. The seamless operation of the proposed ac microgrid system is verified in MATLAB/SIMULINK, and the practical feasibility is verified with an experimental photo-type setup and dSPACE 1202 control kit. Simulation and experimental results are presented to illustrate the seamless operation of the proposed ac microgrid system.
On the improvement of boost and buck-boost converters for battery applications

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